ppxscal/embedding_network huggingface dataset:
A Neural Network Based Short Term Electric Load Forecasting in Ontario Canada
Authors: Fang Liu, Raymond D. Findlay, Qiang Song
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)

Accurate and reliable load forecasting is necessary to ameliorate energy management. Short-term load forecast plays a crucial role in economic and secure system operation. This paper presents a practical method for short-term electric load forecast problem using an artificial neural network with a powerful Levenberg-Marquardt training algorithm approach. The applications of real load from Ontario, Canada with hourly load, daily load, and weekly load predictions have been successfully achieved. Both visual comparison and statistical test are discussed and analyzed to validate training and testing phases of the neural network.
Fast decoding of quasi-perfect Lee distance codes
Authors: Peter Horak, Bader F. Albdaiwi
Venue: Designs, Codes and Cryptography (2006)

A code D over Z 2 n is called a quasi-perfect Lee distance-(2t + 1) code if d L(V,W) ¿ 2t + 1 for every two code words V,W in D, and every word in Z 2 n is at distance ¿ t + 1 from at least one code word, where D L(V,W) is the Lee distance of V and W. In this paper we present a fast decoding algorithm for quasi-perfect Lee codes. The basic idea of the algorithm comes from a geometric representation of D in the 2-dimensional plane. It turns out that to decode a word it suffices to calculate its distance to at most four code words.
Perceptual and Semantic Components of Memory for Objects and Faces: A PET Study
Authors: Jon S. Simons, Kim S. Graham, Adrian M. Owen, Karalyn Patterson, John R. Hodges
Venue: Journal of Cognitive Neuroscience (2001)

Previous studies have suggested differences in the neural substrates of recognition memory when the contributions of perceptual and semantic information are manipulated. In a within-subjects design PET study, we investigated the neural correlates of the following factors: material type (objects or faces), semantic knowledge (familiar or unfamiliar items), and perceptual similarity at study and test (identical or different pictures). There was consistent material-specific lateralization in frontal and temporal lobe regions when the retrieval of different types of nonverbal stimuli was compared, with objects activating bilateral areas and faces preferentially activating the right hemisphere. Retrieval of memories for nameable, familiar items was associated with increased activation in the left ventrolateral prefrontal cortex, while memory for unfamiliar items involved occipital regions. Recognition memory for different pictures of the same item at study and test produced blood flow increase in left inferior temporal cortex. These results have implications for our understanding of the neural correlates of perceptual and semantic contributions to recognition memory.
Automatic Internal Medicine Diagnostics Using Statistical Imaging Methods
Authors: Daniel Smutek, Akinobu Shimiz, Ludvik Tesar, Hidefumi Kobatake, Shigeru Nawano, Stepan Svacina
Venue: Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (2006)

Goal: To develop a computer-aided diagnostic system for diagnosing different internal medicine diseases based on imaging methods. We focus on focal liver lesions in CT images. Methods: The diagnosing process follows the learning phase from known images. For image description, 22 first-order and 108 secondorder texture features are used. They are used as input for network of Bayes classifiers. Results: The best value of 100% success of classification between hepatocellular carcinoma and non-parasitic solitary liver cysts was achieved. Conclusion: The method allows discriminating between different liver diseases based on computer imaging. The method may be very useful in cases where any internal images of patients already diagnosed are available.
Progressive Spatial Join
Authors: Wee Hyong Tok, Stephane Bressan, Mong Li Lee
Venue: Proceedings of the 18th International Conference on Scientific and Statistical Database Management (2006)

In spatial data exploration and analysis, the system would present a user with initial promising results and empower the user to modify runtime query parameters. The high degree of interactivity would significantly reduce users' waiting time for results that are not useful, and then having to re-issue a new query. To support this level of interaction during query processing, it necessitates the study of adaptive and progressive spatial query processing techniques that can deliver initial results quickly and adapt to run-time fluctuations during the delivery of remote data. Our goal is to design a generic framework for adaptive and progressive spatial query processing. In this paper, we present our ongoing work on designing progressive spatial join algorithm as an initial step.
iDM: a unified and versatile data model for personal dataspace management
Authors: Jens-Peter Dittrich, Marcos Antonio Vaz Salles
Venue: Proceedings of the 32nd international conference on Very large data bases (2006)

Personal Information Management Systems require a powerful and versatile data model that is able to represent a highly heterogeneous mix of data such as relational data, XML, file content, folder hierarchies, emails and email attachments, data streams, RSS feeds and dynamically computed documents, e.g. ActiveXML [3]. Interestingly, until now no approach was proposed that is able to represent all of the above data in a single, powerful yet simple data model. This paper fills this gap. We present the iMeMex Data Model (iDM) for personal information management. iDM is able to represent unstructured, semi-structured and structured data inside a single model. Moreover, iDM is powerful enough to represent graph-structured data, intensional data as well as infinite data streams. Further, our model enables to represent the structural information available inside files. As a consequence, the artifical boundary between inside and outside a file is removed to enable a new class of queries. As iDM allows the representation of the whole personal dataspace [20] of a user in a single model, it is the foundation of the iMeMex Personal Dataspace Management System (PDSMS) [16, 14, 47]. This paper also presents results of an evaluation of an initial iDM implementation in iMeMex that show that iDM can be efficiently supported in a real PDSMS.
On detection of belonging of two points to one trajectory
Authors: A. V. Demin
Venue: Proceedings of the 2003 International Conference on Physics and Control - Volume 2 (2003)

The problem of whether two given points belong to one trajectory of given system of nonlinear autonomous differential equations or not is considered. Some variation task with the functional in the form of norm of Hilbert space for set of functions with one parameter is formulated. The solution is given in the form of modified Picard approximations to the solutions of the system of differential equations.
Anisotropy-Induced Effects in the Dynamics of an Ion Confined in a Two-Dimensional Paul Trap
Authors: M. Scala, B. Militello, A. Messina
Venue: Open Systems Information Dynamics (2006)

We investigate the role of anisotropy in the dynamics of a single trapped ion interacting with two orthogonal laser beams, considering how it modifies a scheme for the generation of Schrödinger cat states and the so called parity effect in two-dimensional isotropic Paul traps. We find that anisotropy gives rise to a richer class for the generated states and to a larger number of observables sensitive to the parity of the number of excitation of the vibrational motion of the ion.
SOUNDE: A Hybrid Structure of Service Discovery System for Decentralized E-Commerce
Authors: Dalu Zhang, Jiawen Mou, Zhe Yang
Venue: Proceedings of the IEEE International Conference on e-Business Engineering (2006)

Existing P2P systems are mostly confined to the domain of file sharing systems, which in many aspects differ from decentralized E-Commerce, e.g., peer behavior mode and capability diversity. Therefore, we researched on the distinct properties of decentralized E-Commerce, especially for B2B level E-Commerce, and accordingly, propose a decentralized E-Commerce oriented service discovery system, named SOUNDE. The system can be viewed as a two-layer architecture, with the upper layer composed of many E-Commerce Domains forming a DHT-based topology, then each Domain composed of all the related peers in that specific Commerce Domain, including Service Requesters and Service Providers of it, forming an unstructured topology which applies SophiNode service discovery algorithms. Meanwhile, we also put forward GovNode, which plays the role of joint between the two layers. Gathering all the peers within same Commerce Industry into a logic subnet (Commerce Domain), then all Domains into DHT structure, the fundamental purpose of SOUNDE is to diminish the searching scope of each service query, (i.e. Reduce the network load), and allow the system to scale. At the end, we present our simulation and analyze the results.
Value Creation and its Visualization in E-business
Authors: Almira Karabeg
Venue: Proceedings of the conference on Information Visualization (2006)

The purpose of this article is to view the interplay between technology, concept of value and the role of aesthetics in the Internet-based business through the lens of information design and knowledge and information visualization. We endeavor to show the "big picture", the patterns in structuring and visualizing information around one chosen theme. Showing how to put polyscopic modeling into practice as methodology for designing information and creation and visualization of knowledge, on the example of authentic travel, is the main contribution of this paper.
Solving POMDPs using quadratically constrained linear programs
Authors: Christopher Amato, Daniel S. Bernstein, Shlomo Zilberstein
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

Since the early 1990's, Markov decision processes (MDPs) and their partially observable counterparts (POMDPs) have been widely used by the AI community for planning under uncertainty. POMDPs offer a rich language to describe situations involving uncertainty about the domain, stochastic actions, noisy observations, and a variety of possible objective functions. Even though an optimal solution may be concise, current exact algorithms that use dynamic programming often require an intractable amount of space. POMDP approximation algorithms can operate with a limited amount of memory, but as a consequence they provide very weak theoretical guarantees. In contrast, we describe a new approach that addresses the space requirement of POMDP algorithms while maintaining well-defined optimality guarantees.
Using comparisons of alternate strategies to promote discourse in mathematics classrooms
Authors: Radha R. Kalathil
Venue: Proceedings of the 7th international conference on Learning sciences (2006)

Current mathematics reform calls for greater communication of mathematical thinking in which students share their thinking and ideas with their teachers as well as with their peers in the community. In order to create an environment where mathematical ideas are shared and discussed, teachers are asked to elicit and invite different methods and ideas from students. In addition a key idea in mathematics reform is the need to support discourse in mathematics classrooms. In this paper I examine how comparisons of students' solutions to mathematical tasks were used to foster mathematically significant discourse. Data for this comes from one second-grade classroom in which instruction was based on a reform-based curriculum. I examine how comparisons of students' representations to mathematical problems were used to form discourse patterns and changes in its use over time. I also examine strategies that the teacher used to facilitate the discourse around comparisons.
Brain-Gene Ontology: Integrating Bioinformatics and Neuroinformatics Data, Information and Knowledge to Enable Discoveries
Authors: Nikola Kasabov, Vishal Jain, Paulo C. M. Gottgtroy, Lubica Benuskova, Frances Joseph
Venue: Proceedings of the Sixth International Conference on Hybrid Intelligent Systems (2006)

The paper presents some preliminary results on the brain-gene ontology (BGO) project that is concerned with the collection, presentation and use of knowledge in the form of ontology. BGO includes various concepts, facts, data, software simulators, graphs, videos, animations, and other information forms, related to brain functions, brain diseases, their genetic basis and the relationship between all of them. The first version of the brain-gene ontology has been completed as a hierarchical structure and as an initial implementation in the Protégé ontology building environment.
Tracking Changes in Healthcare Documents
Authors: Sudarshan S. Chawathe
Venue: Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (2006)

We present methods for monitoring a large, diverse, and autonomously modified collection of healthcare documents on the Web. Our methods do not require documentproviders to offer any special services. They are based on explicating changes between document versions on a per-user basis by using differencing algorithms. These changes are presented to users in the context of the documents using special XML elements. In order to effectively browse changes in large document collections, we use a variable-resolution XML browser. A noteworthy feature of this browser is that it produces usable displays at any level of detail specified by a user.
A flexible multi-dimensional QoS performance measure framework for distributed heterogeneous systems
Authors: Jong-Kook Kim, Debra A. Hensgen, Taylor Kidd, Howard Jay Siegel, David St. John, Cynthia Irvine, Tim Levin, N. Wayne Porter, Viktor K. Prasanna, Richard F. Freund
Venue: Cluster Computing (2006)

When users' tasks in a distributed heterogeneous computing environment (e.g., cluster of heterogeneous computers) are allocated resources, the total demand placed on some system resources by the tasks, for a given interval of time, may exceed the availability of those resources. In such a case, some tasks may receive degraded service or be dropped from the system. One part of a measure to quantify the success of a resource management system (RMS) in such a distributed environment is the collective value of the tasks completed during an interval of time, as perceived by the user, application, or policy maker. The Flexible Integrated System Capability (FISC) measure presented here is a measure for quantifying this collective value. The FISC measure is a flexible multi-dimensional measure such that any task attribute can be inserted and may include priorities, versions of a task or data, deadlines, situational mode, security, application- and domain-specific QoS, and task dependencies. For an environment where it is important to investigate how well data communication requests are satisfied, the data communication request satisfied can be the basis of the FISC measure instead of tasks completed. The motivation behind the FISC measure is to determine the performance of resource management schemes if tasks have multiple attributes that needs to be satisfied. The goal of this measure is to compare the results of different resource management heuristics that are trying to achieve the same performance objective but with different approaches.
Optical Digit Recognition for Images of Handwritten Historical Documents
Authors: A. L. I. Oliveira, C. A. B. Mello, E. R. Silva Jr, V. M. O. Alves Alves
Venue: Proceedings of the Ninth Brazilian Symposium on Neural Networks (2006)

This paper presents a study on recognition of handwritten digits extracted from images of historical documents. The work is part of ProHist, a project aimed to make easier the diffusion of historical documents. Our objective herein is to recognize the dates written in the document for indexing purposes. In this paper we investigate the influence of the feature extraction and the classification method on the performance of the recognition system. We report on experiments considering principal component analysis and undersampled bitmaps as feature extraction methods and k-nearest-neighbors, RBF neural networks and support vector machines (SVMs) as classifiers. The conclusion shows that using undersampled bitmaps combined with SVMs yield the best classification result.
Modelling and Implementation of the Astronomy Case Study with an IMS-LD Ontology
Authors: Ricardo Amorim, Manuel Lama, Eduardo Sanchez
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

This paper describes how the Astronomy case study is modelled and implemented using a IMS-LD ontology.
Cardiac Deformation Recovery using a 3D Incompressible Deformable Model
Authors: Arnaud Bistoquet, W. James Parks, Oskar Skrinjar
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

This paper presents a method for cardiac deformation recovery from 3D MR image sequences. The main contribution of this work is that the method is mathematically guaranteed to generate incompressible deformations. This is a desirable property since the myocardium has been shown to be close to incompressible. The method is based on an incompressible deformable model that has a relatively small number of parameters. The myocardium needs to be segmented in an initial frame after which the method automatically determines the tissue deformation everywhere in the myocardium throughout the cardiac cycle. The method has been tested with seven 3D cardiac MR image sequences for the left ventricle, and it has been evaluated against manual segmentation and tagged MR image sequences. The volume agreement between the model and the manual segmentation exceeds 95%, the distance between the model and the manually generated endocardial and epicardial surfaces is 1.5mm on average, and the distance between the model and the intersection of the tagged lines is 0.7mm on average.
Optimizing slicing of formal specifications by deductive verification
Authors: Ingo Brückner, Björn Metzler, Heike Wehrheim
Venue: Nordic Journal of Computing (2006)

Slicing is a technique for extracting parts of programs or specifications with respect to certain criteria of interest. The extraction is carried out in such a way that properties as described by the slicing criterion are preserved, i.e., they hold in the complete program if and only if they hold in the sliced program. During verification, slicing is often employed to reduce the state space of specifications to a size tractable by a model checker.The computation of specification slices relies on the construction of dependence graphs, reflecting (at least) control and data dependencies in specifications. The more dependencies the graph has, the less removal of parts is possible. In this paper we present a technique for optimizing the construction of the dependence graph by using deductive verification techniques. More precisely, we propose a technique for showing that certain control dependencies in the graph can be eliminated. The technique employs small deductive proofs of the enabledness of certain transitions. Thereby we obtain dependence graphs with less control dependencies and as a consequence smaller specification slices which are an easier target for model checking.
A Unified Data Grid Replication Framework
Authors: Tim Ho, David Abramson
Venue: Proceedings of the Second IEEE International Conference on e-Science and Grid Computing (2006)

Modern scientific experiments can generate large amounts of data, which may be replicated and distributed across multiple resources to improve application performance and fault tolerance. Whilst a number of different replica management systems exist, particular communities usually adopt a single system. This creates problems when an application program spans more than one community, because it may need to target more than one middleware layer. One solution to this problem is to build a more flexible data access layer above the specific replica middleware. In this paper, we discuss such an architecture, the Grid Replication Framework, which provides applications with an abstract interface to existing replica systems. Further, the framework's flexible plug-in architecture makes it easy to support new middleware as it becomes available.
Channel based sequential simulation
Authors: Cameron Kiddle, Rob Simmonds, Brian Unger
Venue: Proceedings of the 37th conference on Winter simulation (2005)

Sequential discrete event simulation is widely employed to study the behavior of many systems. Events are typically managed in a central event list which is implemented as a priority queue ordered by event timestamps. Most research to improve sequential simulation performance has focused on improving the priority queue implementations. Recent work has demonstrated that asynchronous conservative parallel discrete event simulation systems can achieve better sequential performance under some conditions, but worse performance under other conditions. This paper introduces a new sequential discrete event simulation algorithm that can exhibit some of the same performance advantages of asynchronous conservative parallel discrete event simulation algorithms and has complexity no more than that of central event list algorithms in the worst case.
On the Relationships between Clustering and Spatial Co-location Pattern Mining
Authors: Yan Huang, Pusheng Zhang
Venue: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (2006)

The goal of spatial co-location pattern mining is to find subsets of spatial features frequently located together in spatial proximity. Example co-location patterns include services requested frequently and located together from mobile devices (e.g., PDAs and cellular phones) and symbiotic species in ecology (e.g., Nile crocodile and Egyptian plover). Spatial clustering groups similar spatial objects together. Reusing research results in clustering, e.g. algorithms and visualization techniques, by mapping colocation mining problem into a clustering problem would be very useful. However, directly clustering spatial objects from various spatial features may not yield well-defined colocation patterns. Clustering spatial objects in each layer followed by overlaying the layers of clusters may not applicable to many application domains where the spatial objects in some layers are not clustered. In this paper, we propose a new approach to the problem of mining co-location patterns using clustering techniques. First, we propose a novel framework for co-location mining using clustering techniques. We show that the proximity of two spatial features can be captured by summarizing their spatial objects embedded in a continuous space via various techniques. We define the desired properties of proximity functions compared to similarity functions in clustering. Furthermore, we summarize the properties of a list of popular spatial statistical measures as the proximity functions. Finally, we show that clustering techniques can be applied to reveal the rich structure formed by co-located spatial features. A case study on real datasets shows that our method is effective for mining co-locations from large spatial datasets.
Sports analogy for modelling of combat in the air domain
Authors: Alan Cowdale
Venue: Proceedings of the 37th conference on Winter simulation (2005)

Aggregated models of Air Warfare invariably rely on a user input value for probability of success (kill) or 'exchange ratio' in Air-to-Air Combat. There is limited historical data available to validate these parameters for engagements between non-peer opponents. This paper explores the potential for gaining insights to non-peer Air-to-Air outcomes from the world of sport, and examines the results from Association Football competitions in England.