ppxscal/embedding_network huggingface dataset:
QoS aware multicast routing protocols evaluation through simulation
Authors: António Costa, Joaquim Macedo, Maria João Nicolau, Alexandre Santos
Venue: Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops (2008)

In networking research, the simulation is often the single way to overcome the lack of equipment needed for laboratory setup of complex experimental topologies, with diverse traffic pattern scenarios. Even for simpler topologies and traffic scenarios, the simulation remains attractive due to the available facilities on data collection, graphics generation and step by step analysis of different protocol machines. Inter-domain multicast scenarios, where heterogeneous QoS requirements should be considered, is a particular example where both complexity and resources availability justify the use of simulation. This paper reports the experience gained by the usage of simulation tools in multicast routing with QoS. It is not focused on the real simulation results reported but on the process used to obtain them. NS-2 has been used as the base of this work.
A Calculational Approach to Control-Flow Analysis by Abstract Interpretation
Authors: Jan Midtgaard, Thomas Jensen
Venue: Proceedings of the 15th international symposium on Static Analysis (2008)

We present a derivation of a control-flow analysis by abstract interpretation. Our starting point is a transition system semantics defined as an abstract machine for a small functional language in continuation-passing style. We obtain a Galois connection for abstracting the machine states by composing Galois connections, most notable an independent-attribute Galois connection on machine states and a Galois connection induced by a closure operator associated with a constituent-parts relation on environments. We calculate abstract transfer functions by applying the state abstraction to the collecting semantics, resulting in a novel characterization of demand-driven 0-CFA.
Autonomy-Oriented Computing (AOC), Self-organized Computability, and Complex Data Mining
Authors: Jiming Liu
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Future data-mining challenges will lie in the breakthroughs in new computational paradigms, models, and tools that can offer scalable and robust solutions to complex data-mining problems. The problems of such a nature can be: (1) petabyte-scale (e.g., mining Google books or social networks), (2) dynamically-evolving (e.g., detecting air traffic patterns, market trends, and social norms), (3) interaction-rich as well as trans-disciplinary (e.g., predicting and preventing world economic and/or ecological crisis), and/or (4) highly-distributed (e.g., security and adaptive computing, such as community evolution, in pervasive environments). Toward this end, various computing ideas and techniques have been proposed and explored that explicitly utilize the models of computational autonomy as inspired by nature. This talk focuses on one of such research initiatives, which concerns the development of an unconventional computing paradigm, called Autonomy-Oriented Computing (AOC). In general, AOC tackles a complex computing problem by defining and deploying a system of local autonomy-oriented entities. The entities spontaneously interact with their environments and operate based on their behavioral rules. They self-organize their structural relationships as well as behavioral dynamics, with respect to some specific forms of interactions and control settings. Such a capability is referred to as the self-organized computability of autonomous entities. In this talk, we will examine basic concepts and principles in the development of an AOC system, and present some related data-mining examples in the area of complex networks, e.g., unveiling community structures, characterizing the empirical laws of WWW user behavior, and/or understanding the dynamic performance of social networks.
Sensor Network-Based Nonlinear System Identification
Authors: Giorgio Biagetti, Paolo Crippa, Francesco Gianfelici, Claudio Turchetti
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

In this paper, a new algorithm for the identification of distributed systems by large scale collaborative sensor networks is suggested. The algorithm, that uses the distributed Karhunen-Loève transform, extends in a decentralized setting the KLT-based identification approach that have recently been proposed for a centralized setting. The effectiveness of the proposed methodology is directly related to the reduction of total distortion in the compression performed by the single nodes of the sensor network, to the identification accuracy as well as to the low computational complexity of the fusion algorithm performed by the fusion center to regulate the intelligent cooperation of the nodes. The results in the identification of a system whose behavior is described by a partial differential equation in a 2-D domain with random excitation confirms the effectiveness of this technique.
Reducing the Dimensionality of Vector Space Embeddings of Graphs
Authors: Kaspar Riesen, Vivian Kilchherr, Horst Bunke
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

Graphs are a convenient representation formalism for structured objects, but they suffer from the fact that only a few algorithms for graph classification and clustering exist. In this paper we propose a new approach to graph classification by embedding graphs in real vector spaces. This approach allows us to apply advanced classification tools while retaining the high representational power of graphs. The basic idea of our approach is to regard the edit distances of a given graph g to a set of training graphs as a vectorial description of g. Once a graph has been transformed into a vector, different dimensionality reduction algorithms are applied such that redundancies are eliminated. To this reduced vectorial data representation, pattern classification algorithms can be applied. Through various experimental results we show that the proposed vector space embedding and subsequent classification with the reduced vectors outperform the classification algorithms in the original graph domain.
k-NN Aggregation with a Stacked Email Representation
Authors: Amandine Orecchioni, Nirmalie Wiratunga, Stewart Massie, Susan Craw
Venue: Proceedings of the 9th European conference on Advances in Case-Based Reasoning (2008)

The variety in email related tasks, as well as the increase in daily email load, has created a need for automated email management tools. In this paper, we provide an empirical evaluation of representational schemes and retrieval strategies for email. In particular, we study the impact of both textual and non-textual email content for case representation applied to Email task management. Our first contribution is Stack, an email representation based on stacking. Multiple casebases are created, each using a different case representation related with attributes corresponding to semi-structured email content. A k-NN classifier is applied to each casebase and the output is used to form a new case representation. Our second contribution is a new evaluation method allowing the creation of random chronological stratified train-test trials that respect both temporal and class distribution aspects, crucial for the email domain. The Enron corpus was used to create a dataset for the email deletion prediction task. Evaluation results show significant improvements with Stack over single casebase retrieval and multiple casebases retrieval combined using majority vote.
Imagistic Database for Medical e-Learning
Authors: Liana Stanescu, Dan Burdescu, Anca Loredana Ion, Andrei Panus
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

The paper presents the multimedia component included in TESYS, a medical e-learning platform that is used nowadays at the University of Medicine and Pharmacy Craiova at disciplines like gastroenterology and urology in order to complete the traditional medical learning process. The multimedia component contains a database with color medical images acquired by the teachers from different patients in the diagnosis process, so represent real cases. A series of alphanumerical information: diagnosis, treatment and patient evolution are added for each image. The multimedia component together with modern search methods: content-based image query and content-based region query is used both in the training process and e-testing one. The content-based visual query uses characteristics that are automatically extracted from medical images (color, texture, regions). Using content-based visual query with other access methods (text-based, hierarchical methods) on a teaching image database allows students to see images and associated information from database in a simple and direct manner. This method stimulates learning, by comparing similar cases along with their particularities, or by comparing cases that are visually similar, but with different diagnoses.
The Application of Improved Genetic Algorithm in Fiber Bragg Grating (FBG) Sensor Network
Authors: Wei Wu, Xin Liu
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

Improved genetic algorithm was used to improve the performance of FBG sensors in a wavelength-division multiplexed sensor network. Simulation and experiment shows that compute speed and capability of multiplexing are enhanced, and the measurement range of sensors in FBG sensor network is increased while using improved genetic algorithms with proper parameters.
An Adaptive and Autonomous Sensor Sampling Frequency Control Scheme for Energy-Efficient Data Acquisition in Wireless Sensor Networks
Authors: Supriyo Chatterjea, Paul Havinga
Venue: Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems (2008)

Wireless sensor networks are increasingly being used in environmental monitoring applications. Collecting raw data from these networks can lead to excessive energy consumption. This is especially true when the application requires specialized sensors that have very high energy consumption, e.g. hydrological sensors for monitoring marine environments. We describe an adaptive sensor sampling scheme where nodes change their sampling frequencies autonomously based on the variability of the measured parameters. The sampling scheme also meets the user's sensing coverage requirements by using information provided by the underlying MAC protocol. This allows the scheme to automatically adapt to topology changes. Our results based on real and synthetic data sets, indicate a reduction in sensor sampling by up to 93%, reduction in message transmissions by up to 99% and overall energy savings of up to 87%. We also show that generally more than 90% of the collected readings fall within the user-defined error threshold.
Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes
Authors: Patrick Laube, Matt Duckham, Thomas Wolle
Venue: Proceedings of the 5th international conference on Geographic Information Science (2008)

Movement patterns, like flocking and converging, leading and following, are examples of high-level process knowledge derived from low-level trajectory data. Conventional techniques for the detection of movement patterns rely on centralized "omniscient" computing systems that have global access to the trajectories of mobile entities. However, in decentralized spatial information processing systems, exemplified by wireless sensor networks, individual processing units may only have access to local information about other individuals in their immediate spatial vicinity. Where the individuals in such decentralized systems are mobile, there is a need to be able to detect movement patterns using collaboration between individuals, each of which possess only partial knowledge of the global system state. This paper presents an algorithm for decentralized detection of the movement pattern flock, with applications to mobile wireless sensor networks. The algorithm's reliability is evaluated through testing on simulated trajectories emerging from unconstrained random movement and correlated random walk.
3D Visualisation of the Radiological Features of Type II Collagenopathies Associated with Skeletal Dysplasias
Authors: Theodor Wyeld, Andreas Zankl
Venue: Proceedings of the 2008 Fifth International Conference BioMedical Visualization: Information Visualization in Medical and Biomedical Informatics (2008)

Preceding this report, a proof-of-concept pilot study was conducted on the REAMS database [see 5]. The REAMS database associates skeletal dysplasia (SD) and their conditions, clinical and radiographic features, with exemplar x-ray images. This study reports on the 3D visualisation of the interconnectedness of the various terms used in the REAMS database. Preliminary results show predictable clustering and outliers based on shared common terms. In 1994 Spranger et al [14] reported on the identification of the 6 type II collagenopathy conditions associated with SDs. To test the accuracy and usefulness of our 3D visualisation of the REAMS database the 6 known type II collagenopathies were colour coded to distinguish their location within the larger database. Distinct clustering was detected. However, we also discovered 10 non type II collagenopathy conditions that share 2 or more common clinical and/or radiographic features with at least 1 of the 6 known type II collagenopathy conditions. This result confirms and quantifies the clinical radiographical overlap among the type II collagenopathies as originally predicted by [14].
Minimum-phase criterion on sampling time for sampled-data interval systems using genetic algorithms
Authors: Chen-Chien Hsu, Tsung-Chi Lu
Venue: Applied Soft Computing (2008)

In this paper, a genetic algorithm-based approach is proposed to determine a desired sampling-time range which guarantees minimum phase behaviour for the sampled-data system of an interval plant preceded by a zero-order hold (ZOH). Based on a worst-case analysis, the identification problem of the sampling-time range is first formulated as an optimization problem, which is subsequently solved under a GA-based framework incorporating two genetic algorithms. The first genetic algorithm searches both the uncertain plant parameters and sampling time to dynamically reduce the search range for locating the desired sampling-time boundaries based on verification results from the second genetic algorithm. As a result, the desired sampling-time range ensuring minimum phase behaviour of the sampled-data interval system can be evolutionarily obtained. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature which requires undertaking a large number of evolution cycles, parallel computation for the proposed genetic algorithm is therefore proposed to accelerate the derivation process. Illustrated examples in this paper have demonstrated that the proposed GA-based approach is capable of accurately locating the boundaries of the desired sampling-time range.
A Proposal for a Lifecycle Process for Hybrid Learning Programs
Authors: Won Kim
Venue: Proceedings of the 1st international conference on Hybrid Learning and Education (2008)

A hybrid learning program is a mixture of traditional in-class learning components and e-learning components. A learning program may be a single course or an entire curriculum. A learning program and courseware must be designed to help learners learn. As such, a hybrid learning program is a rather complex artifact. Commercial software is in general a very complex artifact that has a clearly defined lifecycle for planning, development, deployment, maintenance upgrade, and termination. Although learning programs and courseware are not nearly as complex as commercial software, adapting the lifecycle process for the creation and managing of commercial software to hybrid learning programs and courseware should make it possible for the learning program managers and courseware developers to take a holistic and disciplined approach to the creation and management of hybrid learning programs and courseware. This paper proposes a lifecycle process for hybrid learning programs and e-learning courseware.
University of Alicante at WiQA 2006
Authors: Antonio Toral Ruiz, Georgiana Puşcaşu, Lorenza Moreno Monteagudo, Rubén Izquierdo Beviá, Estela Saquete Boró
Venue: Evaluation of Multilingual and Multi-modal Information Retrieval: 7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006, Alicante, Spain, September 20-22, 2006, Revised Selected Papers (2007)

This paper presents the participation of University of Alicante at the WiQA pilot task organized as part of the CLEF 2006 campaign. For a given set of topics, this task presupposes the discovery of important novel information distributed across different Wikipedia entries. The approach we adopted for solving this task uses Information Retrieval, query expansion by feedback, novelty re-ranking, as well as temporal ordering. Our system has participated both in the Spanish and English monolingual tasks. For each of the two participations the results are promising because, by employing a language independent approach, we obtain scores above the average. Moreover, in the case of Spanish, our result is very close to the best achieved score. Apart from introducing our system, the present paper also provides an in-depth result analysis, and proposes future lines of research, as well as follow-up experiments.Categories and Subject Descriptors: H.3[Information Storage and Retrieval]: H.3.1 Content Analysis and Indexing; H.3.3 Information Search and Retrieval; H.3.4 Systems and Software;
Shape-Preserving Planar Interpolation: An Algorithm
Authors: Harry McLaughlin
Venue: IEEE Computer Graphics and Applications (1983)

If a computer and a draftsman are asked to draw a curve through identical planar data, the draftsman's curve frequently looks better. Must it be this way? The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Capturing Semantics for Information Security and Privacy Assurance
Authors: Mohammad M. Chowdhury, Javier Chamizo, Josef Noll, Juan Miguel Gómez
Venue: Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing (2008)

Security and privacy assurance is indispensable for ubiquitous access to information and resources. This paper focuses on the security and privacy provisions in a restricted organizational environment through access control mechanism. It includes the representation of the semantics of an organization and its access control mechanism exploiting the Web Ontology Language. The system controls access to the resources of an organization through differential access privileges. These are formulated based on the roles of the individuals, and the projects and departments they belong to. Instead of explicit definitions, some additional facts of the mechanism are inferred by executing semantic rules using the Jess rule engine over the designed ontology. These information are then passed back to the ontology to enrich it. The ontology is designed to cope with the organization restructuring with minimal efforts.
Sink-Independent Model in Wireless Sensor Networks
Authors: Sang-Sik Kim, Kwang-Ryul Jung, Ki-Il Kim, Ae-Soon Park
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

Wireless sensor networks generally have three kinds of objects: sensor nodes, sinks, and users that send queries and receive data via the sinks. In addition, the user and the sinks are mostly connected to each other by infrastructure networks. The users, however, should receive the data from the sinks through multi-hop communications between disseminating sensor nodes if such users move into the sensor networks without infrastructure networks. To support mobile users, previous work has studied various user mobility models. Nevertheless, such approaches are not compatible with the existing routing algorithms, and it is difficult for the mobile users to gather data efficiently from sensor nodes due to their mobility. To improve the shortcomings, we propose a view of mobility for wireless sensor networks and propose a model to support a user mobility that is independent of sinks.
Using Eigenvalue Derivatives for Edge Detection in DT-MRI Data
Authors: Thomas Schultz, Hans-Peter Seidel
Venue: Proceedings of the 30th DAGM symposium on Pattern Recognition (2008)

This paper introduces eigenvalue derivatives as a fundamental tool to discern the different types of edges present in matrix-valued images. It reviews basic results from perturbation theory, which allow one to compute such derivatives, and shows how they can be used to obtain novel edge detectors for matrix-valued images. It is demonstrated that previous methods for edge detection in matrix-valued images are simplified by considering them in terms of eigenvalue derivatives. Moreover, eigenvalue derivatives are used to analyze and refine the recently proposed Log-Euclidean edge detector. Application examples focus on data from diffusion tensor magnetic resonance imaging (DT-MRI).
Letters: Null space discriminant locality preserving projections for face recognition
Authors: Liping Yang, Weiguo Gong, Xiaohua Gu, Weihong Li, Yixiong Liang
Venue: Neurocomputing (2008)

In this paper, we propose a null space discriminant locality preserving projections (NDLPP) method for facial feature extraction and recognition. Based on locality preserving projections (LPP) and discriminant locality preserving projections (DLPP) methods, NDLPP comes into the characteristics of DLPP that encodes both the geometrical and discriminant structure of the data manifold, and addresses the small sample size problem by solving an eigenvalue problem in null space. Experiments on synthetic data and ORL, Yale, and FERET face databases are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of NDLPP.
EA-Powered Basin Number Estimation by Means of Preservation and Exploration
Authors: Catalin Stoean, Mike Preuss, Ruxandra Stoean, Dumitru Dumitrescu
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

When using an evolutionary algorithm on an unknown problem, properties like the number of global/local optima must be guessed for properly picking an algorithm and its parameters. It is the aim of current paper to put forward an EA-based method for real-valued optimization to provide an estimate on the number of optima a function exhibits, or at least of the ones that are in reach for a certain algorithm configuration, at low cost. We compare against direct clustering methods applied to different stages of evolved populations; interestingly, there is a turning point (in evaluations) after which our method is clearly better, although for very low budgets, the clustering methods have advantages. Consequently, it is argued in favor of further hybridizations.