OpenRaiser/Intern-Atlas huggingface dataset:
Robust PI Passivity-based Control of Nonlinear Systems: Application to Port-Hamiltonian Systems and Temperature Regulation
Venue: arXiv cs.SY 2015
Year: 2015

This paper deals with the problem of control of partially known nonlinear systems, which have an open-loop stable equilibrium, but we would like to add a PI controller to regulate its behavior around another operating point. Our main contribution is the identification of a class of systems for which a globally stable PI can be designed knowing only the systems input matrix and measuring only the actuated coordinates. The construction of the PI is done invoking passivity theory. The difficulties encountered in the design of adaptive PI controllers with the existing theoretical tools are also discussed. As an illustration of the theory, we consider port--Hamiltonian systems and a class of thermal processes.
Efficient Query Processing for SPARQL Federations with Replicated Fragments
Venue: arXiv cs.DB 2015
Year: 2015

Low reliability and availability of public SPARQL endpoints prevent real-world applications from exploiting all the potential of these querying infras-tructures. Fragmenting data on servers can improve data availability but degrades performance. Replicating fragments can offer new tradeoff between performance and availability. We propose FEDRA, a framework for querying Linked Data that takes advantage of client-side data replication, and performs a source selection algorithm that aims to reduce the number of selected public SPARQL endpoints, execution time, and intermediate results. FEDRA has been implemented on the state-of-the-art query engines ANAPSID and FedX, and empirically evaluated on a variety of real-world datasets.
Strategies for High-Throughput FPGA-based QC-LDPC Decoder Architecture
Venue: arXiv cs.AR 2015
Year: 2015

We propose without loss of generality strategies to achieve a high-throughput FPGA-based architecture for a QC-LDPC code based on a circulant-1 identity matrix construction. We present a novel representation of the parity-check matrix (PCM) providing a multi-fold throughput gain. Splitting of the node processing algorithm enables us to achieve pipelining of blocks and hence layers. By partitioning the PCM into not only layers but superlayers we derive an upper bound on the pipelining depth for the compact representation. To validate the architecture, a decoder for the IEEE 802.11n (2012) QC-LDPC is implemented on the Xilinx Kintex-7 FPGA with the help of the FPGA IP compiler [2] available in the NI LabVIEW Communication System Design Suite (CSDS) which offers an automated and systematic compilation flow where an optimized hardware implementation from the LDPC algorithm was generated in approximately 3 minutes, achieving an overall throughput of 608Mb/s (at 260MHz). As per our knowledge this is the fastest implementation of the IEEE 802.11n QC-LDPC decoder using an algorithmic compiler.
Spreadsheets in an ERP environment: not what the doctor ordered
Venue: arXiv cs.SE 2015
Year: 2015

Modern ERP systems contain flexible report generators but the tendency exists for users to export data to spreadsheets for manipulation, reporting and decision making. A purported reason for this is that some users are more familiar with personal reporting tools (spreadsheets) as opposed to enterprise reporting tools. The author's doctoral research intends to measure the extent of spreadsheet usage in ERP environments and to determine which factors facilitate this.
Solving Local Linear Systems with Boundary Conditions Using Heat Kernel Pagerank
Venue: arXiv cs.DS 2015
Year: 2015

We present an efficient algorithm for solving local linear systems with a boundary condition using the Green's function of a connected induced subgraph related to the system. We introduce the method of using the Dirichlet heat kernel pagerank vector to approximate local solutions to linear systems in the graph Laplacian satisfying given boundary conditions over a particular subset of vertices. With an efficient algorithm for approximating Dirichlet heat kernel pagerank, our local linear solver algorithm computes an approximate local solution with multiplicative and additive error $ε$ by performing $O(ε^{-5}s^3\log(s^3ε^{-1})\log n)$ random walk steps, where $n$ is the number of vertices in the full graph and $s$ is the size of the local system on the induced subgraph.
Learning Classifiers from Synthetic Data Using a Multichannel Autoencoder
Venue: arXiv cs.CV 2015
Year: 2015

We propose a method for using synthetic data to help learning classifiers. Synthetic data, even is generated based on real data, normally results in a shift from the distribution of real data in feature space. To bridge the gap between the real and synthetic data, and jointly learn from synthetic and real data, this paper proposes a Multichannel Autoencoder(MCAE). We show that by suing MCAE, it is possible to learn a better feature representation for classification. To evaluate the proposed approach, we conduct experiments on two types of datasets. Experimental results on two datasets validate the efficiency of our MCAE model and our methodology of generating synthetic data.
Dynamic Partitioning of Physical Memory Among Virtual Machines, ASMI:Architectural Support for Memory Isolation
Venue: arXiv cs.AR 2015
Year: 2015

Cloud computing relies on secure and efficient virtualization. Software level security solutions compromise the performance of virtual machines (VMs), as a large amount of computational power would be utilized for running the security modules. Moreover, software solutions are only as secure as the level that they work on. For example a security module on a hypervisor cannot provide security in the presence of an infected hypervisor. It is a challenge for virtualization technology architects to enhance the security of VMs without degrading their performance. Currently available server machines are not fully equipped to support a secure VM environment without compromising on performance. A few hardware modifications have been introduced by manufactures like Intel and AMD to provide a secure VM environment with low performance degradation. In this paper we propose a novel memory architecture model named \textit{ Architectural Support for Memory Isolation(ASMI)}, that can achieve a true isolated physical memory region to each VM without degrading performance. Along with true memory isolation, ASMI is designed to provide lower memory access times, better utilization of available memory, support for DMA isolation and support for platform independence for users of VMs.
Simple, Accurate, and Robust Nonparametric Blind Super-Resolution
Venue: arXiv cs.CV 2015
Year: 2015

This paper proposes a simple, accurate, and robust approach to single image nonparametric blind Super-Resolution (SR). This task is formulated as a functional to be minimized with respect to both an intermediate super-resolved image and a nonparametric blur-kernel. The proposed approach includes a convolution consistency constraint which uses a non-blind learning-based SR result to better guide the estimation process. Another key component is the unnatural bi-l0-l2-norm regularization imposed on the super-resolved, sharp image and the blur-kernel, which is shown to be quite beneficial for estimating the blur-kernel accurately. The numerical optimization is implemented by coupling the splitting augmented Lagrangian and the conjugate gradient (CG). Using the pre-estimated blur-kernel, we finally reconstruct the SR image by a very simple non-blind SR method that uses a natural image prior. The proposed approach is demonstrated to achieve better performance than the recent method by Michaeli and Irani [2] in both terms of the kernel estimation accuracy and image SR quality.
Lower Bound on the Capacity of Continuous-Time Wiener Phase Noise Channels
Venue: arXiv cs.IT 2015
Year: 2015

A continuous-time Wiener phase noise channel with an integrate-and-dump multi-sample receiver is studied. A lower bound to the capacity with an average input power constraint is derived, and a high signal-to-noise ratio (SNR) analysis is performed. The capacity pre-log depends on the oversampling factor, and amplitude and phase modulation do not equally contribute to capacity at high SNR.
Security challenges in mobile ad hoc networks:a survey
Venue: arXiv cs.NI 2015
Year: 2015

MANET is a kind of Ad hoc network with mobile, wireless nodes. Because of its special characteristics like dynamic topology, hop-by-hop communications and easy and quick setup, MANET faced lots of challenges allegorically routing, security and clustering. The security challenges arise due to MANETs self-configuration and self-maintenance capabilities. In this paper, we present an elaborate view of issues in MANET security. Based on MANETs special characteristics, we define three security parameters for MANET. In addition we divided MANET security into two different aspects and discussed each one in details. A comprehensive analysis in security aspects of MANET and defeating approaches is presented. In addition, defeating approaches against attacks have been evaluated in some important metrics. After analyses and evaluations, future scopes of work have been presented.
Material Approximation of Data Smoothing and Spline Curves Inspired by Slime Mould
Venue: arXiv cs.ET 2015
Year: 2015

Using a particle model of Physarum displaying emer- gent morphological adaptation behaviour we demonstrate how a minimal approach to collective material computation may be used to transform and summarise properties of spatially represented datasets. We find that the virtual material relaxes more strongly to high-frequency changes in data which can be used for the smoothing (or filtering) of data by ap- proximating moving average and low-pass filters in 1D datasets. The relaxation and minimisation properties of the model enable the spatial computation of B-spline curves (approximating splines) in 2D datasets. Both clamped and unclamped spline curves, of open and closed shapes, can be represented and the degree of spline curvature corresponds to the relaxation time of the material. The material computation of spline curves also includes novel quasi-mechanical properties including unwind- ing of the shape between control points and a preferential adhesion to longer, straighter paths. Interpolating splines could not directly be ap- proximated due to the formation and evolution of Steiner points at nar- row vertices, but were approximated after rectilinear pre-processing of the source data. This pre-processing was further simplified by transform- ing the original data to contain the material inside the polyline. These exemplar results expand the repertoire of spatially represented uncon- ventional computing devices by demonstrating a simple, collective and distributed approach to data and curve smoothing.
Dynamic Backhaul Network Configuration in SDN-based Cloud RANs
Venue: arXiv cs.NI 2015
Year: 2015

The coordination of base stations in mobile access networks is an important approach to reduce harmful interference and to deliver high data rates to the users. Such coordination mechanisms, like Coordinated Multi-Point (CoMP) where multiple BSs transmit data to a user equipment, can be easily implemented when centralizing the data processing of the base stations, known as Cloud RAN. This centralization also imposes significant requirements on the backhaul network for high capacities and low latencies for the connections to the base stations. These requirements can be mitigated by (a) a flexible placement of the base station data processing functionality and by (b) dynamically assigning backhaul network resources. We show how these two techniques increase the feasibility of base station coordination in dense mobile access networks by using a heuristic algorithm. We furthermore present a prototype implementation of our approach based on software defined networking (SDN) with OpenDaylight and Maxinet.
Are Heterogeneous Cloud-Based Radio Access Networks Cost Effective?
Venue: arXiv cs.NI 2015
Year: 2015

Mobile networks of the future are predicted to be much denser than today's networks in order to cater to increasing user demands. In this context, cloud based radio access networks have garnered significant interest as a cost effective solution to the problem of coping with denser networks and providing higher data rates. However, to the best knowledge of the authors, a quantitative analysis of the cost of such networks is yet to be undertaken. This paper develops a theoretic framework that enables computation of the deployment cost of a network (modeled using various spatial point processes) to answer the question posed by the paper's title. Then, the framework obtained is used along with a complexity model, which enables computing the information processing costs of a network, to compare the deployment cost of a cloud based network against that of a traditional LTE network, and to analyze why they are more economical. Using this framework and an exemplary budget, this paper shows that cloud-based radio access networks require approximately 10 to 15% less capital expenditure per square kilometer than traditional LTE networks. It also demonstrates that the cost savings depend largely on the costs of base stations and the mix of backhaul technologies used to connect base stations with data centers.
Faster 64-bit universal hashing using carry-less multiplications
Venue: arXiv cs.DS 2015
Year: 2015

Intel and AMD support the Carry-less Multiplication (CLMUL) instruction set in their x64 processors. We use CLMUL to implement an almost universal 64-bit hash family (CLHASH). We compare this new family with what might be the fastest almost universal family on x64 processors (VHASH). We find that CLHASH is at least 60% faster. We also compare CLHASH with a popular hash function designed for speed (Google's CityHash). We find that CLHASH is 40% faster than CityHash on inputs larger than 64 bytes and just as fast otherwise.
Algorithms and Properties for Positive Symmetrizable Matrices
Venue: arXiv cs.DM 2015
Year: 2015

Matrices are the most common representations of graphs. They are also used for the representation of algebras and cluster algebras. This paper shows some properties of matrices in order to facilitate the understanding and locating symmetrizable matrices with specific characteristics, called positive quasi-Cartan companion matrices. Here, symmetrizable matrix are those which are symmetric when multiplied by a diagonal matrix with positive entries called symmetrizer matrix. Four algorithms are developed: one to decide whether there is a symmetrizer matrix; second to find such symmetrizer matrix; another to decide whether the matrix is positive or not; and the last to find a positive quasi-Cartan companion matrix, if there exists. The third algorithm is used to prove that the problem to decide if a matrix has a positive quasi-Cartan companion is NP.
Three-coloring graphs with no induced seven-vertex path II : using a triangle
Venue: arXiv cs.DM 2015
Year: 2015

In this paper, we give a polynomial time algorithm which determines if a given graph containing a triangle and no induced seven-vertex path is 3-colorable, and gives an explicit coloring if one exists. In previous work, we gave a polynomial time algorithm for three-coloring triangle-free graphs with no induced seven-vertex path. Combined, our work shows that three-coloring a graph with no induced seven-vertex path can be done in polynomial time.
An improved return-mapping scheme for nonsmooth yield surfaces: PART I - the Haigh-Westergaard coordinates
Venue: arXiv cs.CE 2015
Year: 2015

The paper is devoted to the numerical solution of elastoplastic constitutive initial value problems. An improved form of the implicit return-mapping scheme for nonsmooth yield surfaces is proposed that systematically builds on a subdifferential formulation of the flow rule. The main advantage of this approach is that the treatment of singular points, such as apices or edges at which the flow direction is multivalued involves only a uniquely defined set of non-linear equations, similarly to smooth yield surfaces. This paper (PART I) is focused on isotropic models containing: $a)$ yield surfaces with one or two apices (singular points) laying on the hydrostatic axis; $b)$ plastic pseudo-potentials that are independent of the Lode angle; $c)$ nonlinear isotropic hardening (optionally). It is shown that for some models the improved integration scheme also enables to a priori decide about a type of the return and investigate existence, uniqueness and semismoothness of discretized constitutive operators in implicit form. Further, the semismooth Newton method is introduced to solve incremental boundary-value problems. The paper also contains numerical examples related to slope stability with available Matlab implementation.
A novel hash based least significant bit (2-3-3) image steganography in spatial domain
Venue: arXiv cs.MM 2015
Year: 2015

This paper presents a novel 2-3-3 LSB insertion method. The image steganography takes the advantage of human eye limitation. It uses color image as cover media for embedding secret message.The important quality of a steganographic system is to be less distortive while increasing the size of the secret message. In this paper a method is proposed to embed a color secret image into a color cover image. A 2-3-3 LSB insertion method has been used for image steganography. Experimental results show an improvement in the Mean squared error (MSE) and Peak Signal to Noise Ratio (PSNR) values of the proposed technique over the base technique of hash based 3-3-2 LSB insertion.
Starting engagement detection towards a companion robot using multimodal features
Venue: arXiv cs.RO 2015
Year: 2015

Recognition of intentions is a subconscious cognitive process vital to human communication. This skill enables anticipation and increases the quality of interactions between humans. Within the context of engagement, non-verbal signals are used to communicate the intention of starting the interaction with a partner. In this paper, we investigated methods to detect these signals in order to allow a robot to know when it is about to be addressed. Originality of our approach resides in taking inspiration from social and cognitive sciences to perform our perception task. We investigate meaningful features, i.e. human readable features, and elicit which of these are important for recognizing someone's intention of starting an interaction. Classically, spatial information like the human position and speed, the human-robot distance are used to detect the engagement. Our approach integrates multimodal features gathered using a companion robot equipped with a Kinect. The evaluation on our corpus collected in spontaneous conditions highlights its robustness and validates the use of such a technique in a real environment. Experimental validation shows that multimodal features set gives better precision and recall than using only spatial and speed features. We also demonstrate that 7 selected features are sufficient to provide a good starting engagement detection score. In our last investigation, we show that among our full 99 features set, the space reduction is not a solved task. This result opens new researches perspectives on multimodal engagement detection.
Practical Realization of the Self-Balancing Robot Using Infrared Sensors
Venue: arXiv cs.RO 2015
Year: 2015

The idea of a two wheel self-balancing robot has become very popular among control system researchers worldwide over the last decade. This paper presents a one variant of the implementation of the self-balancing robot using the VEX Robotics Kit.
Towards 1 Gbps/UE in Cellular Systems: Understanding Ultra-Dense Small Cell Deployments
Venue: arXiv cs.NI 2015
Year: 2015

Todays heterogeneous networks comprised of mostly macrocells and indoor small cells will not be able to meet the upcoming traffic demands. Indeed, it is forecasted that at least a 100x network capacity increase will be required to meet the traffic demands in 2020. As a result, vendors and operators are now looking at using every tool at hand to improve network capacity. In this epic campaign, three paradigms are noteworthy, i.e., network densification, the use of higher frequency bands and spectral efficiency enhancement techniques. This paper aims at bringing further common understanding and analysing the potential gains and limitations of these three paradigms, together with the impact of idle mode capabilities at the small cells as well as the user equipment density and distribution in outdoor scenarios. Special attention is paid to network densification and its implications when transitioning to ultra-dense small cell deployments. Simulation results show that network densification with an average inter site distance of 35 m can increase the cell- edge UE throughput by up to 48x, while the use of the 10GHz band with a 500MHz bandwidth can increase the network capacity up to 5x. The use of beamforming with up to 4 antennas per small cell base station lacks behind with cell-edge throughput gains of up to 1.49x. Our study also shows how network densifications reduces multi-user diversity, and thus proportional fair alike schedulers start losing their advantages with respect to round robin ones. The energy efficiency of these ultra-dense small cell deployments is also analysed, indicating the need for energy harvesting approaches to make these deployments energy- efficient. Finally, the top ten challenges to be addressed to bring ultra-dense small cell deployments to reality are also discussed.
On the Application of Multiuser Detection in Multibeam Satellite Systems
Venue: arXiv cs.IT 2015
Year: 2015

We study the achievable rates by a single user in multibeam satellite scenarios. We show alternatives to the conventional symbol-by-symbol detection applied at user terminals. Single user detection is known to suffer from strong degradation when the terminal is located near the edge of the coverage area, and when aggressive frequency reuse is adopted. For this reason, we consider multiuser detection, and take into account the strongest interfering signal. Moreover, we analyze a different transmission strategy, where the signals from two adjacent beams jointly serve two users in a time division multiplexing fashion. We describe an information-theoretic framework to compare different transmission/detection strategies by computing the information rate of the user in the reference beam.
Event-Triggered Observers and Observer-Based Controllers for a Class of Nonlinear Systems
Venue: arXiv cs.SY 2015
Year: 2015

In this paper, we investigate the stabilization of a nonlinear plant subject to network constraints, under the assumption of partial knowledge of the plant state. The event triggered paradigm is used for the observation and the control of the system. Necessary conditions, making use of the ISS property, are given to guarantee the existence of a triggering mechanism, leading to asymptotic convergence of the observer and system states. The proposed triggering mechanism is illustrated in the stabilization of a robot with a flexible link robot.
Exploiting Image-trained CNN Architectures for Unconstrained Video Classification
Venue: arXiv cs.CV 2015
Year: 2015

We conduct an in-depth exploration of different strategies for doing event detection in videos using convolutional neural networks (CNNs) trained for image classification. We study different ways of performing spatial and temporal pooling, feature normalization, choice of CNN layers as well as choice of classifiers. Making judicious choices along these dimensions led to a very significant increase in performance over more naive approaches that have been used till now. We evaluate our approach on the challenging TRECVID MED'14 dataset with two popular CNN architectures pretrained on ImageNet. On this MED'14 dataset, our methods, based entirely on image-trained CNN features, can outperform several state-of-the-art non-CNN models. Our proposed late fusion of CNN- and motion-based features can further increase the mean average precision (mAP) on MED'14 from 34.95% to 38.74%. The fusion approach achieves the state-of-the-art classification performance on the challenging UCF-101 dataset.