pinecone/core-2020-05-10-deduplication huggingface dataset:
The Loss Rank Principle for Model Selection

We introduce a new principle for model selection in regression and
classification. Many regression models are controlled by some smoothness or
flexibility or complexity parameter c, e.g. the number of neighbors to be
averaged over in k nearest neighbor (kNN) regression or the polynomial degree
in regression with polynomials. Let f_D^c be the (best) regressor of complexity
c on data D. A more flexible regressor can fit more data D' well than a more
rigid one. If something (here small loss) is easy to achieve it's typically
worth less. We define the loss rank of f_D^c as the number of other
(fictitious) data D' that are fitted better by f_D'^c than D is fitted by
f_D^c. We suggest selecting the model complexity c that has minimal loss rank
(LoRP). Unlike most penalized maximum likelihood variants (AIC,BIC,MDL), LoRP
only depends on the regression function and loss function. It works without a
stochastic noise model, and is directly applicable to any non-parametric
regressor, like kNN. In this paper we formalize, discuss, and motivate LoRP,
study it for specific regression problems, in particular linear ones, and
compare it to other model selection schemes.Comment: 16 page
Multi-Objective Supervised Learning

Copyright © 2008 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comBook title: Multiobjective Problem Solving from NatureExtended version of the 2006 workshop paper presented at the Workshop on Multiobjective Problem-Solving from Nature, 9th International Conference on Parallel Problem Solving from Nature (PPSN IX), Reykjavik, Iceland, 9-13 September 2006; see: http://hdl.handle.net/10871/11785This chapter sets out a number of the popular areas in multiobjective supervised learning. It gives empirical examples of model complexity optimization and competing error terms, and presents the recent advances in multi-class receiver operating characteristic analysis enabled by multiobjective optimization. It concludes by highlighting some specific areas of interest/concern when dealing with multiobjective supervised learning problems, and sets out future areas of potential research
Proof Explanation in the DR-DEVICE System

Trust is a vital feature for Semantic Web: If users (humans and agents) are to use and integrate system answers, they must trust them. Thus, systems should be able to explain their actions, sources, and beliefs, and this issue is the topic of the proof layer in the design of the Semantic Web. This paper presents the design and implementation of a system for proof explanation on the Semantic Web, based on defeasible reasoning. The basis of this work is the DR-DEVICE system that is extended to handle proofs. A critical aspect is the representation of proofs in an XML language, which is achieved by a RuleML language extension
Completing Queries: Rewriting of IncompleteWeb
Queries under Schema Constraints

Reactive Web systems, Web services, and Web-based publish/
subscribe systems communicate events as XML messages, and in
many cases require composite event detection: it is not sufficient to react
to single event messages, but events have to be considered in relation to
other events that are received over time.
Emphasizing language design and formal semantics, we describe the
rule-based query language XChangeEQ for detecting composite events.
XChangeEQ is designed to completely cover and integrate the four complementary
querying dimensions: event data, event composition, temporal
relationships, and event accumulation. Semantics are provided as
model and fixpoint theories; while this is an established approach for rule
languages, it has not been applied for event queries before.
Special Features of Galactic Dynamics

The present lecture notes are an introduction to selected topics of {\it Galactic Dynamics}. The focus is on topics that we consider more relevant to the main theme of this workshop, {\it Celestial Mechanics}. This is not intended to be a review article. In fact, any of the topics below could be the subject of a separate review. Only the main ideas and notions are introduced, as well as some important currently open problems in each topic. Some relevant results from our own research are also presented. We discuss topics related mostly to the so-called {\it ellipsoidal components} of galaxies. These are \textbf{a)} the dark halos of both elliptical and disk galaxies, \textbf{b)} the luminous matter in elliptical galaxies, and \textbf{c)} the bulges of disk galaxies. We shall only occasionally refer to the dynamics of disks, bars or spiral structure
Communication Abstractions for Distributed Business Processes

Languages for business process definition generally suffer from myopic approaches to capturing communication between distributed processes. Effective communication between processes requires: support for conversations involving interrelated interactions spread over time; ability to select and group messages based on their content, regardless of format and transport technology; and resolving contention between processes or tasks for common sets of messages. This paper presents a set of communication abstractions that provide a 'glue' between the process layer and the middleware. The paper also reports on an implementation of these abstractions and an experimental evaluation
Refocusing generalised normalisation

When defined with general elimination/application rules, natural
deduction and $\lambda$-calculus become closer to sequent
calculus. In order to get real isomorphism, normalisation has to
be defined in a ``multiary'' variant, in which reduction rules are
necessarily non-local (reason: nomalisation, like cut-elimination,
acts at the \emph{head} of applicative terms, but natural
deduction focuses at the \emph{tail} of such terms). Non-local
rules are bad, for instance, for the mechanization of the system.
A solution is to extend natural deduction even further to a
\emph{unified calculus} based on the unification of cut and
general elimination. In the unified calculus, a sequent term
behaves like in the sequent calculus, whereas the reduction steps
of a natural deduction term are interleaved with explicit steps
for bringing heads to focus. A variant of the calculus has the
symmetric role of improving sequent calculus in dealing with
tail-active permutative conversions
Consumer Profile Identification and Allocation

Proceedings of the 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, San Sebastián, Spain, June 20-22, 2007.We propose an easy-to-use methodology to allocate one of the groups which have been previously built from a complete learning data base, to new individuals. The learning data base contains continuous and categorical variables for each individual. The groups (clusters) are built by using only the continuous variables and described with the help of the categorical ones. For the new individuals, only the categorical variables are available, and it is necessary to define a model which computes the probabilities to belong to each of the clusters, by using only the categorical variables. Then this model provides a decision rule to assign the new individuals and gives an efficient tool to decision-makers. This tool is shown to be very efficient for customers allocation in consumer clusters for marketing purposes, for example
Specialized ensemble of classifiers for traffic sign recognition

Proceeding of: 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, San Sebastían, España, junio, 2007.Several complex problems have to be solved in order to build Advanced Driving Assistance Systems. Among them, an important problem is the detection and classification of traffic signs, which can appear at any position within a captured image. This paper describes a system that employs independent modules to classify several prohibition road signs. Combining the predictions made by the set of classifiers, a unique final classification is achieved. To reduce the computational complexity and to achieve a real-time system, a previous input feature selection is performed. Experimental evaluation confirms that using this feature selection allows a significant input data reduction without an important loss of output accuracy.The research reported here has been supported by the Ministry of Education and Science under project TRA2004-07441-C03-C02
Embodying Cognitive Abilities: Categorization

In previous woks we have introduced a distributed neural\ud
architecture for the generation of complex behaviors in evolutionary\ud
robotics. In this paper we show how this architecture is able to create its\ud
own categories about the sensed world of a robot by direct interaction of\ud
the body with the environment. The distributed elements of the architecture\ud
cooperate to express the categories on an inner world that is easily\ud
accessible from the outside. We conclude the paper with an explanation\ud
of how the inner world created by the robot can be used to gain some\ud
insight into the mind-body problem.Peer ReviewedPostprint (author’s final draft
Emerging behaviors by learning joint coordination in articulated mobile robots

A Policy Gradient Reinforcement Learning (RL) technique is used to design the low level controllers that drives the joints of articulated mobile robots: A search in the controller’s parameters space.\ud
There is an unknown value function that measures the quality of the controller\ud
respect to the parameters of it. The search is orientated by the approximation of the gradient of the value function. The approximation is made by means of the robot experiences and then the behaviors emerge.\ud
This technique is employed in a structure that processes sensor information to achieve coordination. The structure is based on a modularization principle in which complex overall behavior is the result of the interaction of individual ‘simple’  components. The simple components used are\ud
standard low level controllers (PID) which output is combined, sharing information between articulations and therefore taking integrated control actions. Modularization and Learning are cognitive features, here we\ud
endow the robots with this features. Learning experiences in simulated robots are presented as demonstration.Peer ReviewedPostprint (published version
Virtual Environments for Training: From Individual Learning to Collaboration with Humanoids

International audienceThe next generation of virtual environments for training is oriented towards collaborative aspects. Therefore, we have decided to enhance our platform for virtual training environments, adding collaboration opportunities and integrating humanoids. In this paper we put forward a model of humanoid that suits both virtual humans and representations of real users, according to collaborative training activities. We suggest adaptations to the scenario model of our platform making it possible to write collaborative procedures. We introduce a mechanism of action selection made up of a global repartition and an individual choice. These models are currently being integrated and validated in GVT, a virtual training tool for maintenance of military equipments, developed in collaboration with the French company NEXTER-Group
The development of a virtual cycling simulator

The final publication is available at Springer via: http://dx.doi.org/10.1007/978-3-540-73011-8_18.Cycling is one of the current thirteen elite sports in Hong Kong. Despite cycling is one of the well known activities in the world and has numerous advantages for health, it is still far from popular in Hong Kong. In this research, a virtual cycling simulator is developed for exercise and entertainment purpose, and for promoting the cycling activity. The hardware of the cycling simulator consists of four major units including a bike platform, an actuation unit, a sensing unit and a display unit. The control system receives signals from the sensing unit and controls the motions of the actuation unit. It also computes and renders the virtual environment in real-time thereby providing the experience of cycling on different terrain models
An observational study of children interacting with an augmented story book

We present findings of an observational study investigating how young children interact with augmented reality story books. Children aged between 6 and 7 read and interacted with one of two story books aimed at early literacy education. The books pages were augmented using animated virtual 3D characters, sound, and interactive tasks. Introducing novel media to young children requires system and story designers to consider not only technological issues but also questions arising from story design and the design of interactive sequences. We discuss findings of our study and implications regarding the implementation of augmented story books
Informing the specification of a large-scale socio-technical system with models of human activity

In this paper, we present our experience of using rich and detailed models of human activity in an existing socio-technical system in the domain of air traffic control to inform a use case-based specification of an enhanced future system, called DMAN. This work was carried out as part of a real project for Eurocontrol, the European Organisation for the Safety of Air Navigation. We describe, in outline, the kinds of models we used, and present some examples of the ways in which these models influenced the specification of use cases and requirements for the future system. We end with a discussion of lessons learnt
Optimal-Constraint Lexicons for Requirements Specifications

Constrained Natural Languages (CNLs) are becoming an increasingly popular way of writing technical documents such as requirements specifications. This is because CNLs aim to reduce the ambiguity inherent within natural languages, whilst maintaining their readability and expressiveness. The design of existing CNLs appears to be unfocused towards achieving specific quality outcomes, in that the majority of lexical selections have been based upon lexicographer preferences rather than an optimum trade-off between quality factors such as ambiguity, readability, expressiveness, and lexical magnitude. In this paper we introduce the concept of  replaceability  as a way of identifying the lexical redundancy inherent within a sample of requirements. Our novel and practical approach uses Natural Language Processing (NLP) techniques to enable us to make dynamic trade-offs between quality factors to optimise the resultant CNL. We also challenge the concept of a CNL being a one-dimensional static language, and demonstrate that our optimal-constraint process results in a CNL that can adapt to a changing domain while maintaining its expressiveness
Ventricular Tachycardia/Fibrillation Detection Algorithm for 24/7 Personal Wireless Heart Monitoring

This paper describes a Ventricular Tachycardia/Fibrillation (VT/VF) detection algorithm that is specifically designed for a 24/7 personal wireless heart monitoring system. This monitoring system uses Bluetooth enabled bio-sensors and smart phones to monitor continuously cardiac patients  vital signs. Our VT/VF algorithm is optimized for continuous real-time monitoring on smart phones with a high sensitivity and specificity. We studied and compared existing VT/VF algorithms and selected the one which suited best our requirements. However, we modified and improved the existing algorithm for the smart phone to achieve better performance results. We tested the algorithm on full-length signals from the physionet CU, MIT-db and MIT-vfdb databases [16] without any pre-selection of VT/VF or normal QRS-complex signals. We achieved 97% sensitivity, 98% accuracy and 98% specificity for our implementation which is excellent compared to existing algorithms
On reasoning over tracking events

Presentado al 15th Scandinavian Conference on Image Analysis (SCIA-2007) celebrado en Aalborg (Dinamarca) del 10 al 14 de junio.High-level understanding of motion events is a critical task in any system which aims to analyse dynamic human-populated scenes. However, current tracking techniques still do not address complex interaction events among multiple targets. In this paper, a principled event-management framework is proposed, and it is included in a hierarchical and modular tracking architecture. Multiple-target interaction events, and a proper scheme for tracker instantiation and removal according to scene events, are considered. Multiple-target group management allows the system to switch among different operation modes. Robust and accurate tracking results have been obtained in both indoor and outdoor scenarios, without considering a-priori knowledge about either the scene or the targets based on a previous training period.Peer Reviewe
Measuring Red Blood Cell Velocity with a Keyhole Tracking Algorithm

A tracking algorithm is proposed to measure the velocity of red blood cells traveling through microvessels of tumors growing in skin flaps implanted on mice. The tracking is based on a keyhole model that describes the probable movement of a segmented cell between contiguous frames in a video sequence. When a history of movements exists, past, present and a predicted landing position define two regions of probability with a keyhole shape. This keyhole is used to de- termine if cells in contiguous frames should be linked to form tracks. Pre-processing segments cells from background and post-processing joins tracks and discards links that could have been formed due to noise or uncertainty. The algorithm pre- sents several advantages over traditional methods such as kymographs or particle image velocimetry: manual interven- tion is restricted to the thresholding, several vessels can be analyzed simultaneously, algorithm is robust to noise and a wealth of statistical measures can be obtained. Two tumors with different geometries were analyzed; average velocities were 211±136 [μm/s] (mean±std) with a range 15.9-797 [μm/s], and 89±62 [μm/s] with a range 5.5-300 [μm/s] respec- tively, which are consistent with previous results in the litera- ture
Modeling consciousness for autonomous robot exploration

Proceeding of: Second International Work-Conference on the Interplay between Natural and Artificial Computation (IWINAC) 2007. La Manga del Mar Menor, Murcia, Spain, 18-21 June, 2007.This work aims to describe the application of a novel machine consciousness model to a particular problem of unknown environment exploration. This relatively simple problem is analyzed from the point of view of the possible benefits that cognitive capabilities like attention, environment awareness and emotional learning can offer. The model we have developed integrates these concepts into a situated agent control framework, whose first version is being tested in an advanced robotics simulator. The implementation of the relationships and synergies between the different cognitive functionalities of consciousness in the domain of autonomous robotics is also discussed.Publicad
HANNA: a tool for hardware prototyping and benchmarking of ANNs. Poster

For some applications, designers must implement an ANN model over different
platforms to meet performance, cost or power constrains, a process still more
painful when several hardware implementations have to be evaluated. Continuous
advances in VLSI technologies, computer architecture and software development
make it difficult to find the adequate implementation platform. HANNA (Hardware
ANN Architect), is a tool designed to automate the generation of hardware
prototypes of MLP-like neural networks over FPGA devices. Coupled with
traditional Matlab/Simulink environments the model can be synthesized,
downloaded to the FPGA and co-simulated with the software version to trade off
area, speed and precision requirements.This research is being funded by Ministerio de Ciencia y Tecnología TIC 2003-09557-C02-02
Comparison of Classifier for Human Activity Recognition

The human activity recognition in video sequences is a field where many types of classifiers have been used as well as a wide range of input features that feed these classifiers. This work has a double goal. First of all, we extracted the most relevant features for the activity recognition by only utilizing motion features provided by a simple tracker based on the 2D centroid coordinates and the height and width of each persons blob. Second, we present a performance comparison among seven different classifiers (two Hidden Markov Models (HMM), a J.48 tree, two Bayesian classifiers, a classifier based on rules and a Neuro-Fuzzy system). The video sequences under study present four human activities (inactive, active, walking and running) that have been manual labeled previously. The results show that the classifiers reveal different performance according to the number of features employed and the set of classes to sort. Moreover, the basic motion features are not enough to have a complete description of the problem and obtain a good classification
An adaptive Michigan approach PSO for nearest prototype classification

Proceedings of: Second International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2007, La Manga del Mar Menor, Spain, June 18-21, 2007.Nearest Prototype methods can be quite successful on many pattern classification problems. In these methods, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. In this paper we develop a new algorithm (called AMPSO), based on the Particle Swarm Optimization (PSO) algorithm, that can be used to find those prototypes. Each particle in a swarm represents a single prototype in the solution; the swarm evolves using modified PSO equations with both particle competition and cooperation. Experimentation includes an artificial problem and six common application problems from the UCI data sets. The results show that the AMPSO algorithm is able to find solutions with a reduced number of prototypes that classify data with comparable or better accuracy than the 1-NN classifier. The algorithm can also be compared or improves the results of many classical algorithms in each of those problems; and the results show that AMPSO also performs significantly better than any tested algorithm in one of the problems.This article has been financed by the Spanish founded research MEC project OPLINK::UC3M, Ref: TIN2005-08818-C04-02 and CAM project UC3M-TEC-05-029
ParSketch: a Sketch-based Interface for a 2D Parametric Geometry Editor

ParSketch is a software prototype to evaluate the usability and functionality of a sketching interface aimed at defining 2D parametric sections. Currently, ParSketch interprets strokes which can be recognized as geometry (line, arc, circle, ellipse, or composed entities that are automatically segmented into those basic entities), or graphic gestures representing constraints (dimension, parallel, perpendicular, tangent, concentric, horizontal or vertical). From the functionality point of view, ParSketch compares to current commercial parametric CAD applications, as it offers many of the features provided by such applications. A theoretical analysis of the efficiency component of usability is provided that justifies the potential capability of sketching interfaces to compete with classical WIMP applications. Finally, a usability study is presented, which makes special emphasis in the satisfaction component of usability.The Spanish Ministry of Science and Education and the European Union (Project DPI2004-01373) supported this work. It was also partially supported by Fundació Caixa Castelló-Bancaixa under the Universitat Jaume I program for Research Promotion (Project P1-1B2004-02)