OpenRaiser/Intern-Atlas huggingface dataset:
Process Network Models for Embedded System Design Based on the Real-Time BIP Execution Engine
Venue: arXiv cs.SE 2018
Year: 2018

Existing model-based processes for embedded real-time systems support the analysis of various non-functional properties, most notably schedulability, through model checking, simulation or other means. The analysis results are then used for modifying the system's design, so that the expected properties are satisfied. A rigorous model-based design flow differs in that it aims at a system implementation derived from high-level models by applying a sequence of semantics-preserving transformations. Properties established at any design step are preserved throughout the subsequent steps including the executable implementation. We introduce such a design flow using a process network model of computation for application design at a high level, which combines streaming and reactive control processing with task parallelism. The schedulability of the so-called FPPNs (Fixed Priority Process Networks) is well-studied and various solutions have been presented. This article focuses on the design flow's steps for deriving executable implementations on the BIP (Behavior - Interaction - Priority) runtime environment. FPPNs are designed using the TASTE toolset, a convenient architecture description interface. In this way, the developers do not program explicitly low-level real-time OS services and the schedulability properties are guaranteed throughout the design steps by construction. The approach has been validated on the design of a real spacecraft on-board application that has been scheduled for execution on an industrial multicore platform.
On the performance of multi-objective estimation of distribution algorithms for combinatorial problems
Venue: arXiv cs.AI 2018
Year: 2018

Fitness landscape analysis investigates features with a high influence on the performance of optimization algorithms, aiming to take advantage of the addressed problem characteristics. In this work, a fitness landscape analysis using problem features is performed for a Multi-objective Bayesian Optimization Algorithm (mBOA) on instances of MNK-landscape problem for 2, 3, 5 and 8 objectives. We also compare the results of mBOA with those provided by NSGA-III through the analysis of their estimated runtime necessary to identify an approximation of the Pareto front. Moreover, in order to scrutinize the probabilistic graphic model obtained by mBOA, the Pareto front is examined according to a probabilistic view. The fitness landscape study shows that mBOA is moderately or loosely influenced by some problem features, according to a simple and a multiple linear regression model, which is being proposed to predict the algorithms performance in terms of the estimated runtime. Besides, we conclude that the analysis of the probabilistic graphic model produced at the end of evolution can be useful to understand the convergence and diversity performances of the proposed approach.
Open the Black Box Data-Driven Explanation of Black Box Decision Systems
Venue: arXiv cs.AI 2018
Year: 2018

Black box systems for automated decision making, often based on machine learning over (big) data, map a user's features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases hidden in the algorithms, due to human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We introduce the local-to-global framework for black box explanation, a novel approach with promising early results, which paves the road for a wide spectrum of future developments along three dimensions: (i) the language for expressing explanations in terms of highly expressive logic-based rules, with a statistical and causal interpretation; (ii) the inference of local explanations aimed at revealing the logic of the decision adopted for a specific instance by querying and auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of the many local explanations into simple global ones, with algorithms that optimize the quality and comprehensibility of explanations.
Independence of Sources in Social Networks
Venue: arXiv cs.AI 2018
Year: 2018

Online social networks are more and more studied. The links between users of a social network are important and have to be well qualified in order to detect communities and find influencers for example. In this paper, we present an approach based on the theory of belief functions to estimate the degrees of cognitive independence between users in a social network. We experiment the proposed method on a large amount of data gathered from the Twitter social network.
The application of precision time protocol on EAST timing system
Venue: arXiv cs.DC 2018
Year: 2018

The timing system focuses on synchronizing and coordinating each subsystem according to the trigger signals. A new prototype timing slave node based on precision time protocol has been developed by using ARM STM32 platform. The proposed slave timing module is tested and results show that the synchronization accuracy between slave nodes is in sub-microsecond range.
Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach
Venue: arXiv cs.IT 2018
Year: 2018

A key challenge of massive MTC (mMTC), is the joint detection of device activity and decoding of data. The sparse characteristics of mMTC makes compressed sensing (CS) approaches a promising solution to the device detection problem. However, utilizing CS-based approaches for device detection along with channel estimation, and using the acquired estimates for coherent data transmission is suboptimal, especially when the goal is to convey only a few bits of data. First, we focus on the coherent transmission and demonstrate that it is possible to obtain more accurate channel state information by combining conventional estimators with CS-based techniques. Moreover, we illustrate that even simple power control techniques can enhance the device detection performance in mMTC setups. Second, we devise a new non-coherent transmission scheme for mMTC and specifically for grant-free random access. We design an algorithm that jointly detects device activity along with embedded information bits. The approach leverages elements from the approximate message passing (AMP) algorithm, and exploits the structured sparsity introduced by the non-coherent transmission scheme. Our analysis reveals that the proposed approach has superior performance compared to application of the original AMP approach.
Blind Decoding-Metric Estimation for Probabilistic Shaping via Expectation Maximization
Venue: arXiv cs.IT 2018
Year: 2018

An unsupervised learning approach based on expectation maximization is proposed to obtain the parameters of a soft decision forward error correction decoding metric for probabilistic shaping. The algorithm depends only on the channel observations and does not require transmitted data.
Deep $k$-Means: Jointly clustering with $k$-Means and learning representations
Venue: arXiv cs.LG 2018
Year: 2018

We study in this paper the problem of jointly clustering and learning representations. As several previous studies have shown, learning representations that are both faithful to the data to be clustered and adapted to the clustering algorithm can lead to better clustering performance, all the more so that the two tasks are performed jointly. We propose here such an approach for $k$-Means clustering based on a continuous reparametrization of the objective function that leads to a truly joint solution. The behavior of our approach is illustrated on various datasets showing its efficacy in learning representations for objects while clustering them.
The Art of Drafting: A Team-Oriented Hero Recommendation System for Multiplayer Online Battle Arena Games
Venue: arXiv cs.AI 2018
Year: 2018

Multiplayer Online Battle Arena (MOBA) games have received increasing popularity recently. In a match of such games, players compete in two teams of five, each controlling an in-game avatars, known as heroes, selected from a roster of more than 100. The selection of heroes, also known as pick or draft, takes place before the match starts and alternates between the two teams until each player has selected one hero. Heroes are designed with different strengths and weaknesses to promote team cooperation in a game. Intuitively, heroes in a strong team should complement each other's strengths and suppressing those of opponents. Hero drafting is therefore a challenging problem due to the complex hero-to-hero relationships to consider. In this paper, we propose a novel hero recommendation system that suggests heroes to add to an existing team while maximizing the team's prospect for victory. To that end, we model the drafting between two teams as a combinatorial game and use Monte Carlo Tree Search (MCTS) for estimating the values of hero combinations. Our empirical evaluation shows that hero teams drafted by our recommendation algorithm have significantly higher win rate against teams constructed by other baseline and state-of-the-art strategies.
Thermanator: Thermal Residue-Based Post Factum Attacks On Keyboard Password Entry
Venue: arXiv cs.CR 2018
Year: 2018

As a warm-blooded mammalian species, we humans routinely leave thermal residues on various objects with which we come in contact. This includes common input devices, such as keyboards, that are used for entering (among other things) secret information, such as passwords and PINs. Although thermal residue dissipates over time, there is always a certain time window during which thermal energy readings can be harvested from input devices to recover recently entered, and potentially sensitive, information. To-date, there has been no systematic investigation of thermal profiles of keyboards, and thus no efforts have been made to secure them. This serves as our main motivation for constructing a means for password harvesting from keyboard thermal emanations. Specifically, we introduce Thermanator, a new post factum insider attack based on heat transfer caused by a user typing a password on a typical external keyboard. We conduct and describe a user study that collected thermal residues from 30 users entering 10 unique passwords (both weak and strong) on 4 popular commodity keyboards. Results show that entire sets of key-presses can be recovered by non-expert users as late as 30 seconds after initial password entry, while partial sets can be recovered as late as 1 minute after entry. Furthermore, we find that Hunt-and-Peck typists are particularly vulnerable. We also discuss some Thermanator mitigation strategies. The main take-away of this work is three-fold: (1) using external keyboards to enter (already much-maligned) passwords is even less secure than previously recognized, (2) post factum (planned or impromptu) thermal imaging attacks are realistic, and finally (3) perhaps it is time to either stop using keyboards for password entry, or abandon passwords altogether.
An Extensive Evaluation of the Internet's Open Proxies
Venue: arXiv cs.CR 2018
Year: 2018

Open proxies forward traffic on behalf of any Internet user. Listed on open proxy aggregator sites, they are often used to bypass geographic region restrictions or circumvent censorship. Open proxies sometimes also provide a weak form of anonymity by concealing the requestor's IP address. To better understand their behavior and performance, we conducted a comprehensive study of open proxies, encompassing more than 107,000 listed open proxies and 13M proxy requests over a 50 day period. While previous studies have focused on malicious open proxies' manipulation of HTML content to insert/modify ads, we provide a more broad study that examines the availability, success rates, diversity, and also (mis)behavior of proxies. Our results show that listed open proxies suffer poor availability--more than 92% of open proxies that appear on aggregator sites are unresponsive to proxy requests. Much more troubling, we find numerous examples of malicious open proxies in which HTML content is manipulated to mine cryptocurrency (that is, cryptojacking). We additionally detect TLS man-in-the-middle (MitM) attacks, and discover numerous instances in which binaries fetched through proxies were modified to include remote access trojans and other forms of malware. As a point of comparison, we conduct and discuss a similar measurement study of the behavior of Tor exit relays. We find no instances in which Tor relays performed TLS MitM or manipulated content, suggesting that Tor offers a far more reliable and safe form of proxied communication.
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach. We study this problem in the context of grasping, a longstanding challenge in robotic manipulation. In contrast to static learning behaviors that choose a grasp point and then execute the desired grasp, our method enables closed-loop vision-based control, whereby the robot continuously updates its grasp strategy based on the most recent observations to optimize long-horizon grasp success. To that end, we introduce QT-Opt, a scalable self-supervised vision-based reinforcement learning framework that can leverage over 580k real-world grasp attempts to train a deep neural network Q-function with over 1.2M parameters to perform closed-loop, real-world grasping that generalizes to 96% grasp success on unseen objects. Aside from attaining a very high success rate, our method exhibits behaviors that are quite distinct from more standard grasping systems: using only RGB vision-based perception from an over-the-shoulder camera, our method automatically learns regrasping strategies, probes objects to find the most effective grasps, learns to reposition objects and perform other non-prehensile pre-grasp manipulations, and responds dynamically to disturbances and perturbations.
Online Matching in a Ride-Sharing Platform
Venue: arXiv cs.DS 2018
Year: 2018

We propose a formal graph-theoretic model for studying the problem of matching rides online in a ride-sharing platform. Unlike most of the literature on online matching, our model, that we call {\em Online Windowed Non-Bipartite Matching} ($\mbox{OWNBM}$), pertains to online matching in {\em non-bipartite} graphs. We show that the edge-weighted and vertex-weighted versions of our model arise naturally in ride-sharing platforms. We provide a randomized $\frac{1}{4}$-competitive algorithm for the edge-weighted case using a beautiful result of Lehmann, Lehmann and Nisan (EC 2001) for combinatorial auctions. We also provide an $\frac{1}{2} (1 - \frac{1}{e})$-competitive algorithm for the vertex-weighted case (with some constraint relaxation) using insights from an elegant randomized primal-dual analysis technique of Devanur, Jain and Kleinberg (SODA 2013).
Divergence-Free Shape Interpolation and Correspondence
Venue: arXiv cs.CV 2018
Year: 2018

We present a novel method to model and calculate deformation fields between shapes embedded in $\mathbb{R}^D$. Our framework combines naturally interpolating the two input shapes and calculating correspondences at the same time. The key idea is to compute a divergence-free deformation field represented in a coarse-to-fine basis using the Karhunen-Loève expansion. The advantages are that there is no need to discretize the embedding space and the deformation is volume-preserving. Furthermore, the optimization is done on downsampled versions of the shapes but the morphing can be applied to any resolution without a heavy increase in complexity. We show results for shape correspondence, registration, inter- and extrapolation on the TOSCA and FAUST data sets.
MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features
Venue: arXiv cs.CV 2018
Year: 2018

In this work, we propose bag of adversarial features (BAF) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRI) (obtained within one month of injury) by incorporating unsupervised feature learning techniques. MTBI is a growing public health problem with an estimated incidence of over 1.7 million people annually in US. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. Unlike most of previous works, which use hand-crafted features extracted from different parts of brain for MTBI classification, we employ feature learning algorithms to learn more discriminative representation for this task. A major challenge in this field thus far is the relatively small number of subjects available for training. This makes it difficult to use an end-to-end convolutional neural network to directly classify a subject from MR images. To overcome this challenge, we first apply an adversarial auto-encoder (with convolutional structure) to learn patch-level features, from overlapping image patches extracted from different brain regions. We then aggregate these features through a bag-of-word approach. We perform an extensive experimental study on a dataset of 227 subjects (including 109 MTBI patients, and 118 age and sex matched healthy controls), and compare the bag-of-deep-features with several previous approaches. Our experimental results show that the BAF significantly outperforms earlier works relying on the mean values of MR metrics in selected brain regions.
LPRNet: License Plate Recognition via Deep Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes LPRNet - end-to-end method for Automatic License Plate Recognition without preliminary character segmentation. Our approach is inspired by recent breakthroughs in Deep Neural Networks, and works in real-time with recognition accuracy up to 95% for Chinese license plates: 3 ms/plate on nVIDIA GeForce GTX 1080 and 1.3 ms/plate on Intel Core i7-6700K CPU. LPRNet consists of the lightweight Convolutional Neural Network, so it can be trained in end-to-end way. To the best of our knowledge, LPRNet is the first real-time License Plate Recognition system that does not use RNNs. As a result, the LPRNet algorithm may be used to create embedded solutions for LPR that feature high level accuracy even on challenging Chinese license plates.
Towards a formal notion of impact metric for cyber-physical attacks (full version)
Venue: arXiv cs.LO 2018
Year: 2018

Industrial facilities and critical infrastructures are transforming into "smart" environments that dynamically adapt to external events. The result is an ecosystem of heterogeneous physical and cyber components integrated in cyber-physical systems which are more and more exposed to cyber-physical attacks, i.e., security breaches in cyberspace that adversely affect the physical processes at the core of the systems. We provide a formal compositional metric to estimate the impact of cyber-physical attacks targeting sensor devices of IoT systems formalised in a simple extension of Hennessy and Regan's Timed Process Language. Our impact metric relies on a discrete-time generalisation of Desharnais et al.'s weak bisimulation metric for concurrent systems. We show the adequacy of our definition on two different attacks on a simple surveillance system.
Computing the Chromatic Number Using Graph Decompositions via Matrix Rank
Venue: arXiv cs.DS 2018
Year: 2018

Computing the smallest number $q$ such that the vertices of a given graph can be properly $q$-colored is one of the oldest and most fundamental problems in combinatorial optimization. The $q$-Coloring problem has been studied intensively using the framework of parameterized algorithmics, resulting in a very good understanding of the best-possible algorithms for several parameterizations based on the structure of the graph. While there is an abundance of work for parameterizations based on decompositions of the graph by vertex separators, almost nothing is known about parameterizations based on edge separators. We fill this gap by studying $q$-Coloring parameterized by cutwidth, and parameterized by pathwidth in bounded-degree graphs. Our research uncovers interesting new ways to exploit small edge separators. We present two algorithms for $q$-Coloring parameterized by cutwidth $cutw$: a deterministic one that runs in time $O^*(2^{ω\cdot cutw})$, where $ω$ is the matrix multiplication constant, and a randomized one with runtime $O^*(2^{cutw})$. In sharp contrast to earlier work, the running time is independent of $q$. The dependence on cutwidth is optimal: we prove that even 3-Coloring cannot be solved in $O^*((2-\varepsilon)^{cutw})$ time assuming the Strong Exponential Time Hypothesis (SETH). Our algorithms rely on a new rank bound for a matrix that describes compatible colorings. Combined with a simple communication protocol for evaluating a product of two polynomials, this also yields an $O^*((\lfloor d/2\rfloor+1)^{pw})$ time randomized algorithm for $q$-Coloring on graphs of pathwidth $pw$ and maximum degree $d$. Such a runtime was first obtained by Björklund, but only for graphs with few proper colorings. We also prove that this result is optimal in the sense that no $O^*((\lfloor d/2\rfloor+1-\varepsilon)^{pw})$-time algorithm exists assuming SETH.
Online optimal task offloading with one-bit feedback
Venue: arXiv cs.LG 2018
Year: 2018

Task offloading is an emerging technology in fog-enabled networks. It allows users to transmit tasks to neighbor fog nodes so as to utilize the computing resources of the networks. In this paper, we investigate a stochastic task offloading model and propose a multi-armed bandit framework to formulate this model. We consider the fact that different helper nodes prefer different kinds of tasks. Further, we assume each helper node just feeds back one-bit information to the task node to indicate the level of happiness. The key challenge of this problem lies in the exploration-exploitation tradeoff. We thus implement a UCB-type algorithm to maximize the long-term happiness metric. Numerical simulations are given in the end of the paper to corroborate our strategy.
Adult content in Social Live Streaming Services: Characterizing deviant users and relationships
Venue: arXiv cs.SI 2018
Year: 2018

Social Live Stream Services (SLSS) exploit a new level of social interaction. One of the main challenges in these services is how to detect and prevent deviant behaviors that violate community guidelines. In this work, we focus on adult content production and consumption in two widely used SLSS, namely Live.me and Loops Live, which have millions of users producing massive amounts of video content on a daily basis. We use a pre-trained deep learning model to identify broadcasters of adult content. Our results indicate that moderation systems in place are highly ineffective in suspending the accounts of such users. We create two large datasets by crawling the social graphs of these platforms, which we analyze to identify characterizing traits of adult content producers and consumers, and discover interesting patterns of relationships among them, evident in both networks.