ppxscal/embedding_network huggingface dataset:
Visualizing discussion by the use of the conversation chain model
Authors: Sabina Karkin, Elizabeth S. Charles, Janet L. Kolodner
Venue: Proceedings of the 7th international conference on Learning sciences (2006)

Identifying how students acquire an understanding of a particular subject in a classroom involves studying verbal interaction. In middle school science classrooms, we can analyze this interaction through videos and transcripts; however, it is still difficult to track and measure the development of students' scientific reasoning skills without visualizing this change. The conversation chain model offers this visualization by illustrating questions and responses elicited during poster sessions, portraying their interpretation and quality of the discussion.
Accelerated Kernel Feature Analysis
Authors: Xianhua Jiang, Yuichi Motai, Robert R. Snapp, Xingquan Zhu
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (2006)

A fast algorithm, Accelerated Kernel Feature Analysis (AKFA), that discovers salient features evidenced in a sample of n unclassified patterns, is presented. Like earlier kernel-based feature selection algorithms, AKFA implicitly embeds each pattern into a Hilbert space, H, induced by a Mercer kernel. An \ell-dimensional linear subspace of H is iteratively constructed by maximizing a variance condition for the nonlinearly transformed sample. This linear subspace can then be used to define more efficient data representations and pattern classifiers. AKFA requires O(\elln2) operations, as compared to 0(n^3) for Sch¨olkof, Smola, and Müller's Kernel Principal Component Analysis (KPCA), and O(\ell^2 n^2) for Smola, Mangasarian, and Sch¨olkopf's Sparse Kernel Feature Analysis (SKFA). Numerical experiments show that AKFA can generate more concise feature representations than both KPCA and SKFA, and demonstrate that AKFA obtains similar classification performance as KPCA for a face recognition problem.
Discover Bayesian Networks from Incomplete Data Using a Hybrid Evolutionary Algorithm
Authors: Man Leung Wong, Yuan Yuan Guo
Venue: Proceedings of the Sixth International Conference on Data Mining (2006)

This paper proposes a novel hybrid approach for learning Bayesian networks from incomplete data in the presence of missing values, which combines an evolutionary algorithm with the traditional Expectation-Maximization (EM) algorithm. The new algorithm can overcome the problem of getting stuck in sub-optimal solutions which occurs in most existing learning algorithms. The experimental results on the data sets generated from several benchmark networks illustrate that the new algorithm has better performance than some state-of-the-art algorithms. We also apply the approach to a data set of direct marketing and compare the performance of the discovered Bayesian networks obtained by the new algorithm with the networks generated by other methods. In the comparison, the Bayesian networks learned by the new algorithm outperform other networks.
Web-Based Coordination Support in Care Planning
Authors: Kurt Sandkuhl, Anders Carstensen
Venue: Proceedings of the 32nd EUROMICRO Conference on Software Engineering and Advanced Applications (2006)

Starting from a case study in the area of health care, the paper introduces a web-based coordination support application in care planning. Main contributions to research in the field of web computing and CSCW are (1) an experience report on how to shape web-based coordination support for care planning and (2) a discussion of how to apply concepts from the areas communities of practice and coordination mechanisms to the field of care planning. Analysis of facilities useful for communities of practice led to a number of proposals for functional enhancement of the developed application in the field of knowledge sharing. The concept of computational coordination mechanisms was very helpful for understanding the nature of care planning work and evaluating the coordination support provided.
Reducing Latency in Rendezvous-Based Publish-Subscribe Systems for Wireless Ad Hoc Networks
Authors: Nuno Carvalho, Filipe Araujo, Luis Rodrigues
Venue: Proceedings of the 26th IEEE International ConferenceWorkshops on Distributed Computing Systems (2006)

To ensure decoupling between publishers and subscribers, most publish-subscribe systems route notifications through intermediate message brokers. A byproduct of this practice is that notifications often follow suboptimal paths that are much longer than a direct path. Hence, in this paper, we propose a publish-subscribe architecture called GeoRendezvous which aims to reduce the latency experienced by end clients in the delivery of notifications. We base our system on a position-based distributed hash table (DHT) that supports rendezvous points where the interests of publishers and subscribers match. Leveraging from previous work, we replicate the rendezvous points to give multiple choices of paths to the subscribers. We show that in this way, the subscriber is able to achieve latencies comparable to a direct publisher-subscriber path without breaking the decoupling assumptions of the publish-subscribe model. Additionally, we show that scalability is one of the most prominent features of GeoRendezvous, as the number of rendezvous points scales with the network size.
Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models
Authors: Iasonas Kokkinos, Petros Maragos, Alan Yuille
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (2006)

A combination of techniques that is becoming increasingly popular is the construction of part-based object representations using the outputs of interest-point detectors. Our contributions in this paper are twofold: first, we propose a primal-sketch-based set of image tokens that are used for object representation and detection. Second, top-down information is introduced based on an efficient method for the evaluation of the likelihood of hypothesized part locations. This allows us to use graphical model techniques to complement bottom-up detection, by proposing and finding the parts of the object that were missed by the front-end feature detection stage. Detection results for four object categories validate the merits of this joint top-down and bottom-up approach.
Developing Multi-Agent Systems with Dynamic Binding Mechanism
Authors: Jianming Zhao, Xinjun Mao, Ji Wang
Venue: Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology (2006)

Agents in many complex applications are often dynamic, which means that their abilities, services and even design objectives can change at run-time. Though a great number of agent-oriented modeling languages and methodologies have been proposed, it is still a challenge to develop complex multi-agent systems with dynamic agents. In this paper, the technology requirements to analyze, design and implement dynamic agents are discussed. A dynamic binding mechanism to develop multi-agent systems is proposed based on caste that is the basic abstraction to specify agents' behaviors and elementary modular unit to design and implement multi-agent systems. In our approach, the dynamic behaviors of agents are realized as the change of castes that agents bind to by executing "join' and "quit" operations on agent's casteships at run-time. The mechanism also enables agents to change the status of its castes to be either active or inactive at run-time by executing operations 'activate" and "deactivate". In order to specify and analyze the adaptive agents, a graphical notation of caste transition diagram is designed and an example is illustrated. The posed problems and future research about dynamic binding mechanism are also discussed at last.
Dynamic Load Balancing for the Distributed Mining of Molecular Structures
Authors: Giuseppe Di Fatta, Michael R. Berthold
Venue: IEEE Transactions on Parallel and Distributed Systems (2006)

In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute's HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a nondedicated computational environment. These features make it suitable for large-scale, multidomain, heterogeneous environments, such as computational grids.
Masking Interrupts Figure&ndash;Ground Signals in V1
Authors: Victor A. F. Lamme
Venue: Journal of Cognitive Neuroscience (2002)

In a backward masking paradigm, a target stimulus is rapidly (<100 msec) followed by a second stimulus. This typically results in a dramatic decrease in the visibility of the target stimulus. It has been shown that masking reduces responses in V1. It is not known, however, which process in V1 is affected by the mask. In the past, we have shown that in V1, modulations of neural activity that are specifically related to figure&ndash;ground segregation can be recorded. Here, we recorded from awake macaque monkeys, engaged in a task where they had to detect figures from background in a pattern backward masking paradigm. We show that the V1 figure&ndash;ground signals are selectively and fully suppressed at target&ndash;mask intervals that psychophysically result in the target being invisible. Initial response transients, signalling the features that make up the scene, are not affected. As figure&ndash;ground modulations depend on feedback from extrastriate areas, these results suggest that masking selectively interrupts the recurrent interactions between V1 and higher visual areas.
A teacher-friendly interface to assessment data
Authors: Jody S. Underwood, Diego Zapata, Waverely Hester
Venue: Proceedings of the 7th international conference on Learning sciences (2006)

We present the results of research about teachers' use and understanding of assessment data, focusing on (a) questions they want answered, (b) their use of an instructional data management system, and (c) their reactions to a more teacher-friendly interface to the system that is designed to facilitate instruction.
Individualized exercises for self-assessment of programming knowledge: An evaluation of QuizPACK
Authors: Peter Brusilovsky, Sergey Sosnovsky
Venue: Journal on Educational Resources in Computing (JERIC) (2005)

Individualized exercises are a promising feature in promoting modern e-learning. The focus of this article is on the QuizPACK system, which is able to generate parameterized exercises for the C language and automatically evaluate the correctness of student answers. We introduce QuizPACK and present the results of its comprehensive classroom evaluation during four consecutive semesters. Our studies demonstrate that when QuizPACK is used for out-of-class self-assessment, it is an exceptional learning tool. The students' work with QuizPACK significantly improved their knowledge of semantics and positively affected higher-level knowledge and skills. The students themselves praised the system highly as a learning tool. We also demonstrated that the use of the system in self-assessment mode can be significantly increased by basing later classroom paper-and-pencil quizzes on QuizPACK questions, motivating students to practice them more.
Design Trade-Offs for User-Level I/O Architectures
Authors: Lambert Schaelicke, Alan L. Davis
Venue: IEEE Transactions on Computers (2006)

To address the growing I/O bottleneck, next-generation distributed I/O architectures employ scalable point-to-point interconnects and minimize operating system overhead by providing user-level access to the I/O subsystem. Reduced I/O overhead allows I/O intensive applications to efficiently employ latency hiding techniques for improved throughput. This paper presents the design of a novel scalable user-level I/O architecture and evaluates the impact of various architectural mechanisms in terms of overall performance improvement. Results demonstrate that eliminating data movement across protection domains is the dominant contributor to improved scalability. Eliminating system call and interrupt overhead only has a small additional benefit that may not justify the additional hardware support required. While this evaluation is based on one specific design, the conclusions can be generalized to other user-level I/O architectures.
Multiple Simultaneous Projects with One eXtreme Programming Team
Authors: Jason Nocks
Venue: Proceedings of the conference on AGILE 2006 (2006)

The group at SourceXtreme feels that it is essential to deal with multiple simultaneous projects with one eXtreme Programming team. Many people feel that too much time would be wasted task-switching, that the team would suffer too much from fractured thinking, or that a sense of team would be lost. It's probable that these outcomes could happen and that focusing on one project might be better in general. This paper describes some of the reasons why the group feels that working on one project wasn't an option at SourceXtreme and what approaches have emerged over the past year to thrive in this situation.
A Genetic Algorithm Approach for Doing Misuse Detection in Audit Trail Files
Authors: Pedro A. Diaz-Gomez, Dean F. Hougen
Venue: Proceedings of the 15th International Conference on Computing (2006)

This paper focuses on the development of an Intrusion Detection System based on Genetic Algorithms. We present and justify a fitness function independent of variable parameters that addresses the problem of false positives. This fitness function is a generic one that can be used for either off-line or online intrusion detection systems and it allows us consider pseudo intrusions, which could be used to prevent the occurrence of actual intrusions. The paper also describes extending the system to account for the fact that intrusions may be mutually exclusive and defines the union operator which greatly speeds the search for intrusions.
Controlling over-optimism in time-warp via CPU-based flow control
Authors: Vinay Sachdev, Maria Hybinette, Eileen Kraemer
Venue: Proceedings of the 36th conference on Winter simulation (2004)

In standard optimistic parallel event simulation, no restriction exists on the maximum lag in simulation time between the fastest and slowest logical processes (LPs). Over-optimistic applications exhibit a large lag, which encourages rollback and may degrade performance. We investigate an approach for controlling over-optimism that classifies LPs as FAST, MEDIUM, or SLOW and migrates FAST and/or SLOW processes. FAST LPs are aggregated, forcing them to compete for CPU cycles. SLOW LPs are dispersed, to limit their competition for CPU cycles. The approach was implemented on distributed Georgia Tech Time Warp(GTW)(Das et al. 1994) and experiments performed using the synthetic application P-Hold(Fujimoto 1990). For over-optimistic test cases, our approach was found to perform 1.25 to 2.75 times better than the standard approach in terms of useful work and to exhibit execution times shorter than or equal to the standard computation.
New methods for oscillatory systems based on ARKN methods
Authors: J. M. Franco
Venue: Applied Numerical Mathematics (2006)

A one-parameter family of explicit fourth-order methods for oscillatory systems of the form y" + Ky = f (t, y), K being a symmetric positive semi-definite matrix, is obtained. The new methods possess eighth order for the unperturbed problem (f(t,y) ≡ 0), and the free parameter is chosen so that the dispersion or the dissipation are optimized. The stability and phase properties of the new methods are analyzed, obtaining generalized stability regions for the second-order homogeneous linear test model. The numerical experiments carried out show that the new methods are very competitive when they are compared with standard, symplectic and special codes proposed in the scientific literature.
Space-vector structure based synthesis for hierarchically coupled associative memories
Authors: Alcir G. Reis, J. L. Acebal, Rogerio M. Gomes, Henrique E. Borges
Venue: Proceedings of the Ninth Brazilian Symposium on Neural Networks (2006)

The present work introduces a proposal for the training of hierarchically coupled associative memories. The method is based on the eigenvalue and eigenvector structure of the space-vector and on suitable changes the space basis. The approach shows to be useful to the class of models hierarchically coupled associative memories, which has the memorization process organized in many levels of degreesof- freedom and for which the training behaves as a synthesis of previously desired states.
Contributions of the Prefrontal Cortex and Basal Ganglia to Set Shifting
Authors: Susan M. Ravizza, Michael A. Ciranni
Venue: Journal of Cognitive Neuroscience (2002)

Impairments of set shifting have been associated with damage to both the prefrontal cortex (PFC) and to the basal ganglia. The purpose of these experiments was to determine whether damage to the PFC was associated with shifting impairments per se or whether any switching deficits could be attributed to a reduction of working memory capacity. In contrast, shifting impairments were expected for Parkinson patients regardless of memory load, given that these patients seem to have no cognitive deficits other than when having to shift set. To vary working memory demands, a cue to the relevant dimension (letter or shape) in an odd-man-out task was presented or withheld. Pathology to prefrontal areas associated with normal aging was not linked to shifting deficits when working memory load was reduced in a comparison of older and younger adults (Experiment 1). In contrast, set-shifting abilities were still impaired for stroke patients with prefrontal damage regardless of working memory demands (Experiment 2). Parkinson patients were relatively unimpaired on this task (Experiment 2), but began to display shifting deficits when response competition was present in the display (Experiment 3).
A Modular Approach to the Analysis and Evaluation of Particle Filters for Figure Tracking
Authors: Ping Wang, James M. Rehg
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (2006)

This paper presents the first systematic empirical study of the particle filter (PF) algorithms for human figure tracking in video. Our analysis and evaluation follows a modular approach which is based upon the underlying statistical principles and computational concerns that govern the performance of PF algorithms. Based on our analysis, we propose a novel PF algorithm for figure tracking with superior performance called the Optimized Unscented PF. We examine the role of edge and template features, introduce computationally-equivalent sample sets, and describe a method for the automatic acquisition of reference data using standard motion capture hardware. The software and test data are made publicly-available on our project website.
A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI
Authors: F. Prados, A. Bardera, M. Sbert, I. Boada, M. Feixas
Venue: Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (2006)

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach.
A taxonomy for the evaluation of mobile museum guides
Authors: Areti Damala, Hub Kockelkorn
Venue: Proceedings of the 8th conference on Human-computer interaction with mobile devices and services (2006)

Museums are a fertile ground for experimentations with edutainment applications conceived for mobile devices. However the design, implementation and maintenance of mobile multimedia guides is a time and resources consuming iterative process to which ideally all involved stakeholders should participate. Evaluation therefore is of outmost importance. Drawing from already published results and on site experience from DANAE project we define three categories of evaluation key points, under which all possible evaluation questions measuring the effectiveness of an edutainment application can be classified; we then match them with all involved stakeholders, mainly museums, their visitors and information technologies companies. Finally, we argue that the proposed taxonomy can be used for the classification of different evaluation questions so as to constitute a comprehensive and adjustable guide for evaluation purposes of applications for different and heterogeneous museum environments.
The Method of Location Error Detection and Correcting in Smart Home Environments
Authors: Hyung Su Lee, Byunghun Song, Hee Yong Youn, Kwangsue Chung
Venue: Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 02 (2006)

The location awareness system is an essential factor for the development of low cost sensor networks for use in the smart home environments and ubiquitous networking. This paper addresses the problem of tracking a moving intelligent robot using the pharos indoor location system. Our motivating applications include intelligent robot navigation, where location sensors provide position information to a moving robot. All of these applications require the position of a device moving at human speeds to be tracked. Ubiquitous indoor environments often contain substantial amounts of metal and other such reflective materials that affect the propagation of radio frequency signals in non-trivial ways, causing severe multi-path effects, dead-spots, noise, and interference. In this paper, we address the location error detection and correcting problems of the sensor network platform with RF and ultrasonic for location tracking system. Especially we present a novel detecting error of location and correcting using LQI that is particularly well suited to support context-aware smart home computing. It is achieved by considering the speed variance of the moving object and the corrected the interference errors. This proposed scheme applied the method of an error correct to the mobile object. We brought a 5% efficiencies comparing with a three existing schemes (listener inference algorithm). And The system called Pharos, aims to combine the advantages of real-time tracking systems that implement distributed environment with regardless of infrastructure or infrastructure-less wireless sensor networks.