ppxscal/embedding_network huggingface dataset:
Multivariate information bottleneck
Authors: Noam Slonim, Nir Friedman, Naftali Tishby
Venue: Neural Computation (2006)

The information bottleneck (IB) method is an unsupervised model independent data organization technique. Given a joint distribution, p(X, Y), this method constructs a new variable, T, that extracts partitions, or clusters, over the values of X that are informative about Y. Algorithms that are motivated by the IB method have already been applied to text classification, gene expression, neural code, and spectral analysis. Here, we introduce a general principled framework for multivariate extensions of the IB method. This allows us to consider multiple systems of data partitions that are interrelated. Our approach utilizes Bayesian networks for specifying the systems of clusters and which information terms should be maintained. We show that this construction provides insights about bottleneck variations and enables us to characterize the solutions of these variations. We also present four different algorithmic approaches that allow us to construct solutions in practice and apply them to several real-world problems.
Global likelihood optimization via the cross-entropy method with an application to mixture models
Authors: Zdravko Botev, Dirk P. Kroese
Venue: Proceedings of the 36th conference on Winter simulation (2004)

Global likelihood maximization is an important aspect of many statistical analyses. Often the likelihood function is highly multi-extremal. This presents a significant challenge to standard search procedures, which often settle too quickly into an inferior local maximum. We present a new approach based on the cross-entropy (CE) method, and illustrate its use for the analysis of mixture models.
Sparse and Semi-supervised Visual Mapping with the S^3GP
Authors: Oliver Williams, Andrew Blake, Roberto Cipolla
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (2006)

This paper is about mapping images to continuous output spaces using powerful Bayesian learning techniques. A sparse, semi-supervised Gaussian process regression model (S3GP) is introduced which learns a mapping using only partially labelled training data. We show that sparsity bestows efficiency on the S3GP which requires minimal CPU utilization for real-time operation; the predictions of uncertainty made by the S3GP are more accurate than those of other models leading to considerable performance improvements when combined with a probabilistic filter; and the ability to learn from semi-supervised data simplifies the process of collecting training data. The S3GP uses a mixture of different image features: this is also shown to improve the accuracy and consistency of the mapping. A major application of this work is its use as a gaze tracking system in which images of a human eye are mapped to screen coordinates: in this capacity our approach is efficient, accurate and versatile.
Network Intrusion Detection: Automated and Manual Methods Prone to Attack and Evasion
Authors: David J. Chaboya, Richard A. Raines, Rusty O. Baldwin, Barry E. Mullins
Venue: IEEE Security and Privacy (2006)

In this article, the authors describe common intrusion detection techniques, NIDS evasion methods, and how NIDSs detect intrusions. Additionally, we introduce new evasion methods, present test results for confirming attack outcomes based on server responses, and propose a methodology for confirming response validity.
How to Use Events and Rules for Supporting Role-Based Security? (Invited Paper)
Authors: Raman Adaikkalavan, Sharma Chakravarthy
Venue: Proceedings of the 17th International Conference on Database and Expert Systems Applications (2006)

Role-Based Access Control, where object accesses are controlled by roles (or job functions) is a more feasible alternative to traditional access control mechanisms. Constraints play a critical role in realizing and providing finegrained RBAC in diverse domains such as P2P and Grid Computing. In this paper, we have shown how events and authorization rules are used to provide fine-grained RBAC. First, simple events are identified for the RBAC domain. Second, various event operators for modeling constraints such as precedence, non-occurrence, dependency and their combinations are introduced. Third, how eventbased RBAC policies are specified using both simple and complex events are discussed. Finally, how the proposed fine-grained RBAC policies can be exploited for P2P resource management is discussed.
Portal Access to Scientific Computation and Customized Collaborative Visualization on Campus Grid
Authors: Yingjie Xia, Guanghua Song, Yao Zheng
Venue: Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (2006)

In order to integrate the distributed computing resources in Zhejiang University (ZJU), a campus grid is constructed by using the hierarchical combination of middleware, Globus Toolkit and Sun Grid Engine. As one realization of the computational grid, the foundation of the campus grid provides a large amount of scientific computation cycles. Furthermore, our grid system adopts two different approaches, Java 3D and ParaView, to manipulate a collaborative visualization of the computed data respectively. Therefore the users can choose the most appropriate method according to their requirements. Three cases are tested on the campus grid via portal access. The results demonstrate that the ZJU Campus Grid provides powerful computation cycles and an efficient collaborative visualization service.
A parallel out-of-core computing system using PVFS for Linux clusters
Authors: Jianqi Tang, Binxing Fang, Mingzeng Hu, Hongli Zhang
Venue: Proceedings of the international workshop on Storage network architecture and parallel I/Os (2004)

Cluster systems become a new and popular approach to parallel computing. More and more scientists and engineers use clusters to solve problems with large sets of data for its high processing power, low price and good scalability. Since the traditional out-of-core programs are difficult to write and the virtual memory system does not perform well, we develop a parallel out-of-core computing system using PVFS named POCCS. POCCS provides convenient interface to write out-of-core codes and the global view of the out-of-core data. The software architecture, data storage model and system implementation are described in this paper. The experimental results show that POCCS extends the problem sizes that can be solved and the performance of POCCS is better than the virtual memory system while the data set is large.
A Study for Parametric Morpho-Semantic Operators to Assist Architectural Conception at the Drafting Stage
Authors: Jean-Paul Wetzel, Salim Belblidia, Jean-Claude Bignon
Venue: Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation (2006)

In this paper, we make the assumption that a shape modelling process can rely on the application of a set of morpho-semantic operators to initial shapes. In this paper, we will refer to several researches which have attempted to identify such operators. We will attempt to validate this concept through the analysis of some "innovative"buildings. Thus, in different projects of contemporary architects, we can find the trace of some transformations (e.g. "twist", "merge", "overlay", etc.) recorded in the final geometry. These transformations have a symbolic representation. A design system based on the combination of these operators could enable the designer to quickly explore a great number of spatial solutions.
Automatic extraction of bilingual word pairs using inductive chain learning in various languages
Authors: Hiroshi Echizen-ya, Kenji Araki, Yoshio Momouchi
Venue: Information Processing and Management: an International Journal (2006)

In this paper, we propose a new learning method for extracting bilingual word pairs from parallel corpora in various languages. In cross-language information retrieval, the system must deal with various languages. Therefore, automatic extraction of bilingual word pairs from parallel corpora with various languages is important. However, previous works based on statistical methods are insufficient because of the sparse data problem. Our learning method automatically acquires rules, which are effective to solve the sparse data problem, only from parallel corpora without any prior preparation of a bilingual resource (e.g., a bilingual dictionary, a machine translation system). We call this learning method Inductive Chain Learning (ICL). Moreover, the system using ICL can extract bilingual word pairs even from bilingual sentence pairs for which the grammatical structures of the source language differ from the grammatical structures of the target language because the acquired rules have the information to cope with the different word orders of source language and target language in local parts of bilingual sentence pairs. Evaluation experiments demonstrated that the recalls of systems based on several statistical approaches were improved through the use of ICL.
Risk Factor Analysis of West Nile Virus Using Structural Learning with Forgetting Method
Authors: Leilei Pan, Simon X. Yang, Lixu Qin
Venue: Proceedings of the Fifth Mexican International Conference on Artificial Intelligence (2006)

A novel neural network based approach for risk factor analysis of infection of West Nile virus (WNV) is proposed. A multi-factor risk analysis model is developed and learnt by an algorithm called structural learning with forgetting. Through the learning, unnecessary connections fade away and a skeletal network emerges. By analyzing the resulted skeletal networks, significant risk factors can be identified, and thus a more thorough understanding of WNV transmission mechanism can be obtained. The proposed approach is tested with a dead birds surveillance data. The results demonstrate the effectiveness of the proposed approach.
Evolutionary Algorithm for 3D Object Reconstruction from Images
Authors: Renata L. M. E. do Rego, Hansenclever F. Bassani, Aluizio F. R. Araujo, Fernando Buarque de Lima Neto
Venue: Proceedings of the Ninth Brazilian Symposium on Neural Networks (2006)

This work presents an evolutionary algorithm to reconstruct 3D objects based on images of them. In the proposed evolutionary algorithm, we describe a way to evolve 3D initial models to a target object by means of comparisons between images generated from the models and images of target object, in which the acquisition position is known. We proposed a modification in the standard evolutionary strategy algorithm that produced better and faster results in the class of problems at hand (i.e. when many genes must evolve mainly to a direction in the search space). Satisfactory results were achieved reconstructing simple 3D objects, such as an ellipsoid object and a pear from a sphere. Although performance decrease for more detailed objects such as a face, the proposed solution still converges.
Separation of Highlight Reflections on Textured Surfaces
Authors: Ping Tan, Long Quan, Stephen Lin
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (2006)

We present a method for separating highlight reflections on textured surfaces. In contrast to previous techniques that use diffuse color information from outside the highlight area to constrain the solution, the proposed method further capitalizes on the spatial distributions of colors to resolve ambiguities in separation that often arise in real images. For highlight pixels in which a clear-cut separation cannot be determined from color space analysis, we evaluate possible separation solutions based on their consistency with diffuse texture characteristics outside the highlight. With consideration of color distributions in both the color space and the image space, appreciably enhanced separation performance can be attained in challenging cases.
Robust Neural Networks Compensating Motion Control of Reconfigurable Manipulator
Authors: Ying Li, Yuanchun Li
Venue: Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 2 (2006)

There are many uncertainties in real dynamics system of reconfigurable manipulator that makes PID etc. traditional methods control imprecisely. Thus, in this paper, to enhance computed torque control (CTC) based method, robust neural networks (RNN) compensating control scheme is developed to compensate structured and unstructured uncertainties. The controller for a RRP reconfigurable manipulator is designed, uniformly ultimately bounded (UUB) stability is proved by Lyapunov theory and simulations show its effectiveness on satisfactory tracking performance.
Law-abiding and integrity on the internet: a case for agents
Authors: Frances Brazier, Anja Oskamp, Corien Prins, Maurice Schellekens, Niek Wijngaards
Venue: Artificial Intelligence and Law (2004)

Software agents extend the current, information-based Internet to include autonomous mobile processing. In most countries such processes, i.e., software agents are, however, without an explicit legal status. Many of the legal implications of their actions (e.g., gathering information, negotiating terms, performing transactions) are not well understood. One important characteristic of mobile software agents is that they roam the Internet: they often run on agent platforms of others. There often is no pre-existing relation between the "owner" of a running agent's process and the owner of the agent platform on which an agent process runs. When conflicts arise, the position of the agent platform administrator is not clear: is he or she allowed to slow down the process or possibly remove it from the system? Can the interests of the user of the agent be protected? This article explores legal and technical perspectives in protecting the integrity and availability of software agents and agent platforms.
Boosting Sex Identification Performance
Authors: Shumeet Baluja, Henry A. Rowley
Venue: International Journal of Computer Vision (2007)

This paper presents a method based on AdaBoost to identify the sex of a person from a low resolution grayscale picture of their face. The method described here is implemented in a system that will process well over 109 images. The goal of this work is to create an efficient system that is both simple to implement and maintain; the methods described here are extremely fast and have straightforward implementations. We achieve 80% accuracy in sex identification with less than 10 pixel comparisons and 90% accuracy with less than 50 pixel comparisons. The best classifiers published to date use Support Vector Machines; we match their accuracies with as few as 500 comparison operations on a 20× 20 pixel image. The AdaBoost based classifiers presented here achieve over 93% accuracy; these match or surpass the accuracies of the SVM-based classifiers, and yield performance that is 50 times faster.
ConKMeL: a contextual knowledge management framework to support multimedia e-Learning
Authors: Weihong Huang, Alain Mille
Venue: Multimedia Tools and Applications (2006)

As the latest stage of learning and training evolution, e-Learning is supposed to provide intelligent functionalities not only in processing multi-media education resources but also in supporting context-sensitive pedagogical education processes. Towards providing an integrated solution for intelligent multimedia e-Learning, this paper presents a context-aware knowledge management framework named ConKMeL. Proposed framework features a semantic context-based approach for representing and integrating information and knowledge in e-Learning. Requirement analysis in university e-Learning environments shows that knowledge communications are usually in a hybrid mode across different conceptual levels. Based on the fact, a multi-layer contextual knowledge representation model called KG (knowledge graph) is presented. Corresponding key issues in development such as context-based knowledge retrieval and logical knowledge interpretation are discussed. On the application side, a scenario-based learning case study is shown to demonstrate the concepts and techniques developed in the ConKMeL framework.
Two different interfaces to visualize patient histories on a PDA
Authors: Carmelo Ardito, Paolo Buono, Maria Francesca Costabile, Rosa Lanzilotti
Venue: Proceedings of the 8th conference on Human-computer interaction with mobile devices and services (2006)

PHiP (Patient History in Pocket) is a tool designed for a mobile device that displays patient histories and permits to visually query patient data stored in the hospital database. It exploits Information Visualization techniques and it is able to accommodate on the screen a good amount of information that physicians require in their analysis of clinical cases. Two different user interfaces for PHiP have been implemented and informal user testing has been performed to compare their impact on users.
On arguing the safety of large systems
Authors: Gordon R. Stone
Venue: Proceedings of the 10th Australian workshop on Safety critical systems and software - Volume 55 (2006)

The Occupational Health and Safety (Commonwealth Employees) Act (OH&S Act) and technical regulations in the Royal Australian Navy (RAN) both place obligations on the Department of Defence in relation to the safety of its systems. The RAN's technical regulation system requires that evidence of safety be provided for new acquisitions. A safety case is preferred for major systems.The upgrade of a frigate belonging to a class first commissioned over 35 years ago provides significant challenges to the provision of a safety case that should meet the requirements of the OH&S Act and the RAN's technical regulations. This paper examines some of those challenges and discusses techniques and tools that are being applied to them.
Integrated Planning for Telepresence with Time Delays
Authors: Mark D. Johnston, Kenneth J. Rabe
Venue: Proceedings of the 2nd IEEE International Conference on Space Mission Challenges for Information Technology (2006)

Teleoperation of remote robotic systems over time delays in the range of 2-10 seconds poses a unique set of challenges. In the context of a supervisory control system for the JSC Robonaut humanoid robot, we have developed an "intelligent assistant" that integrates an Artificial Intelligence planner (JSHOP2) with execution monitoring of the state of both the human supervisor and the remote robot. The assistant reasons simultaneously about the world state on both sides of the time delay, which represents a novel application of this technology. The purpose of the assistant is to provide advice to the human supervisor about current and future activities, derived from a sequence of highlevel goals to be achieved. To do this, the assistant must simultaneously monitor and react to various data sources, including actions taken by the supervisor who is issuing commands to the robot (e.g. with a data glove), actions taken by the robot, and the environment of the robot, both as currently perceived over the time delay, along with the current sequence of goals. We have developed a "leader/follower" software architecture to handle the dual time-shifted streams of execution feedback. In this paper we describe the integrated planner and its executive, and how it operates in normal and anomaly situations.
Specification-Based Intrusion Detection in WLANs
Authors: Rupinder Gill, Jason Smith, Andrew Clark
Venue: Proceedings of the 22nd Annual Computer Security Applications Conference (2006)

Wireless networking technologies based on the IEEE 802.11 series of standards fail to authenticate management frames and network card addresses and suffer from serious vulnerabilities that may lead to denial of service, session hijacking, and address masquerading attacks. In this paper, we describe and implement a specification-based intrusion detection system for IEEE 802.11 wireless infrastructure networks, which not only provides attack detection, but also implements policy compliance monitoring. The specification used by our intrusion detection system is derived from network protocol state transition models and site security policy constraints. We also perform an experimental and comparative analysis of the technique to assess its effectiveness. The results indicate that the approach is superior at successfully detecting a greater variety of attacks than other existing approaches.
Sensor Bandwidth Assignment through Video Annotation
Authors: Christian Micheloni, Lauro Snidaro, Ingrid Visentini, GianLuca Foresti
Venue: Proceedings of the IEEE International Conference on Video and Signal Based Surveillance (2006)

The state of the art of surveillance systems include a large set of techniques for both low level and high level tasks. In particular, the research community has witnessed in the last decade a high proliferation of techniques that span from object detection and tracking to object recogni- tion and event understanding. Although some techniques have been proven to be very effective those tasks cannot be considered solved. Although more effort is needed in the event analysis field, a new problem arises from the develop- ment of large scale networked surveillance systems: infor- mation sharing. The way information is shared between the nodes of the surveillance network today represents a key- point issue. To provide a first and novel solution to such a problem, we propose an innovative system architecture for a video surveillance system with distributed processing over multiple processing units and with distributed communica- tion over multiple heterogeneous channels (wireless, satel- lite, local IP networks, etc.). In particular, a new real-time technique for changing the video transmission parameters (e.g., frame rate, spatial/color resolution, etc.) according to the bandwidth available will be here presented.
Semantic Data Extraction for B2B Integration
Authors: Bruno Silva, Jorge Cardoso
Venue: Proceedings of the 26th IEEE International ConferenceWorkshops on Distributed Computing Systems (2006)

Business-to-business (B2B) data exchange and integration is a common daily operation in today's organizations. These operations are crucial since they affect organizations' capability to compete in today's marketplace. Data exchange and integration has been proven to be a challenge due to the heterogeneity of the information systems involved."This paper described a Syntactic-to-Semantic (S2S) middleware which, when based on a single query, integrates data residing in different data sources possibly with different formats, structures, schema, and semantics. The middleware uses an ontology-based multi-source data extractor/wrapper approach to transform syntactic data into semantic knowledge.
Performance analysis of some CFAR detectors in homogeneous and non-homogeneous Pearson-distributed clutter
Authors: Hilal Abdenour Meziani, Faouzi Soltani
Venue: Signal Processing (2006)

In this paper, we analyse the performance of the Greatest Of-Constant False Alarm Rate (GO-CFAR) and the Smallest Of (SO)-CFAR detectors in the presence of clutter environments whose amplitude statistics are modelled by the Pearson V distribution and where the clutter dominates the receiver noise. The performance of these detectors are evaluated both in homogeneous and non-homogeneous clutter. The non-homogeneity is modelled as a step function discontinuity in the reference window. The target in the test cell is assumed to fluctuate according to the Swerling I model. We derive closed form expressions for the probability of false alarm (Pfa) and the probability of detection (Pd) in homogeneous and nonhomogeneous Pearson V distributed clutter. The comparison of the two detectors for a non-homogeneous clutter environment showed that the best false alarm rate performance at clutter boundary is obtained for the SO-CFAR detector when the test cell is in low level clutter, while the GO-CFAR detector exhibits better performance when the test cell is from the higher level clutter as expected.