OpenRaiser/Intern-Atlas huggingface dataset:
The Python user interface of the elsA cfd software: a coupling framework for external steering layers
Venue: arXiv cs.HC 2016
Year: 2016

The Python--elsA user interface of the elsA cfd (Computational Fluid Dynamics) software has been developed to allow users to specify simulations with confidence, through a global context of description objects grouped inside scripts. The software main features are generated documentation, context checking and completion, and helpful error management. Further developments have used this foundation as a coupling framework, allowing (thanks to the descriptive approach) the coupling of external algorithms with the cfd solver in a simple and abstract way, leading to more success in complex simulations. Along with the description of the technical part of the interface, we try to gather the salient points pertaining to the psychological viewpoint of user experience (ux). We point out the differences between user interfaces and pure data management systems such as cgns.
Detection of surface defects on ceramic tiles based on morphological techniques
Venue: arXiv cs.CV 2016
Year: 2016

Ceramic tiles have become very popular and are used in the flooring of offices and shopping malls. As testing the quality of tiles manually in a highly polluted environment in the manufacturing industry is a labor-intensive and time consuming process, analysis is carried out on the tile images. This paper discusses an automated system to detect the defects on the surface of ceramic tiles based on dilation, erosion, SMEE and boundary detection techniques.
Synchronization in Networked Systems with Parameter Mismatch: Adaptive Decentralized and Distributed Controls
Venue: arXiv cs.SY 2016
Year: 2016

Here, we study the ultimately bounded stability of network of mismatched systems using Lyapunov direct method. We derive an upper bound on the norm of the error of network states from its average states, which it achieves in finite time. Then, we devise a decentralized compensator to asymptotically pin the network of mismatched systems to a desired trajectory. Next, we design distributed estimators to compensate for the mismatched parameters performances of adaptive decentralized and distributed compensations are analyzed. Our analytical results are verified by several simulations in a network of globally connected Lorenz oscillators.
iDRM: Humanoid Motion Planning with Real-Time End-Pose Selection in Complex Environments
Venue: arXiv cs.RO 2016
Year: 2016

In this paper, we propose a novel inverse Dynamic Reachability Map (iDRM) that allows a floating base system to find valid end-poses in complex and dynamically changing environments in real-time. End-pose planning for valid stance pose and collision-free configuration is an essential problem for humanoid applications, such as providing goal states for walking and motion planners. However, this is non-trivial in complex environments, where standing locations and reaching postures are restricted by obstacles. Our proposed iDRM customizes the robot-to-workspace occupation list and uses an online update algorithm to enable efficient reconstruction of the reachability map to guarantee that the selected end-poses are always collision-free. The iDRM was evaluated in a variety of reaching tasks using the 38 degree-of-freedom (DoF) humanoid robot Valkyrie. Our results show that the approach is capable of finding valid end-poses in a fraction of a second. Significantly, we also demonstrate that motion planning algorithms integrating our end-pose planning method are more efficient than those not utilizing this technique.
Decentralized Dynamic Optimization for Power Network Voltage Control
Venue: arXiv cs.SY 2016
Year: 2016

Voltage control in power distribution networks has been greatly challenged by the increasing penetration of volatile and intermittent devices. These devices can also provide limited reactive power resources that can be used to regulate the network-wide voltage. A decentralized voltage control strategy can be designed by minimizing a quadratic voltage mismatch error objective using gradient-projection (GP) updates. Coupled with the power network flow, the local voltage can provide the instantaneous gradient information. This paper aims to analyze the performance of this decentralized GP-based voltage control design under two dynamic scenarios: i) the nodes perform the decentralized update in an asynchronous fashion, and ii) the network operating condition is time-varying. For the asynchronous voltage control, we improve the existing convergence condition by recognizing that the voltage based gradient is always up-to-date. By modeling the network dynamics using an autoregressive process and considering time-varying resource constraints, we provide an error bound in tracking the instantaneous optimal solution to the quadratic error objective. This result can be extended to more general \textit{constrained dynamic optimization} problems with smooth strongly convex objective functions under stochastic processes that have bounded iterative changes. Extensive numerical tests have been performed to demonstrate and validate our analytical results for realistic power networks.
Rank Correlation Measure: A Representational Transformation for Biometric Template Protection
Venue: arXiv cs.CV 2016
Year: 2016

Despite a variety of theoretical-sound techniques have been proposed for biometric template protection, there is rarely practical solution that guarantees non-invertibility, cancellability, non-linkability and performance simultaneously. In this paper, a ranking-based representational transformation is proposed for fingerprint templates. The proposed method transforms a real-valued feature vector into index code such that the pairwise-order measure in the resultant codes are closely correlated with rank similarity measure. Such a ranking based technique offers two major merits: 1) Resilient to noises/perturbations in numeric values; and 2) Highly nonlinear embedding based on partial order statistics. The former takes care of the accuracy performance mitigating numeric noises/perturbations while the latter offers strong non-invertible transformation via nonlinear feature embedding from Euclidean to Rank space that leads to toughness in inversion. The experimental results demonstrate reasonable accuracy performance on benchmark FVC2002 and FVC2004 fingerprint databases, thus confirm the proposition of the rank correlation. Moreover, the security and privacy analysis justify the strong capability against the existing major privacy attacks.
Architecture for Community-scale Critical Infrastructure Coordination for Security and Resilience
Venue: arXiv cs.CR 2016
Year: 2016

Our Critical Infrastructure (CI) systems are, by definition, critical to the safe and proper functioning of society. Nearly all of these systems utilize industrial Process Control Systems (PCS) to provide clean water, reliable electricity, critical manufacturing, and many other services within our communities - yet most of these PCS incorporate very little cyber-security countermeasures. Cyber-attacks on CI are becoming an attractive target. While many vendor solutions are starting to be deployed at CI sites, these solutions are largely based on network monitoring for intrusion detection. As such, they are not process-aware, nor do they account for inter dependencies among other CI sites in their community. What is proposed is an architecture for coordinating all CI within a community, which defines characteristics to enhance its integration, its resilience to failure and attack, and its ultimate acceptance by CI operators.
Redundancy-free Verbalization of Individuals for Ontology Validation
Venue: arXiv cs.AI 2016
Year: 2016

We investigate the problem of verbalizing Web Ontology Language (OWL) axioms of domain ontologies in this paper. The existing approaches address the problem of fidelity of verbalized OWL texts to OWL semantics by exploring different ways of expressing the same OWL axiom in various linguistic forms. They also perform grouping and aggregating of the natural language (NL) sentences that are generated corresponding to each OWL statement into a comprehensible structure. However, no efforts have been taken to try out a semantic reduction at logical level to remove redundancies and repetitions, so that the reduced set of axioms can be used for generating a more meaningful and human-understandable (what we call redundancy-free) text. Our experiments show that, formal semantic reduction at logical level is very helpful to generate redundancy-free descriptions of ontology entities. In this paper, we particularly focus on generating descriptions of individuals of SHIQ based ontologies. The details of a case study are provided to support the usefulness of the redundancy-free NL descriptions of individuals, in knowledge validation application.
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning
Venue: arXiv cs.CV 2016
Year: 2016

Recently, machine learning based single image super resolution (SR) approaches focus on jointly learning representations for high-resolution (HR) and low-resolution (LR) image patch pairs to improve the quality of the super-resolved images. However, due to treat all image pixels equally without considering the salient structures, these approaches usually fail to produce visual pleasant images with sharp edges and fine details. To address this issue, in this work we present a new novel SR approach, which replaces the main building blocks of the classical interpolation pipeline by a flexible, content-adaptive deep neural networks. In particular, two well-designed structure-aware components, respectively capturing local- and holistic- image contents, are naturally incorporated into the fully-convolutional representation learning to enhance the image sharpness and naturalness. Extensively evaluations on several standard benchmarks (e.g., Set5, Set14 and BSD200) demonstrate that our approach can achieve superior results, especially on the image with salient structures, over many existing state-of-the-art SR methods under both quantitative and qualitative measures.
Doubly Massive mmWave MIMO Systems: Using Very Large Antenna Arrays at Both Transmitter and Receiver
Venue: arXiv cs.IT 2016
Year: 2016

One of the key features of next generation wireless communication systems will be the use of frequencies in the range 10-100GHz (aka mmWave band) in densely populated indoor and outdoor scenarios. Due to the reduced wavelength, antenna arrays with a large number of antennas can be packed in very small volumes, making thus it possible to consider, at least in principle, communication links wherein not only the base-station, but also the user device, are equipped with very large antenna arrays. We denote this configuration as a "doubly-massive" MIMO wireless link. This paper introduces the concept of doubly massive MIMO systems at mmWave, showing that at mmWave the fundamentals of the massive MIMO regime are completely different from what happens at conventional sub-6 GHz cellular frequencies. It is shown for instance that the multiplexing capabilities of the channel and its rank are no longer ruled by the number of transmit and receive antennas, but rather by the number of scattering clusters in the surrounding environment. The implications of the doubly massive MIMO regime on the transceiver processing, on the system energy efficiency and on the system throughput are also discussed.
Spark Parameter Tuning via Trial-and-Error
Venue: arXiv cs.DC 2016
Year: 2016

Spark has been established as an attractive platform for big data analysis, since it manages to hide most of the complexities related to parallelism, fault tolerance and cluster setting from developers. However, this comes at the expense of having over 150 configurable parameters, the impact of which cannot be exhaustively examined due to the exponential amount of their combinations. The default values allow developers to quickly deploy their applications but leave the question as to whether performance can be improved open. In this work, we investigate the impact of the most important of the tunable Spark parameters on the application performance and guide developers on how to proceed to changes to the default values. We conduct a series of experiments with known benchmarks on the MareNostrum petascale supercomputer to test the performance sensitivity. More importantly, we offer a trial-and-error methodology for tuning parameters in arbitrary applications based on evidence from a very small number of experimental runs. We test our methodology in three case studies, where we manage to achieve speedups of more than 10 times.
Unique Set Cover on Unit Disks and Unit Squares
Venue: arXiv cs.CG 2016
Year: 2016

We study the Unique Set Cover problem on unit disks and unit squares. For a given set $P$ of $n$ points and a set $D$ of $m$ geometric objects both in the plane, the objective of the Unique Set Cover problem is to select a subset $D'\subseteq D$ of objects such that every point in $P$ is covered by at least one object in $D'$ and the number of points covered uniquely is maximized, where a point is covered uniquely if the point is covered by exactly one object in $D'$. In this paper, (i) we show that the Unique Set Cover is NP-hard on both unit disks and unit squares, and (ii) we give a PTAS for this problem on unit squares by applying the mod-one approach of Chan and Hu (Comput. Geom. 48(5), 2015).
Phase Retrieval by Linear Algebra
Venue: arXiv cs.IT 2016
Year: 2016

The null vector method, based on a simple linear algebraic concept, is proposed as a solution to the phase retrieval problem. In the case with complex Gaussian random measurement matrices, a non-asymptotic error bound is derived, yielding an asymptotic regime of accurate approximation comparable to that for the spectral vector method.
Task-space coordinated tracking of multiple heterogeneous manipulators via controller-estimator approaches
Venue: arXiv cs.SY 2016
Year: 2016

This paper studies the task-space coordinated tracking of a time-varying leader for multiple heterogeneous manipulators (MHMs), containing redundant manipulators and nonredundant ones. Different from the traditional coordinated control, distributed controller-estimator algorithms (DCEA), which consist of local algorithms and networked algorithms, are developed for MHMs with parametric uncertainties and input disturbances. By invoking differential inclusions, nonsmooth analysis, and input-to-state stability, some conditions (including sufficient conditions, necessary and sufficient conditions) on the asymptotic stability of the task-space tracking errors and the subtask errors are developed. Simulation results are given to show the effectiveness of the presented DCEA.
Latency-Optimal Uplink Scheduling Policy in Training-based Large-Scale Antenna Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, an uplink scheduling policy problem to minimize the network latency, defined as the air-time to serve all of users with a quality-of-service (QoS), under an energy constraint is considered in a training-based large-scale antenna systems (LSAS) employing a simple linear receiver. An optimal algorithm providing the exact latency-optimal uplink scheduling policy is proposed with a polynomial-time complexity. Via numerical simulations, it is shown that the proposed scheduling policy can provide several times lower network latency over the conventional ones in realistic environments. In addition, the proposed scheduling policy and its network latency are analyzed asymptotically to provide better insights on the system behavior. Four operating regimes are classified according to the average received signal quality, $ρ$, and the number of BS antennas, $M$. It turns out that orthogonal pilots are optimal only in the regime $ρ\gg1$ and $ M\ll \log^2ρ$. In other regimes ($ρ\ll 1$ or $ M\gg \log^2ρ$), it turns out that non-orthogonal pilots become optimal. More rigorously, the use of non-orthogonal pilots can reduce the network latency by a factor of $Θ(M)$ when $ρ\ll 1$ or by a factor of $Θ(\sqrt{M}/\log M)$ when $ρ\gg 1$ and $M\gg \logρ$, which would be a critical guideline for designing 5G future cellular systems.
OntoCat: Automatically categorizing knowledge in API Documentation
Venue: arXiv cs.SE 2016
Year: 2016

Most application development happens in the context of complex APIs; reference documentation for APIs has grown tremendously in variety, complexity, and volume, and can be difficult to navigate. There is a growing need to develop well-organized ways to access the knowledge latent in the documentation; several research efforts deal with the organization (ontology) of API-related knowledge. Extensive knowledge-engineering work, supported by a rigorous qualitative analysis, by Maalej & Robillard [3] has identified a useful taxonomy of API knowledge. Based on this taxonomy, we introduce a domain independent technique to extract the knowledge types from the given API reference documentation. Our system, OntoCat, introduces total nine different features and their semantic and statistical combinations to classify the different knowledge types. We tested OntoCat on python API reference documentation. Our experimental results show the effectiveness of the system and opens the scope of probably related research areas (i.e., user behavior, documentation quality, etc.).
Grounded Lexicon Acquisition - Case Studies in Spatial Language
Venue: arXiv cs.CL 2016
Year: 2016

This paper discusses grounded acquisition experiments of increasing complexity. Humanoid robots acquire English spatial lexicons from robot tutors. We identify how various spatial language systems, such as projective, absolute and proximal can be learned. The proposed learning mechanisms do not rely on direct meaning transfer or direct access to world models of interlocutors. Finally, we show how multiple systems can be acquired at the same time.
Scale Invariant Interest Points with Shearlets
Venue: arXiv cs.CV 2016
Year: 2016

Shearlets are a relatively new directional multi-scale framework for signal analysis, which have been shown effective to enhance signal discontinuities such as edges and corners at multiple scales. In this work we address the problem of detecting and describing blob-like features in the shearlets framework. We derive a measure which is very effective for blob detection and closely related to the Laplacian of Gaussian. We demonstrate the measure satisfies the perfect scale invariance property in the continuous case. In the discrete setting, we derive algorithms for blob detection and keypoint description. Finally, we provide qualitative justifications of our findings as well as a quantitative evaluation on benchmark data. We also report an experimental evidence that our method is very suitable to deal with compressed and noisy images, thanks to the sparsity property of shearlets.
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks
Venue: arXiv cs.CV 2016
Year: 2016

One impressive advantage of convolutional neural networks (CNNs) is their ability to automatically learn feature representation from raw pixels, eliminating the need for hand-designed procedures. However, recent methods for single image super-resolution (SR) fail to maintain this advantage. They utilize CNNs in two decoupled steps, i.e., first upsampling the low resolution (LR) image to the high resolution (HR) size with hand-designed techniques (e.g., bicubic interpolation), and then applying CNNs on the upsampled LR image to reconstruct HR results. In this paper, we seek an alternative and propose a new image SR method, which jointly learns the feature extraction, upsampling and HR reconstruction modules, yielding a completely end-to-end trainable deep CNN. As opposed to existing approaches, the proposed method conducts upsampling in the latent feature space with filters that are optimized for the task of image SR. In addition, the HR reconstruction is performed in a multi-scale manner to simultaneously incorporate both short- and long-range contextual information, ensuring more accurate restoration of HR images. To facilitate network training, a new training approach is designed, which jointly trains the proposed deep network with a relatively shallow network, leading to faster convergence and more superior performance. The proposed method is extensively evaluated on widely adopted data sets and improves the performance of state-of-the-art methods with a considerable margin. Moreover, in-depth ablation studies are conducted to verify the contribution of different network designs to image SR, providing additional insights for future research.
Customer Churn in Mobile Markets A Comparison of Techniques
Venue: arXiv cs.CY 2016
Year: 2016

The high increase in the number of companies competing in mature markets makes customer retention an important factor for any company to survive. Thus, many methodologies (e.g., data mining and statistics) have been proposed to analyse and study customer retention. The validity of such methods is not yet proved though. This paper tries to fill this gap by empirically comparing two techniques: Customer churn-decision tree and logistic regression models. The paper proves the superiority of decision tree technique and stresses the needs for more advanced methods to churn modelling.
Influence of personal values and the adoption of analytical tools using laddering methodology
Venue: arXiv cs.HC 2016
Year: 2016

Analytical tools in business management are understood as a combination of information technologies and quantitative methods used to assist stakeholders to make better decisions. The contemporary business environment is dramatically changing by the massive accumulation of data. Now, as never before, the use of analytical tools must be expanded to take advantage of this growing digital universe. This article will apply the laddering technique to see how personal values (or managerial functions) influence a companys adoption of analytical tools. A set of ten in-depth interviews are conducted with CEOs, analytics consultants, academics and businessmen in order to establish quantitative relations among attributes, consequences and personal values. Two easy-to-read outputs are provided to interpret our results. The most important links are quantitatively associated through an implication matrix, and then visually represented on a hierarchical value map. Guidelines for improving the use of analytical tools are provided in the last section
Equivalence of Quasi-cyclic Codes over Finite Fields
Venue: arXiv cs.IT 2016
Year: 2016

This paper considers the equivalence problem for quasi-cyclic codes over finite fields. The results obtained are used to construct isodual quasi-cyclic codes.
Regular Behaviours with Names
Venue: arXiv cs.LO 2016
Year: 2016

Nominal sets provide a framework to study key notions of syntax and semantics such as fresh names, variable binding and $α$-equivalence on a conveniently abstract categorical level. Coalgebras for endofunctors on nominal sets model, e.g., various forms of automata with names as well as infinite terms with variable binding operators (such as $λ$-abstraction). Here, we first study the behaviour of orbit-finite coalgebras for functors $\bar F$ on nominal sets that lift some finitary set functor $F$. We provide sufficient conditions under which the rational fixpoint of $\bar F$, i.e. the collection of all behaviours of orbit-finite $\bar F$-coalgebras, is the lifting of the rational fixpoint of $F$. Second, we describe the rational fixpoint of the quotient functors: we introduce the notion of a sub-strength of an endofunctor on nominal sets, and we prove that for a functor $G$ with a sub-strength the rational fixpoint of each quotient of $G$ is a canonical quotient of the rational fixpoint of $G$. As applications, we obtain a concrete description of the rational fixpoint for functors arising from so-called binding signatures with exponentiation, such as those arising in coalgebraic models of infinitary $λ$-terms and various flavours of automata.