OpenRaiser/Intern-Atlas huggingface dataset:
On the Resilience of a QKD Key Synchronization Protocol for IPsec
Venue: arXiv cs.CR 2018
Year: 2018

This paper presents a practical solution to the problem of limited bandwidth in Quantum Key Distribution (QKD)- secured communication through using rapidly rekeyed Internet Protocol security (IPsec) links. QKD is a cutting-edge security technology that provides mathematically proven security by using quantum physical effects and information theoretical axioms to generate a guaranteed non-disclosed stream of encryption keys. Although it has been a field of theoretical research for some time, it has only been producing market-ready solutions for a short period of time. The downside of this technology is that its key generation rate is only around 52,000 key bits per second over a distance of 50 km. As this rate limits the data throughput to the same rate, it is substandard for normal modern communications, especially for securely interconnecting networks. IPsec, on the other hand, is a well-known security protocol that uses classical encryption and is capable of exactly creating site-to-site virtual private networks. This paper presents a solution that combines the performance advantages of IPsec with QKD. The combination sacrifices only a small portion of QKD security by using the generated keys a limited number of times instead of just once. As a part of this, the solution answers the question of how many data bits per key bit make sensible upper and lower boundaries to yield high performance while maintaining high security. While previous approaches complement the Internet Key Exchange protocol (IKE), this approach simplifies the implementation with a new key synchronization concept, proposing a lightweight protocol that uses relatively few, slim control messages and sparse acknowledgement. Furthermore, it provides a Linux-based module for the AIT QKD software using the Netlink XFRM Application Programmers Interface to feed the quantum key to the IP***ABSTRACT TRUNCATED TO 1920 CHARS***
Optimal Vehicle Dimensioning for Multi-Class Autonomous Electric Mobility On-Demand Systems
Venue: arXiv cs.SY 2018
Year: 2018

Autonomous electric mobility on demand (AEMoD) has recently emerged as a cyber-physical system aiming to bring automation, electrification, and on-demand services for the future private transportation market. The expected massive demand for such services and its resulting insufficient charging time/resources prohibit the use of centralized management and full vehicle charging. A fog-based multi-class solution for these challenges was recently suggested, by enabling per-zone management and partial charging for different classes of AEMoD vehicles. This paper focuses on finding the optimal vehicle dimensioning for each zone of these systems in order to guarantee a bounded response time of its vehicles. Using a queuing model representing the multi-class charging and dispatching processes, we first derive the stability conditions and the number of system classes to guarantee the response time bound. Decisions on the proportions of each class vehicles to partially/fully charge, or directly serve customers are then optimized so as to minimize the vehicles in-flow to any given zone. Excess waiting times of customers in rare critical events, such as limited charging resources and/or limited vehicles availabilities, are also investigated. Results show the merits of our proposed model compared to other schemes and in usual and critical scenarios.
On randomized counting versus randomised decision
Venue: arXiv cs.CC 2018
Year: 2018

We study the question of which counting problems admit f.p.r.a.s., under a structural complexity perspective. Since problems in #P with NP-complete decision version do not admit f.p.r.a.s. (unless NP = RP), we study subclasses of #P, having decision version either in P or in RP. We explore inclusions between these subclasses and we present all possible worlds with respect to NP v.s. RP and RP v.s. P.
Vectorization of Line Drawings via PolyVector Fields
Venue: arXiv cs.GR 2018
Year: 2018

Image tracing is a foundational component of the workflow in graphic design, engineering, and computer animation, linking hand-drawn concept images to collections of smooth curves needed for geometry processing and editing. Even for clean line drawings, modern algorithms often fail to faithfully vectorize junctions, or points at which curves meet; this produces vector drawings with incorrect connectivity. This subtle issue undermines the practical application of vectorization tools and accounts for hesitance among artists and engineers to use automatic vectorization software. To address this issue, we propose a novel image vectorization method based on state-of-the-art mathematical algorithms for frame field processing. Our algorithm is tailored specifically to disambiguate junctions without sacrificing quality.
QBM - Mapping User-Specified Functions to Programmable Logic through a QBF Satisfiability Problem
Venue: arXiv cs.LO 2018
Year: 2018

This is a brief overview on the background behind the test set formulas generated by the QBM tool. After establishing its application context, its formal approach to the generation of QBF formulas and the concrete test set formulas are described. Finally, some related work will be credited and the source to obtain the open-source tool will be identified.
Future Mobile Network Architecture: Challenges and Issues
Venue: arXiv cs.NI 2018
Year: 2018

The future mobile networks facing many challenges and to cope these challenges, different standards and project has been proposed so far. Most recently Cognitive Networks has opened a new ground to present suitable architecture and mechanism for these challenges. The objective of this paper is to identify and discuss the challenges to the future mobile networks and to discuss some workable solutions to these challenges. Finally, on the basis of discussion a simple flexible network architecture is proposed.
Handover Control in Wireless Systems via Asynchronous Multi-User Deep Reinforcement Learning
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, we propose a two-layer framework to learn the optimal handover (HO) controllers in possibly large-scale wireless systems supporting mobile Internet-of-Things (IoT) users or traditional cellular users, where the user mobility patterns could be heterogeneous. In particular, our proposed framework first partitions the user equipments (UEs) with different mobility patterns into clusters, where the mobility patterns are similar in the same cluster. Then, within each cluster, an asynchronous multi-user deep reinforcement learning scheme is developed to control the HO processes across the UEs in each cluster, in the goal of lowering the HO rate while ensuring certain system throughput. In this scheme, we use a deep neural network (DNN) as an HO controller learned by each UE via reinforcement learning in a collaborative fashion. Moreover, we use supervised learning in initializing the DNN controller before the execution of reinforcement learning to exploit what we already know with traditional HO schemes and to mitigate the negative effects of random exploration at the initial stage. Furthermore, we show that the adopted global-parameter-based asynchronous framework enables us to train faster with more UEs, which could nicely address the scalability issue to support large systems. Finally, simulation results demonstrate that the proposed framework can achieve better performance than the state-of-art on-line schemes, in terms of HO rates.
Convex Relaxations for Pose Graph Optimization with Outliers
Venue: arXiv cs.RO 2018
Year: 2018

Pose Graph Optimization involves the estimation of a set of poses from pairwise measurements and provides a formalization for many problems arising in mobile robotics and geometric computer vision. In this paper, we consider the case in which a subset of the measurements fed to pose graph optimization is spurious. Our first contribution is to develop robust estimators that can cope with heavy-tailed measurement noise, hence increasing robustness to the presence of outliers. Since the resulting estimators require solving nonconvex optimization problems, we further develop convex relaxations that approximately solve those problems via semidefinite programming. We then provide conditions under which the proposed relaxations are exact. Contrarily to existing approaches, our convex relaxations do not rely on the availability of an initial guess for the unknown poses, hence they are more suitable for setups in which such guess is not available (e.g., multi-robot localization, recovery after localization failure). We tested the proposed techniques in extensive simulations, and we show that some of the proposed relaxations are indeed tight (i.e., they solve the original nonconvex problem 10 exactly) and ensure accurate estimation in the face of a large number of outliers.
Threshold Auto-Tuning Metric Learning
Venue: arXiv cs.LG 2018
Year: 2018

It has been reported repeatedly that discriminative learning of distance metric boosts the pattern recognition performance. A weak point of ITML-based methods is that the distance threshold for similarity/dissimilarity constraints must be determined manually and it is sensitive to generalization performance, although the ITML-based methods enjoy an advantage that the Bregman projection framework can be applied for optimization of distance metric. In this paper, we present a new formulation of metric learning algorithm in which the distance threshold is optimized together. Since the optimization is still in the Bregman projection framework, the Dykstra algorithm can be applied for optimization. A nonlinear equation has to be solved to project the solution onto a half-space in each iteration. Naïve method takes $O(LMn^{3})$ computational time to solve the nonlinear equation. In this study, an efficient technique that can solve the nonlinear equation in $O(Mn^{3})$ has been discovered. We have proved that the root exists and is unique. We empirically show that the accuracy of pattern recognition for the proposed metric learning algorithm is comparable to the existing metric learning methods, yet the distance threshold is automatically tuned for the proposed metric learning algorithm.
Publish-Subscribe Framework for Event Management in IoT-based Applications
Venue: arXiv cs.NI 2018
Year: 2018

The incredible growth of sensors and microcontroller units makes the task of real-time event monitoring in the Internet of Things (IoT) based applications easier and more practical. In order to effectively support the event management in IoT-based applications, we propose a framework that is based on the publish-subscribe model for detecting events from IoT sensor nodes and sending notifications to subscribers (end-users) via Internet, SMS, and Calling. With the exception of the advantages inherited from the publish-subscribe model, the further advantages of the proposed framework are the ease of use in terms of user configuration without any need of technical skills, the aid of security mechanisms to prevent network intrusion, and the minimum hardware resource requirement. Additionally, the proposed framework is applicable and adaptable to various platforms since it has been developed by using Boost C++ Libraries and CMake. To evaluate the proposed framework, we develop a prototype of a real-time event monitoring system that is also presented in this paper.
Finding Faster Configurations using FLASH
Venue: arXiv cs.SE 2018
Year: 2018

Finding good configurations for a software system is often challenging since the number of configuration options can be large. Software engineers often make poor choices about configuration or, even worse, they usually use a sub-optimal configuration in production, which leads to inadequate performance. To assist engineers in finding the (near) optimal configuration, this paper introduces FLASH, a sequential model-based method, which sequentially explores the configuration space by reflecting on the configurations evaluated so far to determine the next best configuration to explore. FLASH scales up to software systems that defeat the prior state of the art model-based methods in this area. FLASH runs much faster than existing methods and can solve both single-objective and multi-objective optimization problems. The central insight of this paper is to use the prior knowledge (gained from prior runs) to choose the next promising configuration. This strategy reduces the effort (i.e., number of measurements) required to find the (near) optimal configuration. We evaluate FLASH using 30 scenarios based on 7 software systems to demonstrate that FLASH saves effort in 100% and 80% of cases in single-objective and multi-objective problems respectively by up to several orders of magnitude compared to the state of the art techniques.
Heuristic algorithms for the min-max edge 2-coloring problem
Venue: arXiv cs.DS 2018
Year: 2018

In multi-channel Wireless Mesh Networks (WMN), each node is able to use multiple non-overlapping frequency channels. Raniwala et al. (MC2R 2004, INFOCOM 2005) propose and study several such architectures in which a computer can have multiple network interface cards. These architectures are modeled as a graph problem named \emph{maximum edge $q$-coloring} and studied in several papers by Feng et. al (TAMC 2007), Adamaszek and Popa (ISAAC 2010, JDA 2016). Later on Larjomaa and Popa (IWOCA 2014, JGAA 2015) define and study an alternative variant, named the \emph{min-max edge $q$-coloring}. The above mentioned graph problems, namely the maximum edge $q$-coloring and the min-max edge $q$-coloring are studied mainly from the theoretical perspective. In this paper, we study the min-max edge 2-coloring problem from a practical perspective. More precisely, we introduce, implement and test four heuristic approximation algorithms for the min-max edge $2$-coloring problem. These algorithms are based on a \emph{Breadth First Search} (BFS)-based heuristic and on \emph{local search} methods like basic \emph{hill climbing}, \emph{simulated annealing} and \emph{tabu search} techniques, respectively. Although several algorithms for particular graph classes were proposed by Larjomaa and Popa (e.g., trees, planar graphs, cliques, bi-cliques, hypergraphs), we design the first algorithms for general graphs. We study and compare the running data for all algorithms on Unit Disk Graphs, as well as some graphs from the DIMACS vertex coloring benchmark dataset.
Theory of Deep Learning IIb: Optimization Properties of SGD
Venue: arXiv cs.LG 2018
Year: 2018

In Theory IIb we characterize with a mix of theory and experiments the optimization of deep convolutional networks by Stochastic Gradient Descent. The main new result in this paper is theoretical and experimental evidence for the following conjecture about SGD: SGD concentrates in probability -- like the classical Langevin equation -- on large volume, "flat" minima, selecting flat minimizers which are with very high probability also global minimizers
Evaluation of Machine Learning Algorithms for Intrusion Detection System
Venue: arXiv cs.CR 2018
Year: 2018

Intrusion detection system (IDS) is one of the implemented solutions against harmful attacks. Furthermore, attackers always keep changing their tools and techniques. However, implementing an accepted IDS system is also a challenging task. In this paper, several experiments have been performed and evaluated to assess various machine learning classifiers based on KDD intrusion dataset. It succeeded to compute several performance metrics in order to evaluate the selected classifiers. The focus was on false negative and false positive performance metrics in order to enhance the detection rate of the intrusion detection system. The implemented experiments demonstrated that the decision table classifier achieved the lowest value of false negative while the random forest classifier has achieved the highest average accuracy rate.
On Enhancing Genetic Algorithms Using New Crossovers
Venue: arXiv cs.NE 2018
Year: 2018

This paper investigates the use of more than one crossover operator to enhance the performance of genetic algorithms. Novel crossover operators are proposed such as the Collision crossover, which is based on the physical rules of elastic collision, in addition to proposing two selection strategies for the crossover operators, one of which is based on selecting the best crossover operator and the other randomly selects any operator. Several experiments on some Travelling Salesman Problems (TSP) have been conducted to evaluate the proposed methods, which are compared to the well-known Modified crossover operator and partially mapped Crossover (PMX) crossover. The results show the importance of some of the proposed methods, such as the collision crossover, in addition to the significant enhancement of the genetic algorithms performance, particularly when using more than one crossover operator.
Step Detection Algorithm For Accurate Distance Estimation Using Dynamic Step Length
Venue: arXiv cs.OH 2018
Year: 2018

In this paper, a new Smartphone sensor based algorithm is proposed to detect accurate distance estimation. The algorithm consists of two phases, the first phase is for detecting the peaks from the Smartphone accelerometer sensor. The other one is for detecting the step length which varies from step to step. The proposed algorithm is tested and implemented in real environment and it showed promising results. Unlike the conventional approaches, the error of the proposed algorithm is fixed and is not affected by the long distance. Keywords distance estimation, peaks, step length, accelerometer.
Optimal Time Scheduling for Wireless-Powered Backscatter Communication Networks
Venue: arXiv cs.NI 2018
Year: 2018

This letter introduces a novel wireless-powered backscatter communication system which allows sensors to utilize RF signals transmitted from a dedicated RF energy source to transmit data. In the proposed system, when the RF energy source transmits RF signals, the sensors are able to backscatter the RF signals to transmit date to the gateway and/or harvest energy from the RF signals for their operations. By integrating backscattering and energy harvesting techniques, we can optimize the network throughput of the system. In particular, we first formulate the time scheduling problem for the system, and then propose an optimal solution using convex optimization to maximize the overall network throughput. Numerical results show a significant throughput gain achieved by our proposed design over two other baseline schemes.
Acceleration of Mean Square Distance Calculations with Floating Close Structure in Metadynamics Simulations
Venue: arXiv cs.CE 2018
Year: 2018

Molecular dynamics simulates the~movements of atoms. Due to its high cost, many methods have been developed to "push the~simulation forward". One of them, metadynamics, can hasten the~molecular dynamics with the~help of variables describing the~simulated process. However, the~evaluation of these variables can include numerous mean square distance calculations that introduce substantial computational demands, thus jeopardize the~benefit of the~approach. Recently, we proposed an~approximative method that significantly reduces the~number of these distance calculations. Here we evaluate the~performance and the~scalability on two molecular systems. We assess the~maximal theoretical speed-up based on the reduction of distance computations and Ahmdal's law and compare it to the~practical speed-up achieved with our implementation.
AUV Optimal Path for Leak Detection
Venue: arXiv cs.SY 2018
Year: 2018

This paper studies an optimal autonomous underwater vehicule (AUV) path planning method for both reducing average delay before pollutants detection in underwater mining, oil or gas fields and reducing AUV occupancy time. The proposed technique, based on the bayesian search theory framework and multi-objective optimization, extracts optimal boustrophedon paths for leak detection in complex environment. We describe a multi-objective nonlinear mixed integer optimization model for both reducing global nondetection probability and path duration. We then propose a hierarchical algorithm combining two functions. The main function is a multi-objective cross entropy which places the tracklines. The second function sets the optimal speeds on each trackline by means of an interior point method. Numerical simulations show that the proposed framework is a very promising approach because the optimal paths cross spill of highly probable leaks before less probable ones. We show that our optimized paths outperform boustrophedon paths of same duration with uniform speed and spacing of trackline. Thanks to Pareto efficiency approach, our tool propose optimal trajectories for numerous AUV autonomies. Hence it can be used for both real time path planning and design purpose.
Benchmarking Crimes: An Emerging Threat in Systems Security
Venue: arXiv cs.CR 2018
Year: 2018

Properly benchmarking a system is a difficult and intricate task. Unfortunately, even a seemingly innocuous benchmarking mistake can compromise the guarantees provided by a given systems security defense and also put its reproducibility and comparability at risk. This threat is particularly insidious as it is generally not a result of malice and can easily go undetected by both authors and reviewers. Moreover, as modern defenses often trade off security for performance in an attempt to find an ideal design point in the performance-security space, the damage caused by benchmarking mistakes is increasingly worrisome. To analyze the magnitude of the phenomenon, we identify a set of 22 "benchmarking crimes" that threaten the validity of systems security evaluations and perform a survey of 50 defense papers published in top venues. To ensure the validity of our results, we perform the complete survey twice, with two independent readers. We find only a very small number of disagreements between readers, showing that our assessment of benchmarking crimes is highly reproducible. We show that benchmarking crimes are widespread even in papers published at tier-1 venues. We find that tier-1 papers commit an average of five benchmarking crimes and we find only a single paper in our sample that committed no benchmarking crimes. Moreover, we find that the scale of the problem is constant over time, suggesting that the community is not yet addressing it despite the problem being now more relevant than ever. This threatens the scientific process, which relies on reproducibility and comparability to ensure that published research advances the state of the art. We hope to raise awareness of these issues and provide recommendations to improve benchmarking quality and safeguard the scientific process in our community.
Attacking Speaker Recognition With Deep Generative Models
Venue: arXiv cs.SD 2018
Year: 2018

In this paper we investigate the ability of generative adversarial networks (GANs) to synthesize spoofing attacks on modern speaker recognition systems. We first show that samples generated with SampleRNN and WaveNet are unable to fool a CNN-based speaker recognition system. We propose a modification of the Wasserstein GAN objective function to make use of data that is real but not from the class being learned. Our semi-supervised learning method is able to perform both targeted and untargeted attacks, raising questions related to security in speaker authentication systems.
Age-Optimal Updates of Multiple Information Flows
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we study an age of information minimization problem, where multiple flows of update packets are sent over multiple servers to their destinations. Two online scheduling policies are proposed. When the packet generation and arrival times are synchronized across the flows, the proposed policies are shown to be (near) optimal for minimizing any time-dependent, symmetric, and non-decreasing penalty function of the ages of the flows over time in a stochastic ordering sense.