ppxscal/embedding_network huggingface dataset:
Differential Evolution as a viable tool for satellite image registration
Authors: I. De Falco, A. Della Cioppa, D. Maisto, E. Tarantino
Venue: Applied Soft Computing (2008)

A software system grounded on Differential Evolution to automatically register multiview and multitemporal images is designed, implemented and tested through a set of 2D satellite images on two problems, i.e. mosaicking and changes in time. Registration is effected by looking for the best affine transformation in terms of maximization of the mutual information between the first image and the transformation of the second one, and no control points are needed in this approach. This method is compared against five widely available tools, and its effectiveness is shown.
Exploration of E-learning of science & technology integration in science and technology curriculums of primary school
Authors: Rong-Jyue Fang, Hung-Jen Yang, Hua-Lin Tsai, Chi-Jen Lee, Pofen Wang, Shu-Hui Hsieh
Venue: Proceedings of the 8th WSEAS International Conference on Multimedia systems and signal processing (2008)

It is very important that understand the mobile technology content for bring up the new century students' technology equipment, and it is the headstone of technology development. The mobile technology include handle mobile device, digital transmission modes, mobile information software and so on. The mobile technology realized the perpetual contact, the network tools of teenagers' social intercourse. In the science and technology curriculum, the students had to learn something, such as: what is mobile technology? How did the things execute? What kinds of value and belief effect influenced the mobile technology? What idea foster the mobile technology? How did we put idea into practice? What influence between mobile technology and sociality? The more important is to promote the students' adaptation to the new environment via technology instruction. The mobile technology content integrated into instruction could apply instructional strategies of dissemination, facilitation, inside collaboration, outside collaboration, apprenticeship, generative development an so on. Therefore, there are perplexities and countermeasures in the paper by theory exploration and experience of teaching. The result will provide administrations and teachinf to refer.
Interest Based Selection of User Generated Content for Rich Multimedia Services
Authors: Olivier Van Laere, Matthias Strobbe, Samuel Dauwe, Bart Dhoedt, Filip De Turck, Piet Demeester, Orlando Verde, Frank Hülsken
Venue: Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services (2008)

In view of the overwhelming popularity of user generated content, both in terms of production and consumption, new intelligent services are needed to help users finding the content they need and enhance existing services with suitably selected content. In this paper we present a set of algorithms for retrieving content, based on dynamic user profiles and learning capabilities (e.g. based on user feedback). The profile information is used in content searches as well as for assisting the user input analysis process (i.e. speech recognition). To illustrate the approach taken, a rich communication service is presented. Here, the basic service (i.e. voice/video conferencing) is enhanced by showing pictures in real time to the users based on the topic of their conversation and their specific interests.
Enhancing Text Categorization Using Sentence Semantics
Authors: Shady Shehata, Fakhri Karray, Mohamed Kamel
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Most of text categorization techniques are based on word and/or phrase analysis of the text. Statistical analysis of a term frequency captures the importance of the term within a document only. However, two terms can have the same frequency in their documents, but one term contributes more to the meaning of its sentences than the other term. Thus, the underlying model should indicate terms that capture the semantics of text. In this case, the model can capture terms that present the concepts of the sentence, which leads to discover the topic of the document.A new concept-based model that analyzes terms on the sentence and document levels rather than the traditional analysis of document only is introduced. The concept-based model can effectively discriminate between non-important terms with respect to sentence semantics and terms which hold the concepts that represent the sentence meaning.A set of experiments using the proposed concept-based model on different datasets in text categorization is conducted. The experiments demonstrate the comparison between traditional weighting and the concept-based weighting enhances the quality of categorization quality of sets of documents substantially.
Multimedia Chat for Helpdesks: A Practical SOA Architecture
Authors: Zon-Yin Shae, Tony Bergstrom, Claudio Pinhanez, Mark Podlaseck
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

The existing 3rd party chat services for help desk are text based only and more effective communication between user and agent for better help desk support in multimedia chat services is called for. It would be a very expensive approach to totally re-write the code to convert the current text based chat product for supporting multimedia in chat services. An innovative SOA chat system architecture is therefore presented in this paper to create a new multimedia chat services for help desk by novel decomposition and re-composition of the services from the existing 3rd party text based chat product. A regular web mashup is memory-less which the current mashup operation is independent from the previous one. Our SOA architecture implements uniquely the session based interactive mashup mechanism which the current mashup operation is related and bundled to the previous operations in the same chat session.
Partial vs. Complete Domination: t-Dominating Set
Authors: Joachim Kneis, Daniel Mölle, Peter Rossmanith
Venue: Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science (2007)

We examine the parameterized complexity of t -Dominating Set, the problem of finding a set of at most k nodes that dominate at least t nodes of a graph G = (V,E). The classic NP-complete problem Dominating Set, which can be seen to be t -Dominating Set with the restriction that t = n, has long been known to be W[2]-complete when parameterized in k. Whereas this implies W[2]-hardness for t -Dominating Set and the parameter k, we are able to prove fixed-parameter tractability for t -Dominating Set and the parameter t. More precisely, we obtain a quintic problem kernel and a randomized $O((4+\varepsilon)^t\textit{poly}(n))$ algorithm. The algorithm is based on the divide-and-color method introduced to the community earlier this year, rather intuitive and can be derandomized using a standard framework.
Ontology Design Principles and Normalization Techniques in the Web
Authors: Xiaoshu Wang, Jonas S. Almeida, Arlindo L. Oliveira
Venue: Proceedings of the 5th international workshop on Data Integration in the Life Sciences (2008)

The fundamental issue of knowledge sharing in the web is the ability to share the ontological constrains associated with the Uniform Resource Identifiers (URI). To maximize the expressiveness and robustness of an ontological system in the web, each ontology should be ideally designed for a confined conceptual domain and deployed with minimal dependencies upon others. Through a retrospective analysis of the existing design of BioPAX ontologies, we illustrate the often encountered problems in ontology design and deployment. In this paper, we identify three design principles --- minimal ontological commitment, granularity separation, and orthogonal domain --- and two deployment techniques --- intensionally normalized form (INF) and extensionally normalized form (ENF) --- as the potential remedies for these problems.
Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification
Authors: Björn Andres, Ullrich Köthe, Moritz Helmstaedter, Winfried Denk, Fred A. Hamprecht
Venue: Proceedings of the 30th DAGM symposium on Pattern Recognition (2008)

Three-dimensional electron-microscopic image stacks with almost isotropic resolution allow, for the first time, to determine the complete connection matrix of parts of the brain. In spite of major advances in staining, correct segmentation of these stacks remains challenging, because very few local mistakes can lead to severe global errors. We propose a hierarchical segmentation procedure based on statistical learning and topology-preserving grouping. Edge probability maps are computed by a random forest classifier (trained on hand-labeled data) and partitioned into supervoxels by the watershed transform. Over-segmentation is then resolved by another random forest. Careful validation shows that the results of our algorithm are close to human labelings.
Commitment-Based Policies in Persuasion Dialogues with Defeasible Beliefs
Authors: Ioan Alfred Letia, Raluca Vartic
Venue: Agent Communication II: International Workshops on Agent Communication, AC 2005 and AC 2006, Utrecht, Netherlands, July 25, 2005, and Hakodate, Japan, May 9, 2006, Selected and Revised Papers (2006)

We advance a model to express the preconditions for engaging in dialogues in terms of the agents' mental attitudes, in a defeasible logic context (beliefs are divided into strict and defeasible ones). Then, we give a protocol for the persuasion dialogue between two agents, which provides the means of identifying both false premises and logical fallacies. Communicative acts are organized in a hierarchy, and relations between speech acts and social commitments are expressed via policies involving operations on social commitments. Including commitments renders an observable behaviour of the communicating agents.
Termination of Innermost Context-Sensitive Rewriting Using Dependency Pairs
Authors: Beatriz Alarcón, Salvador Lucas
Venue: Proceedings of the 6th international symposium on Frontiers of Combining Systems (2007)

Innermost context-sensitive rewriting has been proved useful for modeling computations of programs of algebraic languages like Maude, OBJ, etc. Furthermore, innermost termination of rewriting is often easier to prove than termination. Thus, under appropriate conditions, a useful strategy for proving termination of rewriting is trying to prove termination of innermost rewriting. This phenomenon has also been investigated for context-sensitive rewriting (CSR). Up to now, only few transformations have been proposed and used to prove termination of innermost CSR. In this paper, we investigate direct methods for proving termination of innermost CSR. We adapt the recently introduced context-sensitive dependency pairs approach to innermost CSR and show that they can be advantageously used for proving termination of innermost CSR. We have implemented them as part of the termination tool mu-term.
Agent-Based Social Simulation and Modeling in Social Computing
Authors: Xiaochen Li, Wenji Mao, Daniel Zeng, Fei-Yue Wang
Venue: Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics (2008)

Agent-based social simulation (ABSS) as a main computational approach to social simulation has attracted increasing attention in the field of social computing. With the development of computer and information technologies, many new ABSS approaches have been proposed with wide application.. In this paper, we aim at reviewing research and applications of agent-based social simulation and modeling in recent years from a social computing perspective. We identify the underlying social theories for ABSS, its simulation and modeling techniques, and computational frameworks from both individual agent and multi-agent system perspective. We finally address some future research issues in agent-based social simulation and modeling.
Attribute Reduction Based on Fuzzy Rough Sets
Authors: Degang Chen, Xizhao Wang, Suyun Zhao
Venue: Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms (2007)

In T ¿fuzzy rough sets a fuzzy T ¿similarity relation is employed to describe the similar degree between two objects and to construct lower and upper approximations for arbitrary fuzzy sets. The existing researches on T ¿fuzzy rough sets mainly concentrate on constructive and axiomatic approaches of lower and upper approximation operators. In this paper we define attribute reduction based on T ¿fuzzy rough sets. The structure of proposed attribute reduction is investigated in detail by the approach of discernibility matrix. At last an example is proposed to illustrate our idea in this paper.
Home Automation with ZigBee
Authors: Maxim Osipov
Venue: Proceedings of the 8th international conference, NEW2AN and 1st Russian Conference on Smart Spaces, ruSMART on Next Generation Teletraffic and Wired/Wireless Advanced Networking (2008)

This article discusses a topic of home automation, also called domotics, and provides an overview of state-of-the-art in communication protocols for this application area. Comparitive analysis for different networking standards and communication media is provided. Special focus is given to wireless technologies and advantages provided by ZigBee wireless networking standard for home automation solutions. ZigBee protocol implementation details are covered together with examples of possible applications in conjunction with Linux Open Source software platform.
Towards a Bootstrapping NLIDB System
Authors: Catalina Hallett, David Hardcastle
Venue: Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems (2008)

This paper presents the results of a feasibility study for a bootstrapping natural language database query interface which uses natural language generation (nlg) technology to address the interpretation problem faced by existing nlidb systems. In particular we assess the feasibility of automatically acquiring the requisite semantic and linguistic resources for the nlg component using the database metadata and data content, a domain-specific ontology and a corpus of associated text documents, such as end-user manuals, for example.
Combining Lexical Information with Machine Learning for Answer Validation at QA@CLEF 2007
Authors: M. Á. García-Cumbreras, J. M. Perea-Ortega, F. Martínez-Santiago, L. Alfonso Ureña-López
Venue: Advances in Multilingual and Multimodal Information Retrieval: 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007, Budapest, Hungary, September 19-21, 2007, Revised Selected Papers (2008)

This document contains the description of the experiments carried out by the SINAI group. We have developed an approach based on several lexical measures integrated by means of different machine learning models. Based on lexical features it obtains a 41% of accuracy in answer validation for the Question-Answering task.
Web-Based Integrated Service Discovery Using Agent Platform for Pervasive Computing Environments
Authors: Kyu Min Lee, Dong-Uk Kim, Kee-Hyun Choi, Dong-Ryeol Shin
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

Although current service discovery protocols provide the same basic function of service discovery, they differ significantly in architecture, message exchange pattern, expected operating environment and service representation/description. These differences prevent service interoperability among protocols. To solve this problem, we propose a web-based integrated service discovery mechanism, using an agent platform to guarantee scalability of scope of available services without modifying existing service discovery protocols. The proposed web-based integrated service discovery mechanism provides users with a wider selection of services, and convenient search methods.
Towards a Formal Foundation for Aggregating Scientific Workflows
Authors: Frank Terpstra, Zhiming Zhao, Wico Mulder, Pieter Adriaans
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

In e-Science, scientific workflow systems are used to share data and knowledge in collaborative experiments. In recent work we discussed the concepts of a workflow bus [1], allowing multiple workflow systems to be coupled in a meta-workflow system with multiple execution models. In this paper we propose an approach for a formal model to perform the task of reasoning of about the execution models of such workflow systems. We propose that I/O Automata can be used as a formalism to prove the correctness of complicated workflows involving multiple workflow engines and execution models.
Molecular Diagnosis of Tumor Based on Independent Component Analysis and Support Vector Machines
Authors: Shulin Wang, Huowang Chen, Ji Wang, Dingxing Zhang, Shutao Li
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

Gene expression data that is being used to gather information from tissue samples is expected to significantly improve the development of efficient tumor diagnosis. For more accurate classification of tumor, extracting discriminant components from thousands of genes is an important problem which becomes a challenging task due to its characteristics such as the large number of genes and small sample size. We propose a novel approach which combines gene ranking with independent component analysis that has been developing recently to further improve the classification performance of gene expression data based on support vector machines. Two sets of gene expression data (colon dataset and leukemia dataset) are examined to confirm that the proposed approach can extract a small quantity of independent components which can drastically reduce the dimensionality of the original gene expression data when retaining higher recognition rate. The cross-validation accuracy of 100% has been achieved with extracting only 3 independent components from the leukemia dataset, and 93.55% for the colon dataset.
Privacy Preserving DBSCAN Algorithm for Clustering
Authors: K. Anil Kumar, C. Pandu Rangan
Venue: Proceedings of the 3rd international conference on Advanced Data Mining and Applications (2007)

In this paper we address the issue of privacy preserving clustering. Specially, we consider a scenario in which two parties owning confidential databases wish to run a clustering algorithm on the union of their databases, without revealing any unnecessary information. This problem is a specific example of secure multi-party computation and as such, can be solved using known generic protocols. However there are several clustering algorithms are available. They are applicable to specific type of data, but DBSCAN [4] is applicable for all types of data and the clusters obtained by DBSCAN are similar to natural clusters. However, DBSCAN [4] algorithm is basically designed as an algorithm working on a single database. In this paper we proposed a protocols for how the distances are measured between data points, when the data is distributed across two parties. By using these protocols we propose the first novel method for running DBSCAN algorithm operating over vertically and horizontally partitioned data sets, distributed in two different databases in a privacy preserving manner.
A Neural Network Approach for Video Object Segmentation in Traffic Surveillance
Authors: R. M. Luque, E. Domínguez, E. J. Palomo, J. Muñoz
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

This paper presents a neural background modeling based on subtraction approach for video object segmentation. A competitive neural network is proposed to form a background model for traffic surveillance. The unsupervised neural classifier handles the segmentation in natural traffic sequences with changes in illumination. The segmentation performance of the proposed neural network is qualitatively examined and compared to mixture of Gaussian models. The proposed algorithm is designed to enable efficient hardware implementation and to achieve real-time processing at great frame rates.
An Operational Approach to Validate the Path of BGP
Authors: Ping Li, Wanlei Zhou, Ke Li
Venue: Proceedings of the 8th international conference on Algorithms and Architectures for Parallel Processing (2008)

BGP (Border Gateway Protocol) is a fundamental component of the current Internet infrastructure. However, BGP is vulnerable to a variety of attacks, since it cannot ensure the authenticity of the path attributes announced by BGP routers. Despite several solutions have been proposed to address this vulnerability, none of them is operational in real-world due to their immense impact on original BGP. In this paper, we propose a Deployable Path Validation Authentication scheme, which can effectively validate the path of BGP. Through analysis and simulation we show that this scheme has little impact on the performance and memory usage for the original BGP, and can be adopted in practice as an operational approach.
Ballistic Missile Target Recognition Based on Improved Bispectra Feature
Authors: Wenchong Xie, Jianwen Chen
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

According to the characteristic of ballistic missile target recognition, the reduced-dimensional bispectra methods that are applied in high-resolution range profile recognition are studied, and an improved selected bispectra method based on extended Fisher class separability discriminant measure is proposed. The improved selected bispectra features are evaluated using computer simulation experiment. Compared to conventional selected bispectra and integrated bispectra methods, the improved selected bispectra method is highly discriminative and provides proper recognition of high-resolution radar ballistic missile target range profiles.
Robust H∞ control for uncertain delayed nonlinear systems based on standard neural network models
Authors: Meiqin Liu
Venue: Neurocomputing (2008)

A neural-network-based robust output feedback H"~ control design is suggested for control of a class of nonlinear systems both with time delays and with uncertainties. In this paper, a full-order dynamic output feedback controller is designed for the delayed uncertain nonlinear system approximated by the neural network (e.g. multilayer perceptron, recurrent neural network, etc.), of which the activation functions satisfy the sector conditions. The closed-loop neural control system is transformed into a novel neural network model both with uncertainties and with time delays termed standard neural network model (SNNM). Based on the optimal robust H"~ performance analysis of the SNNM, the parameters of output feedback controllers can be obtained by solving some linear matrix inequalities (LMIs). The optimal H"~ controller ensures the robust global asymptotic stability of the closed-loop system and eliminates the effect of approximation errors, parametric uncertainties, and external disturbances. Finally, a simple example is presented to illustrate the effectiveness and the applicability of the proposed design approach.