pinecone/core-2020-05-10-deduplication huggingface dataset:
The use of graph techniques for identifying objects and scenes in indoor building environments for mobile robots

Trabajo presentado al Joint IAPR International Workshop on Structural, Syntactic and Statistical Pattern Recognition (SSPR&SPR) celebrado en Lisboa del 18 al 20 de agosto de 2004.In this paper we present some of the common issues that appear when we try to recognize objects in indoor scenes of a building, and we describe some strategies for recognizing them by using graph techniques. These scene images are captured by the colour cameras of a mobile robot, which in the learning phase, learn the objects by taken a set of 2D images of the projective object views. Then afterwards, the robot must identify the objects once its moves through the area that has been used to learn the objects. We describe two strategies to use graph techniques for object and scene recognition, some algorithms and preliminary results.Peer Reviewe
Understanding Human-Computer Interactions
in Map Revision

It is difficult to track, parse and model human-computer interactions during editing and revising of documents, but it is necessary if we are to develop automated technologies that will aid or replace humans. This paper introduces a system for accessing and recording a stream of events related to human actions in a real-time cartographic map revision system. The recorded events are parsed into a sequence of meaningful user actions and an action representation in XML format is generated. We also report results of experiments on predicting user actions such as view changes, edits, road tracking/production using hidden Markov models.Faculty of Science, Environment, Engineering and TechnologyFull Tex
An Algorithmic Argument for Nonadaptive Query Complexity Lower Bounds on
  Advised Quantum Computation

This paper employs a powerful argument, called an algorithmic argument, to
prove lower bounds of the quantum query complexity of a multiple-block ordered
search problem in which, given a block number i, we are to find a location of a
target keyword in an ordered list of the i-th block. Apart from much studied
polynomial and adversary methods for quantum query complexity lower bounds, our
argument shows that the multiple-block ordered search needs a large number of
nonadaptive oracle queries on a black-box model of quantum computation that is
also supplemented with advice. Our argument is also applied to the notions of
computational complexity theory: quantum truth-table reducibility and quantum
truth-table autoreducibility.Comment: 16 pages. An extended abstract will appear in the Proceedings of the
  29th International Symposium on Mathematical Foundations of Computer Science,
  Lecture Notes in Computer Science, Springer-Verlag, Prague, August 22-27,
  200
Mutual information and topology 2: Symmetric network

Proceedings of International Symposium on Neural Networks, Dalian, China, August 2004, Part IThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-28647-9_4Following Gardner, we calculate the information capacity and other phase transition related parameters for a symmetric Hebb network with small word topology in mean-field approximation. It was found that the topology dependence can be described by very small number of parameters, namely the probability of existence of loops with given length. In the case of small world topology, closed algebraic set of equations with only three parameters was found that is easily to be solved.Supported by the Spanish MCyT BFI-2003-07276 and TIC 2002-572-C02
Mosaics from arbitrary stereo video sequences

lthough mosaics are well established as a compact and non-redundant representation of image sequences, their application still suffers from restrictions of the camera motion or has to deal with parallax errors. We present an approach that allows construction of mosaics from arbitrary motion of a head-mounted camera pair. As there are no parallax errors when creating mosaics from planar objects, our approach first decomposes the scene into planar sub-scenes from stereo vision and creates a mosaic for each plane individually. The power of the presented mosaicing technique is evaluated in an office scenario, including the analysis of the parallax error
Feature and viewpoint selection for industrial car assembly

Abstract. Quality assurance programs of today’s car manufacturers show increasing demand for automated visual inspection tasks. A typical example is just-in-time checking of assemblies along production lines. Since high throughput must be achieved, object recognition and pose estimation heavily rely on offline preprocessing stages of available CAD data. In this paper, we propose a complete, universal framework for CAD model feature extraction and entropy index based viewpoint selection that is developed in cooperation with a major german car manufacturer
Experimental Comparison of Classification Uncertainty for Randomised and Bayesian Decision Tree Ensembles

Copyright © 2004 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comBook title: Intelligent Data Engineering and Automated Learning – IDEAL 20045th International Conference on Intelligent Data Engineering and Automated Learning – IDEAL 2004, Exeter, UK. August 25-27, 2004In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community. For quantitative evaluation of classification uncertainty, we use an Uncertainty Envelope dealing with the class posterior distribution and a given confidence probability. Counting the classifier outcomes, this technique produces feasible evaluations of the classification uncertainty. Using this technique in our experiments, we found that the Bayesian DT technique is superior to the randomised DT ensemble technique
Cardinality constrained portfolio optimisation

Copyright © 2004 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comBook title: Intelligent Data Engineering and Automated Learning – IDEAL 20045th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2004), Exeter, UK. August 25-27, 2004The traditional quadratic programming approach to portfolio optimisation is difficult to implement when there are cardinality constraints. Recent approaches to resolving this have used heuristic algorithms to search for points on the cardinality constrained frontier. However, these can be computationally expensive when the practitioner does not know a priori exactly how many assets they may desire in a portfolio, or what level of return/risk they wish to be exposed to without recourse to analysing the actual trade-off frontier.This study introduces a parallel solution to this problem. By extending techniques developed in the multi-objective evolutionary optimisation domain, a set of portfolios representing estimates of all possible cardinality constrained frontiers can be found in a single search process, for a range of portfolio sizes and constraints. Empirical results are provided on emerging markets and US asset data, and compared to unconstrained frontiers found by quadratic programming
A Variable Metric Probabilistic k-Nearest-Neighbours Classifier

Copyright © 2004 Springer Verlag. The final publication is available at link.springer.com5th International Conference, Exeter, UK. August 25-27, 2004. ProceedingsBook title: Intelligent Data Engineering and Automated Learning – IDEAL 2004k-nearest neighbour (k-nn) model is a simple, popular classifier. Probabilistic k-nn is a more powerful variant in which the model is cast in a Bayesian framework using (reversible jump) Markov chain Monte Carlo methods to average out the uncertainy over the model parameters.The k-nn classifier depends crucially on the metric used to determine distances between data points. However, scalings between features, and indeed whether some subset of features is redundant, are seldom known a priori. Here we introduce a variable metric extension to the probabilistic k-nn classifier, which permits averaging over all rotations and scalings of the data. In addition, the method permits automatic rejection of irrelevant features. Examples are provided on synthetic data, illustrating how the method can deform feature space and select salient features, and also on real-world data
PC-filter: a Robust filtering technique for duplicate record detection in large databases

[Abstract]: In this paper, we will propose PC-Filter (PC stands for Partition Comparison), a robust data filter for approximately duplicate record detection in\ud
large databases. PC-Filter distinguishes itself from all of existing methods by using the notion of partition in duplicate detection. It first sorts the whole database\ud
and splits the sorted database into a number of record partitions. The Partition Comparison Graph (PCG) is then constructed by performing fast partition\ud
pruning. Finally, duplicate records are effectively detected by using internal and external partition comparison based on PCG. Four properties, used as heuristics, have been devised to achieve a remarkable efficiency of the filter based on triangle inequity of record similarity. PC-Filter is insensitive to the key used to sort the database, and can achieve a very good recall level that is comparable to that of the pair-wise record comparison method but only with a complexity of\ud
O(N4/3). Equipping existing detection methods with PC-Filter, we are able to well solve the “Key Selection” problem, the “Scope Specification” problem and the\ud
“Low Recall” problem that the existing methods suffer from
Applying a Fuzzy Approach to Relaxing Cardinality Constraints

9 pages, 3 figures.-- Contributed to: 15th International Conference on Database and Expert Systems Applications (DEXA 2004, Zaragoza, Spain, Aug 30 - Sep 3, 2004).In database applications the verification of cardinality constraints is a serious and complex problem that appears when the modifications operations are performed in a large cascade. Many efforts have been devoted to solve this problem, but some solutions lead to other problems such as the complex execution model or an impact on the database performance. In this paper a method to reducing and simplifying the complex verification of cardinality constraints by relaxing these constraints using fuzzy concepts is proposed.Publicad
The support for different democracy models by the use of a web-based discussionboard

Published version of a chapter published in Lecture Notes in Computer Science, 3183, 23-26. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-540-30078-6_4Different initiatives are initiated to utilize ICT to maintain and develop democracy. Democracy models are developed to explain differences between different democracies. This paper reports from a case study where a web-based discussion board is developed and implemented. The findings indicate that politicians and citizens add support to different democracy models by their contributions to the web-based discussion board. Contributions from the citizens add stronger support for the neo-republican model where as politicians mainly support the demo-elitist model. These differences might become obstacles to the utilization of the technology. Identifying potential differences early on in a project may increase the opportunity to develop services capable of handling requests from different stakeholders
On the strong uniqueness of highly sparse expansions from redundant dictionaries

International audienceA series of recent results shows that if a signal admits a sufficiently sparse representation (in terms of the number of nonzero coefficients) in an ``incoherent'' dictionary, this solution is unique and can be recovered as the unique solution of a linear programming problem. We generalize these results to a large class of sparsity measures which includes the ell^p-sparsity measures for 0 \le p \le 1. We give sufficient conditions on a signal such that the simple solution of a linear programming problem simultaneously solves all the non-convex (and generally hard combinatorial) problems of sparsest representation w.r.t. arbitrary admissible sparsity measures. Our results should have a practical impact on source separation methods based on sparse decompositions, since they indicate that a large class of sparse priors can be efficiently replaced with a Laplacian prior without changing the resulting solution
Analyzing sensory data using non-linear preference learning with feature subset selection

15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004The quality of food can be assessed from different points of view. In this paper, we deal with those aspects that can be appreciated through sensory impressions. When we are aiming to induce a function that maps object descriptions into ratings, we must consider that consumers’ ratings are just a way to express their preferences about the products presented in the same testing session. Therefore, we postulate to learn from consumers’ preference judgments instead of using an approach based on regression. This requires the use of special purpose kernels and feature subset selection methods. We illustrate the benefits of our approach in two families of real-world data base
Digging into acceptor splice site prediction : an iterative feature selection approach

Feature selection techniques are often used to reduce data dimensionality, increase classification performance, and gain insight into the processes that generated the data. In this paper, we describe an iterative procedure of feature selection and feature construction steps, improving the classification of acceptor splice sites, an important subtask of gene prediction.
We show that acceptor prediction can benefit from feature selection, and describe how feature selection techniques can be used to gain new insights in the classification of acceptor sites. This is illustrated by the identification of a new, biologically motivated feature: the AG-scanning feature.
The results described in this paper contribute both to the domain of gene prediction, and to research in feature selection techniques, describing a new wrapper based feature weighting method that aids in knowledge discovery when dealing with complex datasets
Field Programmable Logic and Application

A previous study [1] demonstrates the advantages of replacing registers by FPGA embedded memories during the storage allocation phase of High-Level Synthesis. The trend in new FPGAs to have large amounts of on-chip embedded memories motivated this proposition and resulted in substantial area decrease in the synthesized designs. This paper elaborates further on the various possibilities involved during storage allocation onto embedded memories, and presents new memory binding techniques. These techniques include modifications to the memory mapping procedure presented in [1] and cater to various memory specifications. The embedded memories differ in their assumptions of the number of memory banks, the number of ports on each bank, and the read/write types of each port. The paper highlights the benefits of the new techniques and discusses the pros and cons involved in each case. The Discrete Cosine Transform (DCT) benchmark illustrates the area improvements obtained in the new approaches compared to conventional register binding (up to 47%). The results are evaluated through an analysis of both area and delay performances.N/
Development of DSP Unit for Online Tuning and Application to Neural Pattern Recognition System

Recently, smaller and more effective recognition system is required in various fields. In our previous researches, the recognition system using neural network (NN) and DSP had been developed. In some research fields, such as biometrics, the target of the system was to recognize patterns whose data varied widely because of the difference of individuals and surroundings, but its recognition ability was not enough. So the online tuning system is proposed in this paper. The proposed system, which is consisted of DSP unit and the NN continuous learning and recognition parts, is used to recognize the patterns whose data varied widely. We discuss to adjust the recognition system to data varied widely by continuous leaning. In experiment, performance of the online tuning system is checked, and we attempt to apply the system to electromyogram (EMG) pattern recognition.Knowledge-Based Intelligent Information and Engineering Systems
8th International Conference, KES 2004, Wellington, New Zealand, September 20-25, 2004, Proceedings, Part
Using Evolution Strategies to Find a Dynamical Model of the M81 Triplet

In this work we present Evolution Strategies (ES) as an efficient optimization method for dynamic modelling of the main interacting group of three galaxies in M81. The M81 group is one of the nearest groups of galaxies; its biggest galaxy, M81, sits in the core of the group together with its two companions M82 and NGC3077. The interaction among these three galaxies is very well defined in an image taken in HI. In this first attempt we use non-self-gravitating  simulations for modelling dynamically the group; even with this restriction our method reproduces the density distribution of the three galaxies with great precision. Results presented here show that ES is an excellent method to find an accurate model of groups of interacting galaxies, where a global search for a large number of real-valued parameters needs to be performed.status: publishe
How explicit are the barriers to failure in safety arguments?

Safety cases embody arguments that demonstrate how safety properties of a system are upheld. Such cases implicitly document the barriers that must exist between hazards and vulnerable components of a system. For safety certification, it is the analysis of these barriers that provide confidence in the safety of the system. The explicit representation of hazard barriers can provide additional insight for the design and evaluation of system safety. They can be identified in a hazard analysis to allow analysts to reflect on particular design choices. Barrier existence in a live system can be mapped to abstract barrier representations to provide both verification of barrier existence and a basis for quantitative measures between the predicted barrier behaviour and performance of the actual barrier. This paper explores the first stage of this process, the binding between explicit mitigation arguments in hazard analysis and the barrier concept. Examples from the domains of computer-assisted detection in mammography and free route airspace feasibility are examined and the implications for system certification are considered
Coherence compilation: applying AIED techniques to the reuse of educational resources

The HomeWork project is building an exemplar system to provide individualised experiences for individual and groups of children aged 6-7 years, their parents, teachers and classmates at school. It employs an existing set of broadcast video media and associated resources that tackle both numeracy and literacy at Key Stage 1. The system employs a learner model and a pedagogical model to identify what resource is best used with an individual child or group of children collaboratively at a particular learning point and at a particular location. The Coherence Compiler is that component of the system which is designed to impose an overall narrative coherence on the materials that any particular child is exposed to. This paper presents a high level vision of the design of the Coherence Compiler and sets its design within the overall framework of the HomeWork project and its learner and pedagogical models
Role-based specification of the behaviour of an agent for the interactive resolution of mathematical problems

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-30139-4_18Proceedings of 7th International Conference, ITS 2004, Maceió, Alagoas, Brazil, August 30 - September 3, 2004In this paper we describe how a computer system, which includes an authoring tool for teachers and an execution tool for students, is able to generate interactive dialogs in a Mathematics teaching application. This application is named ConsMath, and allows students to learn how to solve problems of Mathematics that involve symbolic calculations. This is done by means of an agent that is in charge of delivering the dialogs to students. The design process entails the dynamic generation of the user interface intended for learners with the insertion of decision points. During the execution, a tracking agent matches students’ actions with the ones previously associated to decision points by the designer, thereby activating dynamic modifications in the interface by using a hierarchy of production rules. Students can use ConsMath both at their own pace or in a collaborative setting.The work described in this paper is part of the Ensenada and Arcadia projects, funded by the National Plan of Research and Development of Spain, projects TIC 2001-0685-C02-01 and TIC2002-01948 respectivel
Context adaptive interaction with an automatically created spoken interface for intelligent environments

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-30179-0_11Proceedings of IFIP International Conference, INTELLCOMM 2004, Bangkok, Thailand, November 23-26, 2004.In this paper we present the interpretation and generation processes of a spoken dialogue interface for intelligent environments. The interface is automatically created for each specific environment and the interpretation and generation vary depending on the environment and its context. These processes rely on a dialogue tree structure. Several modules process the tree structure and the environment context information to produce specific dialogues for the current environment state. Dialogues are provided with clarification, error recovering, anaphora resolution and other capabilities. The interface is implemented in a real intelligent environment laboratory.This work has been sponsored by the Spanish Ministry of Science and Education, project number TIN2004-0314
Bounded version vectors

Version vectors play a central role in update tracking under optimistic distributed systems, allowing the detection of obsolete or inconsistent versions of replicated data. Version vectors do not have a bounded representation; they are based on integer counters that grow indefinitely as updates occur. Existing approaches to this problem are scarce; the mechanisms proposed are either unbounded or operate only under specific settings. This paper examines version vectors as a mechanism for data causality tracking and clarifies their role with respect to vector clocks. Then, it introduces bounded stamps and proves them to be a correct alternative to integer counters in version vectors. The resulting mechanism, bounded version vectors, represents the first bounded solution to data causality tracking between replicas subject to local updates and pairwise symmetrical synchronization.FCT project POSI/ICHS/44304/2002, FCT under grant BSAB/390/2003
Identifying left-right deterministic linear languages

Recently an algorithm to identify in the limit with polynomial time and data Left Deterministic Linear Languages (Left DLL) and, consequently Right DLL, was proposed. In this paper we show that the class of the Left-Right DLL formed by the union of both classes is also identifiable. To do that, we introduce the notion of n-negative characteristic sample, that is a sample that forces an inference algorithm to output an hypothesis of size bigger than n when strings from a non identifiable language are provided.This work was supported by the Spanish CICyT trough project TIC2003-08496-C04 and supported in part by the IST Programme of the European Community, under the Pascal Network of Excellence, IST-2002-506778