ppxscal/embedding_network huggingface dataset:
MOVEMENT --- A Modular and Versatile Mobility Enhancement System
Authors: Gernot Kronreif, Paul Panek, Alexander Hüntemann, Ger Cremers, Andreas Hochgatterer, Martin Fürst, Peter Mayer, Gert Jan Gelderblom
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

This paper describes a novel robotic system which aims on supporting the mobility of elderly citizens and persons with disabilities. The key innovation of this research project is the use of the concept of "Modular Mobility". According to this concept the system consists of a mobile (robotic) platform as core system and several dedicated "application modules". On demand the platform is being assigned to a particular task and automatically connects to an appropriate application module in order to accomplish the given task.
Claw-free graphs. V. Global structure
Authors: Maria Chudnovsky, Paul Seymour
Venue: Journal of Combinatorial Theory Series B (2008)

A graph is claw-free if no vertex has three pairwise nonadjacent neighbours. In earlier papers of this series we proved that every claw-free graph either belongs to one of several basic classes that we described explicitly, or admits one of a few kinds of decomposition. In this paper we convert this ''decomposition'' theorem into a theorem describing the global structure of claw-free graphs.
Fingerprint Verification Using Local Interest Points and Descriptors
Authors: Javier Ruiz-Del-Solar, Patricio Loncomilla, Christ Devia
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

A new approach to automatic fingerprint verification based on a general-purpose wide baseline matching methodology is here proposed. The approach is not based on the standard ridge-minutiae-based framework. Instead of detecting and matching the standard structural features, local interest points are detected in the fingerprints, then local descriptors are computed in the neighborhood of these points, and afterwards these descriptors are compared using local and global matching procedures. Then, a final verification is carried out by a Bayes classifier. The methodology is validated using the FVC2004 dataset, where competitive results are obtained.
Refining Aggregation Functions for Improving Document Ranking in Information Retrieval
Authors: Mohand Boughanem, Yannick Loiseau, Henri Prade
Venue: Proceedings of the 1st international conference on Scalable Uncertainty Management (2007)

Classical information retrieval (IR) methods use the sum for aggregating term weights. In some cases, this may diminish the discriminating power between documents because some information is lost in this aggregation. To cope with this problem, the paper presents an approach for ranking documents in IR, based on a refined vector-based ordering technique taken from multiple criteria analysis methods. Different vector representations of the retrieval status values are considered and compared. Moreover, another refinement of the sum-based evaluation that controls if a term is worth adding or not (in order to avoid noise effect) is considered. The proposal is evaluated on a benchmark collection that allows us to compare the effectiveness of the approach with respect to a classical one. The proposed method provides some improvement of the precision w.r.t Mercure IR system.
Private Key Management in Hierarchical Identity-Based Encryption
Authors: Zhongren Liu, Li Xu, Zhide Chen, Fuchun Guo
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

Hierarchical Identity-Based Encryption (HIBE) is a generalization of IBE that mirrors an organizational hierarchy, which consists of a root private key generator (PKG), domain PKGs and users. In a traditional HIBE system, users retrieve private keys from their domain PKGs, while domain PKGs request domain secret keys from the root PKG. What's more, the manager of each private key is a single one. Unfortunately, some of the PKGs(private key manager) could be untrusty, such that they would leak lower level's private keys. In this paper, we propose a novel private key management scheme in HIBE, where a domain master key is not charged by a single one, buta group PKGs aiming at avoiding unreliable PKGs' attack.
Sequential-Merge Facets for Two-Dimensional Group Problems
Authors: Santanu S. Dey, Jean-Philippe P. Richard
Venue: Proceedings of the 12th international conference on Integer Programming and Combinatorial Optimization (2007)

In this paper, we show how to generate strong cuts for unstructured mixed integer programs through the study of high-dimensional group problems. We present a new operation that generates facet-defining inequalities for two-dimensional group problems by combining two facet-defining inequalities of one-dimensional group problems. Because the procedure allows the use of a large variety of one-dimensional constituent inequalities, it yields large families of new cutting planes for MIPs that we call sequential-merge inequalities. We show that sequential-merge inequalities can be used to generate inequalities whose continuous variable coefficients are stronger than those of one-dimensional cuts and can be used to derive the three-gradient facet-defining inequality introduced by Dey and Richard [4].
Line Extraction from Mechanically Scanned Imaging Sonar
Authors: David Ribas, Pere Ridao, José Neira, Juan Domingo Tardós
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

The extraction of reliable features is a key issue for autonomous underwater vehicle navigation. Imaging sonars can produce acoustic images of the surroundings of the vehicle. Despite of the noise, the phantoms and reflections, we believe that they are a good source for features since they can work in turbid water where other sensors like vision fail. Moreover, they can cover wide areas incrementing the number of features visible within a scan. This work presents an algorithm to extract linear features from underwater structured environments including as major contributions a novel sonar model sensor and an adapted implementation of the Hough transform.
An Improved Error Concealment Algorithm for Intra-frames of H.264/AVC
Authors: Haoyang Tang, Haoshan Shi, Honggang Zhao
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

In this paper, we propose a novel error concealment algorithm for intra frames of h.264/AVC. By using the high correlation of adjacent frames sequence, the proposed algorithm performs quadratic interpolation to the error pixel value of the concealed intra frame from the corresponding pixels of the previously decoded frame and the adjacent blocks received correctly while remaining the least distortion. The algorithm for intra frames was tested over 3GPP mobile IP networks. Simulation results demonstrate that our method outperforms the other methods by up to 4dB.
Automatic live tagging of videos using chronicles
Authors: Marta Rey-López, Ana Fernández-Vilas, Rebeca P. Díaz-Redondo, José J. Pazos-Arias
Venue: Proceedings of the 2008 Ambi-Sys workshop on Ambient media delivery and interactive television (2008)

Creating and editing videos is a costly process in terms of time and money. For this reason, reusability is a major issue in this field; hence, videos should be appropriately labelled, describing its structure and content, but this process is costly too. However, for some types of videos, this information is already expressed as structured text. In this paper, we present an automatic tagging system to describe the structure and semantics of the videos for sports events, extracting information from the chronicles for these events written in the web pages of some journals or sports organisations.
NEU-FACES: A Neural Network-Based Face Image Analysis System
Authors: Ioanna-Ourania Stathopoulou, George A. Tsihrintzis
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

Towards building more efficient human control interactive systems, we developed a neural network-based image processing system (called NEU-FACES), which first determines automatically whether or not there are any faces in given images and, if so, returns the location and extent of each face. Next, NEU-FACES uses neural network-based classifiers, which allow the classification of several facial expressions from features that we develop and describe. NEU-FACES is fully implemented and evaluated to assess its performance.
Vessel recognition in color doppler ultrasound imaging
Authors: Linda G. Shapiro
Venue:  (2008)

Diagnostic ultrasound imaging is an important non-invasive tool for diagnosis and treatment decisions with a variety of clinical applications such as vascular disease, cardiology disease, and obstetrics. Color Doppler ultrasound imaging modality in particular offers complete 2-D cross sectional images depicting velocities of moving reflectors, such as blood flow. Color Doppler imaging is a crucial diagnostic tool for vascular disorders such as arterial atherosclerosis and deep vein thrombosis. Nowadays the vascular ultrasound exam is a tedious and time-consuming process that requires a high level of expertise. This is mainly due to the large number of manual controls offered by the ultrasound system to the clinical users to optimize the images for the best clinical outcome. Another reason is the lack of automation and quantification tools by the ultrasound system since the system utilizes very little high-level information from the acquired images. Moreover musculoskeletal injuries due to operating the ultrasound systems have become very common and a great concern for the medical community. Another concern is the standardization of the clinical exam outcome to minimize interobserver and intraobserver variability among diagnostic ultrasound practitioners. Introducing advanced automation and quantification capabilities to the ultrasound system will help with all these issues and streamline the clinical exam operation and outcome. In this thesis a complete vessel recognition system based on the analysis of the color Doppler ultrasound images is developed. The system includes the design and realization of many automated tasks including image acquisition, vessel segmentation, true velocity restoration, feature extraction, and classifier design. The involved tasks represent the building blocks for the automation and quantification applications that can be incorporated within the ultrasound system to achieve ease of use and more standardized clinical outcome. The thesis will also address a number of important clinical problems in the field of ultrasound vascular imaging and propose novel solutions that rely on the developed image understanding techniques.
On Stability of Switched Linear Hyperbolic Conservation Laws with Reflecting Boundaries
Authors: Saurabh Amin, Falk M. Hante, Alexandre M. Bayen
Venue: Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control (2008)

We consider stability of an infinite dimensional switching system, posed as a system of linear hyperbolic partial differential equations (PDEs) with reflecting boundaries, where the system parameters and the boundary conditions switch in time. Asymptotic stability of the solution for arbitrary switching is proved under commutativity of the advective velocity matrices and a joint spectral radius condition involving the boundary data.
Smith normal form and Laplacians
Authors: Dino Lorenzini
Venue: Journal of Combinatorial Theory Series B (2008)

Let M denote the Laplacian matrix of a graph G. Associated with G is a finite group @F(G), obtained from the Smith normal form of M, and whose order is the number of spanning trees of G. We provide some general results on the relationship between the eigenvalues of M and the structure of @F(G), and address the question of how often the group @F(G) is cyclic.
Bookshelf
Authors: Diomidis Spinellis, Naseem Mariam, Caroline Pepa
Venue: IEEE Software (2007)

Book reviews of CMMI for Outsourcing: Guidelines for Software, Systems, and IT Acquisition by Hubert F. Hofmann, Deborah K. Yedlin, John W. Mishler, and Susan Kushner; Code Craft: The Practice of Writing Excellent Code by Pete Goodliffe; and Software Specification and Design: An Engineering Approach by John C. Munson.
SEHAS 2003: The Future of High-Assurance Systems
Authors: Nancy R. Mead
Venue: IEEE Security and Privacy (2003)

Software Engineering for High Assurance Systems (SEHAS) 2003 was the third in an informal series of workshops on high-assurance systems (HASs).This is the first workshop on the subject held in conjunction with the International Conference on Software Engineering (ICSE). ICSE attracts a broader software engineering audience than other venues, but the workshop attendee profiles are similar researchers and practitioners with special interest in the HASs that control nuclear plants, life-critical medical systems, aerospace and weapons systems, and so on.
Topic 13: High-Performance Networks
Authors: Tor Skeie, Daniel Ortega, José Flich, Raimir Holanda
Venue: Proceedings of the 14th international Euro-Par conference on Parallel Processing (2008)

The communication network is the key component of every parallel and distributed system. The trend of always aiming at bigger and more complex cores has shifted towards having many simpler cores, sharing yet another complex communication layer at the chip level. Moreover, advancements on scaling out at the cluster level have pushed communication and storage networks to new limits. All these technological opportunities bring out new and exciting research challenges.
Virtual Patients for Clinical Therapist Skills Training
Authors: Patrick Kenny, Thomas D. Parsons, Jonathan Gratch, Anton Leuski, Albert A. Rizzo
Venue: Proceedings of the 7th international conference on Intelligent Virtual Agents (2007)

Virtual humans offer an exciting and powerful potential for rich interactive experiences. Fully embodied virtual humans are growing in capability, ease, and utility. As a result, they present an opportunity for expanding research into burgeoning virtual patient medical applications. In this paper we consider the ways in which one may go about building and applying virtual human technology to the virtual patient domain. Specifically we aim to show that virtual human technology may be used to help develop the interviewing and diagnostics skills of developing clinicians. Herein we proffer a description of our iterative design process and preliminary results to show that virtual patients may be a useful adjunct to psychotherapy education.
Emotion Classification of Audio Signals Using Ensemble of Support Vector Machines
Authors: Taner Danisman, Adil Alpkocak
Venue: Proceedings of the 4th IEEE tutorial and research workshop on Perception and Interactive Technologies for Speech-Based Systems: Perception in Multimodal Dialogue Systems (2008)

This study presents an approach for emotion classification of speech utterances based on ensemble of support vector machines. We considered feature level fusion of the MFCC, total energy and F0 as input feature vectors, and choose bagging method for the classification. Additionally, we also present a new emotional dataset based on a popular animation film, Finding Nemo where emotions are much emphasized to attract attention of spectators. Speech utterances are directly extracted from video audio channel including all background noise. Totally 2054 utterances from 24 speakers were annotated by a group of volunteers based on seven emotion categories. We concentrated on perceived emotion. Our approach has been tested on our newly developed dataset besides publically available datasets of DES and EmoDB. Experiments showed that our approach achieved 77.5% and 66.8% overall accuracy for four and five class classification on EFN dataset respectively. In addition, we achieved 67.6% accuracy on DES (five classes) and 63.5% on EmoDB (seven classes) dataset using ensemble of SVM's with 10 fold cross-validation.
Beyond Nash Equilibrium: Solution Concepts for the 21st Century
Authors: Joseph Y. Halpern
Venue: Proceedings of the 19th international conference on Concurrency Theory (2008)

Nash equilibrium is the most commonly-used notion of equilibrium in game theory. However, it suffers from numerous problems. Some are well known in the game theory community; for example, the Nash equilibrium of repeated prisoner's dilemma is neither normatively nor descriptively reasonable. However, new problems arise when considering Nash equilibrium from a computer science perspective: for example, Nash equilibriumis not robust (it does not tolerate "faulty" or "unexpected" behavior), it does not deal with coalitions, it does not take computation cost into account, and it does not deal with cases where players are not aware of all aspects of the game. Solution concepts that try to address these shortcomings of Nash equilibrium are discussed.The paper appears in the Proceedings of the Twenty-Seventh Annual ACM Symposium on Principles of Distributed Computing, 2008.
Symmetry and asymmetry analysis and its significance in neuro-imaging applications
Authors: Andrew Laine
Venue:  (2008)

Advances in computer technologies over the last decade have catalyzed the development of modern computerized schemes for lesion detections in radiological images. One biggest challenge is that computers generally lack sufficient perceptibility and intelligence in terms of discovering pathological patterns; the decision making process is hence hindered. As it is known that knowledge plays an indispensable role in computer vision and artificial intelligence, integrating anatomical knowledge into such computer system holds great promise for facilitating decision making and improving patient care in neuro-radiology. Based on the assumption that the brain exhibits a high level of bi-fold symmetry and that this symmetry is violated in the presence of pathological conditions, a principle goal of this effort was motivated to construct a symmetry-based paradigm for automatic localization and segmentation of brain lesions. The framework of this methodology is grounded on the hypothesis that the systematic correlation between asymmetry and pathologies can be a key to the improvement of existing detection algorithms. Integrating symmetry/asymmetry information as the prior knowledge or heuristics into a computer aided diagnostic (CAD) system, ought to enhance the system performance in the analysis of brain pathologies. The methodology of this study is two-fold: First a symmetry axis or, the symmetry plane needs to be spatially oriented because it is valuable for the correction of possible misalignment of radiological scans and for hemisphere-wise asymmetry evaluation. In a second step automatic detection and quantification of brain lesions such as stroke and tumor is required. In this dissertation, I explore and discuss the discriminating capacity of symmetry/asymmetry in the context of extracting pathological findings in various radiological applications with different modalities, such as MRI and CT. In other words, the first part of this research focuses on solving an image registration problem, and the second part relies upon the performance of pattern recognition and segmentation algorithms applied to asymmetry detection. It should be noted, however, that the methods developed in this thesis for a set of particular neuro-applications may have a more general applicability since many other parts of human body, not only limited to the brain, are highly symmetrical in nature.
GeCiM: A Novel Generalized Approach to C-Means Clustering
Authors: László Szilágyi, David Iclănzan, Sándor M. Szilágyi, Dan Dumitrescu
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

All three conventional c-means clustering algorithms have their advantages and disadvantages. This paper presents a novel generalized approach to c-means clustering: the objective function is considered to be a mixture of the FCM, PCM, and HCM objective functions. The optimal solution is obtained via evolutionary computation. Our main goal is to reveal the properties of such mixtures and to formulate some rules that yield accurate partitions.
Using Genetic Algorithms for Parameter Optimization in Building Predictive Data Mining Models
Authors: Ashish Sureka, Kishore Varma Indukuri
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

We present an application of genetic algorithms to search the space of model building parameters for optimizing the score function or accuracy of a predictive data mining model. The goal of predictive modeling is to build a classification or regression model that can accurately predict the value of a target column by observing the values of the input attributes. The process of finding an optimal algorithm and its control parameters for building a predictive model is a non-trivial process because of two reasons. The first reason is that the number of classification algorithms and its control parameters are very large. The second reason is that it can be quite time consuming to build a model for datasets containing a large number of records and attributes. These two reasons makes it impractical to enumerate through every algorithm and its possible control parameters for finding an optimal model. Genetic Algorithms are adaptive heuristic search algorithm and have been successfully applied to solve optimization problems in diverse domains. In this work, we formulate the problem of finding optimal predictive model building parameter as an optimization problem and examine the usefulness of genetic algorithms. We perform experiments on several datasets and report empirical results to show the applicability of genetic algorithms to the problem of finding optimal predictive model building parameters.
Motivations, design and business models of wireless community networks
Authors: Csaba Szabó, Károly Farkas, Zoltán Horváth
Venue: Mobile Networks and Applications (2008)

In this paper we provide an integrated presentation of applications, technologies and business models for wireless community network together with design considerations and examples. An overview is given of the state-of-the-affairs of wireless community networks. Driving forces and stakeholders of the projects and the applications and services will be presented for some carefully selected cases. We suggest a design methodology and illustrate its application to an ongoing digital city project in Hungary. Relevant business models are also analyzed.
Interactive In-Job Workflows
Authors: Branislav Šimo, Ondrej Habala, Emil Gatial, Ladislav Hluchý
Venue: Proceedings of the 8th international conference on Computational Science, Part I (2008)

This paper describes a new approach to interactive workflow management in the grid. By modification of existing system for management of applications composed of web and grid services an interactive workflow management system has been created, which allows users to manage complex jobs, composed of several program executions, interactively. The system uses an interactivity functionality provided by the Interactive European Grid project to forward commands from a GUI to a workflow manager running inside of a grid job. The tool is able to visualize the inner workflow of the application and the user has complete in-execution control over the job, can see its partial results, and can even alter it while it is running. This allows not only to accommodate the job workflow to the data it produces, extend or shorten it, but also to interactively debug and tune the job.