ppxscal/embedding_network huggingface dataset:
Cooperative Visual Tracking in a Team of Autonomous Mobile Robots
Authors: Walter Nisticò, Matthias Hebbel, Thorsten Kerkhof, Christine Zarges
Venue: RoboCup 2006: Robot Soccer World Cup X (2006)

Robot soccer is a challenging domain for sensor fusion and object tracking techniques, due to its team oriented, fast-paced, dynamic and competitive nature. Since each robot has a limited view about the world surrounding it, the sharing of information with its teammates is often crucial in order to be ready to react to situations which might involve it in the near future. In this paper we propose a Particle Filter based approach that addresses the problem of cooperative global sensor fusion by explicitly modeling the uncertainty concerning the robots' positions, the data association about the tracked object, and the loss of information over the network.
Program Verification by Using DISCOVERER
Authors: Lu Yang, Naijun Zhan, Bican Xia, Chaochen Zhou
Venue: Verified Software: Theories, Tools, Experiments: First IFIP TC 2/WG 2.3 Conference, VSTTE 2005, Zurich, Switzerland, October 10-13, 2005, Revised Selected Papers and Discussions (2005)

Recent advances in program verification indicate that various verification problems can be reduced to semi-algebraic system (SAS for short) solving. An SAS consists of polynomial equations and polynomial inequalities. Algorithms for quantifier elimination of real closed fields are the general method for those problems. But the general method usually has low efficiency for specific problems. To overcome the bottleneck of program verification with a symbolic approach, one has to combine special techniques with the general method. Based on the work of complete discrimination systems of polynomials [33,31],, we invented new theories and algorithms [32,30,35] for SAS solving and partly implemented them as a real symbolic computation tool in Maple named DISCOVERER. In this paper, we first summarize the results that we have done so far both on SAS-solving and program verification with DISCOVERER, and then discuss the future work in this direction, including SAS-solving itself, termination analysis and invariant generation of programs, and reachability computation of hybrid systems etc.
Automatic classification of security messages based on text categorization
Authors: Fatiha Benali, Stéphane Ubéda, Véronique Legrand
Venue: Proceedings of the 8th international conference on New technologies in distributed systems (2008)

The generated messages by the security devices are the necessary data for the detection of the malicious activities in an information system. The heterogeneity of the devices and the lack of a standard for the security messages make the automatic processing of the messages difficult. The messages are short, use a very wide vocabulary and have different formats. We propose in this article the application of the text categorization technics for the automatic classification of security log files messages, in categories defined by an ontology. We develop an extraction module for the message attributes to reduce the vocabulary size. Then we apply two training algorithms: the k-nearest neighbour algorithm and the naive bayes, on two corpus of security log messages.
Scalable Content-Based Ranking in P2P Information Retrieval
Authors: Maroje Puh, Toan Luu, Ivana Podnar Zarko, Martin Rajman
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

Numerous retrieval models have been defined within the field of information retrieval (IR) to produce a ranked and ordered list of documents relevant to a given query. Existing models are in general well-explored and thoroughly evaluated using traditionally centralized IR engines. However, the problem of producing global relevance scores to enable document ranking in peer-to-peer (P2P) IR systems has largely been neglected. Traditional ranking models in general require global document collection metrics such as document frequency, average document length, or the number of collection documents, which are not readily available in P2P IR systems. In this paper, we present a scalable solution for content-based ranking using global relevance scores in P2P IR systems that has been implemented as a part of ALVIS PEERS, a full-text IR engine developed for structured P2P networks. The provided experimental results show efficient and scalable performance of here proposed ranking implementation.
Ear Recognition with Variant Poses Using Locally Linear Embedding
Authors: Zhaoxia Xie, Zhichun Mu
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

Ear recognition with variant poses is an important problem. In this paper locally linear embedding (LLE) is introduced considering the advantages of LLE, by analyzing the shortcomings of most ear recognition methods currently when dealing with pose variations. Experimental results demonstrate that applying LLE for ear recognition with variant poses is feasible and can obtain the better recognition rate, which shows the validity of this algorithm.
Prosodic Events Recognition in Evaluation of Speech-Synthesis System Performance
Authors: France Mihelič, Boštjan Vesnicer, Janez Žibert, Elmar Nöth
Venue: Proceedings of the 11th international conference on Text, Speech and Dialogue (2008)

We present an objective-evaluation method of the prosody modeling in an HMM-based Slovene speech-synthesis system. Method is based on the results of the automatic recognition of syntactic-prosodic boundary positions and accented words in the synthetic speech. We have shown that the recognition results represent a close match with the prosodic notations, labeled by the human expert on the natural-speech counterpart that was used to train the speech-synthesis system. The recognition rate of the prosodic events is proposed as an objective evaluation measure for the quality of the prosodic modeling in the speech-synthesis system. The results of the proposed evaluation method are also in accordance with previous subjective-listening assesment evaluations, where high scores for the naturalness for such a type of speech synthesis were observed.
Learning about Space
Authors: Dedre Gentner
Venue: Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space (2008)

Spatial cognition is important in human learning, both in itself and as a major substrate of learning in other domains. Although some aspects of spatial cognition may be innate, it is clear that many important spatial concepts must be learned from experience. For example, Dutch and German use three spatial prepositions--op, aan, and om in Dutch--to describe containment and support relations, whereas English requires just one preposition--on--to span this range. How do children learn these different ways of partitioning the world of spatial relations? More generally, how do people come to understand powerful spatial abstractions like parallel, convergent, proportionate, and continuous?I suggest that two powerful contributors to spatial learning are analogical mapping--structural alignment and abstraction--and language, especially relational language, which both invites and consolidates the insights that arise from analogical processes. I will present evidence that (1) analogical processes are instrumental in learning new spatial relational concepts; and, further, that (2) spatial relational language fosters analogical processing. I suggest that mutual bootstrapping between structure-mapping processes and relational language is a major contributor to spatial learning in humans.
11th International Workshop on Aspect-Oriented Modeling
Authors: Jörg Kienzle, Jeff Gray, Dominik Stein, Walter Cazzola, Omar Aldawud, Tzilla Elrad
Venue: Models in Software Engineering: Workshops and Symposia at MoDELS 2007, Nashville, TN, USA, September 30 - October 5, 2007, Reports and Revised Selected Papers (2008)

This report summarizes the results and discussions from the 11th Workshop on Aspect-Oriented Modeling (AOM). The workshop was held in conjunction with the International Conference on Model-Driven Engineering, Languages, and Systems (MODELS), which was located in Nashville, Tennes-see, on September 30, 2007. Over 20 researchers and practitioners attended the workshop with various backgrounds in aspect-oriented software development and software model engineering. The workshop provided a forum for discuss-ing the state of the art in modeling crosscutting concerns at different stages of the software development process: requirements elicitation and analysis, soft-ware architecture, detailed design, and mapping to aspect-oriented programm-ing constructs. This workshop summary provides an overview of the accepted submissions and summarizes the results of the different discussion groups. Papers, presentation slides, and photos from the workshop are available at http://www.aspect-modeling.org/.
Engineering Stochastic Local Search for the Low Autocorrelation Binary Sequence Problem
Authors: Steven Halim, Roland H. Yap, Felix Halim
Venue: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming (2008)

This paper engineers a new state-of-the-art Stochastic Local Search (SLS) for the Low Autocorrelation Binary Sequence (LABS) problem. The new SLS solver is obtained with white-box visualization to get insights on how an SLS can be effective for LABS; implementation improvements; and black-box parameter tuning.
Unsupervised Feature Selection for Detection Using Mutual Information Thresholding
Authors: C. Ó Conaire, N. E. O'Connor
Venue: Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services (2008)

This paper proposes a method for unsupervised selection of features for detecting important events in a surveillance context. While traditional feature selection requires manually annotated ground truth to choose the best features, we examine the possibility of exploiting the redundancy between a pair of independent data sources for selecting good detection features. Building on our prior work on mutual information thresholding, we show that strong agreement between data sources indicates strong detection performance. Experimental tests, combining visual and audio data, show that the best performing features can be automatically selected by taking advantage of the common information shared by the sensors.
Mixed Criteria Packet Scheduling
Authors: Chad R. Meiners, Eric Torng
Venue: Proceedings of the 3rd international conference on Algorithmic Aspects in Information and Management (2007)

Packet scheduling in networks with quality of service constraints has been extensively studied as a single criterion scheduling problem. The assumption underlying single criterion packet scheduling is that the value of all packets can be normalized to a single scale, even in cases when packets have different requirements. We demonstrate that this approach can lead to inefficient utilization of network resources.To improve network efficiency, we model packet scheduling as a mixed criteria scheduling problem where there are two distinct sets of jobs: deadline jobs which represent real-time packets in a network and flow jobs which represent other packets in the network. As the names imply, the jobs in these two sets differ by the criteria associated with them.For this problem, the flow jobs are scheduled to minimize the sum of their flow times, and the deadline jobs are scheduled to maximize the value of jobs that complete by their deadlines.We demonstrate that even when there is only a single deadline job, this mixed criteria scheduling problem is NP-Complete. We give a polynomial time optimal algorithm Slacker for the variant where all jobs have unit size and the value of deadline jobs processed by the deadline must be maximized. Given this constraint Slacker minimizes the total flow time. Furthermore, we show that online Slacker is optimal for flow time while being 2-competitive with respect to the deadline jobs when compared to an optimal algorithm like Slacker that maximizes the value of deadline jobs.
Temporal Nearest End-Effectors for Real-Time Full-Body Human Actions Recognition
Authors: Oscar Mena, Luis Unzueta, Basilio Sierra, Luis Matey
Venue: Proceedings of the 5th international conference on Articulated Motion and Deformable Objects (2008)

In this paper we present a novel method called Temporal Nearest End-Effectors (TNEE) to automatically classify full-body human actions captured in real-time. This method uses a simple representation for modeling actions based exclusively on the recent positions of the user's end-effectors, i.e. hands, head and feet, relative to the pelvis. With this method, the essential information of full-body movements is retained in a reduced form. The recognition procedure combines the evaluation of the performed poses and the temporal coherence. The performance of TNEE is tested with real motion capture data obtaining satisfactory results for real-time applications.
DCaseLP: A Prototyping Environment for Multi-language Agent Systems
Authors: Viviana Mascardi, Maurizio Martelli, Ivana Gungui
Venue: Languages, Methodologies and Development Tools for Multi-Agent Systems: First International Workshop, LADS 2007, Durham, UK, September 4-6, 2007. Revised Selected Papers (2008)

This paper describes DCaseLP, a multi-language prototyping environment for Multi-Agent Systems. DCaseLP provides tools, languages, and methodological suggestions for engineering a MAS prototype from the late requirement analysis to the prototype implementation and testing. Full support for validating the MAS model by running the prototype in the JADE platform is offered. DCaseLP and its ancestor, CaseLP, have been employed to develop many applications in collaboration with Italian companies, thus demonstrating the feasibility of the proposed approach.
A Geometric Constraint over k-Dimensional Objects and Shapes Subject to Business Rules
Authors: Mats Carlsson, Nicolas Beldiceanu, Julien Martin
Venue: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming (2008)

This paper presents a global constraint that enforces rules written in a language based on arithmetic and first-order logic to hold among a set of objects. In a first step, the rules are rewritten to Quantifier-Free Presburger Arithmetic (QFPA) formulas. Secondly, such formulas are compiled to generators of k-dimensional forbidden sets. Such generators are a generalization of the indexicals of cc(FD). Finally, the forbidden sets generated by such indexicals are aggregated by a sweep-based algorithm and used for filtering.The business rules allow to express a great variety of packing and placement constraints, while admitting effective filtering of the domain variables of the k-dimensional object, without the need to use spatial data structures.
Categorization as Persuasion: Considering the Nature of the Mind
Authors: Signe Dahl Iversen, Maria Elisabeth Pertou
Venue: Proceedings of the 3rd international conference on Persuasive Technology (2008)

Categorization is inevitable in the process of designing persuasive software. Having said that, there has not yet been paid much attention to the role of categorization within the field of Persuasive Design. When a designer categorizes what elements should be part of the persuasive software he must always consider the user in order to achieve the most suitable categorization in relation to his intention. This article will show how theories of Epistemic Rhetoric and Cognitive Science both emphasize the role of categorization and how they supplement each other in an understanding of the user and how human beings categorize in order to comprehend and make sense of concepts. In a concluding example it will show how two furniture warehouses with seemingly similar persuasive intentions have chosen different categorizations that consequently result in different persuasive outcomes.
Building and using temporal knowledge in archaeological documentation
Authors: Tiphaine Accary-Barbier, Sylvie Calabretto
Venue: Journal of Intelligent Information Systems (2008)

Our previous work on specialized digital libraries showed that each expert was brought to enrich the documentary corpus with his/her point of view (expressed by a model of knowledge). These points of view can be contradictory and therefore, it would be interesting to provide expert communities with tools, enabling them to point out meaning dissensions. In Archaeology, the comparison between different temporal models of knowledge make possible the publication of relative models of knowledge. These different models tend towards the emerging of a global knowledge shared by a community. In this paper we present a method and a set of concrete relations proposed to the annotator, which allows to extend some existing models of knowledge and to build new temporal knowledge. The proposed model, based on temporal algebra relations, permits the use of some efficient constraint propagation algorithms to extract new information.
ProCarSur: A System for Dynamic Prognostic Reasoning in Cardiac Surgery
Authors: Niels Peek, Marion Verduijn, Winston G. Sjoe-Sjoe, Peter J. Rosseel, Evert Jonge, Bas A. Mol
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

We present the ProCarSur system for prognostic reasoning in the domain of cardiac surgery. The system has a three-tiered architecture consisting of a Bayesian network, a task layer, and a graphical user interface. In contrast to traditional prognostic tools, that are usually based on logistic regression, ProCarSur implements a dynamic, process-oriented view on prognosis. The system distinguishes between the various phases of peri-surgical care, explicates the scenarios that lead to different clinical outcomes, and can be used to update predictions when new information becomes available. To support users in their interaction with the Bayesian network, a set of predefined prognostic reasoning tasks is implemented in the task layer. The user communicates with the system through an interface that hides the underlying Bayesian network and aggregates the results of probabilistic inferences.
Air gap formation by UV-assisted decomposition of CVD material
Authors: M. Pantouvaki, A. Humbert, E. VanBesien, E. Camerotto, Y. Travaly, O. Richard, M. Willegems, H. Volders, K. Kellens, R. Daamen, R. J. O. M. Hoofman, G. Beyer
Venue: Microelectronic Engineering (2008)

A sacrificial material deposited by CVD is used to demonstrate air gap formation in single damascene structures by UV-assisted decomposition. The material is removed through a porous low-k cap, after completion of the damascene scheme. The porosity of the low-k cap is shown to be critical for efficient air gap formation. Capacitance reduction of ~50% is demonstrated using this technique compared to conventional SiOC(H) interconnects and an effective dielectric constant of 1.7 is extrapolated.
Neural Network Based Retinal Image Analysis
Authors: J. David, Rekha Krishnan, Sukesh Kumar A.
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02 (2008)

Diabetic-retinopathy contributes to serious health problem in many parts of the world. With the motivation of the needs of the medical community system for early screening of diabetics and other diseases a computer aided diagnosis system is proposed. This work is aimed to develop an automated system to analyze the retinal images for important features of diabetic retinopathy using image processing techniques and an image classifier based on artificial neural network which classify the images according to the disease conditions. The consistent identifying and quantifying of changes in blood vessels and different findings such as exudates in the retina over time can be used for the early detection of diabetic retinopathy. The algorithm has been tested on an image data base and the results are presented. Vascular network, optic disc and lesions like exudates are identified. A neural network classifier is developed and a comparative study on the performance is also presented.
Ensuring Time in Real-Time Commitments
Authors: Martí Navarro, Stella Heras, Vicente Julián
Venue: Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence (2008)

This paper presents a framework for the estimation of the temporal cost of agent commitments. The work here presented focuses on the development of a commitment manager as a special module in a real-time agent. This manager has been constructed for the previous analysis of a commitment request in a temporal bounded way. The proposed commitment manager incorporates as CBR module whose aim is to decide if the agent will be able to perform a requested service without exceeding a specified deadline. The proposal has been tested over a mobile robot example and this paper presents the results obtained.
Exploring Steganography: Seeing the Unseen
Authors: Neil F. Johnson, Sushil Jajodia
Venue: Computer (1998)

Steganography is an ancient art of hiding information. Digital technology gives us new ways to apply steganographic techniques, including one of the most intriguing—that of hiding information in digital images.
Modelling Prostate Gland Motion for Image-Guided Interventions
Authors: Yipeng Hu, Dominic Morgan, Hashim Uddin Ahmed, Doug Pendsé, Mahua Sahu, Clare Allen, Mark Emberton, David Hawkes, Dean Barratt
Venue: Proceedings of the 4th international symposium on Biomedical Simulation (2008)

A direct approach to using finite element analysis (FEA) to predict organ motion typically requires accurate boundary conditions, which can be difficult to measure during surgical interventions, and accurate estimates of soft-tissue properties, which vary significantly between patients. In this paper, we describe a method that combines FEA with a statistical approach to overcome these problems. We show how a patient-specific, statistical motion model (SMM) of the prostate gland, generated from FE simulations, can be used to predict the displacement field over the whole gland given sparse surface displacements. The method was validated using 3D transrectal ultrasound images of the prostates of five patients, acquired before and after expanding the balloon covering the ultrasound probe. The mean target registration error, calculated for anatomical landmarks within the gland, was 1.9mm.