ppxscal/embedding_network huggingface dataset:
Goal-oriented modularity in agent programming
Authors: M. Birna van Riemsdijk, Mehdi Dastani, John-Jules Ch. Meyer, Frank S. de Boer
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

Modularization is widely recognized as a central issue in software engineering. In this paper we address the issue of modularization in cognitive agent programming languages. We discuss existing approaches to modularity in cognitive agent programming. Then, we propose a new kind of modularity, i.e., goal-oriented modularity, which takes the goals of an agent as the basis for modularization. Further, we present a formal semantics of goal-oriented modularity in the context of the 3APL agent programming language.
Designing for Recovery
Authors: Rebecca Wirfs-Brock
Venue: IEEE Software (2006)

When things go right, software hums along like well-oiled machinery--receive an event, twiddle with inputs, send a flurry of messages, change the system state, interact with the environment or users, then wait for the next chunk of work. Smooth. Mechanical. Predictable. But what happens when something goes wrong? How should you design your software to detect, react, and recover from exceptional conditions?
Diffusion Maps and Coarse-Graining: A Unified Framework for Dimensionality Reduction, Graph Partitioning, and Data Set Parameterization
Authors: Stephane Lafon, Ann B. Lee
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)

We provide evidence that nonlinear dimensionality reduction, clustering, and data set parameterization can be solved within one and the same framework. The main idea is to define a system of coordinates with an explicit metric that reflects the connectivity of a given data set and that is robust to noise. Our construction, which is based on a Markov random walk on the data, offers a general scheme of simultaneously reorganizing and subsampling graphs and arbitrarily shaped data sets in high dimensions using intrinsic geometry. We show that clustering in embedding spaces is equivalent to compressing operators. The objective of data partitioning and clustering is to coarse-grain the random walk on the data while at the same time preserving a diffusion operator for the intrinsic geometry or connectivity of the data set up to some accuracy. We show that the quantization distortion in diffusion space bounds the error of compression of the operator, thus giving a rigorous justification for k-means clustering in diffusion space and a precise measure of the performance of general clustering algorithms.
Validation and calibration of human performance models to support simulation-based acquisition
Authors: Floyd Glenn, Kelly Neville, James Stokes, Joan Ryder
Venue: Proceedings of the 36th conference on Winter simulation (2004)

We present a methodology under development for calibration and validation of human performance models in support of simulation-based acquisition processes --- a human performance modeling validation program. We describe a conceptual framework based on an investigation of the characteristics of a wide variety of performance modeling frameworks and application domains. We offer initial taxonomies of model actions and empirical performance actions that will support the necessary mappings between model predictions and empirical observations for all models over the full range of required representation detail, and across all stages of the acquisition process in order to establish a solid analytic basis for calibration and validation of SBA processes and human performance modeling frameworks. We describe methods for specifying performance measures so that for any given design decision, performance measures captured using a model can be mapped to performance measures obtained during live test and evaluation.
Study of an energy efficient multi rate scheme for wireless sensor network MAC protocol
Authors: Wei Wang, Dongming Peng, Honggang Wang, Hamid Sharif
Venue: Proceedings of the 2nd ACM international workshop on Quality of service security for wireless and mobile networks (2006)

Real-time Digital Signal Processing (DSP) applications in wireless sensor network are often multi-rate in nature. These multi rate attributes provide additional significant opportunities in network design for achieving higher energy efficiency, besides traditional approaches of saving energy. In this paper, we propose a novel multi rate sensor network scheme to take advantage of power scaling for saving energy consumption. It can be easily synthesized into existing wireless sensor network MAC protocols, providing multi rate service for upper layer, and achieving communication energy efficiency. By monitoring a desirable BER threshold as the critical system parameter, the quality of received multi-rate data is assured. Based on this BER and the corresponding modulation scheme, the SNR at receiver end can be derived. The transmission power can be supplied optimally according to this SNR and the channel attenuation between sender and receiver. This dynamic power scaling scheme can lead to optimal energy efficiency since it demands that only a necessary amount of communication energy be consumed for multi-rate DSP applications. Our simulation shows that this multi data rate scheme with dynamic power scaling achieves energy saving significantly
Blind quadratic and time-frequency based detectors from training data
Authors: D. L. Jones, A. M. Sayeed
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02 (1995)

Time-frequency based methods, particularly quadratic (Cohen's-class) representations, are often considered for detection in applications ranging from sonar to machine monitoring. We propose a method of obtaining near-optimal quadratic detectors directly from training data using Fisher's optimal linear discriminant to design a quadratic detector. This detector is optimal in terms of Fisher's scatter criterion as applied to the quadratic outer product of the data vector, and in early simulations appears to closely approximate the true optimal quadratic detector. By relating this quadratic detector to an equivalent operation on the Wigner distribution of a signal, we derive near-optimal time-frequency detectors. A simple example demonstrates the excellent performance of the method.
High performance energy efficient file storage system
Authors: Jun Wang
Venue:  (2006)

Recently energy has become a critical resource in modern computing systems. In mobile computers, disk sub-systems consume a major portion of available energy, which reduces battery life and thus limits the utility the whole system. In data intensive environments, the energy consumption of the I/O storage subsystem may easily surpass the energy consumed by the rest of the computing system. The direct and indirect cost of energy consumption is a limiting factor in deploying and operating data-intensive computing systems. This dissertation aims to provide energy saving solutions for both client side and server side environments, with the latter as the focus. For the client side systems, this work proposes a new energy-efficient file system called EEFS, to reduce energy consumption and improve performance by separately managing small-sized files with good group access locality. Because of the workload intensity, energy-efficient I/O solutions for client systems usually cannot be applied to data servers. Multi-speed disks were introduced by previous research work to make it possible to lower the disk energy consumption in I/O intensive environments. For multi-speed disk based server systems, this dissertation provides an energy-efficient RAID system named EERAID. Furthermore, this work addresses the open question of saving energy for conventional disk based storage servers by providing the policies EPRAID and eRAID, for RAID5 and RAID1 systems respectively.
Service-Oriented Distributed Data Mining
Authors: William K. Cheung, Xiao-Feng Zhang, Ho-Fai Wong, Jiming Liu, Zong-Wei Luo, Frank C. H. Tong
Venue: IEEE Internet Computing (2006)

Data mining research currently faces two great challenges: how to embrace data mining services with just-in-time and autonomous properties and how to mine distributed and privacy-protected data. To address these problems, the authors adopt the Business Process Execution Language for Web Services in a service oriented distributed data mining (DDM) platform to choreograph DDM component services and fulfill global data mining requirements. They also use the learning-from-abstraction methodology to achieve privacy-preserving DDM. Finally,they illustrate how localized autonomy on privacy-policy enforcement plusa bidding process can help the service-oriented system self-organize.
A measurement-friendly network (MFN) architecture
Authors: Sridhar Machiraju, Darryl Veitch
Venue: Proceedings of the 2006 SIGCOMM workshop on Internet network management (2006)

Using active Techniques to measure networks, that is by injecting probe packets, has proved to be quite challenging for properties beyond simple end-to-end delay and loss. Some of the greatest difficulties have resulted from our inability to design techniques robust to multi-hop queueing effects. This difficulty is only compounded by the need to keep measurements non-intrusive, that is to minimally affect ongoing data flows. In this paper, we show that novel network primitives based on hop-dependent priority queueing are very effective in addressing these challenges. By enabling these primitives, network operators can perform a variety of active measurements accurately. Such measurement-friendliness results from many factors including ease of applying fundamentally single-hop methods, better measurement capabilities, and easier clock synchronization. Other advantages of our architecture include ease of deployment, simplicity, low overhead and generality, i.e., no constraints on scheduling policies for data packets. We also discuss the challenges faced, for example, in coping with small but unavoidable inaccuracies and with exposing the primitives to end-users.
A Loss and Queuing-Delay Controller for Router Buffer Management
Authors: Long Le, Kevin Jeffay, F. Donelson Smith
Venue: Proceedings of the 26th IEEE International Conference on Distributed Computing Systems (2006)

Active queue management (AQM) in routers has been proposed as a solution to some of the scalability issues associated with TCP's pure end-to-end approach to congestion control. However, beyond congestion control, controlling queues in routers is important because unstable router queues can cause poor application performance. Existing AQM schemes explicitly try to control router queues by probabilistically dropping (or marking) packets. We argue that while controlling router queues is important, this control needs to be tempered by a consideration of the overall lossrate at the router. Solely attempting to control queue length can induce loss-rates that have as negative an effect on application and network performance as the large queues that existing AQM schemes were trying to avoid. Thus controlling queue length without regard to loss-rate can be counterproductive. In this work we demonstrate that by jointly controlling queue length and loss-rate, both network and application performance are improved. We present a novel AQM design that attempts to simultaneously optimize queue length and loss-rate. Our algorithm, called loss and queuing delay control (LQD), is a control theoretic scheme that explicitly treats loss-rate as a control parameter. LQD is shown to provide stable control analytically and is evaluated empirically by comparing its performance against other control theoretic AQM designs (PI and REM). The results of evaluation in a laboratory testbed under realistic traffic mixes and loads show that LQD results in lower overall loss rates and that applications see lower average queue lengths than with PI or REM.
Electrical characteristics of GaN-based metal-oxide-semiconductor (MOS) structures
Authors: K. A. Abdullah, M. J. Abdullah, F. K. Yam, Z. Hassan
Venue: Microelectronic Engineering (2005)

Gallium nitride (GaN) has attracted considerable interest for electronic device applications at high temperature environment with high power conditions. The large lattice mismatch and the large thermal expansion coefficient difference between the GaN film and silicon substrate makes it difficult to get film of high quality and suitable for the metal-oxide-semiconductor (MOS) devices. However, deposited films can be subjected to different fabrication processes to exhibit good electrical characteristics. In this paper, we report on the fabrication and characterization of MOS capacitor based on GaN grown on silicon substrates at low growth temperatures (200 and 600^oC). The roughness, morphology, composition and crystalline quality of the GaN films were determined by atomic force microscopy (AFM), scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDX) and X-ray diffraction (XRD) measurements. The fabricated MOS structures were characterized using capacitance-voltage (C-V) measurements.
Concept, topic, and pattern discovery using clustering
Authors: Dennis Mcleod
Venue:  (2005)

In this dissertation, we present mining framework to extract useful pattern, concept, and topic from multi-dimensional dataset using clustering. In general, there are two kinds of datasets, incremental data and static data. Incremental data is the one where data items are inserted over time. However, not all datasets are incremental. In many cases, with static data, there is no incremental insertion. Thus, depending on the nature of data, relevant data mining algorithms should be developed. Thus, this dissertation is basically composed of two parts: incremental clustering for incremental data, and batch clustering for static data. For incremental data, we target news streams, and for static data, we target gene expression data. In the first part, we propose a mining framework that supports the identification of useful patterns based on incremental data clustering. Given the popularity of Web news services, we focus our attention on news streams mining. A key challenging issue within news repository management is the high rate of document insertion. To address this problem, we present an incremental hierarchical document clustering algorithm using a neighborhood search. The novelty of the proposed algorithm is the ability to identify meaningful patterns (e.g., news events, and news topics) while reducing the amount of computations by maintaining cluster structure incrementally. In addition, we propose a topic ontology learning framework that utilizes the obtained document hierarchy. Experimental results demonstrate that the proposed clustering algorithm produces high-quality clusters, and a topic ontology provides interpretations of news topics at different levels of abstraction. In the second part, we focus our attention on mining yeast cell cycle dataset. In molecular biology, a set of co-expressed genes tend to share a common biological function. Thus, it is essential to develop an effective clustering algorithm to identify the set of co-expressed genes. Toward this end, we propose genome-wide expression clustering based on a density-based approach. By addressing the strengths and limitations of previous density-based clustering approaches, we present a novel density clustering algorithm, which utilizes a neighborhood defined by k-nearest mutual neighbors. Experimental results indicate that the proposed method successfully identifies co-expressed and biologically meaningful gene clusters.
Robust target detection and tracking through integration of motion, color, and geometry
Authors: Harini Veeraraghavan, Paul Schrater, Nikos Papanikolopoulos
Venue: Computer Vision and Image Understanding (2006)

Vision-based tracking is a basic elementary task in many computer vision-based applications such as video surveillance and monitoring, sensing and navigation in robotics, video compression, video annotation, and many more. However, reliable recovery of targets and their trajectories in an uncontrolled environment is affected by a wide range of conditions exhibited by the environment such as sudden illumination changes and clutter. This work addresses the problem of (i) combining information from a set of cues in order to obtain reasonably accurate estimates of multiple targets in uncontrolled environments and (ii) a collection of data association methods for cues containing less information for robust tracking through persistent clutter. Specifically, we introduce a novel geometric template constrained data association method for robust tracking of point features, while using the Joint Probabilistic Data Association (JPDA) method for blob cue measurements. Extensive experimental validation of the tracking and the data association framework is presented in the work for several real-world outdoor traffic intersection image sequences.
Automatic Landmark Tracking and its Application to the Optimization of Brain Conformal Mapping
Authors: Lok Ming Lui, Yalin Wang, Tony F. Chan, Paul M. Thompson
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (2006)

Anatomical features on cortical surfaces are usually represented by landmark curves, called sulci/gyri curves. These landmark curves are important information for neuroscientists to study brain diseases and to match different cortical surfaces. Manual labelling of these landmark curves is time-consuming, especially when there is a large set of data. In this paper, we proposed to trace the landmark curves on cortical surfaces automatically based on the principal directions. Suppose we are given the global conformal parametrization of a cortical surface, By fixing two endpoints, the anchor points, we propose to trace the landmark curves iteratively on the spherical/rectangular parameter domain along the principal direction. Consequently, the landmark curves can be mapped onto the cortical surface. To speed up the iterative scheme, a good initial guess of the landmark curve is necessary. We proposed a method to get a good initialization by extracting the high curvature region on the cortical surface using the Chan-Vese segmentation. This involves solving a PDE on the manifold using our global conformal parametrization technique. Experimental results show that the landmark curves detected by our algorithm closely resemble to those manually labelled curves. As an application, we used these automatically labelled landmark curves to build average cortical surfaces with an optimized brain conformal mapping method. Experimental results show our method can help automatically matching brain cortical surfaces.
The borderline of science: on the value of factor analysis for understanding presence
Authors: David Waller, Eric R. Bachmann
Venue: Presence: Teleoperators and Virtual Environments (2006)

The use of exploratory factor analysis in research on presence is summarized and critically examined. We conclude that, as with research on human intelligence in the first half of the twentieth century, exploratory factor analysis has been extremely limited in its ability to test hypotheses and to advance theory. To illustrate these ideas, we examine a recent analysis by Witmer, Jerome, and Singer (2005, Presence 14, 298-312). We suggest that analyses based on confirmatory methods are in general better suited to advance our understanding of the concept of presence.
Scrap++: scrap your boilerplate in C++
Authors: Gustav Munkby, Andreas Priesnitz, Sibylle Schupp, Marcin Zalewski
Venue: Proceedings of the 2006 ACM SIGPLAN workshop on Generic programming (2006)

"Scrap Your Boilerplate" (SYB) is a well studied and widely used design pattern for generic traversal in the Haskell language, but almost unknown to generic programmers in C++. This paper shows that SYB can be implemented in C++. It identifies the features and idioms of C++ that correspond to the Haskell constructs that implement SYB, or can be used to emulate them, and provides a prototype C++ implementation.
Performance of various detectors in non-Gaussian noise and weak stochastic signal environment
Authors: L. C. Godara, P. M. Schultheiss
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04 (1993)

Many locally optimum, sub-optimum and ad hoc detectors exploit knowledge of the noise probability density function (PDF) to obtain their test statistic for the detection of stochastic signals in non-Gaussian noise and appear to offer potential for highly improved performance for very small signal to noise ratios. The authors explore the realism of this conclusion by comparing performance of a locally optimum detector with that of the power detector and of the log-likelihood ratio detector by deriving analytical expressions for a performance measure using a double-sided exponential PDF as the dominant noise. The conclusion is that the performance of the generalized energy detector can become poorer than that of the power detector well within the practical range of signal to noise ratios, and the apparently large gains predicted by the asymptotic theory may not be reachable in practice.
Modeling and Analysis of Peer-to-Peer Multicast Streaming
Authors: Dominik Zalewski
Venue: Proceedings of the Second International Conference on Automated Production of Cross Media Content for Multi-Channel Distribution (2006)

The article is a study of current progress in peer-to-peer multicast streaming. The idea of peer-to-peer networks is now about seven years old. Its use for multicast streaming is even younger. Many software packages have been written and deployed. The most astonishing fact about the subject however, is the lack of theoretical background knowledge. The article reviews this knowledge and as its contribution tries to develop this knowledge. As a result the definition of parameters that a model for such a network should have follows. After that, the design of a simulation environment for checking model applicability is introduced.
Is Morton layout competitive for large two-dimensional arrays yet&quest;: Research Articles
Authors: Jeyarajan Thiyagalingam, Olav Beckmann, Paul H. J. Kelly
Venue: Concurrency and Computation: Practice Experience (2006)

Two-dimensional arrays are generally arranged in memory in row-major order or column-major order. Traversing a row-major array in column-major order, or vice versa, leads to poor spatial locality. With large arrays the performance loss can be a factor of 10 or more. This paper explores the Morton storage layout, which has substantial spatial locality whether traversed in row-major or column-major order. Using a small suite of dense kernels working on two-dimensional arrays, we have carried out an extensive study of the impact of poor array layout and of whether Morton layout can offer an attractive compromise. We show that Morton layout can lead to better performance than the worse of the two canonical layouts; however, the performance of Morton layout compared to the better choice of canonical layout is often disappointing. We further study one simple improvement of the basic Morton scheme: we show that choosing the correct alignment for the base address of an array in Morton layout can sometimes significantly improve the competitiveness of this layout. Copyright &copy; 2006 John Wiley & Sons, Ltd.