OpenRaiser/Intern-Atlas huggingface dataset:
Collaborative P2P Streaming of Interactive Live Free Viewpoint Video
Venue: arXiv cs.MM 2012
Year: 2012

We study an interactive live streaming scenario where multiple peers pull streams of the same free viewpoint video that are synchronized in time but not necessarily in view. In free viewpoint video, each user can periodically select a virtual view between two anchor camera views for display. The virtual view is synthesized using texture and depth videos of the anchor views via depth-image-based rendering (DIBR). In general, the distortion of the virtual view increases with the distance to the anchor views, and hence it is beneficial for a peer to select the closest anchor views for synthesis. On the other hand, if peers interested in different virtual views are willing to tolerate larger distortion in using more distant anchor views, they can collectively share the access cost of common anchor views. Given anchor view access cost and synthesized distortion of virtual views between anchor views, we study the optimization of anchor view allocation for collaborative peers. We first show that, if the network reconfiguration costs due to view-switching are negligible, the problem can be optimally solved in polynomial time using dynamic programming. We then consider the case of non-negligible reconfiguration costs (e.g., large or frequent view-switching leading to anchor-view changes). In this case, the view allocation problem becomes NP-hard. We thus present a locally optimal and centralized allocation algorithm inspired by Lloyd's algorithm in non-uniform scalar quantization. We also propose a distributed algorithm with guaranteed convergence where each peer group independently make merge-and-split decisions with a well-defined fairness criteria. The results show that depending on the problem settings, our proposed algorithms achieve respective optimal and close-to-optimal performance in terms of total cost, and outperform a P2P scheme without collaborative anchor selection.
Multicore Dynamic Kernel Modules Attachment Technique for Kernel Performance Enhancement
Venue: arXiv cs.OS 2012
Year: 2012

Traditional monolithic kernels dominated kernel structures for long time along with small sized kernels,few hardware companies and limited kernel functionalities. Monolithic kernel structure was not applicable when the number of hardware companies increased and kernel services consumed by different users for many purposes. One of the biggest disadvantages of the monolithic kernels is the inflexibility due to the need to include all the available modules in kernel compilation causing high time consuming. Lately, new kernel structure was introduced through multicore operating systems. Unfortunately, many multicore operating systems such as barrelfish and FOS are experimental. This paper aims to simulate the performance of multicore hybrid kernels through dynamic kernel module customized attachment/ deattachment for multicore machines. In addition, this paper proposes a new technique for loading dynamic kernel modules based on the user needs and machine capabilities.
Domain Adaptations for Computer Vision Applications
Venue: arXiv cs.CV 2012
Year: 2012

A basic assumption of statistical learning theory is that train and test data are drawn from the same underlying distribution. Unfortunately, this assumption doesn't hold in many applications. Instead, ample labeled data might exist in a particular `source' domain while inference is needed in another, `target' domain. Domain adaptation methods leverage labeled data from both domains to improve classification on unseen data in the target domain. In this work we survey domain transfer learning methods for various application domains with focus on recent work in Computer Vision.
The Complexity of Non-Monotone Markets
Venue: arXiv cs.CC 2012
Year: 2012

We introduce the notion of non-monotone utilities, which covers a wide variety of utility functions in economic theory. We then prove that it is PPAD-hard to compute an approximate Arrow-Debreu market equilibrium in markets with linear and non-monotone utilities. Building on this result, we settle the long-standing open problem regarding the computation of an approximate Arrow-Debreu market equilibrium in markets with CES utility functions, by proving that it is PPAD-complete when the Constant Elasticity of Substitution parameter ρis any constant less than -1.
Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices
Venue: arXiv cs.IT 2012
Year: 2012

In this paper we consider the problem of recovering a high dimensional data matrix from a set of incomplete and noisy linear measurements. We introduce a new model that can efficiently restrict the degrees of freedom of the problem and is generic enough to find a lot of applications, for instance in multichannel signal compressed sensing (e.g. sensor networks, hyperspectral imaging) and compressive sparse principal component analysis (s-PCA). We assume data matrices have a simultaneous low-rank and joint sparse structure, and we propose a novel approach for efficient compressed sensing (CS) of such data. Our CS recovery approach is based on a convex minimization problem that incorporates this restrictive structure by jointly regularizing the solutions with their nuclear (trace) norm and l2/l1 mixed norm. Our theoretical analysis uses a new notion of restricted isometry property (RIP) and shows that, for sampling schemes satisfying RIP, our approach can stably recover all low-rank and joint-sparse matrices. For a certain class of random sampling schemes satisfying a particular concentration bound (e.g. the subgaussian ensembles) we derive a lower bound on the number of CS measurements indicating the near-optimality of our recovery approach as well as a significant enhancement compared to the state-of-the-art. We introduce an iterative algorithm based on proximal calculus in order to solve the joint nuclear and l2/l1 norms minimization problem and, finally, we illustrate the empirical recovery phase transition of this approach by series of numerical experiments.
Study And Performance Evaluation Of Security-Throughput Tradeoff With Link Adaptive Encryption Scheme
Venue: arXiv cs.CR 2012
Year: 2012

With the ever increasing volume of information over wireless medium, security has assumed an important dimension. The security of transmitted data over a wireless channel aims at protecting the data from unauthorized intrusion. Wireless network security is achieved using cryptographic primitives. Some properties that give encryption mechanism their cryptographic strength also make them very sensitive to channel error as well. Therefore, security for data transmission over wireless channel results in throughput loss. Trade-off between security and throughput is always a major concern in wireless networks. In this paper, a Link Adaptive Encryption scheme is evaluated that adapts to channel variations and enhances the security level of WLANs without making any compromise with the network performance. Numerical results obtained through simulation are compared with the fixed block length encryption technique in two different modes of operation- Electronic Code Book (ECB) & Cipher Block Chaining (CBC). Optimal block length is also computed, which is assumed to be the effective strength of the cipher. It has been observed that security attained with link adaptive scheme operating in ECB mode of cipher is a better solution for security and throughput trade-off. However, it is found that if computational security is a major concern, link adaptive scheme in CBC mode should be preferred.
Optimal Sequence-Based Control and Estimation of Networked Linear Systems
Venue: arXiv cs.SY 2012
Year: 2012

In this paper, a unified approach to sequence-based control and estimation of linear networked systems with multiple sensors is proposed. Time delays and data losses in the controller-actuator-channel are compensated by sending sequences of control inputs. The sequence-based design paradigm is further extended to the sensor-controller-channels without increasing the load of the network. In this context, we present a recursive solution based on the Hypothesizing Distributed Kalman Filter (HKF) that is included in the overall sequence-based controller design.
To Relay or Not To Relay in Cognitive Radio Sensor Networks
Venue: arXiv cs.NI 2012
Year: 2012

Recent works proposed the relaying at the MAC layer in cognitive radio networks whereby the primary packets are forwarded by the secondary node maintaining an extra queue devoted to the relaying function. However, relaying of primary packets may introduce delays on the secondary packets (called secondary delay) and require additional power budget in order to forward the primary packets that is especially crucial when the network is deployed using sensors with limited power resources. To this end, an admission control can be employed in order to manage efficiently the relaying in cognitive radio sensor networks. In this paper, we first analyse and formulate the secondary delay and the required power budget of the secondary sensor node in relation with the acceptance factor that indicates whether the primary packets are allowed to be forwarded or not. Having defined the above, we present the tradeoff between the secondary delay and the required power budget when the acceptance factor is adapted. In the sequel, we formulate an optimization problem to minimize the secondary delay over the admission control parameter subject to a limit on the required power budget plus the constraints related to the stabilities of the individual queues due to their interdependencies observed by the analysis. The solution of this problem is provided using iterative decomposition methods i.e. dual and primal decompositions using Lagrange multipliers that simplifies the original complicated problem resulting in a final equivalent dual problem that includes the initial Karush Kuhn Tucker conditions. Using the derived equivalent dual problem, we obtain the optimal acceptance factor while in addition we highlight the possibilities for extra delay minimization that is provided by relaxing the initial constraints through changing the values of the Lagrange multipliers.
On the Compressed Measurements over Finite Fields: Sparse or Dense Sampling
Venue: arXiv cs.IT 2012
Year: 2012

We consider compressed sampling over finite fields and investigate the number of compressed measurements needed for successful L0 recovery. Our results are obtained while the sparseness of the sensing matrices as well as the size of the finite fields are varied. One of interesting conclusions includes that unless the signal is "ultra" sparse, the sensing matrices do not have to be dense.
Understanding Complex Service Systems Through Different Lenses: An Overview
Venue: arXiv cs.OH 2012
Year: 2012

The 2011 Grand Challenge in Service conference aimed to explore, analyse and evaluate complex service systems, utilising a case scenario of delivering on improved perception of safety in the London Borough of Sutton, which provided a common context to link the contributions. The key themes that emerged included value co-creation, systems and networks, ICT and complexity, for which we summarise the contributions. Contributions on value co-creation are based mainly on empirical research and provide a variety of insights including the importance of better understanding collaboration within value co-creation. Contributions on the systems perspective, considered to arise from networks of value co-creation, include efforts to understand the implications of the interactions within service systems, as well as their interactions with social systems, to co-create value. Contributions within the technological sphere, providing ever greater connectivity between entities, focus on the creation of new value constellations and new demand being fulfilled through hybrid offerings of physical assets, information and people. Contributions on complexity, arising from the value co- creation networks of technology enabled services systems, focus on the challenges in understanding, managing and analysing these complex service systems. The theory and applications all show the importance of understanding service for the future.
Accurate Demarcation of Protein Domain Linkers based on Structural Analysis of Linker Probable Region
Venue: arXiv cs.CE 2012
Year: 2012

In multi-domain proteins, the domains are connected by a flexible unstructured region called as protein domain linker. The accurate demarcation of these linkers holds a key to understanding of their biochemical and evolutionary attributes. This knowledge helps in designing a suitable linker for engineering stable multi-domain chimeric proteins. Here we propose a novel method for the demarcation of the linker based on a three-dimensional protein structure and a domain definition. The proposed method is based on biological knowledge about structural flexibility of the linkers. We performed structural analysis on a linker probable region (LPR) around domain boundary points of known SCOP domains. The LPR was described using a set of overlapping peptide fragments of fixed size. Each peptide fragment was then described by geometric invariants (GIs) and subjected to clustering process where the fragments corresponding to actual linker come up as outliers. We then discover the actual linkers by finding the longest continuous stretch of outlier fragments from LPRs. This method was evaluated on a benchmark dataset of 51 continuous multi-domain proteins, where it achieves F1 score of 0.745 (0.83 precision and 0.66 recall). When the method was applied on 725 continuous multi-domain proteins, it was able to identify novel linkers that were not reported previously. This method can be used in combination with supervised / sequence based linker prediction methods for accurate linker demarcation.
Efficient Hybrid Execution of C++ Applications using Intel(R) Xeon Phi(TM) Coprocessor
Venue: arXiv cs.DC 2012
Year: 2012

The introduction of Intel(R) Xeon Phi(TM) coprocessors opened up new possibilities in development of highly parallel applications. The familiarity and flexibility of the architecture together with compiler support integrated into the Intel C++ Composer XE allows the developers to use familiar programming paradigms and techniques, which are usually not suitable for other accelerated systems. It is now easy to use complex C++ template-heavy codes on the coprocessor, including for example the Intel Threading Building Blocks (TBB) parallelization library. These techniques are not only possible, but usually efficient as well, since host and coprocessor are of the same architectural family, making optimization techniques designed for the Xeon CPU also beneficial on Xeon Phi. As a result, highly optimized Xeon codes (like the TBB library) work well on both. In this paper we present a new parallel library construct, which makes it easy to apply a function to every member of an array in parallel, dynamically distributing the work between the host CPUs and one or more coprocessor cards. We describe the associated runtime support and use a physical simulation example to demonstrate that our library construct can be used to quickly create a C++ application that will significantly benefit from hybrid execution, simultaneously exploiting CPU cores and coprocessor cores. Experimental results show that one optimized source code is sufficient to make the host and the coprocessors run efficiently.
Improving Perceptual Color Difference using Basic Color Terms
Venue: arXiv cs.CV 2012
Year: 2012

We suggest a new color distance based on two observations. First, perceptual color differences were designed to be used to compare very similar colors. They do not capture human perception for medium and large color differences well. Thresholding was proposed to solve the problem for large color differences, i.e. two totally different colors are always the same distance apart. We show that thresholding alone cannot improve medium color differences. We suggest to alleviate this problem using basic color terms. Second, when a color distance is used for edge detection, many small distances around the just noticeable difference may account for false edges. We suggest to reduce the effect of small distances.
Hardware-Accelerated Raycasting: Towards an Effective Brain MRI Visualization
Venue: arXiv cs.CG 2012
Year: 2012

The rapid development in information technology has immensely contributed to the use of modern approaches for visualizing volumetric data. Consequently, medical volume visualization is increasingly attracting attention towards achieving an effective visualization algorithm for medical diagnosis and pre-treatment planning. Previously, research has been addressing implementation of algorithm that can visualize 2-D images into 3-D. Meanwhile, achieving such a rendering algorithm at an interactive speed and of good robustness to handle mass data still remains a challenging issue. However, in medical diagnosis, finding the exact location of brain tumor or diseases is an important step of surgery / disease management. This paper proposes a GPU-based raycasting algorithm for accurate allocation and localization of human brain abnormalities using magnetic resonance (MRI) images of normal and abnormal patients. Keywords: Brain tumor, graphic processing units, magnetic resonance imaging, volume visualization
A survey of computational methods for protein complex prediction from protein interaction networks
Venue: arXiv cs.CE 2012
Year: 2012

Complexes of physically interacting proteins are one of the fundamental functional units responsible for driving key biological mechanisms within the cell. Their identification is therefore necessary not only to understand complex formation but also the higher level organization of the cell. With the advent of high-throughput techniques in molecular biology, significant amount of physical interaction data has been cataloged from organisms such as yeast, which has in turn fueled computational approaches to systematically mine complexes from the network of physical interactions among proteins (PPI network). In this survey, we review, classify and evaluate some of the key computational methods developed till date for the identification of protein complexes from PPI networks. We present two insightful taxonomies that reflect how these methods have evolved over the years towards improving automated complex prediction. We also discuss some open challenges facing accurate reconstruction of complexes, the crucial ones being presence of high proportion of errors and noise in current high-throughput datasets and some key aspects overlooked by current complex detection methods. We hope this review will not only help to condense the history of computational complex detection for easy reference, but also provide valuable insights to drive further research in this area.
Shadows and headless shadows: a worlds-based, autobiographical approach to reasoning
Venue: arXiv cs.AI 2012
Year: 2012

Many cognitive systems deploy multiple, closed, individually consistent models which can represent interpretations of the present state of the world, moments in the past, possible futures or alternate versions of reality. While they appear under different names, these structures can be grouped under the general term of worlds. The Xapagy architecture is a story-oriented cognitive system which relies exclusively on the autobiographical memory implemented as a raw collection of events organized into world-type structures called {\em scenes}. The system performs reasoning by shadowing current events with events from the autobiography. The shadows are then extrapolated into headless shadows corresponding to predictions, hidden events or inferred relations.
Analysis-suitable T-splines: characterization, refineability, and approximation
Venue: arXiv cs.GR 2012
Year: 2012

We establish several fundamental properties of analysis-suitable T-splines which are important for design and analysis. First, we characterize T-spline spaces and prove that the space of smooth bicubic polynomials, defined over the extended T-mesh of an analysis-suitable T-spline, is contained in the corresponding analysis-suitable T-spline space. This is accomplished through the theory of perturbed analysis-suitable T-spline spaces and a simple topological dimension formula. Second, we establish the theory of analysis-suitable local refinement and describe the conditions under which two analysis-suitable T-spline spaces are nested. Last, we demonstrate that these results can be used to establish basic approximation results which are critical for analysis.
The Survey of Data Mining Applications And Feature Scope
Venue: arXiv cs.DB 2012
Year: 2012

In this paper we have focused a variety of techniques, approaches and different areas of the research which are helpful and marked as the important field of data mining Technologies. As we are aware that many Multinational companies and large organizations are operated in different places of the different countries.Each place of operation may generate large volumes of data. Corporate decision makers require access from all such sources and take strategic decisions.The data warehouse is used in the significant business value by improving the effectiveness of managerial decision-making. In an uncertain and highly competitive business environment, the value of strategic information systems such as these are easily recognized however in todays business environment,efficiency or speed is not the only key for competitiveness.This type of huge amount of data are available in the form of tera-topeta-bytes which has drastically changed in the areas of science and engineering.To analyze,manage and make a decision of such type of huge amount of data we need techniques called the data mining which will transforming in many fields.This paper imparts more number of applications of the data mining and also focuses scope of the data mining which will helpful in the further research.
Critical Utility Infrastructural Resilience
Venue: arXiv cs.PF 2012
Year: 2012

The paper refers to CRUTIAL, CRitical UTility InfrastructurAL Resilience, a European project within the research area of Critical Information Infrastructure Protection, with a specific focus on the infrastructures operated by power utilities, widely recognized as fundamental to national and international economy, security and quality of life. Such infrastructures faced with the recent market deregulations and the multiple interdependencies with other infrastructures are becoming more and more vulnerable to various threats, including accidental failures and deliberate sabotage and malicious attacks. The subject of CRUTIAL research are small scale networked ICT systems used to control and manage the electric power grid, in which artifacts controlling the physical process of electricity transportation need to be connected with corporate and societal applications performing management and maintenance functionality. The peculiarity of such ICT-supported systems is that they are related to the power system dynamics and its emergency conditions. Specific effort need to be devoted by the Electric Power community and by the Information Technology community to influence the technological progress in order to allow commercial intelligent electronic devices to be effectively deployed for the protection of citizens against cyber threats to electric power management and control systems. A well-founded know-how needs to be built inside the industrial power sector to allow all the involved stakeholders to achieve their service objectives without compromising the resilience properties of the logical and physical assets that support the electric power provision.