ppxscal/embedding_network huggingface dataset:
Distribution of route-impacting control information in a publish/subscribe system with delivery guarantees
Authors: Zvi M. Kedem, Daniel C. Sturman
Venue:  (2006)

Event-driven middleware is a popular infrastructure for building large-scale asynchronous distributed systems. Content-based publish/subscribe systems are a type of event-driven middleware that provides service flexibility and specification expressiveness, creating opportunities for improving reliability and efficiency of the system. The use of route-impacting control information, such as subscription filters and access control rules, has the potential to enable efficient routing for applications that require selective and regional distribution of events. Such applications range from financial information systems to sensor networks to service-oriented architectures. However, it has been a great challenge to design correct and efficient protocols for distributing control information and exploiting it to achieve efficient and highly available message routing. In this dissertation, we study the problem of distributing and utilizing route-impacting control information. We present an abstract model of content-based routing and reliable delivery in redundant broker networks. Based on this model, we design a generic algorithm that propagates control information and performs content-based routing and delivers events reliably. The algorithm is efficient and light-weight in that it does not require heavy-weight consensus protocols between redundant brokers. We extend this generic algorithm to support consolidation and merging of control information. Existing protocols can be viewed as particular encodings and optimizations of the generic algorithm. We show an encoding using virtual time vectors that supports reliable delivery and deterministic dynamic access control in redundant broker networks. In our system, the semantics of reliable delivery is clearly defined even if subscription information and access control policy can dynamically change. That is, one or more subscribers of same principal will receive exactly the same sequence of messages (modulo subscription filter differences) regardless of where they are connected and the network latency and failure conditions in their parts of the network. We have implemented these protocols in a fully-functioning content-based publish/subscribe system---Gryphon. We evaluate its efficiency, scalability and high availability.
Robust chirp parameter estimation for Hann windowed signals
Authors: A. S. Master, Yi-Wen Liu
Venue: Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1 (2003)

The sinusoidal model has been a fundamentally important signal representation for coding and analysis of audio. We present an enhancement to sinusoidal modeling in the form of a linear frequency chirp parameter estimator applicable to Hann-windowed quasi-sinusoidal signals. The estimator relies on models of the phase curvature and peak width of a given chirp signal's FFT magnitude domain peak. We show that different models are applicable for smaller and larger values of the chirp parameter, derived respectively from Taylor series and Fresnel integral analysis of the signal. We construct an estimator for the transition region between the two models via a neural net. Results indicate that the estimator is robust to noise and outperforms any known chirp parameter estimators for Hann windowed signals.
Towards automatic social bootstrapping of peer-to-peer protocols
Authors: David Hales, Ozalp Babaoglu
Venue: ACM SIGOPS Operating Systems Review (2006)

Current peer-to-peer systems rely on user intervention to install and update the software (protocols) that run on each node. We propose another direction where protocols are dynamically and automatically rolled-out over peers, with the peers themselves selecting those that are beneficial and rejecting those which are not. To achieve this, we argue that, new protocols should be "injected" live into a running P2P system, with peers themselves replicating them. This requires that peers select "socially beneficial" protocols even though they need to base this on their own individual performance evaluations. What we are proposing can be seen as a meta-protocol, which we call Automatic Social Bootstrapping, that intelligently selects and replicates those protocols that are for the social good --- that is, maximize the average utility of the entire population. We sketch an outline of the protocol and present some initial high-level simulation results. Finally we identify several open issues that need to be addressed in order to further develop the approach.
Moving Architectural Description from Under the Technology Lamppost
Authors: Nenad Medvidovic
Venue: Proceedings of the 32nd EUROMICRO Conference on Software Engineering and Advanced Applications (2006)

Software architecture description languages (ADLs) were a particularly active research area in the 1990s. In 2000, I co-authored an extensive study of existing ADLs, which has served as a useful reference to software architecture researchers and practitioners. However, the field of software architecture and our understanding of it have undergone a number of changes in the past several years. In particular, the Unified Modeling Language (UML) has gained a lot of popularity and wide adoption, and as a result many of the ADLs I had studied have been pushed into obscurity. In this talk, I will argue that the main reason behind this is that the early ADLs focused almost exclusively on the technological aspects of architecture, and mostly ignored the application domain and business contexts within which software systems, and development organizations, exist. Together, these three concerns--technology, domain, and business--constitute the three "lampposts" needed to appropriately "illuminate" software architecture and architectural description. I will use this new framework to evaluate both the languages from my original study, as well as several more recent ADLs (including UML 2.0).
Belief Propagation Driven Method for Facial Gestures Recognition in Presence of Occlusions
Authors: Wei-Kai Liao, Isaac Cohen
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

In this paper, we present a framework for automatic facial gestures recognition in a highly interactive environment. We propose to use a region-based representation of the face, and model the interdependencies of these regions using a graphical model. A parametric modeling of the local facial deformations and the use of a graphical model facilitate the characterization of intra-region dynamic patterns and the inter-region dependencies. We augment this formalism with a belief propagation algorithm to infer missing data, as well as correcting for erroneous estimations of the local deformations. The resulting approach hence handles complete observation and partial occlusion cases in an unified way, and allows for the recognition of facial gestures in presence of head motion and partial occlusions of the face. Experimental results on recognizing facial expression in various real world situations, such as non-frontal views, moving head, and occlusions illustrate the proposed approach.
Using Statistical Similarity to Identify Corresponding Attributes between Heterogeneous Spatial Databases
Authors: Su-ling Wang, Jun Xu, Qi Zeng
Venue: Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (2006)

One step in integrating heterogeneous database systems is matching equivalent attributes: Determine which fields in two databases refer to same real world entity character. In this paper, we present a methodology based on instance statistic similarity for identifying corresponding attributes in heterogonous spatial databases which has three basic steps. In first step, the corresponding of the same entities in heterogeneous spatial datasets is built by means of geometrical matching. In second step, attribute similarity, which is expressed by a value between 0 and 1, is computed according to attributes values of the same entity. We design different similarity compute models for different kind of attributes. At last, corresponding attributes are identified by means of comparing attribute similarity value with the predefined threshold. An experimental result is presented which shows the effectiveness and precision of this approach.
Efficient cryptographic tools for secure distributed computing
Authors: James Aspnes
Venue:  (2006)

This thesis presents a set of efficient and practical tools that can be used to enhance security of existing distributed systems. Our tools achieve marked improvements in bit, round, and communication complexity compared to generic multi-party solutions. We begin by introducing a new cryptographic primitive, called the blind coupon mechanism (BCM). The BCM is effectively an AND-homomorphic bit commitment scheme. It provides a missing link in protocol design because it was not known how to construct such commitments before. Next, we give a simple construction of a verifiable random function (VRF). A VRF is a pseudo-random function that provides publically verifiable proofs of its outputs' correctness. Our VRF is quite practical compared to prior constructions. It has already been used to construct a compact e-cash scheme [CHL05] and a distributed electronic lottery [CHYC05]. We then use our VRF to construct an O(1) round distributed protocol for the Luby-Rackoff construction, yielding the first reasonably efficient threshold pseudo-random permutation (PRP). Many protocols that use PRPs like a CBC block cipher mode can now be translated into the distributed setting. Finally, we consider incentive compatibility in the context of distributed systems. We propose a simple game for modeling containment of the spread of viruses in a network of users, and show that allowing selfish users decide whether or not to install a security technology can be highly undesirable.
Generating a Test Strategy with Bayesian Networks and Common Sense
Authors: Jean-Jacques Gras, Rishabh Gupta, Elena Perez-Minana
Venue: Proceedings of the Testing: Academic & Industrial Conference on Practice And Research Techniques (2006)

Testing still represents an important share of the overall development effort and, coming late in the software life cycle, it is on the critical path both from a schedule and quality perspective. In an effort to conduct smarter software testing, Motorola Labs have developed the Bayesian Test Assistant (BTA), an advanced decision support tool to optimize all Verification and Validation activities, in Development With Bayesian Networks, the theory underlying BTA, Motorola Labs built a library of causal models to predict, from key process, people and product factors, the quality of artefacts at each step of the software development. In this paper we present how BTA links the predictions from development models by mapping dependencies between components or subsystems to predict the level of risk in each system feature. As a result, and well before System Testing starts, BTA generates a test strategy that optimizes the writing of test cases. During System Test, BTA scores test cases to select an optimum set for each test step, leading to a faster discovery of defects. We also describe how BTA was deployed on large Telecomm system releases in several Motorola organizations and the improvement driven so far in System testing.
Discovering Expressive Process Models by Clustering Log Traces
Authors: Gianluigi Greco Antonella Guzzo Luigi Pontieri Domenico Sacca
Venue: IEEE Transactions on Knowledge and Data Engineering (2006)

Process mining techniques have recently received notable attention in the literature for their ability to assist in the (re)design of complex processes by automatically discovering models that explain the events registered in some log traces provided as input. Following this line of research, the paper investigates an extension of such basic approaches, where the identification of different variants for the process is explicitly accounted for, based on the clustering of log traces. Indeed, modeling each group of similar executions with a different schema allows us to single out "conformant” models, which, specifically, minimize the number of modeled enactments that are extraneous to the process semantics. Therefore, a novel process mining framework is introduced and some relevant computational issues are deeply studied. As finding an exact solution to such an enhanced process mining problem is proven to require high computational costs, in most practical cases, a greedy approach is devised. This is founded on an iterative, hierarchical, refinement of the process model, where, at each step, traces sharing similar behavior patterns are clustered together and equipped with a specialized schema. The algorithm guarantees that each refinement leads to an increasingly sound model, thus attaining a monotonic search. Experimental results evidence the validity of the approach with respect to both effectiveness and scalability.
Phonological Processing in Visual Rhyming: A Developmental ERP Study
Authors: Giordana Grossi, Donna Coch, Sharon Coffey-corina, Phillip J. Holcomb, Helen J. Neville
Venue: Journal of Cognitive Neuroscience (2001)

We employed a visual rhyming priming paradigm to characterize the development of brain systems important for phonological processing in reading. We studied 109 righthanded, native English speakers within eight age groups: 7-8, 9-10, 11-12, 13-14, 15-16, 17-18, 19-20, and 21-23. Participants decided whether two written words (prime-target) rhymed (JUICE-MOOSE) or not (CHAIR-MOOSE). In similar studies of adults, two main event-related potential (ERP) effects have been described: a negative slow wave to primes, larger over anterior regions of the left hemisphere and hypothesized to index rehearsal of the primes, and a negative deflection to targets, peaking at 400-450 msec, maximal over right temporal-parietal regions, larger for nonrhyming than rhyming targets, and hypothesized index phonological matching. In this study, these two ERP effects were observed in all age groups; however, the two effects showed different developmental timecourses. On the one hand, the frontal asymmetry to primes increased with age; moreover, this asymmetry was correlated with reading and spelling scores, even after controlling for age. On the other hand, the distribution and onset of the more posterior rhyming effect (RE) were stable across age groups, suggesting that phonological matching relied on similar neural systems across these ages. Behaviorally, both reaction times and accuracy improved with age. These results suggest that different aspects of phonological processing rely on different neural systems that have different to developmental timecourses.
Automatic Segmentation of MR Brain Images Using Spatial-Varying Gaussian Mixture and Markov Random Field Approach
Authors: Zhigang Peng, William Wee, Jing-Huei Lee
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

We present an automatic segmentation method using the Maximum a posterior (MAP)-Markov random field (MRF) framework that possesses regional adaptive capability for the segmentation of MR brain images with the presence of noise and the intensity inhomogeneity. A spatial-varying Gaussian mixture (SVGM) is used to model the conditional probability distribution of each of the three brain tissues (WM, GM and CSF), and the MRF is used to represent prior probabilities. A three-component random vector consisting of spatial and intensity information is used in SVGM such that the model can represent more complex image characteristics, such as intensity inhomogeneity. The regional adaptive capability is achieved by imposing a similarity criterion that minimizes the intensity variation of the segmented tissues in local regions. Initial parameters of SVGM are estimated from a brain atlas using either the expectation maximization (EM) algorithm or modified k-mean (MKM) algorithm, and the iterated conditional modes (ICM) algorithm is used to perform the segmentation. The parameters estimation and the ICM algorithm are alternatively operated until a stopping criterion is reached. The implement of the method is validated by quantitatively comparing with other published classification methods on fifteen simulated and twenty in vivo brain volumes using the same evaluation criteria. Our algorithm demonstrates superior performance in all cases other than two, and is especially suitable for the volumes with higher noise and inhomogeneity. The complete experiments illustrate that the incorporations of the spatial information and the regional adaptive processing into MAP-MRF framework provide better solution to the segmentation of MR brain images.
A weighted L2-based method for the design of arbitrary one-dimensional FIR digital filters
Authors: Emmanouil Z. Psarakis
Venue: Signal Processing (2006)

In FIR filter design problems, filter specifications do not constrain in any way the ideal frequency response inside the transition regions. Most existing L"2-based filter design techniques utilize this flexibility in order to improve their performance. In this paper we propose a new general method for the weighted L"2-based design of arbitrary FIR filters. In particular, we propose a well-defined optimization criterion that depends on the selection of the desired response inside the transition regions. By optimizing our criterion we obtain desired responses that produce weighted mean square error optimum filters with extremely good characteristics. The proposed method is computationally simple since it requires the solution of a linear system of equations.
Linear and Non-Linear Dimensional Reduction via Class Representatives for Text Classification
Authors: Dimitrios Zeimpekis, Efstratios Gallopoulos
Venue: Proceedings of the Sixth International Conference on Data Mining (2006)

We address the problem of building fast and effective text classification tools. We describe a "representatives methodology" related to feature extraction and illustrate its performance using as vehicles a centroid based method and a method based on clustered LSI that were recently proposed as useful tools for low rank matrix approximation and cost effective alternatives to LSI. The methodology is very flexible, providing the means for accelerating existing algorithms. It is also combined with kernel techniques to enable the analysis of data for which linear techniques are insufficient. Numerous classification examples indicate that the proposed technique is effective and efficient with an overall performance superior than existing linear and nonlinear LSI-based approaches.
Lifetime Analysis in Heterogeneous Sensor Networks
Authors: Falko Dressler, Isabel Dietrich
Venue: Proceedings of the 9th EUROMICRO Conference on Digital System Design (2006)

Wireless sensor networks (WSN) are composed of battery-driven communication entities performing multiple, usually different tasks. In order to complete a given task, all sensor nodes, which are deployed in an ad-hoc fashion, have to collaborate by exchanging and forwarding measurement data. We define the behavior of the overall sensor network based on the parameters lifetime and functional density. The functional density describes the distribution of all necessary tasks in a given geographical area. The lifetime is primarily given by the time each task is successfully performed by at least one node, i.e. the functional density of all necessary tasks. Nodes can become unavailable due to insufficient remaining energy. We assume that sensor nodes can be reconfigured or reprogrammed by a mobile robot system. There are various reasons for considering robots for this reconfiguration, e.g. reliability, security, and deployment issues. In this paper, we evaluate the advantages of exploiting reconfiguration and reprogramming schemes WSN using mobile robots. The primary objective is to increase the lifetime of the overall network. This goal is achieved by optimizing the functional density of heterogeneous tasks. Based on a developed simulation model, we discuss the advantages and performance characteristics.
Using simulated data in support of research on regression analysis
Authors: Christopher M. Hill, Linda C. Malone
Venue: Proceedings of the 36th conference on Winter simulation (2004)

Using simulated data to develop and study diagnostic tools for data analysis is very beneficial. The user can gain insight about what happens when assumptions are violated since the true model is known. However, care must be taken to be sure that the simulated data is a reasonable representation of what one would usually expect in the real world. This paper discusses the construction of simulated data sets and provides specific examples using linear and logistic regression analysis. It also addresses the execution of simulation based data studies following data construction.
Quantifying trends accurately despite classifier error and class imbalance
Authors: George Forman
Venue: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (2006)

This paper promotes a new task for supervised machine learning research: quantification - the pursuit of learning methods for accurately estimating the class distribution of a test set, with no concern for predictions on individual cases. A variant for cost quantification addresses the need to total up costs according to categories predicted by imperfect classifiers. These tasks cover a large and important family of applications that measure trends over time.The paper establishes a research methodology, and uses it to evaluate several proposed methods that involve selecting the classification threshold in a way that would spoil the accuracy of individual classifications. In empirical tests, Median Sweep methods show outstanding ability to estimate the class distribution, despite wide disparity in testing and training conditions. The paper addresses shifting class priors and costs, but not concept drift in general.
Fuzzy Immune Self-Regulating PID Control of Networked Control System
Authors: Laihua Fang, Zongzhi Wu, Aiguo Wu, Aihong Zheng
Venue: Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (2006)

Network-induced time delay is the main factor that deteriorates the performance of networked control system (NCS). In order to effectively restrain the impact of network delay on NCS, a fuzzy immune self-regulating PID control scheme is proposed. With ideas from the biological immune system, fuzzy immune PID control strategy is applied to NCS, in which parameter P is adaptively modulated by means of fuzzy immune algorithm, parameters I and D are dynamically regulated by fuzzy logic scheme. Simulation on network-based controlled DC motor is carried out so as to evaluate application of the presented control scheme in NCS. The result of simulation experiment shows validity of the control scheme and improvement in dynamic performance and robustness of NCS.