OpenRaiser/Intern-Atlas huggingface dataset:
On the Security of Warning Message Dissemination in Vehicular Ad Hoc Networks
Venue: arXiv cs.NI 2017
Year: 2017

Information security is an important issue in vehicular networks as the accuracy and integrity of information is a prerequisite to satisfactory performance of almost all vehicular network applications. In this paper, we study the information security of a vehicular ad hoc network whose message may be tampered by malicious vehicles. An analytical framework is developed to analyze the process of message dissemination in a vehicular network with malicious vehicles randomly distributed in the network. The probability that a destination vehicle at a fixed distance away can receive the message correctly from the source vehicle is obtained. Simulations are conducted to validate the accuracy of the theoretical analysis. Our results demonstrate the impact of network topology and the distribution of malicious vehicles on the correct delivery of a message in vehicular ad hoc networks, and may provide insight on the design of security mechanisms to improve the security of message dissemination in vehicular networks.
Étude sur les portails et agrégateurs des ressources pédagogiques universitaires francophones en accès libre
Venue: arXiv cs.DL 2017
Year: 2017

This study responds to the first measure undertaken on July 17, 2015 by IDNEUF prject, that of an exploratory analysis of the existing portals and aggregators of free French-language academic resources. The idea is to provide an overview of the most common trends and practices in the constitution and organization of digital online learning resource portals. The study of these trends would help to define the appropriate choices and conditions for designing the future common French-language portal and to optimize its services for the conservation, exchange, integration and pooling of educational resources within the distributed technological framework of French-language universities. This framework should therefore be interconnected, transparent and interoperable, reflecting both the linguistic and cultural specificities of partner institutions and their ambitions for technological and economic developments. The development of this first exploratory study of portals would take into account the two technological solutions discussed at the task force meeting on 17 July 2015.1. The alternative of extending the capabilities of France's Digital University's search engine to French-language academic institutions will require that the experience of the UNT (Numerical Thematic University) be taken into account as a key player in the digital portal In higher education (supnumerique.gouv.fr). Thus, the present study should first target the portals of the UNT as models replicable or extensible to the French context by analyzing their technological choices, their modes of organization and their modes of use and communication;2. Next, the study will not be limited to analyzing UNT portals. The proposed hypothesis to create a common portal of French portals from the existing university portals also requires exploring this possibility and proposing an analysis of existing portals that function according to other organizational models.
Multicast Transmissions in Directional mmWave Communications
Venue: arXiv cs.IT 2017
Year: 2017

Multicast transmissions have been widely analyzed in traditional networks as a way to improve spectrum efficiency when multiple users are interested in the same data. However, their application to mmWave communications has been studied only marginally so far. The goal of this paper is to partially fill this gap by investigating optimal and suboptimal multicast schemes for mmWave communications with directional beams. In particular, we propose a Markov setup to model the retransmission status of the unsuccessfully transmitted packets and, because of the computational complexity of the optimal solution, we introduce a suboptimal hierarchical optimization procedure, which is much easier to derive. Finally, we numerically show that restricting the link to unicast beams is strongly suboptimal, especially when many packets have to be transmitted.
Everware toolkit. Supporting reproducible science and challenge-driven education
Venue: arXiv cs.CY 2017
Year: 2017

Modern science clearly demands for a higher level of reproducibility and collaboration. To make research fully reproducible one has to take care of several aspects: research protocol description, data access, environment preservation, workflow pipeline, and analysis script preservation. Version control systems like git help with the workflow and analysis scripts part. Virtualization techniques like Docker or Vagrant can help deal with environments. Jupyter notebooks are a powerful platform for conducting research in a collaborative manner. We present project Everware that seamlessly integrates git repository management systems such as Github or Gitlab, Docker and Jupyter helping with a) sharing results of real research and b) boosts education activities. With the help of Everware one can not only share the final artifacts of research but all the depth of the research process. This been shown to be extremely helpful during organization of several data analysis hackathons and machine learning schools. Using Everware participants could start from an existing solution instead of starting from scratch. They could start contributing immediately. Everware allows its users to make use of their own computational resources to run the workflows they are interested in, which leads to higher scalability of the toolkit.
Parallel energy-stable phase field crystal simulations based on domain decomposition methods
Venue: arXiv cs.CE 2017
Year: 2017

In this paper, we present a parallel numerical algorithm for solving the phase field crystal equation. In the algorithm, a semi-implicit finite difference scheme is derived based on the discrete variational derivative method. Theoretical analysis is provided to show that the scheme is unconditionally energy stable and can achieve second-order accuracy in both space and time. An adaptive time step strategy is adopted such that the time step size can be flexibly controlled based on the dynamical evolution of the problem. At each time step, a nonlinear algebraic system is constructed from the discretization of the phase field crystal equation and solved by a domain decomposition based, parallel Newton--Krylov--Schwarz method with improved boundary conditions for subdomain problems. Numerical experiments with several two and three dimensional test cases show that the proposed algorithm is second-order accurate in both space and time, energy stable with large time steps, and highly scalable to over ten thousands processor cores on the Sunway TaihuLight supercomputer.
Custom Hypergraph Categories via Generalized Relations
Venue: arXiv cs.LO 2017
Year: 2017

Process theories combine a graphical language for compositional reasoning with an underlying categorical semantics. They have been successfully applied to fields such as quantum computation, natural language processing, linear dynamical systems and network theory. When investigating a new application, the question arises of how to identify a suitable process theoretic model. We present a conceptually motivated parameterized framework for the construction of models for process theories. Our framework generalizes the notion of binary relation along four axes of variation, the truth values, a choice of algebraic structure, the ambient mathematical universe and the choice of proof relevance or provability. The resulting categories are preorder-enriched and provide analogues of relational converse and taking graphs of maps. Our constructions are functorial in the parameter choices, establishing mathematical connections between different application domains. We illustrate our techniques by constructing many existing models from the literature, and new models that open up ground for further development.
EmotioNet Challenge: Recognition of facial expressions of emotion in the wild
Venue: arXiv cs.CV 2017
Year: 2017

This paper details the methodology and results of the EmotioNet challenge. This challenge is the first to test the ability of computer vision algorithms in the automatic analysis of a large number of images of facial expressions of emotion in the wild. The challenge was divided into two tracks. The first track tested the ability of current computer vision algorithms in the automatic detection of action units (AUs). Specifically, we tested the detection of 11 AUs. The second track tested the algorithms' ability to recognize emotion categories in images of facial expressions. Specifically, we tested the recognition of 16 basic and compound emotion categories. The results of the challenge suggest that current computer vision and machine learning algorithms are unable to reliably solve these two tasks. The limitations of current algorithms are more apparent when trying to recognize emotion. We also show that current algorithms are not affected by mild resolution changes, small occluders, gender or age, but that 3D pose is a major limiting factor on performance. We provide an in-depth discussion of the points that need special attention moving forward.
Deep Collaborative Learning for Visual Recognition
Venue: arXiv cs.CV 2017
Year: 2017

Deep neural networks are playing an important role in state-of-the-art visual recognition. To represent high-level visual concepts, modern networks are equipped with large convolutional layers, which use a large number of filters and contribute significantly to model complexity. For example, more than half of the weights of AlexNet are stored in the first fully-connected layer (4,096 filters). We formulate the function of a convolutional layer as learning a large visual vocabulary, and propose an alternative way, namely Deep Collaborative Learning (DCL), to reduce the computational complexity. We replace a convolutional layer with a two-stage DCL module, in which we first construct a couple of smaller convolutional layers individually, and then fuse them at each spatial position to consider feature co-occurrence. In mathematics, DCL can be explained as an efficient way of learning compositional visual concepts, in which the vocabulary size increases exponentially while the model complexity only increases linearly. We evaluate DCL on a wide range of visual recognition tasks, including a series of multi-digit number classification datasets, and some generic image classification datasets such as SVHN, CIFAR and ILSVRC2012. We apply DCL to several state-of-the-art network structures, improving the recognition accuracy meanwhile reducing the number of parameters (16.82% fewer in AlexNet).
An Extended Consideration of Joint Exploration and Tracking: JET
Venue: arXiv cs.RO 2017
Year: 2017

Autonomous exploration and multi-object tracking by a team of agents have traditionally been considered as two separate, yet related, problems which are usually solved in two phases: an exploration phase then a tracking phase. The exploration problem is usually viewed through an information theoretic framework where a robotic agent attempts to gather as much information about the environment or an Object of Interest (OI). Conversely, the tracking problem attempts to maintain precise location information about an OI over time. This work proposes a single framework which enables the multi-robot multi-object problem to be solved simultaneously. A hierarchical architecture is used to coordinate robotic agents in the tracking of multiple OIs while simultaneously allowing the task to remain computationally efficient. The primary contributions of this work are a probabilistic constraint on the tracked OIs' covariances guarantees tracking performance throughout the entire mission. The automatic discovery of new OIs, a seamless transition to guaranteed tracking of discovered OIs, and the automatic balancing of exploration with the requirements of tracking.
Building Interoperable and Cross-Domain Semantic Web of Things Applications
Venue: arXiv cs.SE 2017
Year: 2017

The Web of Things (WoT) is rapidly growing in popularity getting the interest of not only technologist and scientific communities but industrial, system integrators and solution providers. The key aspect of the WoT to succeed is the relatively, easy-to-build ecosystems nature inherited from the web and the capacity for building end-to-end solutions. At the WoT connecting physical devices such as sensors, RFID tags or any devices that can send data through the Internet using the Web is almost automatic. The WoT shared data can be used to build smarter solutions that offer business services in the form of IoT applications. In this chapter, we review the main WoT challenges, with particular interest on highlighting those that rely on combining heterogeneous IoT data for the design of smarter services and applications and that benefit from data interoperability. Semantic web technologies help for overcoming with such challenges by addressing, among other ones the following objectives: 1) semantically annotating and unifying heterogeneous data, 2) enriching semantic WoT datasets with external knowledge graphs, and 3) providing an analysis of data by means of reasoning mechanisms to infer meaningful information. To overcome the challenge of building interoperable semantics-based IoT applications, the Machine-to-Machine Measurement (M3) semantic engine has been designed to semantically annotate WoT data, build the logic of smarter services and deduce meaningful knowledge by linking it to the external knowledge graphs available on the web. M3 assists application and business developers in designing interoperable Semantic Web of Things applications. Contributions in the context of European semantic-based WoT projects are discussed and a particular use case within FIESTA-IoT project is presented.
Recurrent Poisson Factorization for Temporal Recommendation
Venue: arXiv cs.SI 2017
Year: 2017

Poisson factorization is a probabilistic model of users and items for recommendation systems, where the so-called implicit consumer data is modeled by a factorized Poisson distribution. There are many variants of Poisson factorization methods who show state-of-the-art performance on real-world recommendation tasks. However, most of them do not explicitly take into account the temporal behavior and the recurrent activities of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback. RPF treats time as a natural constituent of the model and brings to the table a rich family of time-sensitive factorization models. To elaborate, we instantiate several variants of RPF who are capable of handling dynamic user preferences and item specification (DRPF), modeling the social-aspect of product adoption (SRPF), and capturing the consumption heterogeneity among users and items (HRPF). We also develop a variational algorithm for approximate posterior inference that scales up to massive data sets. Furthermore, we demonstrate RPF's superior performance over many state-of-the-art methods on synthetic dataset, and large scale real-world datasets on music streaming logs, and user-item interactions in M-Commerce platforms.
Using Matching to Detect Infeasibility of Some Integer Programs
Venue: arXiv cs.DS 2017
Year: 2017

A novel matching based heuristic algorithm designed to detect specially formulated infeasible zero-one IPs is presented. The algorithm input is a set of nested doubly stochastic subsystems and a set E of instance defining variables set at zero level. The algorithm deduces additional variables at zero level until either a constraint is violated (the IP is infeasible), or no more variables can be deduced zero (the IP is undecided). All feasible IPs, and all infeasible IPs not detected infeasible are undecided. We successfully apply the algorithm to a small set of specially formulated infeasible zero-one IP instances of the Hamilton cycle decision problem. We show how to model both the graph and subgraph isomorphism decision problems for input to the algorithm. Increased levels of nested doubly stochastic subsystems can be implemented dynamically. The algorithm is designed for parallel processing, and for inclusion of techniques in addition to matching.
Distributed Partial Clustering
Venue: arXiv cs.DS 2017
Year: 2017

Recent years have witnessed an increasing popularity of algorithm design for distributed data, largely due to the fact that massive datasets are often collected and stored in different locations. In the distributed setting communication typically dominates the query processing time. Thus it becomes crucial to design communication efficient algorithms for queries on distributed data. Simultaneously, it has been widely recognized that partial optimizations, where we are allowed to disregard a small part of the data, provide us significantly better solutions. The motivation for disregarded points often arise from noise and other phenomena that are pervasive in large data scenarios. In this paper we focus on partial clustering problems, $k$-center, $k$-median and $k$-means, in the distributed model, and provide algorithms with communication sublinear of the input size. As a consequence we develop the first algorithms for the partial $k$-median and means objectives that run in subquadratic running time. We also initiate the study of distributed algorithms for clustering uncertain data, where each data point can possibly fall into multiple locations under certain probability distribution.
Detection of Nonlinearly Distorted OFDM Signals via Generalized Approximate Message Passing
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we propose a practical receiver for multicarrier signals subjected to a strong memoryless nonlinearity. The receiver design is based on a generalized approximate message passing (GAMP) framework, and this allows real-time algorithm implementation in software or hardware with moderate complexity. We demonstrate that the proposed receiver can provide more than a 2dB gain compared with an ideal uncoded linear OFDM transmission at a BER range $10^{-4}\div10^{-6}$ in the AWGN channel, when the OFDM signal is subjected to clipping nonlinearity and the crest-factor of the clipped waveform is only 1.9dB. Simulation results also demonstrate that the proposed receiver provides significant performance gain in frequency-selective multipath channels
L2GSCI: Local to Global Seam Cutting and Integrating for Accurate Face Contour Extraction
Venue: arXiv cs.CV 2017
Year: 2017

Current face alignment algorithms can robustly find a set of landmarks along face contour. However, the landmarks are sparse and lack curve details, especially in chin and cheek areas where a lot of concave-convex bending information exists. In this paper, we propose a local to global seam cutting and integrating algorithm (L2GSCI) to extract continuous and accurate face contour. Our method works in three steps with the help of a rough initial curve. First, we sample small and overlapped squares along the initial curve. Second, the seam cutting part of L2GSCI extracts a local seam in each square region. Finally, the seam integrating part of L2GSCI connects all the redundant seams together to form a continuous and complete face curve. Overall, the proposed method is much more straightforward than existing face alignment algorithms, but can achieve pixel-level continuous face curves rather than discrete and sparse landmarks. Moreover, experiments on two face benchmark datasets (i.e., LFPW and HELEN) show that our method can precisely reveal concave-convex bending details of face contours, which has significantly improved the performance when compared with the state-ofthe- art face alignment approaches.
Neural Machine Translation and Sequence-to-sequence Models: A Tutorial
Venue: arXiv cs.CL 2017
Year: 2017

This tutorial introduces a new and powerful set of techniques variously called "neural machine translation" or "neural sequence-to-sequence models". These techniques have been used in a number of tasks regarding the handling of human language, and can be a powerful tool in the toolbox of anyone who wants to model sequential data of some sort. The tutorial assumes that the reader knows the basics of math and programming, but does not assume any particular experience with neural networks or natural language processing. It attempts to explain the intuition behind the various methods covered, then delves into them with enough mathematical detail to understand them concretely, and culiminates with a suggestion for an implementation exercise, where readers can test that they understood the content in practice.
Approximation algorithms for TSP with neighborhoods in the plane
Venue: arXiv cs.CG 2017
Year: 2017

In the Euclidean TSP with neighborhoods (TSPN), we are given a collection of n regions (neighborhoods) and we seek a shortest tour that visits each region. As a generalization of the classical Euclidean TSP, TSPN is also NP-hard. In this paper, we present new approximation results for the TSPN, including (1) a constant-factor approximation algorithm for the case of arbitrary connected neighborhoods having comparable diameters; and (2) a PTAS for the important special case of disjoint unit disk neighborhoods (or nearly disjoint, nearly-unit disks). Our methods also yield improved approximation ratios for various special classes of neighborhoods, which have previously been studied. Further, we give a linear-time O(1)-approximation algorithm for the case of neighborhoods that are (infinite) straight lines.
End-to-End Network Delay Guarantees for Real-Time Systems using SDN
Venue: arXiv cs.NI 2017
Year: 2017

We propose a novel framework that reduces the management and integration overheads for real-time network flows by leveraging the capabilities (especially global visibility and management) of software-defined networking (SDN) architectures. Given the specifications of flows that must meet hard real-time requirements, our framework synthesizes paths through the network and associated switch configurations - to guarantee that these flows meet their end-to-end timing requirements. In doing so, our framework makes SDN architectures "delay-aware" - remember that SDN is otherwise not able to reason about delays. Hence, it is easier to use such architectures in safety-critical and other latency-sensitive applications. We demonstrate our principles as well as the feasibility of our approach using both - exhaustive simulations as well as experiments using real hardware switches.
Measuring Affectiveness and Effectiveness in Software Systems
Venue: arXiv cs.SE 2017
Year: 2017

The summary presented in this paper highlights the results obtained in a four-years project aiming at analyzing the development process of software artifacts from two points of view: Effectiveness and Affectiveness. The first attribute is meant to analyze the productivity of the Open Source Communities by measuring the time required to resolve an issue, while the latter provides a novel approach for studying the development process by analyzing the affectiveness ex-pressed by developers in their comments posted during the issue resolution phase. Affectivenes is obtained by measuring Sentiment, Politeness and Emotions. All the study presented in this summary are based on Jira, one of the most used software repositories.