ppxscal/embedding_network huggingface dataset:
Combining ECA Rules with Process Algebras for the Semantic Web
Authors: Erik Behrends, Oliver Fritzen, Wolfgang May, Franz Schenk
Venue: Proceedings of the Second International Conference on Rules and Rule Markup Languages for the Semantic Web (2006)

We describe how Event-Condition-Action (ECA) rules can be combined with Process Algebras like CCS as specification of the action part to obtain a powerful, declarative formalism that also covers intuitively procedural tasks in an appropriate way. Since both formalisms have a concise formal semantics, verification and other kinds of reasoning about such specifications are possible. Using a rule markup with cleanly distinguished rule components allows for such a compositional approach. The approach is currently under implementation in a General ECA Framework for the Web and the Semantic Web.
Function-approximation-based importance sampling for pricing American options
Authors: Nomesh Bolia, Sandeep Juneja, Paul Glasserman
Venue: Proceedings of the 36th conference on Winter simulation (2004)

Monte Carlo simulation techniques that use function approximations have been successfully applied to approximately price multi-dimensional American options. However, for many pricing problems the time required to get accurate estimates can still be prohibitive, and this motivates the development of variance reduction techniques. In this paper, we describe a zero-variance importance sampling measure for American options. We then discuss how function approximation may be used to approximately learn this measure; we test this idea in simple examples. We also note that the zero-variance measure is fundamentally connected to a duality result for American options. While our methodology is geared towards developing an estimate of an accurate lower bound for the option price, we observe that importance sampling also reduces variance in estimating the upper bound that follows from the duality.
Constrained Circular Hidden Markov Models for Recognizing Deformed Shapes
Authors: Jinhai Cai
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)

In this paper, we analyse the properties of the standard circular hidden Markov model (HMM) on 2D shape recognition. We point out the limitations of the circular HMMs and further propose to impose the constraint on the relationship between the initial and final states of circular HMMs to improve the performance. We develop two modified Viterbi algorithms to implement our proposal. The proposed algorithms have been tested on the database of the MPEG-7 Core Experiments Shape-1, Part B. The experiments show that both proposed algorithms can achieve better performance than that of the standard circular HMM in terms of accuracy. In particular, the second proposed algorithm, which is faster than elastic matching algorithms, has much potential due to its accuracy and speed.
Extending a Resource Broker for Advance Reservation and Charging
Authors: Marco Fargetta, Donal Fellows, Dean Kuo
Venue: Proceedings of the Second IEEE International Conference on e-Science and Grid Computing (2006)

Existing resource brokers for computational Grids only focus on locating a machine (computational resource) within a cluster that has the least load. Such simple brokers are ineffective in commercial and complex environments where negotiation over price, time and service-level agreements are required to establish a contract before users are authorised to use a computational resource. This paper presents a brokering system, utilising the Job Submission Description Language (JSDL) standard and extends the Unicore/GS Resource Broker, that extends brokering with scheduling (resource availability) and simple pricing policy management. The brokering system improves the effectiveness in negotiating contracts for the use of computational resources on the Grid. The architecture and implementation have developed a plug-in for Unicore/GS Resource broker and integrates an externally hosted calendaring service to support the described functionality, and demonstrates the feasibility of building systems by composing autonomous services hosted on the network.
Application Profiles for Learning
Authors: Erik Duval, Neil Smith, Marc Van Coillie
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

Application profiles enable "mixing and matching" metadata elements, in order to meet specific requirements for a particular context. As an example, some communities may want to make certain elements mandatory or restrict the value space of a particular element. However, there is much confusion and only limited experience and expertise in the development and deployment of application profiles. That is why the CEN/ISSS Learning Technologies Workshop has decided to develop guidelines on the use of application profiles for (e-)learning. This paper highlights the main results of this work, and was co-authored by the three experts that drafted the CEN document [4].
Imaginal, Semantic, and Surface-Level Processing of Concrete and Abstract Words: An Electrophysiological Investigation
Authors: W. Caroline West, Phillip J. Holcomb
Venue: Journal of Cognitive Neuroscience (2000)

Words representing concrete concepts are processed more quickly and efficiently than words representing abstract concepts. Concreteness effects have also been observed in studies using event-related brain potentials (ERPs). The aim of this study was to examine concrete and abstract words using both reaction time (RT) and ERP measurements to determine (1) at what point in the stream of cognitive processing concreteness effects emerge and (2) how different types of cognitive operations influence these concreteness effects. Three groups of subjects performed a sentence verification task in which the final word of each sentence was concrete or abstract. For each group the truthfulness judgment required either (1) image generation, (2) semantic decision, or (3) evaluation of surface characteristics. Concrete and abstract words produced similar RTs and ERPs in the surface task, suggesting that postlexical semantic processing is necessary to elicit concreteness effects. In both the semantic and imagery tasks, RTs were shorter for concrete than for abstract words. This difference was greatest in the imagery task. Also, in both of these tasks concrete words elicited more negative ERPs than abstract words between 300 and 550 msec (N400). This effect was widespread across the scalp and may reflect activation in a linguistic semantic system common to both concrete and abstract words. ERPs were also more negative for concrete than abstract words between 550 and 800 msec. This effect was more frontally distributed and was most evident in the imagery task. We propose that this later anterior effect represents a distinct ERP component (N700) that is sensitive to the use of mental imagery. The N700 may reflect the a access of specific characteristics of the imaged item or activation in a working memory system specific to mental imagery. These results also support the extended dual-coding hypothesis that superior associative connections and the use of mental imagery both contribute to processing advantages for concrete words over abstract words.
Advanced memory management and disk scheduling techniques for general-purpose operating systems
Authors: Peter Druschel
Venue:  (2006)

Operating systems have evolved into sophisticated, high-performance virtualizing platforms, to support and be fair towards concurrently running applications. However, since applications usually run oblivious of each other and prefer narrow system interfaces, they inadvertently contend for resources, resulting in inappropriate allocations and significant performance degradations. This dissertation identifies and eliminates two such problems: one we call rigidity in physical memory management which we solve using adaptive memory management, and a second we call deceptive idleness in disk schedulers that we solve through anticipatory disk scheduling . Many applications, their libraries, and runtimes can trade memory consumption for performance by maintaining caches, triggering garbage collection, etc. However, due to ignorance of memory pressure in the system, they are forced to be conservative about memory usage. Adaptive memory management is a technique that informs applications of the severity of memory pressure via a metric that quantifies the cost of using memory. This enables applications to allocate memory liberally when available (with performance benefits of 20% to 300%); and to release it under contention. The system thus reaches an equilibrium that balances the impact of memory pressure on each application; adapts to avoid paging during load bursts and improves stability and responsiveness; and reduces the need for manual configuration of memory footprints. It also provides finer control on memory usage by adapting proportional to application priorities. Disk schedulers generally schedule a request as soon as the previous request has finished. Unfortunately, many applications perform synchronous I/O by issuing a request after the previous request has been served. This causes the scheduler to suffer from deceptive idleness, a condition where it incorrectly assumes that the process has no further requests, and seeks to a request from another process. Anticipatory dish scheduling transparently solves this problem by sometimes injecting a small, controlled delay into the disk scheduler before it makes a scheduling decision, whenever it expects the current request to be quickly followed by another nearby request. This improves performance by up to 70% and enables proportional schedulers to achieve their contracts. Anticipatory scheduling has been ported to Linux, where it is now the default disk scheduler.
A fractal approach to Sabina vulgaris branch structure
Authors: Zhen-jie Jiang, Guo-sheng Wen, Zhao-feng Geng, Dan Meng
Venue: Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 02 (2006)

Sabina vulgaris patches have superior capability against drought, wind-break and sand-control. It has been highly regarded in the research of the eco- physiological characteristics of Sabina vulgaris in recent years. But rare report of the approach using fractal theory is seen on this topic. The fractal structure of Sabina vulgaris branch is analyzed in this paper, Its fractal dimension is calculated as 1.432, the angle between acicular leaf and branch is about 55.65°, the morphological structure is very close to the Golden mean, 0.618, revealing certain optimal structure of Sabina vulgaris patches.
Manipulating Deformable Linear Objects: Fuzzy-Based Active Vibration Damping Skill
Authors: Shigang Yue, Dominik Henrich
Venue: Journal of Intelligent and Robotic Systems (2006)

Human can handle a deformable object and damp its vibration with recognized skill. However, for an industrial robot, handling a deformable object with acute vibration is often a difficult task. This paper addresses the problem of active damping skill for handling deformable linear objects (DLOs) by using a strategy inspired from human manipulation skills. The strategy is illustrated by several rules, which are explained by a fuzzy and a P controller. A proportional-integral-derivative (PID) controller is also employed to explain the rules as a comparison. The interpretations from controllers are translated into high level commands in a robotic language V+. A standard industrial robot with a force/torque sensor mounted on the wrist was employed to demonstrate the skill. Experimental results showed the fuzzy based damping skill is quite effective and stable even without any previous acknowledge of the deformable linear objects.
Remote Sensing Data Distribution by a Grid-Based Data Distribution System
Authors: ZhenChun Huang, GuoQing Li
Venue: Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (2006)

Data distribution is one of the most important steps in the Remote Sensing applications; it is the bridge between consumers and providers of remote sensing data. Most of the conventional data distribution systems provide web-based user interface and support a small quantity of data types only. It is not friendly for integrated Remote Sensing applications. So, a distributed and platform-independent remote sensing data distribution framework which supports multi data type, multi data source, and machine-to-machine intelligent integration is needed. Based on Grid technology, a 3-layered Remote Sensing Data Distribution System named SIG-DDS is designed and implemented in this paper. In order to make the system simple, extensible and platform-independent, a well-defined extensible data accessing language based on XML is adopted as the accessing language of data sources and data brokers. The distribution system can distribute different sources, types and formats of Remote Sensing Data in one system and make it easier to build up a distributed integrated remote sensing application.
Recency-based collaborative filtering
Authors: Yi Ding, Xue Li, Maria E. Orlowska
Venue: Proceedings of the 17th Australasian Database Conference - Volume 49 (2006)

Collaborative filtering is regarded as one of the most promising recommendation algorithms. Traditional approaches for collaborative filtering do not take concept drift into account. For example, user purchase interests may be volatile. A new mother may be interested in baby toys, although previously she had no interest in these. A man may like romantic films while he preferred action movies one year ago. Collaborative filtering is characterized by concept drift in the real world. To make time-critical predictions, we argue that the target users' recent ratings reflect his/her future preferences more than older ratings. In this paper, we present a novel algorithm namely recency-based collaborative filtering to explore the weights for items based on their expected accuracy on the future preferences. Our proposed approach is based on item-based collaborative filtering algorithms. Specifically, we design a new similarity function to produce similarity scores that better reflect the reality. Our experimental results have shown that the new algorithm substantially improves the precision of traditional collaborative filtering algorithms.
Responding to terrorist attacks and natural disasters: a case study using simulation
Authors: Pavel Albores, Duncan Shaw
Venue: Proceedings of the 37th conference on Winter simulation (2005)

The heightened threat of terrorism has caused governments worldwide to reconsider their plans for responding in the immediate aftermath to large-scale catastrophic incidents. This paper discusses the use of discrete event simulation modeling to understand how a Fire Service might position its resources before an attack takes place, to best respond to a combination of different attacks at different locations if they happen. Two models are built for this purpose. The first model deals with mass decontamination of a population following biological or chemical attack --- aiming to study resource requirements (vehicles, equipment and manpower) necessary to meet performance targets. The second model deals with the allocation of resources across regions --- aiming to study cover level and response times, analyzing different allocations of resources, both centralized and decentralized. Contributions to theory and practice are outlined.
SVD Based Robust Image Content Retrieval
Authors: Yuhua Jiao, Bian Yang, He Wang, Xiamu Niu
Venue: Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia (2006)

In some image content retrieval applications, the query maybe some image processed, geometrically transformed, or noise contaminated versions of their original ones, and therefore requires robustness against these unmalicious modifications, while possessing good discrimination ability for different image contents. We investigate a robust way for image content retrieval based on singular value decomposition to improve the performance of content discrimination. The singular value is used to gain robustness against geometrical variance. To gain higher robustness against other signal processing modifications, an adaptive image thresholding method is used as a preprocessing to SVD. Our experiments are based on 1,000 original images (with different contents) and their 23,000 modified versions, showing improved results of discrimination ability for image contents.
Web latency reduction via client-side prefetching
Authors: A. N. Eden
Venue: Proceedings of the 2000 IEEE International Symposium on Performance Analysis of Systems and Software (2000)

The rapid growth of the WWW has inspired numerous techniques to reduce Web latency. While some of these techniques have not been implemented because they either increase network traffic or require cooperation between tiers, recent studies cast a shadow on techniques already in use (e.g. proxy caching) as a result of the increasingly dynamic aspects of the WWW. In particular, the proliferation of dynamically generated Web pages (i.e. cgi, ASP), which are either linked to a database, or extract information from cookies, reduces the effectiveness of caching techniques. Most techniques attempt to improve on part of the overall latency, and often neglect to address the internal latency, which can be a serious bottleneck in heterogeneous environments. We propose a client-side prefetching mechanism, where the decision of what to prefetch is left to the user. We found it has the potential of reducing latency by up to 81% in a homogeneous environment and 63% in a heterogeneous environment. In data taken on the client, the technique depicted the potential to decrease latency by three-fold. Client-side prefetching does not increase network traffic, it attempts to improve on all parts of latency, and it can be implemented on the client side, without the cooperation of any other tier. Moreover, it can work seamlessly with any other latency reduction technique. We advocate the inclusion of a suitable mechanism in future Web browsers to support client-side prefetching.
Detection and estimation of multiple cisoids in colored noise by Bayesian predictive densities
Authors: Chao-Ming Cho, P. M. Djuric
Venue: Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04 (1994)

A new criterion based on Bayesian predictive densities and subspace decomposition is proposed to estimate the number and the frequencies of close cisoids in colored noise. The colored noise is modeled by an autoregression whose order has also to be estimated. The proposed criterion significantly outperforms the MDL and AIC in correctly determining the number of cisoids and the order of the autoregressive process. Furthermore, an algorithm for frequency estimation is proposed that considerably reduces the computational complexity of the criterion.
On the kernel MUSIC algorithm
Authors: H. Shimotahira
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02 (1995)

The kernel MUSIC (multiple signal classification) algorithm was proposed as an improvement over the existing MUSIC algorithm. The proposed algorithm is based on the orthogonality between the image and kernel space of an Hermitian mapping constructed from a signal. The major part of the computation is Gaussian elimination of a matrix, the required processing time as a function of data number grows slower than that of the existing MUSIC based on eigenstate analysis. And thus this algorithm is advantageous for processing large size data. This algorithm was applied to image reconstruction process of a laser radar and its superior spatial resolution higher than that limited by the wavelength scanning range was demonstrated. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Unified bias analysis for DOA estimation algorithms
Authors: F. Li, Y. Lu
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04 (1993)

A statistical analysis using subspace perturbation expansion is applied to the direction of arrival (DOA) estimation bias for MUSIC, Min-Norm, ESPRIT, State-Space Realization (TAM), and Matrix-Pencil algorithms, assuming that only a finite amount of array data is available. The authors obtain an unified analytical expression of the DOA estimation bias for those algorithms in a simple and self-contained fashion. The tractable formula provides insight to the algorithms. Simulation results verify the analytically predicted performance.
Optimal positioning of sensors for a microphone array
Authors: S. Gazor, Y. Grenier
Venue: Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04 (1994)

Sensor positioning has an important influence on the performance of array processing. The main contributions of the paper are the proposal of criteria to evaluate and to optimize the performance of an array of sensors through its geometric configuration, the number and the positions of the sensors. Each criterion evaluates the performance of the array for some applications, particularly for beamforming and source localization. The simulation results show that the criteria are related, and allow proper optimization of the array configuration.
Skin Colour-Based Face Detection in Colour Images
Authors: Wen-Hsiang Lai, Chang-Tsun Li
Venue: Proceedings of the IEEE International Conference on Video and Signal Based Surveillance (2006)

We propose in this work a method for detecting faces in colour images with complex backgrounds. The approach starts with the transformation of the image pixels from the RGB colour space to the chrominance space (YCbCr). Secondly, a Gaussian model is fitted on the transformed image in order to calculate the likelihood of skin for each pixel and to create a likelihood image. Thirdly, by thresholding the likelihood image, skin pixels are segmented to form a binary skin map, which contains the candidate face regions. Finally, a verification process is carried out to determine whether these candidate face regions are real faces or not.
Multi-view Appearance-based 3D Hand Pose Estimation
Authors: Haiying Guan, Jae Sik Chang, Longbin Chen, Rogerio S. Feris, Matthew Turk
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

We describe a novel approach to appearance-based hand pose estimation which relies on multiple cameras to improve accuracy and resolve ambiguities caused by selfocclusions. Rather than estimating 3D geometry as most previous multi-view imaging systems, our approach uses multiple views to extend current exemplar-based methods that estimate hand pose by matching a probe image with a large discrete set of labeled hand pose images. We formulate the problem in a MAP (maximum a posteriori) framework, where the information from multiple cameras is fused to provide reliable hand pose estimation. Our quantitative experimental results show that correct estimation rate is much higher using our multi-view approach than using a single-view approach.