ppxscal/embedding_network huggingface dataset:
On-line Boosting and Vision
Authors: Helmut Grabner, Horst Bischof
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (2006)

Boosting has become very popular in computer vision, showing impressive performance in detection and recognition tasks. Mainly off-line training methods have been used, which implies that all training data has to be a priori given; training and usage of the classifier are separate steps. Training the classifier on-line and incrementally as new data becomes available has several advantages and opens new areas of application for boosting in computer vision. In this paper we propose a novel on-line AdaBoost feature selection method. In conjunction with efficient feature extraction methods the method is real time capable. We demonstrate the multifariousness of the method on such diverse tasks as learning complex background models, visual tracking and object detection. All approaches benefit significantly by the on-line training.
Professional development, cognitive tools, and thinking skills
Authors: Katherine McMillan Culp, Lauren B. Goldenberg, Dara Wexler
Venue: Proceedings of the 7th international conference on Learning sciences (2006)

This poster shares findings from an on-going, formative evaluation of a professional development workshop for K-12 teachers that is designed to engage teachers in creating classroom projects that use cognitive tools to foster higher-order thinking and 21st century skills across the disciplines. Findings show teachers used the tools to "make students' thinking visible," help students compare ideas, and to stimulate group discussions; the remaining challenge is to explicitly target students' higher order thinking skills.
Nondeterminism in ORBs: The Perception and the Reality
Authors: Joseph Slember, Priya Narasimhan
Venue: Proceedings of the 17th International Conference on Database and Expert Systems Applications (2006)

Nondeterminism is a source of problems for distributed replication because it makes it difficult to keep replicas consistent as they execute, process invocations and modify their internal states. Even if a middleware application is completely deterministic, the underlying middleware, e.g., the ORB, can continue to remain a source of nondeterminism. The paper presents our analysis of an open-source ORB from the viewpoint of nondeterminism. Our approach identifies the various sources of nondeterminism within the ORB. Our results demonstrate that while ORBs can contain several apparently nondeterministic system calls and functions, only a fraction of them manifest as actual nondeterminism and pose a threat to replica consistency.
An Agent-Supported Multimodal Scaffolding Infrastructure
Authors: M. Brian Blake, Jerome Butcher-Green, Jayfus T. Doswell
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

Scaffolding is a well-established instructional approach that facilitates learning by incrementally removing training aids as the learner progresses. By combining multiple training aids (i.e. multimodal interfaces), a trainer, either human or virtual, must make real-time decisions about which aids to remove throughout the training scenario. A significant problem occurs in implementing scaffolding techniques since the speed and choice of removing training aids must be strongly correlated to the individual traits of a specific trainee. We detail an agent-based infrastructure that supports the customization of scaffolding routines per individual user. We describe the integration of this agent-based approach into a simulated augmented reality (AR) environment
Guest Editors' Introduction: Distributed Data Mining--Framework and Implementations
Authors: Anup Kumar, Mehmed Kantardzic, Samuel Madden
Venue: IEEE Internet Computing (2006)

It is now possible to gather and store incredible volumes of data, creating opportunities for large-scale data-driven knowledge discovery. Data mining technology has emerged as a means of performing this discovery, and the added dimension of distributed data mining increases this process'scomplexity.
The Dependency Management Framework: A Case Study of the ION CubeSat
Authors: Hui Ding, Leon Arber, Lui Sha, Marco Caccamo
Venue: Proceedings of the 18th Euromicro Conference on Real-Time Systems (2006)

Due to the complexity and requirements of modern realtime systems, multiple teams must often work concurrently and independently to develop the various components of the system. Since a team typically only knows the dependency relations between the components they wrote and those they directly use, keeping track of system-wide dependency relations is not possible for any individual team. To further complicate matters, dependency relations often change as software components are refined or their interactions modified. Because the robustness of any real-time system hinges on the availability of essential services in spite of faults and failures in useful but non-essential components, keeping track of the constantly evolving dependency relations between the system's components is crucial. If a system's designers cannot ensure that critical services only USE but do not DEPEND ON less critical components, a seemingly minor fault can propagate along complex and unforeseen dependency chains and bring down the entire system. Therefore, automatically tracking and analyzing system-wide dependency relations given only local dependency information is vital for the development of robust real time systems.
Exploring the Maintenance Process through the Defect Management in the Open Source Projects - Four Case Studies
Authors: Heli Lintula, Timo Koponen, Virpi Hotti
Venue: Proceedings of the International Conference on Software Engineering Advances (2006)

While Open Source Software are becoming evermore widespread and used these days, their maintenance is coming important issue. Earlier studies have shown that defect and version management systems are rich and valuable sources for evaluation of maintenance but they have not studied the use of separate management system for support and feature request. Therefore, in this research we study defect reports, support and feature requests of Open Source Software projects through four case studies from SourceForge. Results showed that most of the case studies used actively those systems but discussion forums were even more active. Although reports and requests were submitted, most of them did not cause any changes or further actions because they were closed shortly as duplicates, invalid or without any resolution.
Regional Brain Activation Evoked When Approaching a Virtual Human on a Virtual Walk
Authors: James P. Morris, Kevin A. Pelphrey, Gregory Mccarthy
Venue: Journal of Cognitive Neuroscience (2005)

We investigated the necessity of biological motion for activation of the posterior superior temporal sulcus (pSTS) in circumstances in which the rapid approach of the observer to a virtual human induced the observer to make inferences about the characters intentions. Using a virtual reality environment, subjects experienced themselves walking towards a complex scene composed of animate and/or inanimate objects. During "person" trials, the scene contained a virtual human either making a simple gesture such as scratching his face (Study 1) or standing completely still (Study 2). During "object" trials, the scenes contained items such as furniture, a face portrait, and a clock, but not the virtual human. Using functional MRI to measure brain activity, we demonstrated strong activity in the pSTS while the observer approached the social scene, but only when the virtual human was making gestures. This result emphasizes the importance of biological motion in inferring the intentions of others.
Complete-data spaces and generalized EM algorithms
Authors: J. A. Fessler, A. O. Hero
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04 (1993)

Expectation-maximization (EM) algorithms have been applied extensively for computing maximum-likelihood and penalized-likelihood parameter estimates in signal processing applications. Intrinsic to each EM algorithm is a complete-data space (CDS)-a hypothetical set of random variables that is related to the parameters more naturally than the measurements are. The authors describe two generalizations of the EM paradigm: (i) allowing the relationship between the CDS and the measured data to be nondeterministic, and (ii) using a sequence of alternating complete-data spaces. These generalizations are motivated in part by the influence of the CDS on the convergence rate, a relationship that is formalized through a data-processing inequality for Fisher information. These concepts are applied to the problem of estimating superimposed signals in Gaussian noise, and it is shown that the new space alternating generalized EM algorithm converges significantly faster than the ordinary EM algorithm.
Many cyclic block diagrams do not need parallel semantics
Authors: Ben Denckla
Venue: ACM SIGPLAN Notices (2006)

Some cyclic block diagrams need parallel semantics: they are syntactically invalid or semantically nonterminating in any block diagram language with sequential semantics. Yet, many cyclic block diagrams do not need parallel semantics: they behave the same in a block diagram language with non-strict sequential semantics. We show that a block diagram used to motivate the parallel semantics of the Ptolemy SR domain behaves the same in BdHas, a block diagram language with non-strict sequential semantics. BdHas is implemented as syntactic sugar on top of Haskell.
Alternative Architectures and Protocols for Providing Strong Consistency in Dynamic Web Applications
Authors: M. Hossein Sheikh Attar, M. Tamer Özsu
Venue: World Wide Web (2006)

Dynamic Web applications have gained a great deal of popularity. Improving the performance of these applications has recently attracted the attention of many researchers. One of the most important techniques proposed for this purpose is caching, which can be done at different locations and within different stages of the process of generating a dynamic Web page. Most of the caching schemes proposed in literature are lenient about the issue of consistency; they assume that users can tolerate receiving stale data. However, an important class of dynamic Web applications are those in which users always expect to get the freshest data available. Any caching scheme has to incur a significant overhead to be able to provide this level of consistency (i.e., strong consistency); the overhead may be so much that it neutralizes the benefits of caching. In this paper, three alternative architectures are investigated for dynamic Web applications that require strong consistency. A proxy caching scheme is designed and implemented, which performs caching at the level of database queries. This caching system is used in one of the alternative architectures. The performance experiments show that, despite the high overhead of providing strong consistency in database caching, this technique can improve the performance of dynamic Web applications, especially when there is a long network latency between clients and the (origin) server.
A systematic approach to the assessment of fuzzy association rules
Authors: Didier Dubois, Eyke Hüllermeier, Henri Prade
Venue: Data Mining and Knowledge Discovery (2006)

In order to allow for the analysis of data sets including numerical attributes, several generalizations of association rule mining based on fuzzy sets have been proposed in the literature. While the formal specification of fuzzy associations is more or less straightforward, the assessment of such rules by means of appropriate quality measures is less obvious. Particularly, it assumes an understanding of the semantic meaning of a fuzzy rule. This aspect has been ignored by most existing proposals, which must therefore be considered as ad-hoc to some extent. In this paper, we develop a systematic approach to the assessment of fuzzy association rules. To this end, we proceed from the idea of partitioning the data stored in a database into examples of a given rule, counterexamples, and irrelevant data. Evaluation measures are then derived from the cardinalities of the corresponding subsets. The problem of finding a proper partition has a rather obvious solution for standard association rules but becomes less trivial in the fuzzy case. Our results not only provide a sound justification for commonly used measures but also suggest a means for constructing meaningful alternatives.
Mining for proposal reviewers: lessons learned at the national science foundation
Authors: Seth Hettich, Michael J. Pazzani
Venue: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (2006)

In this paper, we discuss a prototype application deployed at the U.S. National Science Foundation for assisting program directors in identifying reviewers for proposals. The application helps program directors sort proposals into panels and find reviewers for proposals. To accomplish these tasks, it extracts information from the full text of proposals both to learn about the topics of proposals and the expertise of reviewers. We discuss a variety of alternatives that were explored, the solution that was implemented, and the experience in using the solution within the workflow of NSF.
Domain Ontology for Personalized E-Learning in Educational Systems
Authors: Jose M. Gascuena, Antonio Fernandez-Caballero, Pascual Gonzalez
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

This paper introduces a domain ontology to describe learning material that compose a course, capable of providing adaptive e-learning environments and reusable educational resources. Two characteristics have been considered to describe each resource: (1) the most appropriate learning style and, (2) the most satisfactory hardware and software features of the used device.
Mixture distribution-based forecasting using stochastic volatility models: Research Articles
Authors: A. E. Clements, S. Hurn, S. I. White
Venue: Applied Stochastic Models in Business and Industry (2006)

Many traditional econometric methods forecast the conditional distribution of asset returns by a point prediction of volatility. Alternatively, forecasts of this distribution may be generated from a mixture of distributions. This paper proposes a method by which information extracted from the estimation of a standard stochastic volatility model (using non-linear filtering) can be used to generate mixture distribution forecasts. In general, it is found that forecasts based on mixture distributions are superior to those simply using point predictions of volatility. In terms of mixture distribution forecasts, the method proposed in this paper is found to be superior to a number of competing approaches. Copyright &copy; 2006 John Wiley & Sons, Ltd.
Exceptional coupling constants for the Coulomb-Dirac operator with anomalous magnetic moment
Authors: K. M. Schmidt
Venue: Journal of Computational and Applied Mathematics (2006)

It was recently shown that the point spectrum of the separated Coulomb-Dirac operator H0(k) is the limit of the point spectrum of the Dirac operator with anomalous magnetic moment Ha(k) as the anomaly parameter tends to 0; this spectral stability holds for all Coulomb coupling constants c for which H0(k) has a distinguished self-adjoint extension if the angular momentum quantum number k is negative, but for positive k there are certain exceptional values for c. Here we obtain an explicit formula for these exceptional values. In particular, it implies spectral stability for the three-dimensional Coulomb-Dirac operator if |c| < 1, covering all physically relevant cases.
Keystroke Biometric Recognition Studies on Long-Text Input under Ideal and Application-Oriented Conditions
Authors: Mary Villani, Charles Tappert, Giang Ngo, Justin Simone, Huguens St. Fort, Sung-Hyuk Cha
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

A long-text-input keystroke biometric system was developed for applications such as identifying perpetrators of inappropriate e-mail or fraudulent Internet activity. A Java applet collected raw keystroke data over the Internet, appropriate long-text-input features were extracted, and a pattern classifier made identification decisions. Experiments were conducted on a total of 118 subjects using two input modes - copy and free-text input - and two keyboard types - desktop and laptop keyboards. Results indicate that the keystroke biometric can accurately identify an individual who sends inappropriate email (free text) if sufficient enrollment samples are available and if the same type of keyboard is used to produce the enrollment and questioned samples. For laptop keyboards we obtained 99.5% accuracy on 36 users, which decreased to 97.9% on a larger population of 47 users. For desktop keyboards we obtained 98.3% accuracy on 36 users, which decreased to 93.3% on a larger population of 93 users. Accuracy decreases significantly when subjects used different keyboard types or different input modes for enrollment and testing.
Further explorations into ternary complementary pairs
Authors: R. Craigen, S. Georgiou, Will Gibson, C. Koukouvinos
Venue: Journal of Combinatorial Theory Series A (2006)

In [R. Craigen, C. Koukouvinos, A theory of ternary complementary pairs, J. Combin. Theory Ser. A 96 (2001) 358-375], we proposed a systematic approach to the theory of ternary complementary pairs (TCPs) and showed how all pairs known then could be constructed using a single elementary product, the natural equivalence relations, and a handful of pairs which we called primitive. We also introduced more new primitive pairs than could be inferred previously, concluding with some conjectures reflecting the patterns that were beginning to arise in light of the new approach.In this paper we take what appears to be the natural next step, by investigating these patterns among those lengths and weights that are within easy computational distance from the last length considered therein, length 14. We give complete results up to length 21, and partial results up to length 28. (Ironically, although we proceed analytically by weight first then length, for computational reasons we are bound, in this empirical investigation, to proceed according to length first.)Thus we provide support for the previous conjectures, and shed enough new light to speculate further as to the likely ultimate shape of the theory. Since short term work on TCPs will require massive acquisition of data about small pairs, we also discuss affixes--a computational strategy that arose out of the investigations culminating in this article.
Smart Home Sensor Networks Pose Goal-Driven Solutions to Wireless Vacuum Systems
Authors: Huan Chen, Bo-Chao Cheng, Chih-Chuan Cheng, Li-Kuang Tsai
Venue: Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 02 (2006)

Home sensor nodes are devices embedded in home appliances and are designed to sense environments, to process collected information, to perform a specific task, and to cooperate with other units. The advances of VLSI technologies and wireless sensor networks (WSN) turn the inspirational idea of intelligent home appliances into reality. In this paper, we focus on a category of home appliances, Smart Home Vacuum (SHV), where mobility and battery are critical design concerns. The emphasis of this paper is on the design of SHV system and the development of a goal driven task planning (GDTP) engine which can be implemented in the wireless vacuum systems to maximize the network lifetime as well as the cleaning efficiency. Unlike LEACH, proposed GDTP engine is a goal-driven approach to select the cluster heads to satisfy the design goals. Simulations are conducted by the network simulator (ns-2) and the experiment results indicate that GDTP performs better than other algorithms in terms of the network lifetime and the cleaning area coverage.
Representing Process-Based Ontology Using Topic Maps for Web Services
Authors: Jeong-Youn Yu, Yun-Young Hwang, Kyu-Chul Lee
Venue: Proceedings of the 15th International Conference on Computing (2006)

The intelligent discovery of Web Services is an important factor in automatic Web Services. Thus far, discovery methods have been heavily dependent on the input/output and theprecondition/effect of a service. In addition, they have not used ontology describing internal operations. Thus, users have much difficulty in finding the service that they want with the previous methods. In this study, a process-based ontology, TMS (Topic Maps-based Ontology for Service) is developed. The characteristics of TM-S are benejicial in terms of filtering out unrelated services and enhancing a user S retrieval capabilities. Additionally, TMS-QL (TM-S Query Language) was developed; directing users in how services can be found using TM-S.
Extensive Individual Differences in Brain Activations Associated with Episodic Retrieval are Reliable Over Time
Authors: Michael B. Miller, John Darrell Van Horn, George L. Wolford, Todd C. Handy, Monica Valsangkar-smyth, Souheil Inati, Scott Grafton, Michael S. Gazzaniga
Venue: Journal of Cognitive Neuroscience (2002)

The localization of brain functions using neuroimaging techniques is commonly dependent on statistical analyses of groups of subjects in order to identify sites of activation, particularly in studies of episodic memory. Exclusive reliance on group analysis may be to the detriment of understanding the true underlying cognitive nature of brain activations. In the present study, we found that the patterns of brain activity associated with episodic retrieval are very distinct for individual subjects from the patterns of brain activity at the group level. These differences go beyond the relatively small variations due to cyctoarchitectonic differences or spatial normalization. We quantify this individual variability by cross-correlating volumes of brain images. We demonstrate that individual patterns of brain activity are reliable over time despite their extensive variability. We suggest that varied but reliable individual patterns of significant brain activity may be indicative of different cognitive strategies used to produce a recognition response. We believe that individual analysis in conjunction with group analysis may be critical to fully understanding the relationship between retrieval processes and underlying brain regions.