OpenRaiser/Intern-Atlas huggingface dataset:
An Extensive Systematic Review on Model-Driven Development of Secure Systems
Venue: arXiv cs.SE 2015
Year: 2015

Context: Model-Driven Security (MDS) is as a specialised Model-Driven Engineering research area for supporting the development of secure systems. Over a decade of research on MDS has resulted in a large number of publications. Objective: To provide a detailed analysis of the state of the art in MDS, a systematic literature review (SLR) is essential. Method: We conducted an extensive SLR on MDS. Derived from our research questions, we designed a rigorous, extensive search and selection process to identify a set of primary MDS studies that is as complete as possible. Our three-pronged search process consists of automatic searching, manual searching, and snowballing. After discovering and considering more than thousand relevant papers, we identified, strictly selected, and reviewed 108 MDS publications. Results: The results of our SLR show the overall status of the key artefacts of MDS, and the identified primary MDS studies. E.g. regarding security modelling artefact, we found that developing domain-specific languages plays a key role in many MDS approaches. The current limitations in each MDS artefact are pointed out and corresponding potential research directions are suggested. Moreover, we categorise the identified primary MDS studies into 5 principal MDS studies, and other emerging or less common MDS studies. Finally, some trend analyses of MDS research are given. Conclusion: Our results suggest the need for addressing multiple security concerns more systematically and simultaneously, for tool chains supporting the MDS development cycle, and for more empirical studies on the application of MDS methodologies. To the best of our knowledge, this SLR is the first in the field of Software Engineering that combines a snowballing strategy with database searching. This combination has delivered an extensive literature study on MDS.
Fast Detection of Curved Edges at Low SNR
Venue: arXiv cs.CV 2015
Year: 2015

Detecting edges is a fundamental problem in computer vision with many applications, some involving very noisy images. While most edge detection methods are fast, they perform well only on relatively clean images. Indeed, edges in such images can be reliably detected using only local filters. Detecting faint edges under high levels of noise cannot be done locally at the individual pixel level, and requires more sophisticated global processing. Unfortunately, existing methods that achieve this goal are quite slow. In this paper we develop a novel multiscale method to detect curved edges in noisy images. While our algorithm searches for edges over a huge set of candidate curves, it does so in a practical runtime, nearly linear in the total number of image pixels. As we demonstrate experimentally, our algorithm is orders of magnitude faster than previous methods designed to deal with high noise levels. Nevertheless, it obtains comparable, if not better, edge detection quality on a variety of challenging noisy images.
Efficient computation of generalized Ising polynomials on graphs with fixed clique-width
Venue: arXiv cs.LO 2015
Year: 2015

Graph polynomials which are definable in Monadic Second Order Logic (MSOL) on the vocabulary of graphs are Fixed-Parameter Tractable (FPT) with respect to clique-width. In contrast, graph polynomials which are definable in MSOL on the vocabulary of hypergraphs are fixed-parameter tractable with respect to tree-width, but not necessarily with respect to clique width. No algorithmic meta-theorem is known for the computation of graph polynomials definable in MSOL on the vocabulary of hypergraphs with respect to clique-width. We define an infinite class of such graph polynomials extending the class of graph polynomials definable in MSOL on the vocabulary of graphs and prove that they are Fixed-Parameter Polynomial Time (FPPT) computable, i.e. that they can be computed in time $O(n^{f(k)})$, where $n$ is the number of vertices and $k$ is the clique-width.
Discrete Independent Component Analysis (DICA) with Belief Propagation
Venue: arXiv cs.CV 2015
Year: 2015

We apply belief propagation to a Bayesian bipartite graph composed of discrete independent hidden variables and discrete visible variables. The network is the Discrete counterpart of Independent Component Analysis (DICA) and it is manipulated in a factor graph form for inference and learning. A full set of simulations is reported for character images from the MNIST dataset. The results show that the factorial code implemented by the sources contributes to build a good generative model for the data that can be used in various inference modes.
Smelly Maps: The Digital Life of Urban Smellscapes
Venue: arXiv cs.SI 2015
Year: 2015

Smell has a huge influence over how we perceive places. Despite its importance, smell has been crucially overlooked by urban planners and scientists alike, not least because it is difficult to record and analyze at scale. One of the authors of this paper has ventured out in the urban world and conducted smellwalks in a variety of cities: participants were exposed to a range of different smellscapes and asked to record their experiences. As a result, smell-related words have been collected and classified, creating the first dictionary for urban smell. Here we explore the possibility of using social media data to reliably map the smells of entire cities. To this end, for both Barcelona and London, we collect geo-referenced picture tags from Flickr and Instagram, and geo-referenced tweets from Twitter. We match those tags and tweets with the words in the smell dictionary. We find that smell-related words are best classified in ten categories. We also find that specific categories (e.g., industry, transport, cleaning) correlate with governmental air quality indicators, adding validity to our study.
Tight Mobile Byzantine Tolerant Atomic Storage
Venue: arXiv cs.DC 2015
Year: 2015

This paper proposes the first implementation of an atomic storage tolerant to mobile Byzantine agents. Our implementation is designed for the round-based synchronous model where the set of Byzantine nodes changes from round to round. In this model we explore the feasibility of multi-writer multi-reader atomic register prone to various mobile Byzantine behaviors. We prove upper and lower bounds for solving the atomic storage in all the explored models. Our results, significantly different from the static case, advocate for a deeper study of the main building blocks of distributed computing while the system is prone to mobile Byzantine failures.
Identifying Dwarfs Workloads in Big Data Analytics
Venue: arXiv cs.DB 2015
Year: 2015

Big data benchmarking is particularly important and provides applicable yardsticks for evaluating booming big data systems. However, wide coverage and great complexity of big data computing impose big challenges on big data benchmarking. How can we construct a benchmark suite using a minimum set of units of computation to represent diversity of big data analytics workloads? Big data dwarfs are abstractions of extracting frequently appearing operations in big data computing. One dwarf represents one unit of computation, and big data workloads are decomposed into one or more dwarfs. Furthermore, dwarfs workloads rather than vast real workloads are more cost-efficient and representative to evaluate big data systems. In this paper, we extensively investigate six most important or emerging application domains i.e. search engine, social network, e-commerce, multimedia, bioinformatics and astronomy. After analyzing forty representative algorithms, we single out eight dwarfs workloads in big data analytics other than OLAP, which are linear algebra, sampling, logic operations, transform operations, set operations, graph operations, statistic operations and sort.
Complexity Analysis of Precedence Terminating Infinite Graph Rewrite Systems
Venue: arXiv cs.CC 2015
Year: 2015

The general form of safe recursion (or ramified recurrence) can be expressed by an infinite graph rewrite system including unfolding graph rewrite rules introduced by Dal Lago, Martini and Zorzi, in which the size of every normal form by innermost rewriting is polynomially bounded. Every unfolding graph rewrite rule is precedence terminating in the sense of Middeldorp, Ohsaki and Zantema. Although precedence terminating infinite rewrite systems cover all the primitive recursive functions, in this paper we consider graph rewrite systems precedence terminating with argument separation, which form a subclass of precedence terminating graph rewrite systems. We show that for any precedence terminating infinite graph rewrite system G with a specific argument separation, both the runtime complexity of G and the size of every normal form in G can be polynomially bounded. As a corollary, we obtain an alternative proof of the original result by Dal Lago et al.
Is Massive MIMO Energy Efficient?
Venue: arXiv cs.IT 2015
Year: 2015

Massive multi-input multi-output (MIMO) can support high spectral efficiency (SE) with simple linear transceivers, and is expected to provide high energy efficiency (EE). In this paper, we analyze the EE of downlink multi-cell massive MIMO systems under spatially correlated channel model, where both transmit and circuit power consumptions, training overhead, channel estimation and pilot contamination (PC) are taken into account. We obtain the maximal EE for the systems with maximum-ratio transmission (MRT) and zero-forcing beamforming (ZFBF) for given number of antennas and users by optimizing the transmit power. The closed-form expressions of approximated optimal transmit power and maximal EE, and their scaling laws with the number of antennas M are derived for the systems with MRT and ZFBF. Our analysis shows that the maximal EE decreases with M for both systems with and without PC, but with different descending speeds. For the system without PC, the optimal transmit power should be configured to increases with M, while for the system with PC, the optimal transmit power should be configured as a constant independent from M. The analytical results are validated by simulations under a more realistic three-dimensional channel model.
Energy Harvesting Noncoherent Cooperative Communications
Venue: arXiv cs.IT 2015
Year: 2015

This paper investigates simultaneous wireless information and power transfer (SWIPT) in energy harvesting (EH) relay systems. Unlike existing SWIPT schemes requiring the channel state information (CSI) for coherent information delivery, we propose a noncoherent SWIPT framework for decode-and-forward (DF) relay systems bypassing the need for CSI and consequently saving energy in the network. The proposed SWIPT framework embraces power-splitting noncoherent DF (PS-NcDF) and time-switching noncoherent DF (TS-NcDF) in a unified form, and supports arbitrary M-ary noncoherent frequency-shift keying (FSK) and differential phase-shift keying (DPSK). Exact (noncoherent) maximum-likelihood detectors (MLDs) for PS-NcDF and TS-NcDF are developed in a unified form, which involves integral evaluations yet serves as the optimum performance benchmark for noncoherent SWIPT. To reduce the computational cost of the exact MLDs, we also propose closed-form approximate MLDs achieving near-optimum performance, thus serving as a practical solution for noncoherent SWIPT. Numerical results demonstrate a performance tradeoff between the first and second hops through the adjustment of time switching or power splitting parameters, whose optimal values minimizing the symbol-error rate (SER) are strictly between 0 and 1. We demonstrate that M-FSK results in a significant energy saving over M-DPSK for M >= 8; thus M-FSK may be more suitable for EH relay systems.
A First Step Towards Dynamic Hybrid Traffic Modeling
Venue: arXiv cs.MA 2015
Year: 2015

Hybrid traffic modeling and simulation provide an important way to represent and evaluate large-scale traffic networks at different levels of details. The first level, called "microscopic" allows the description of individual vehicles and their interactions as well as the study of driver's individual behavior. The second, based on the analogy with fluidic dynamic, is the "macroscopic" one and provides an efficient way to represent traffic flow behavior in large traffic infrastructures, using three aggregated variables: traffic density, mean speed and traffic volume. An intermediate level called "mesoscopic" considers a group of vehicles sharing common properties such as a same origin and destination. The work conducted in this paper presents a first step allowing simulation of wide area traffic network on the basis of dynamic hybrid modeling, where the representation associated to a network section can change at runtime. The proposed approach is implemented in a simulation platform, called Jam-free.
Prototyping Information Visualization in 3D City Models: a Model-based Approach
Venue: arXiv cs.HC 2015
Year: 2015

When creating 3D city models, selecting relevant visualization techniques is a particularly difficult user interface design task. A first obstacle is that current geodata-oriented tools, e.g. ArcGIS, have limited 3D capabilities and limited sets of visualization techniques. Another important obstacle is the lack of unified description of information visualization techniques for 3D city models. If many techniques have been devised for different types of data or information (wind flows, air quality fields, historic or legal texts, etc.) they are generally described in articles, and not really formalized. In this paper we address the problem of visualizing information in (rich) 3D city models by presenting a model-based approach for the rapid prototyping of visualization techniques. We propose to represent visualization techniques as the composition of graph transformations. We show that these transformations can be specified with SPARQL construction operations over RDF graphs. These specifications can then be used in a prototype generator to produce 3D scenes that contain the 3D city model augmented with data represented using the desired technique.
Higher weight distribution of linearized Reed-Solomon codes
Venue: arXiv cs.IT 2015
Year: 2015

Linearized Reed-Solomon codes are defined. Higher weight distribution of those codes are determined.
Generalized communities in networks
Venue: arXiv cs.SI 2015
Year: 2015

A substantial volume of research has been devoted to studies of community structure in networks, but communities are not the only possible form of large-scale network structure. Here we describe a broad extension of community structure that encompasses traditional communities but includes a wide range of generalized structural patterns as well. We describe a principled method for detecting this generalized structure in empirical network data and demonstrate with real-world examples how it can be used to learn new things about the shape and meaning of networks.
Some remarks on multiplicity codes
Venue: arXiv cs.IT 2015
Year: 2015

Multiplicity codes are algebraic error-correcting codes generalizing classical polynomial evaluation codes, and are based on evaluating polynomials and their derivatives. This small augmentation confers upon them better local decoding, list-decoding and local list-decoding algorithms than their classical counterparts. We survey what is known about these codes, present some variations and improvements, and finally list some interesting open problems.
A Practical Guide to Randomized Matrix Computations with MATLAB Implementations
Venue: arXiv cs.MS 2015
Year: 2015

Matrix operations such as matrix inversion, eigenvalue decomposition, singular value decomposition are ubiquitous in real-world applications. Unfortunately, many of these matrix operations so time and memory expensive that they are prohibitive when the scale of data is large. In real-world applications, since the data themselves are noisy, machine-precision matrix operations are not necessary at all, and one can sacrifice a reasonable amount of accuracy for computational efficiency. In recent years, a bunch of randomized algorithms have been devised to make matrix computations more scalable. Mahoney (2011) and Woodruff (2014) have written excellent but very technical reviews of the randomized algorithms. Differently, the focus of this manuscript is on intuition, algorithm derivation, and implementation. This manuscript should be accessible to people with knowledge in elementary matrix algebra but unfamiliar with randomized matrix computations. The algorithms introduced in this manuscript are all summarized in a user-friendly way, and they can be implemented in lines of MATLAB code. The readers can easily follow the implementations even if they do not understand the maths and algorithms.
Networking technologies for robotic applications
Venue: arXiv cs.CY 2015
Year: 2015

The ongoing progress in networking security, together with the growing range of robot applications in many fields of everyday life, makes robotics tangible reality in our near future. Accordingly, new advanced services, depends on the interplay between the robotics and cyber security, are being an important role in robotics world. This paper addresses technological implications of security enhancement to the Internet of Thing (IoT)-aided robotics domain, where networked robots are expected to work in complex environments. The security enhancement suggested by the NIST (National Institute of Standards and Technology) creates a security template for secure communications over the network are also discussed.
Design and Optimization of OpenFOAM-based CFD Applications for Hybrid and Heterogeneous HPC Platforms
Venue: arXiv cs.DC 2015
Year: 2015

Hardware-aware design and optimization is crucial in exploiting emerging architectures for PDE-based computational fluid dynamics applications. In this work, we study optimizations aimed at acceleration of OpenFOAM-based applications on emerging hybrid heterogeneous platforms. OpenFOAM uses MPI to provide parallel multi-processor functionality, which scales well on homogeneous systems but does not fully utilize the potential per-node performance on hybrid heterogeneous platforms. In our study, we use two OpenFOAM applications, icoFoam and laplacianFoam, both based on Krylov iterative methods. We propose a number of optimizations of the dominant kernel of the Krylov solver, aimed at acceleration of the overall execution of the applications on modern GPU-accelerated heterogeneous platforms. Experimental results show that the proposed hybrid implementation significantly outperforms the state-of-the-art implementation.
Pignistic Probability Transforms for Mixes of Low- and High-Probability Events
Venue: arXiv cs.AI 2015
Year: 2015

In some real world information fusion situations, time critical decisions must be made with an incomplete information set. Belief function theories (e.g., Dempster-Shafer theory of evidence, Transferable Belief Model) have been shown to provide a reasonable methodology for processing or fusing the quantitative clues or information measurements that form the incomplete information set. For decision making, the pignistic (from the Latin pignus, a bet) probability transform has been shown to be a good method of using Beliefs or basic belief assignments (BBAs) to make decisions. For many systems, one need only address the most-probable elements in the set. For some critical systems, one must evaluate the risk of wrong decisions and establish safe probability thresholds for decision making. This adds a greater complexity to decision making, since one must address all elements in the set that are above the risk decision threshold. The problem is greatly simplified if most of the probabilities fall below this threshold. Finding a probability transform that properly represents mixes of low- and high-probability events is essential. This article introduces four new pignistic probability transforms with an implementation that uses the latest values of Beliefs, Plausibilities, or BBAs to improve the pignistic probability estimates. Some of them assign smaller values of probabilities for smaller values of Beliefs or BBAs than the Smets pignistic transform. They also assign higher probability values for larger values of Beliefs or BBAs than the Smets pignistic transform. These probability transforms will assign a value of probability that converges faster to the values below the risk threshold. A probability information content (PIC) variable is also introduced that assigns an information content value to any set of probability. Four operators are defined to help simplify the derivations.
A Geometric Interpretation of the Boolean Gilbert-Johnson-Keerthi Algorithm
Venue: arXiv cs.CG 2015
Year: 2015

The Gilbert-Johnson-Keerthi (GJK) algorithm is an iterative improvement technique for finding the minimum distance between two convex objects. It can easily be extended to work with concave objects and return the pair of closest points. [4] The key operation of GJK is testing whether a Voronoi region of a simplex contains the origin or not. In this paper we show that, in the context where one is interested only in the Boolean value of whether two convex objects intersect, and not in the actual distance between them, the number of test cases in GJK can be significantly reduced. This results in a simpler and more efficient algorithm that can be used in many computational geometry applications.
An Accuracy-Assured Privacy-Preserving Recommender System for Internet Commerce
Venue: arXiv cs.CR 2015
Year: 2015

Recommender systems, tool for predicting users' potential preferences by computing history data and users' interests, show an increasing importance in various Internet applications such as online shopping. As a well-known recommendation method, neighbourhood-based collaborative filtering has attracted considerable attention recently. The risk of revealing users' private information during the process of filtering has attracted noticeable research interests. Among the current solutions, the probabilistic techniques have shown a powerful privacy preserving effect. When facing $k$ Nearest Neighbour attack, all the existing methods provide no data utility guarantee, for the introduction of global randomness. In this paper, to overcome the problem of recommendation accuracy loss, we propose a novel approach, Partitioned Probabilistic Neighbour Selection, to ensure a required prediction accuracy while maintaining high security against $k$NN attack. We define the sum of $k$ neighbours' similarity as the accuracy metric alpha, the number of user partitions, across which we select the $k$ neighbours, as the security metric beta. We generalise the $k$ Nearest Neighbour attack to beta k Nearest Neighbours attack. Differing from the existing approach that selects neighbours across the entire candidate list randomly, our method selects neighbours from each exclusive partition of size $k$ with a decreasing probability. Theoretical and experimental analysis show that to provide an accuracy-assured recommendation, our Partitioned Probabilistic Neighbour Selection method yields a better trade-off between the recommendation accuracy and system security.