OpenRaiser/Intern-Atlas huggingface dataset:
Optimal Limited Contingency Planning
Venue: arXiv cs.AI 2012
Year: 2012

For a given problem, the optimal Markov policy can be considerred as a conditional or contingent plan containing a (potentially large) number of branches. Unfortunately, there are applications where it is desirable to strictly limit the number of decision points and branches in a plan. For example, it may be that plans must later undergo more detailed simulation to verify correctness and safety, or that they must be simple enough to be understood and analyzed by humans. As a result, it may be necessary to limit consideration to plans with only a small number of branches. This raises the question of how one goes about finding optimal plans containing only a limited number of branches. In this paper, we present an any-time algorithm for optimal k-contingency planning (OKP). It is the first optimal algorithm for limited contingency planning that is not an explicit enumeration of possible contingent plans. By modelling the problem as a Partially Observable Markov Decision Process, it implements the Bellman optimality principle and prunes the solution space. We present experimental results of applying this algorithm to some simple test cases.
Markov Random Walk Representations with Continuous Distributions
Venue: arXiv cs.LG 2012
Year: 2012

Representations based on random walks can exploit discrete data distributions for clustering and classification. We extend such representations from discrete to continuous distributions. Transition probabilities are now calculated using a diffusion equation with a diffusion coefficient that inversely depends on the data density. We relate this diffusion equation to a path integral and derive the corresponding path probability measure. The framework is useful for incorporating continuous data densities and prior knowledge.
A Generalized Mean Field Algorithm for Variational Inference in Exponential Families
Venue: arXiv cs.LG 2012
Year: 2012

The mean field methods, which entail approximating intractable probability distributions variationally with distributions from a tractable family, enjoy high efficiency, guaranteed convergence, and provide lower bounds on the true likelihood. But due to requirement for model-specific derivation of the optimization equations and unclear inference quality in various models, it is not widely used as a generic approximate inference algorithm. In this paper, we discuss a generalized mean field theory on variational approximation to a broad class of intractable distributions using a rich set of tractable distributions via constrained optimization over distribution spaces. We present a class of generalized mean field (GMF) algorithms for approximate inference in complex exponential family models, which entails limiting the optimization over the class of cluster-factorizable distributions. GMF is a generic method requiring no model-specific derivations. It factors a complex model into a set of disjoint variable clusters, and uses a set of canonical fix-point equations to iteratively update the cluster distributions, and converge to locally optimal cluster marginals that preserve the original dependency structure within each cluster, hence, fully decomposed the overall inference problem. We empirically analyzed the effect of different tractable family (clusters of different granularity) on inference quality, and compared GMF with BP on several canonical models. Possible extension to higher-order MF approximation is also discussed.
Boltzmann Machine Learning with the Latent Maximum Entropy Principle
Venue: arXiv cs.LG 2012
Year: 2012

We present a new statistical learning paradigm for Boltzmann machines based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from Jaynes maximum entropy principle and from standard maximum likelihood estimation.We demonstrate the LME principle BY deriving new algorithms for Boltzmann machine parameter estimation, and show how robust and fast new variant of the EM algorithm can be developed.Our experiments show that estimation based on LME generally yields better results than maximum likelihood estimation, particularly when inferring hidden units from small amounts of data.
Learning Measurement Models for Unobserved Variables
Venue: arXiv cs.LG 2012
Year: 2012

Observed associations in a database may be due in whole or part to variations in unrecorded (latent) variables. Identifying such variables and their causal relationships with one another is a principal goal in many scientific and practical domains. Previous work shows that, given a partition of observed variables such that members of a class share only a single latent common cause, standard search algorithms for causal Bayes nets can infer structural relations between latent variables. We introduce an algorithm for discovering such partitions when they exist. Uniquely among available procedures, the algorithm is (asymptotically) correct under standard assumptions in causal Bayes net search algorithms, requires no prior knowledge of the number of latent variables, and does not depend on the mathematical form of the relationships among the latent variables. We evaluate the algorithm on a variety of simulated data sets.
A Learning Framework for Morphological Operators using Counter-Harmonic Mean
Venue: arXiv cs.CV 2012
Year: 2012

We present a novel framework for learning morphological operators using counter-harmonic mean. It combines concepts from morphology and convolutional neural networks. A thorough experimental validation analyzes basic morphological operators dilation and erosion, opening and closing, as well as the much more complex top-hat transform, for which we report a real-world application from the steel industry. Using online learning and stochastic gradient descent, our system learns both the structuring element and the composition of operators. It scales well to large datasets and online settings.
A General Framework for Distributed Vote Aggregation
Venue: arXiv cs.SI 2012
Year: 2012

We present a general model for opinion dynamics in a social network together with several possibilities for object selections at times when the agents are communicating. We study the limiting behavior of such a dynamics and show that this dynamics almost surely converges. We consider some special implications of the convergence result for gossip and top-$k$ selective gossip models. In particular, we provide an answer to the open problem of the convergence property of the top-$k$ selective gossip model, and show that the convergence holds in a much more general setting. Moreover, we propose an extension of the gossip and top-$k$ selective gossip models and provide some results for their limiting behavior.
A Study on Fuzzy Systems
Venue: arXiv cs.AI 2012
Year: 2012

We use princiles of fuzzy logic to develop a general model representing several processes in a system's operation characterized by a degree of vagueness and/or uncertainy. Further, we introduce three altenative measures of a fuzzy system's effectiveness connected to the above model. An applcation is also developed for the Mathematical Modelling process illustrating our results.
Mixture Models of Endhost Network Traffic
Venue: arXiv cs.NI 2012
Year: 2012

In this work we focus on modeling a little studied type of traffic, namely the network traffic generated from endhosts. We introduce a parsimonious parametric model of the marginal distribution for connection arrivals. We employ mixture models based on a convex combination of component distributions with both heavy and light-tails. These models can be fitted with high accuracy using maximum likelihood techniques. Our methodology assumes that the underlying user data can be fitted to one of many modeling options, and we apply Bayesian model selection criteria as a rigorous way to choose the preferred combination of components. Our experiments show that a simple Pareto-exponential mixture model is preferred for a wide range of users, over both simpler and more complex alternatives. This model has the desirable property of modeling the entire distribution, effectively segmenting the traffic into the heavy-tailed as well as the non-heavy-tailed components. We illustrate that this technique has the flexibility to capture the wide diversity of user behaviors.
Diametrical Mesh Of Tree (D2D-MoT) Architecture: A Novel Routing Solution For NoC
Venue: arXiv cs.ET 2012
Year: 2012

Network-on-chip (NoC) is a new aspect for designing of future System-On-Chips (SoC) where a vast number of IP cores are connected through interconnection network. The communication between the nodes occurred by routing packets rather than wires. It supports high degree of scalability, reusability and parallelism in communication. In this paper, we present a Mesh routing architecture, which is called Diametrical 2D Mesh of Tree, based on Mesh-of-Tree (MoT) routing and Diametrical 2D Mesh. It has the advantage of having small diameter as well as large bisection width and small node degree clubbed with being the fastest network in terms of speed. The routing algorithm ensures that the packets will always reach from source to sink through shortest path and is deadlock free.
Keyword Extraction for Identifying Social Actors
Venue: arXiv cs.IR 2012
Year: 2012

Identifying the social actor has become one of tasks in Artificial Intelligence, whereby extracting keyword from Web snippets depend on the use of web is steadily gaining ground in this research. We develop therefore an approach based on overlap principle for utilizing a collection of features in web snippets, where use of keyword will eliminate the un-relevant web pages.
A use case driven approach for system level testing
Venue: arXiv cs.SE 2012
Year: 2012

Use case scenarios are created during the analysis phase to specify software system requirements and can also be used for creating system level test cases. Using use cases to get system tests has several benefits including test design at early stages of software development life cycle that reduces over all development cost of the system. Current approaches for system testing using use cases involve functional details and does not include guards as passing criteria i.e. use of class diagram that seem to be difficult at very initial level which lead the need of specification based testing without involving functional details. In this paper, we proposed a technique for system testing directly derived from the specification without involving functional details. We utilize initial and post conditions applied as guards at each level of the use cases that enables us generation of formalized test cases and makes it possible to generate test cases for each flow of the system. We used use case scenarios to generate system level test cases, whereas system sequence diagram is being used to bridge the gap between the test objective and test cases, derived from the specification of the system. Since, a state chart derived from the combination of sequence diagrams can model the entire behavior of the system.Generated test cases can be employed and executed to state chart in order to capture behavior of the system with the state change.All these steps enable us to systematically refine the specification to achieve the goals of system testing at early development stages.
The application of cause effect graph for the college placement process
Venue: arXiv cs.SE 2012
Year: 2012

This paper presents a case study on the application of cause effect graph for representing the college placement process. This paper begins with giving a brief overview of the college placement process which will serve as the basis for developing the cause effect graph and the decision table for the same in a systematic manner. Finally, it concludes with the design of test cases thus giving a complete and clear representation about the application of cause-effect graph in the software testing domain.
The Modified Direct Method: an Approach for Smoothing Planar and Surface Meshes
Venue: arXiv cs.CG 2012
Year: 2012

The Modified Direct Method (MDM) is an iterative mesh smoothing method for smoothing planar and surface meshes, which is developed from the non-iterative smoothing method originated by Balendran [1]. When smooth planar meshes, the performance of the MDM is effectively identical to that of Laplacian smoothing, for triangular and quadrilateral meshes; however, the MDM outperforms Laplacian smoothing for tri-quad meshes. When smooth surface meshes, for trian-gular, quadrilateral and quad-dominant mixed meshes, the mean quality(MQ) of all mesh elements always increases and the mean square error (MSE) decreases during smoothing; For tri-dominant mixed mesh, the quality of triangles always descends while that of quads ascends. Test examples show that the MDM is convergent for both planar and surface triangular, quadrilateral and tri-quad meshes.
Improving Macrocell - Small Cell Coexistence through Adaptive Interference Draining
Venue: arXiv cs.GT 2012
Year: 2012

The deployment of underlay small base stations (SBSs) is expected to significantly boost the spectrum efficiency and the coverage of next-generation cellular networks. However, the coexistence of SBSs underlaid to an existing macro-cellular network faces important challenges, notably in terms of spectrum sharing and interference management. In this paper, we propose a novel game-theoretic model that enables the SBSs to optimize their transmission rates by making decisions on the resource occupation jointly in the frequency and spatial domains. This procedure, known as interference draining, is performed among cooperative SBSs and allows to drastically reduce the interference experienced by both macro- and small cell users. At the macrocell side, we consider a modified water-filling policy for the power allocation that allows each macrocell user (MUE) to focus the transmissions on the degrees of freedom over which the MUE experiences the best channel and interference conditions. This approach not only represents an effective way to decrease the received interference at the MUEs but also grants the SBSs tier additional transmission opportunities and allows for a more agile interference management. Simulation results show that the proposed approach yields significant gains at both macrocell and small cell tiers, in terms of average achievable rate per user, reaching up to 37%, relative to the non-cooperative case, for a network with 150 MUEs and 200 SBSs.
Image Authentication Technique in Frequency Domain based on Discrete Fourier Transformation (IATFDDFT)
Venue: arXiv cs.CR 2012
Year: 2012

In this paper a novel data embedding technique in frequency domain has been proposed using Discrete Fourier Transform (DFT) for image authentication and secured message transmission based on hiding a large volume of data into gray images. Image authentication is done by embedding message or image in frequency domain by choosing image blocks of size 2 x 2, called mask, from the source image in row major order and transform it into the frequency domain using DFT. Three bits of authenticating message/image/message-digest are fabricated within the real parts of each source image byte except first frequency component of each mask. The dimension of authenticating image followed by message digest (MD) and the content of authenticating message/image are also embedded. Inverse DFT (IDFT) is performed on embedded data to transform embedded frequency component to spatial component. In order to keep the quantum value positive and non negative in spatial domain a strong and robust technique is incorporated mainly on the first frequency component and sometimes on other component depends upon situations. The decoding is done by applying the reverse algorithm. Experimental results conform that the proposed algorithm performs better than DCT, QFT and SCDFT schemes.
Optimal Cuts and Partitions in Tree Metrics in Polynomial Time
Venue: arXiv cs.DS 2012
Year: 2012

We present a polynomial time dynamic programming algorithm for optimal partitions in the shortest path metric induced by a tree. This resolves, among other things, the exact complexity status of the optimal partition problems in one dimensional geometric metric settings. Our method of solution could be also of independent interest in other applications. We discuss also an extension of our method to the class of metrics induced by the bounded treewidth graphs.
Parallel grid library for rapid and flexible simulation development
Venue: arXiv cs.DC 2012
Year: 2012

We present an easy to use and flexible grid library for developing highly scalable parallel simulations. The distributed cartesian cell-refinable grid (dccrg) supports adaptive mesh refinement and allows an arbitrary C++ class to be used as cell data. The amount of data in grid cells can vary both in space and time allowing dccrg to be used in very different types of simulations, for example in fluid and particle codes. Dccrg transfers the data between neighboring cells on different processes transparently and asynchronously allowing one to overlap computation and communication. This enables excellent scalability at least up to 32 k cores in magnetohydrodynamic tests depending on the problem and hardware. In the version of dccrg presented here part of the mesh metadata is replicated between MPI processes reducing the scalability of adaptive mesh refinement (AMR) to between 200 and 600 processes. Dccrg is free software that anyone can use, study and modify and is available at [https://gitorious.org/dccrg]. Users are also kindly requested to cite this work when publishing results obtained with dccrg.
State-Dependent Multiple Access Channels with Feedback
Venue: arXiv cs.IT 2012
Year: 2012

In this paper, we examine discrete memoryless Multiple Access Channels (MACs) with two-sided feedback in the presence of two correlated channel states that are correlated in the sense of Slepian-Wolf (SW). We find achievable rate region for this channel when the states are provided non-causally to the transmitters and show that our achievable rate region subsumes Cover-Leung achievable rate for the discrete memoryless MAC with two-sided feedback as its special case. We also find the capacity region of discrete memoryless MAC with two-sided feedback and with SW-type correlated states available causally or strictly causally to the transmitters. We also study discrete memoryless MAC with partial feedback in the presence of two SW-type correlated channel states that are provided non-causally, causally, or strictly causally to the transmitters. An achievable rate region is found when channel states are non-causally provided to the transmitters whereas capacity regions are characterized when channel states are causally, or strictly causally available at the transmitters.
Learning efficient sparse and low rank models
Venue: arXiv cs.LG 2012
Year: 2012

Parsimony, including sparsity and low rank, has been shown to successfully model data in numerous machine learning and signal processing tasks. Traditionally, such modeling approaches rely on an iterative algorithm that minimizes an objective function with parsimony-promoting terms. The inherently sequential structure and data-dependent complexity and latency of iterative optimization constitute a major limitation in many applications requiring real-time performance or involving large-scale data. Another limitation encountered by these modeling techniques is the difficulty of their inclusion in discriminative learning scenarios. In this work, we propose to move the emphasis from the model to the pursuit algorithm, and develop a process-centric view of parsimonious modeling, in which a learned deterministic fixed-complexity pursuit process is used in lieu of iterative optimization. We show a principled way to construct learnable pursuit process architectures for structured sparse and robust low rank models, derived from the iteration of proximal descent algorithms. These architectures learn to approximate the exact parsimonious representation at a fraction of the complexity of the standard optimization methods. We also show that appropriate training regimes allow to naturally extend parsimonious models to discriminative settings. State-of-the-art results are demonstrated on several challenging problems in image and audio processing with several orders of magnitude speedup compared to the exact optimization algorithms.
Discovering Links for Metadata Enrichment on Computer Science Papers
Venue: arXiv cs.DL 2012
Year: 2012

At the very beginning of compiling a bibliography, usually only basic information, such as title, authors and publication date of an item are known. In order to gather additional information about a specific item, one typically has to search the library catalog or use a web search engine. This look-up procedure implies a manual effort for every single item of a bibliography. In this technical report we present a proof of concept which utilizes Linked Data technology for the simple enrichment of sparse metadata sets. This is done by discovering owl:sameAs links be- tween an initial set of computer science papers and resources from external data sources like DBLP, ACM and the Semantic Web Conference Corpus. In this report, we demonstrate how the link discovery tool Silk is used to detect additional information and to enrich an initial set of records in the computer science domain. The pros and cons of silk as link discovery tool are summarized in the end.
Cross-Layer Design to Maintain Earthquake Sensor Network Connectivity After Loss of Infrastructure
Venue: arXiv cs.NI 2012
Year: 2012

We present the design of a cross-layer protocol to maintain connectivity in an earthquake monitoring and early warning sensor network in the absence of communications infrastructure. Such systems, by design, warn of events that severely damage or destroy communications infrastructure. However, the data they provide is of critical importance to emergency and rescue decision making in the immediate aftermath of such events, as is continued early warning of aftershocks, tsunamis, or other subsequent dangers. Utilizing a beyond line-of-sight (BLOS) HF physical layer, we propose an adaptable cross-layer network design that meets these specialized requirements. We are able to provide ultra high connectivity (UHC) early warning on strict time deadlines under worst-case channel conditions along with providing sufficient capacity for continued seismic data collection from a 1000 sensor network.
Biologically Inspired Spiking Neurons : Piecewise Linear Models and Digital Implementation
Venue: arXiv cs.LG 2012
Year: 2012

There has been a strong push recently to examine biological scale simulations of neuromorphic algorithms to achieve stronger inference capabilities. This paper presents a set of piecewise linear spiking neuron models, which can reproduce different behaviors, similar to the biological neuron, both for a single neuron as well as a network of neurons. The proposed models are investigated, in terms of digital implementation feasibility and costs, targeting large scale hardware implementation. Hardware synthesis and physical implementations on FPGA show that the proposed models can produce precise neural behaviors with higher performance and considerably lower implementation costs compared with the original model. Accordingly, a compact structure of the models which can be trained with supervised and unsupervised learning algorithms has been developed. Using this structure and based on a spike rate coding, a character recognition case study has been implemented and tested.