ppxscal/embedding_network huggingface dataset:
An overlay approach to data security in ad-hoc networks
Authors: Jörg Liebeherr, Guangyu Dong
Venue: Ad Hoc Networks (2007)

While it has been argued that application-layer overlay protocols can enhance services in mobile ad-hoc networks, hardly any empirical data is available on the throughput and delay performance achievable in this fashion. This paper presents performance measurements of an application-layer overlay approach that ensures integrity and confidentiality of application data in an ad-hoc environment. A key management and encryption scheme, called neighborhood key method, is presented where each node shares secrets only with authenticated neighbors in the ad-hoc network, thus avoiding global re-keying operations. All proposed solutions have been implemented and empirically evaluated in an existing software system for application-layer overlay networking. Results from indoor and outdoor measurement experiments with mobile handheld devices provide insight into the performance and overhead of overlay networking and application-layer security services in ad-hoc networks.
Challenge--Construction of an Adequate Digital Forensics Testbed
Authors: Adel Elmaghraby, James Graham, Jana Godwin, Michael Losavio, Deborah Wilson
Venue: Proceedings of the Second International Workshop on Systematic Approaches to Digital Forensic Engineering (2007)

The Challenge - What is an adequate laboratory specification for emulating network attacks and experimenting with network forensics, other digital forensics techniques and social behavioral traits? Could published specifications for different scales of research assist in research development? Is the specification described here adequate for research purposes? What additional considerations are needed for simulating network attacks and validating forensic tools?
An unsupervised approach to language identification
Authors: F. Pellegrino
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02 (1999)

This paper presents an unsupervised approach to automatic language identification (ALI) based on vowel system modeling. Each language vowel system is modeled by a Gaussian mixture model (GMM) trained with automatically detected vowels. Since this detection is unsupervised and language independent, no labeled data are required. GMMs are initialized using an efficient data-driven variant of the LBG algorithm: the LBG-Rissanen (1983) algorithm. With 5 languages from the OGI MLTS corpus and in a close set identification task, we reach 79% of correct identification using only the vowel segments detected in 45 second duration utterances for the male speakers.
Processing Mesoscale Climatology in a Grid Environment
Authors: R. P. Souto, R. B. Avila, P. O. A. Navaux, M. X. Py, T. A. Diverio, H. F. Campos Velho, S. Stephany, A. J. Preto, J. Panetta, E. R. Rodrigues, E. S. Almeida, P. L. Silva Dias, A. W. Gandu
Venue: Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid (2007)

Enhancing the quality of weather and climate forecasts are central scientific research objectives worldwide. However, simulations of the atmosphere, usually demand high processing power and large storage resources. In this context, we present the GBRAMS project, that applies grid computing to speed up the generation of a regional model climatology for Brazil. A grid infrastructure was built to perform long-term integrations of a mesoscale numerical model (BRAMS), managing a queue of up to nine independent jobs submitted to three clusters spread over Brazil. Three distinct middlewares, Globus Toolkit, OurGrid and OAR/CIGRI, were compared in their ability to manage these jobs, and results on the usage of each node of the grid are provided. We analyze the impact of the resulted climatology in the accuracy of climate forecast, showing model bias removal which indicates correctness of the generated climatology. Our central contribution are how to use grid computing to speed-up climatology generation and the middleware impact on this enterprise.
Increasing Acceptability of Decision Trees with Domain Attributes Partial Orders
Authors: Joan Albert Lopez-Vallverdu, David Riano, Antoni Collado
Venue: Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems (2007)

There are several domains, such as health-care, in which the decision process usually has a background knowledge that must be considered. We need to maximize the accuracy of the models, but we also need them to be meaningful. Otherwise it will lead to the problem that the expert finds the obtained models incomprehensible. We propose a way for representing the knowledge of the experts in order to modify the C4.5 algorithm to produce decision trees which are more comprehensible to medical doctors without losing accuracy.
Geodesic Active Contour Based Fusion of Visible and Infrared Video for Persistent Object Tracking
Authors: F. Bunyak, K. Palaniappan, S. K. Nath, G. Seetharaman
Venue: Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision (2007)

Persistent object tracking in complex and adverse environments can be improved by fusing information from multiple sensors and sources. We present a new moving object detection and tracking system that robustly fuses infrared and visible video within a level set framework. We also introduce the concept of the flux tensor as a generalization of the 3D structure tensor for fast and reliable motion detection without eigen-decomposition. The infrared flux tensor provides a coarse segmentation that is less sensitive to illumination variations and shadows. The Beltrami color metric tensor is used to define a color edge stopping function that is fused with the infrared edge stopping function based on the grayscale structure tensor. The min fusion operator combines salient contours in either the visible or infrared video and drives the evolution of the multispectral geodesic active contour to refine the coarse initial flux tensor motion blobs. Multiple objects are tracked using correspondence graphs and a cluster trajectory analysis module that resolves incorrect merge events caused by under- segmentation of neighboring objects or partial and full occlusions. Long-term trajectories for object clusters are estimated using Kalman filtering and watershed segmentation. We have tested the persistent object tracking system for surveillance applications and demonstrate that fusion of visible and infrared video leads to significant improvements for occlusion handling and disambiguating clustered groups of objects.
The separability theory of hyperbolic tangent kernels and support vector machines for pattern classification
Authors: M. Sellathurai
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02 (1999)

A new theory is developed for the feature spaces of hyperbolic tangent used as an activation kernel for non-linear support vector machines. The theory developed herein is based on the distinct features of hyperbolic geometry, which leads to an interesting geometrical interpretation of the higher-dimensional feature spaces of neural networks using hyperbolic tangent as the activation function. The new theory is used to explain the separability of hyperbolic tangent kernels where we show that the separability is possible only for a certain class of hyperbolic kernels. Simulation results are given supporting the separability theory.
Smoothing methods in maximum entropy language modeling
Authors: S. C. Martin
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01 (1999)

This paper discusses various aspects of smoothing techniques in maximum entropy language modeling, a topic not sufficiently covered by previous publications. We show: (1) that straightforward maximum entropy models with nested features, e.g. tri-, bi-, and unigrams, result in unsmoothed relative frequencies models; (2) that maximum entropy models with nested features and discounted feature counts approximate backing-off smoothed relative frequencies models with Kneser's (see IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Detroit, MI, vol. I, p.181-84, 1995) advanced marginal backoff distribution; this explains some of the reported success of maximum entropy models in the past; and (3) perplexity results for nested and non-nested features, e.g, trigrams and distance-trigrams, on a 4-million word subset of the Wall Street Journal corpus, showing that the smoothing method has more effect on the perplexity than the method used to combine information.
Fast algorithm for finite-length MMSE equalizers with application to discrete multitone systems
Authors: N. Lashkarian
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 05 (1999)

This paper presents a new, fast algorithm for finite-length minimum mean square error (MMSE) equalizers. The research exploits the asymptotic equivalence of Toeplitz and circulant matrices to estimate the Hessian matrix of a quadratic form. Research shows that the Hessian matrix exhibits a specific structure. As a result, when combined with the Rayleigh minimization algorithm, it provides an efficient method to obtain the global minimum of constrained optimization problem. A salient feature of this algorithm is that extreme eigenvector of the Hessian matrix can be obtained without direct computation of the matrix. In comparison to the previous methods, the algorithm is more computationally efficient and highly parallelizable, which makes the algorithm more attractive for real time applications. The algorithm is applied for equalization of discrete multitone (DMT) systems for asynchronous digital subscriber line (ADSL) applications.
Revisiting Boltzmann learning: parameter estimation in Markov random fields
Authors: L. K. Hansen
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06 (1996)

This article presents a generalization of the Boltzmann machine that allows us to use the learning rule for a much wider class of maximum likelihood and maximum a posteriori problems, including both supervised and unsupervised learning. Furthermore, the approach allows us to discuss regularization and generalization in the context of Boltzmann machines. We provide an illustrative example concerning parameter estimation in an inhomogeneous Markov field. The regularized adaptation produces a parameter set that closely resembles the "teacher" parameters, hence, will produce segmentations that closely reproduce those of the inhomogeneous teacher network.
Impact of Memory Contention on Dynamic Scheduling on Numa Multiprocessors
Authors: M. D. Durand, T. Montaut, L. Kervella, W. Jalby
Venue: Proceedings of the 1993 International Conference on Parallel Processing - Volume 01 (1993)

Self-scheduling is a method for task schedul ing in parallel programs, in which each processor acquires a new block of tasks for execution whenever it becomes idle. To get the best performance, the block size must be chosen to balance the scheduling overhead against the load im balance. To determine the best block size, better analytical models of self-scheduling are needed. We present an experimental study of self-scheduling on a BBN TC2000, a NUMA machine. Previously published models to predict running time and optimal block size were tested using our experimental results. Although the models gave good predictions for small block sizes, for large block sizes the models fail, underestimating the running time by almost a factor of two. We present an upper bound on the running time and use it to explain this failure.
An introduction to engineering design: teaching the engineering process through teaming and the continuous improvement philosophy
Authors: L. Bellamy, B. McNeill, J. Balley, R. Roedel, W. Moor, I. Zwiebel, D. Laananen
Venue: Proceedings of the Frontiers in Education Conference, on 1995. Proceedings., 1995 - Volume 02 (1995)

Summary form only given, as follows. We describe a first year required course in engineering design, initiated at ASU in the Fall'94 semester. The organizing thread and philosophy for the course is the process of engineering, utilizing teaming and continuous improvement, based on Deming's fourteen points. Process is defined as a collection of interrelated tasks that take one from input to output in the engineering environment. The course has three components: Process Concepts, Design Laboratory, and Computer Modeling. In the concepts section, the emphasis is on a problem solving heuristic similar to the Deming Plan-Do-Check-Act process or the Boeing Seven Step problem solving process. The concepts section meets once a week for two hours in a large, multimedia classroom with a center podium and tables for teams of four students. The capacity of the concepts class is 120 students. The design laboratory component of the class has two main portions: (1) A Mechanical Dissection and Reassembly of an Artifact, in which the reassembly process is developed, documented, and evaluated using community volunteers for testing, and (2) An Artifact Design for Reproducible Performance in which an object is designed, constructed, and evaluated. In the Fall '94 semester students dissected a telephone for the reassembly process and constructed a mouse trap powered model airplane launcher for the artifact design process. In the computer modeling component of the course, students learn how to develop models conceptually and then evaluate these models with Excel spreadsheets and TKSolver. Nine different computer models are generated and evaluated in this portion of the course, which meets in a computer classroom which contains approximately 25 computers. The class combines active learning and technology enhanced education. More details of the course content and the assessment and evaluation of the student performance will be described in the talk.
Self-organizing Replica Placement - A Case Study on Emergence
Authors: Klaus Herrmann
Venue: Proceedings of the First International Conference on Self-Adaptive and Self-Organizing Systems (2007)

The concept of self-organization is rapidly gaining importance in the area of distributed computing systems. However; we still lack the necessary means for engineering such system in a standardized way since their common properties are rather abstract, and the mechanisms from which self-organization emerges are too diverse. Therefore, it has become common practice to engineer computing systems by taking inspirations from well-known case studies of biological systems. However; the concepts found in such systems are in many cases only partially transferable to the domain of distributed computing systems since biological systems are subject to vastly different constraints compared to those in a computing system. Our contributions in this paper are the following: (i) We present a case study of a self-organizing software system that originates from the domain of distributed computing systems. Therefore, its concepts can be exploited in other distributed computing systems much more directly. (ii) We give a detailed analysis of the emergent properties of the system and the meclzanisms by which they arise. (iii) We generalize the meckanisms by which self-organization emerges in this system and present a catalog of design questions that may help engineers in creating arbitrary self-organizing systems.
Vehicle Class Recognition from Video-Based on 3D Curve Probes
Authors: Dongjin Han, M. J. Leotta, D. B. Cooper, J. L. Mundy
Venue: Proceedings of the 14th International Conference on Computer Communications and Networks (2005)

A new approach is presented to vehicle-class recognition from a video clip. Two new concepts introduced are: probes consisting of local 3D curve-groups which when projected into video frames are features for recognizing vehicle classes in video clips; and Bayesian recognition based on class probability densities for groups of 3D distances between pairs of 3D probes. The most stable image features for vehicle class recognition appear to be image curves associate with 3D ridges on the vehicle surface. These ridges are mostly those occurring at metal/glass interfaces, two-surface intersections such as back and side, and self occluding contours such as wheel wells or vehicle-body apparent contours, i.e., silhouettes. There are other detectable surface curves, but most do not provide useful discriminatory features, and many of these are clutter, i.e., due to reflections from the somewhat shiny vehicle surface. Models are built and used for the considerable variability that exists in the features used. A Bayesian recognizer is then used for vehicle class recognition from a sequence of frames. The ultimate goal is a recognizer to deal with essentially all classes of civilian vehicles seen from arbitrary directions, at a broad range of distances and under the broad range of lighting ranging from sunny to cloudy. Experiments are run with a small set of classes to prove feasibility. This work uses estimated knowledge of the motion and position of the vehicle. We briefly indicate one way of inferring that information which uses ID projectivity invariance.
Separable Karhunen Loeve transforms for the weighted universal transform coding algorithm
Authors: Hanying Feng
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 05 (1999)

The weighted universal transform code (WUTC) is a two-stage transform code that replaces JPEG's single, non-optimal transform code with a jointly designed collection of transform codes to achieve good performance across a broader class of possible sources. Unfortunately, the performance gains of WUTC are achieved at the expense of significant increases in computational complexity and larger codes. We here present a faster, more space-efficient WUTC algorithm. The new algorithm uses separable coding instead of direct KLT. While separable coding gives performance comparable to that of WUTC, it uses only 1/8 of the floating-point multiplications and 1/32 of storage of direct KLT. Experimental results included in this work compare the performance of new separable WUTC with both the WUTC and other fast variations of that algorithm.
Building a Coordination Framework to Support Behavior-Based Adaptive Checkpointing for Open Distributed Embedded Systems
Authors: Nianen Chen, Shangping Ren
Venue: Proceedings of the 40th Annual Hawaii International Conference on System Sciences (2007)

Checkpointing is a commonly used approach to provide fault-tolerance and improve system dependability. However, using a constant and pre-configured checkpointing frequency may compromise other QoS criteria when there are resource and timing constraints. The frequency of checkpointing should be based on the importance of corresponding entities' behaviors and should also be runtime adaptable. The paper presents a behavior-based coordination framework. In this framework, behavior-based adaptive checkpointing is treated as a coordination concern; different coordination constraints are applied based on the behaviors that the entities are currently engaged in. Our prototyping and empirical experiments have shown that we are able to achieve behavior-based coordination in a more modular way with limited performance overhead. The experiments also demonstrate that the framework scales well when the number of entities involved in the system increases.
A Performance Debugger for a Language Supporting Data Distribution Primitives
Authors: G. Hurteau, A. Singh, M. Hancu, V. Van Dongen, H. Hum
Venue: Proceedings of the 1994 International Conference on Parallel Processing - Volume 02 (1994)

Data parallel languages based on user-supplied data distribution directives significantly simplify the development of the initial version of a parallel application. However, selection of good data distributions leading to efficient computations is often quite difficult. Therefore, performance debuggers are usually employed to yield insights into effects of data distribution. This paper presents design and implementation of a performance debugger that is specifically targeted to meet the performance debugging requirements of a data-parallel programming model based on user-specified data distributions. The visual interface of the performance debugger provides feedback regarding data distributions amongst processor nodes, data access patterns during program execution, as well as statistical performance data. The feedback provided by the performance debugger is explicitly related back to objects in the programmer's source code.
Innovative manufacturing engineering program: a "SCCEME" way
Authors: R. Williams, A. Zehrabchi, M. Das
Venue: Proceedings of the Frontiers in Education Conference, on 1995. Proceedings., 1995 - Volume 02 (1995)

Summary form only given. The Southern California Coalition for Education in Manufacturing Engineering (SCCEME) is a coalition of six major California universities which was formed in 1993 and is now one of only eight National Science Foundation Engineering Centers nationwide. The mission of SCCEME is to develop a comprehensive system of inter-university programs of undergraduate manufacturing engineering education. The Coalition focuses on the long-term systematic reform of manufacturing engineering education across the curricular enhancing exiting manufacturing-related programs, creating new degree programs and degree options related to manufacturing that emphasize integrated product and process development, and providing training programs to enhance the knowledge and skills of the manufacturing workforce.
A second order recursive algorithm for subspace tracking
Authors: A. Andersson
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05 (1996)

In the present paper a second order recursive algorithm for subspace tracking is proposed. The tracking capabilities of the proposed algorithm is demonstrated via some simulation examples and the algorithm is compared with the corresponding first order algorithm. The proposed algorithm has good tracking capabilities, such as smooth behavior of the trajectories and fast adaptation. The proposed algorithm has the same computational complexity as has the first order algorithm, but some extra memory storage is required.
A provisioning model and its comparison with best-effort for performance-cost optimization in grids
Authors: Gurmeet Singh, Carl Kesselman, Ewa Deelman
Venue: Proceedings of the 16th international symposium on High performance distributed computing (2007)

The resource availability in Grids is generally unpredictable due to the autonomous and shared nature of the Grid resources and stochastic nature of the workload resulting in a best effort quality of service. The resource providers optimize for throughput and utilization whereas the users optimize for application performance. We present a cost-based model where the providers advertise resource availability to the user community. We also present a multi-objective genetic algorithm formulation for selecting the set of resources to be provisioned that optimizes the application performance while minimizing the resource costs. We use trace-based simulations to compare the application performance and cost using the provisioned and the best effort approach with a number of artificially generated workflow-structured applications and a seismic hazard application from the earthquake science community. The provisioned approach shows promising results when the resources are under high utilization and/or the applications have significant resource requirements.