pinecone/core-2020-05-10-deduplication huggingface dataset:
Natural landmark detection for visually-guided robot navigation

Presentado al 10th Congress of the Italian Association for Artificial Intelligence celebrado en 2007 en Roma (Italia).The main difficulty to attain fully autonomous robot navigation outdoors is the fast detection of reliable visual references, and their subsequent characterization as landmarks for immediate and unambiguous recognition. Aimed at speed, our strategy has been to track salient regions along image streams by just performing on-line pixel sampling. Persistent regions are considered good candidates for landmarks, which are then characterized by a set of subregions with given color and normalized shape. They are stored in a database for posterior recognition during the navigation process. Some experimental results showing landmark-based navigation of the legged robot Lauron III in an outdoor setting are provided.This work was supported by the project 'Sistema reconfigurable para la navegación basada en visión de robots caminantes y rodantes en entornos naturales.' (00).This work has been supported by the Spanish\ud
Ministerio de Ciencia y Tecnología and FEDER under the project SIRVENT (DPI2003-05193-C02-01).Peer Reviewe
Semantic annotation of complex human scenes for multimedia surveillance

Presentado al 10th Congress of the Italian Association for Artificial Intelligence (AI*IA-2007) celebrado en Roma (Italia) del 10 al 13 de septiembre.A Multimedia Surveillance System (MSS) is considered for automatically retrieving semantic content from complex outdoor scenes, involving both human behavior and traffic domains. To characterize the dynamic information attached to detected objects, we consider a deterministic modeling of spatio-temporal features based on abstraction processes towards fuzzy logic formalism. A situational analysis over conceptualized information will not only allow us to describe human actions within a scene, but also to suggest possible interpretations of the behaviors perceived, such as situations involving thefts or dangers of running over. Towards this end, the different levels of semantic knowledge implied throughout the process are also classified into a proposed taxonomy.This work has been supported by EC grant IST-027110 for the HERMES project and by the Spanish MEC under projects TIC-2003-08865 and DPI\ud
-2004-5414.\ud
Jordi Gonz`alez also acknowledges the support of a Juan de la\ud
Cierva Postdoctoral\ud
fellowship from the Spanish MEC.Peer Reviewe
Managing email overload with an automatic nonparametric clustering approach

Email overload is a recent problem that there is increasingly difficulty people have faced to process the large number of emails received daily. Currently this problem becomes more and more serious and it has already affected the normal usage of email as a knowledge management tool. It has been recognized that categorizing emails into meaningful groups can greatly save cognitive load to process emails and thus this is an effective way to manage email overload problem. However, most current approaches still require significant human input when categorizing emails. In this paper we develop an automatic email clustering system, underpinned by a new nonparametric text clustering algorithm. This system does not require any predefined input parameters and can automatically generate meaningful email clusters. Experiments show our new algorithm outperforms existing text clustering algorithms with higher efficiency in terms of computational time and clustering quality measured by different gauges.<br /
Spatial information extraction for cognitive mapping with a mobile robot

When animals (including humans) first explore a new environment, what they remember is fragmentary knowledge about the places visited. Yet, they have to use such fragmentary knowledge to find their way home. Humans naturally use more powerful heuristics while lower animals have shown to develop a variety of methods that tend to utilize two key pieces of information, namely distance and orientation information. Their methods differ depending on how they sense their environment. Could a mobile robot be used to investigate the nature of such a process, commonly referred to in the psychological literature as cognitive mapping? What might be computed in the initial explorations and how is the resulting "cognitive map" be used for localization? In this paper, we present an approach using a mobile robot to generate a "cognitive map", the main focus being on experiments conducted in large spaces that the robot cannot apprehend at once due to the very limited range of its sensors. The robot computes a "cognitive map" and uses distance and orientation information for localization. © Springer-Verlag Berlin Heidelberg 2007
Expressiveness of Recursion, Replication and Scope Mechanisms in Process Calculi

International audienceIn this paper we shall survey and discuss in detail the work on the relative expressiveness of recursion and replication in various process calculi. Namely, CCS, the pi-calculus, the Ambient calculus, Concurrent Constraint Programming and calculi for Cryptographic Protocols. We shall give evidence that the ability of expressing recursive behaviour via replication often depends on the scoping mechanisms of the given calculus which compensate for the restriction of replication
Improving users’ comprehension of changes with animation and sound: an empirical assessment

Animation or sound is often used in user interfaces as an attempt to improve users' perception and comprehension of evolving situations and support them in decision-making. However, empirical data establishing their real effectiveness on the comprehension of changes are still lacking. We have carried out an experiment using four combinations of visual and auditory feedback in a split attention task. The results not only confirm that such feedback improves the perception of changes, but they also demonstrate that animation and sound used alone or combined bring major improvements on the comprehension of a changing situation. Based on these results, we propose design guidelines about the most efficient combinations to be used in user interfaces
Sequence classification using statistical pattern recognition

Proceeding of: 7th International Symposium on Intelligent Data Analysis. IDA-2007. Ljubljana, Slovenia, September, 6th-8th, 2007.Sequence classification is a significant problem that arises in many different real-world applications. The purpose of a sequence classifier is to assign a class label to a given sequence. Also, to obtain the pattern that characterizes the sequence is usually very useful. In this paper, a technique to discover a pattern from a given sequence is presented followed by a general novel method to classify the sequence. This method considers mainly the dependencies among the neighbouring elements of a sequence. In order to evaluate this method, a UNIX command environment is presented, but the method is general enough to be applied to other environments.This work has been supported by the Spanish Government under project TRA2004-07441-C03-2/IA.Publicad
A Method for Visualising Possible Contexts

This paper presents a method for visualising possible sequences of information. Possible sequences of information segments, called contexts, scenarios, narratives, or storytellings, have been used as a tool for exploring and stimulating thinking about possible events, assumptions relating to these events, and courses of actions, i.e. chances for a broad range of information designers from public audiences searching on the internet to analysts and policy makers. Thanks to the search technology, it has become easier to obtain information that a person is looking for, however, it is a laborious task to grab an overview of information space so that an information designer can find contextually relevant information pieces and sequence them into contextually meaningful ways. In this research, we design and develop a series of generative systems that visualises possible contexts
Random Germs and Stochastic Watershed for Unsupervised Multispectral Image Segmentation

International audienceThis paper extends the use of stochastic watershed, recently introduced by Angulo and Jeulin [1], to unsupervised segmentation of multispectral images. Several probability density functions (pdf), derived from Monte Carlo simulations (M realizations of N random markers), are used as a gradient for segmentation: a weighted marginal pdf a vectorial pdf and a probabilistic gradient. These gradient-like functions are then segmented by a volume-based watershed algorithm to define the R largest regions. The various gradients are computed in multispectral image space and in factor image space, which gives the best segmentation. Results are presented on PLEIADES satellite simulated images
Mixing 3-colourings in bipartite graphs.

For a 3-colourable graph G, the 3-colour graph of G, denoted  C3(G), is the graph with node set the proper vertex 3-colourings of G, and two nodes adjacent whenever the corresponding colourings differ on precisely one vertex of G. We consider the following question : given G, how easily can we decide whether or not  C3(G) is connected? We show that the 3-colour graph of a 3-chromatic graph is never connected, and characterise the bipartite graphs for which  C3(G) is connected. We also show that the problem of deciding the connectedness of the 3-colour graph of a bipartite graph is coNP-complete, but that restricted to planar bipartite graphs, the question is answerable in polynomial time
On restrictions of balanced 2-interval graphs

International audienceThe class of 2-interval graphs has been introduced for modelling scheduling and allocation problems, and more recently for specific bioinformatic problems. Some of those applications imply restrictions on the 2-interval graphs, and justify the introduction of a hierarchy of subclasses of 2-interval graphs that generalize line graphs: balanced 2-interval graphs, unit 2-interval graphs, and (x,x)-interval graphs. We provide instances that show that all the inclusions are strict. We extend the NP-completeness proof of recognizing 2-interval graphs to the recognition of balanced 2-interval graphs. Finally we give hints on the complexity of unit 2-interval graphs recognition, by studying relationships with other graph classes: proper circular-arc, quasi-line graphs, K_{1,5}-free graphs, ..
A grid-based infrastructure for distributed retrieval

In large-scale distributed retrieval, challenges of latency, heterogeneity, and dynamicity emphasise the importance of infrastructural support in reducing the development costs of state-of-the-art solutions. We present a service-based infrastructure for distributed retrieval which blends middleware facilities and a design framework to ‘lift’ the resource sharing approach and the computational services of a European Grid platform into the domain of e-Science applications. In this paper, we give an overview of the DILIGENT Search Framework and illustrate its exploitation in the ﬁeld of Earth Science
Virtual Consumption: Using Player Types to Explore Virtual Consumer Behavior

As virtual economies begin to interact more with real world economies, it is important to consider the in-game activities that players engage in, which create and transfer game currency. Specifically, we examine products and services that players offer to each other within MMORPGs. We use Bartle’s taxonomy of player types to illustrate the different behavioral approaches players have in consuming these products and services. We introduce the term virtual consumption to describe these in-game exchanges and highlight the need for further research into this phenomenon
Entertainment modeling in physical play through physiology beyond heart-rate

An investigation into capturing the relation of physiology, beyond heart rate recording, to expressed preferences of entertainment in children’s physical gameplay is presented in this paper. An exploratory survey experiment raises the difficulties of isolating elements derived (solely) from heart rate recordings attributed to reported entertainment and a control experiment for surmounting those difficulties is proposed. Then a survey experiment on a larger scale is devised where more physiological signals (Blood Volume Pulse and Skin Conductance) are collected and analyzed. Given effective data collection a set of numerical features is extracted from the child’s physiological state. A preference learning mechanism based on neuro-evolution is used to construct a function of single physiological features that models the players’ notion of ‘fun’ for the games under investigation. Performance of the model is evaluated by the degree to which the preferences predicted by the model match those expressed by the children. Results indicate that there appears to be increased mental/emotional effort in preferred games of children.peer-reviewe
Interactive storytelling with literary feelings

In this paper, the authors describe the integration of Natural Language Processing (NLP) within an emotional planner to support Interactive Storytelling. Their emotional planner is based on a standard HSP planner, whose originality is drawn from altering the agents’ beliefs and emotional states. Each character is driven by its own planner, while characters are able to operate on their reciprocal feelings thus affecting each other. Our baseline story is constituted by a classic XIXth century French novel from Gustave Flaubert in which characters feelings play a dominant role. This approach benefits from the fact that Flaubert has described a specific ontology for his characters feelings. The objective of NLP should be to uncover from natural language utterances the same kind of affective elements, which requires an integration between NLP and the planning component at the level of semantic content. This research is illustrated with examples from a first fully integrated prototype comprising NLP, emotional planning and real-time 3D animation
Neuroevolution of Agents Capable of Reactive and Deliberative Behaviours in Novel and Dynamic Environments

Both reactive and deliberative qualities are essential for a good action selection mechanism. We present a model that embodies a hybrid of two very different neural network architectures inside an animat: one that controls their high level deliberative behaviours, such as the selection of sub-goals, and one that provides reactive and navigational capabilities. Animats using this model are evolved in novel and dynamic environments, on complex tasks requiring deliberative behaviours: tasks that cannot be solved by reactive mechanisms alone and which would traditionally have their solutions formulated in terms of search-based planning. Significantly, no a priori information is given to the animats, making explicit forward search through state transitions impossible. The complexity of the problem means that animats must first learn to solve sub-goals without receiving any reward. Animats are shown increasingly complex versions of the task, with the results demonstrating, for the first time, incremental neuro-evolutionary learning on such tasks
Lambda theories of effective lambda models

International audienceA longstanding open problem is whether there exists a non-syntactical model of untyped lambda-calculus whose theory is exactly the least lambda-theory (i.e. beta-conversion). In this paper we investigate the more general question of whether the equational (resp. order) theory of a non syntactical model M, say Eq(M) (resp. Ord(M)) can be recursively enumerable (= r.e. below). We conjecture that no such model exists in Scott's semantics and its refinements, and prove the conjecture for several large classes of models. In particular we introduce a notion of effective lambda-model and show that for all effective models M, Eq(M) is different from beta-conversion, and Ord(M) is not r.e. If moreover M belongs to the stable or strongly stable semantics, then Eq(M) is not r.e. Concerning Scott's continuous semantics we explore the class of (all) graph models, show that it satisfies a Lowenheim Skolem theorem, that there exists a minimum order/equational graph theory, and that both are the order/equ theories of an effective graph model. We deduce that no graph model can have an r.e. order theory, and also show that for some large subclasses, the same is true for Eq(M)
MSO on the Infinite Binary Tree: Choice and Order

International audienceWe give a new proof showing that it is not possible to define in monadic second-order logic (MSO) a choice function on the infinite binary tree. This result was first obtained by Gurevich and Shelah using set theoretical arguments. Our proof is much simpler and only uses basic tools from automata theory. We discuss some applications of the result concerning unambiguous tree automata and definability of winning strategies in infinite games. In a second part we strengthen the result of the non-existence of an MSO-definable well-founded order on the infinite binary tree by showing that every infinite binary tree with a well-founded order has an undecidable MSO-theory
Building Decision Procedures in the Calculus of Inductive Constructions

International audienceIt is commonly agreed that the success of future proof assistants will rely on their ability to incorporate computations within deduction in order to mimic the mathematician when replacing the proof of a proposition P by the proof of an equivalent proposition P' obtained from P thanks to possibly complex calculations. In this paper, we investigate a new version of the calculus of inductive constructions which incorporates arbitrary decision procedures into deduction via the conversion rule of the calculus. The novelty of the problem in the context of the calculus of inductive constructions lies in the fact that the computation mechanism varies along proof-checking: goals are sent to the decision procedure together with the set of user hypotheses available from the current context. Our main result shows that this extension of the calculus of constructions does not compromise its main properties: confluence, subject reduction, strong normalization and consistency are all preserved
Phenotypic Variation and Bistable Switching in Bacteria

Microbial research generally focuses on clonal populations. However, bacterial cells with identical genotypes frequently display different phenotypes under identical conditions. This microbial cell individuality is receiving increasing attention in the literature because of its impact on cellular differentiation, survival under selective conditions, and the interaction of pathogens with their hosts. It is becoming clear that stochasticity in gene expression in conjunction with the architecture of the gene network that underlies the cellular processes can generate phenotypic variation. An important regulatory mechanism is the so-called positive feedback, in which a system reinforces its own response, for instance by stimulating the production of an activator. Bistability is an interesting and relevant phenomenon, in which two distinct subpopulations of cells showing discrete levels of gene expression coexist in a single culture. In this chapter, we address techniques and approaches used to establish phenotypic variation, and relate three well-characterized examples of bistability to the molecular mechanisms that govern these processes, with a focus on positive feedback.
Clustering trees with instance level constraints

Constrained clustering investigates how to incorporate domain knowledge in the clustering process. The domain knowledge takes the form of constraints that must hold on the set of clusters. We consider instance level constraints, such as must-link and cannot-link. This type of constraints has been successfully used in popular clustering algorithms, such as k-means and hierarchical agglomerative clustering. This paper shows how clustering trees can support instance level constraints. Clustering trees are decision trees that partition the instances into homogeneous clusters. Clustering trees provide a symbolic description for each cluster. To handle non-trivial constraint sets, we extend clustering trees to support disjunctive descriptions. The paper's main contribution is ClusILC, an efficient algorithm for building such trees. We present experiments comparing ClusILC to COP-k-means.status: publishe
Seeing the forest through the trees: Learning a comprehensible model from an ensemble

Ensemble methods are popular learning methods that usually increase the predictive accuracy of a classifier though at the cost of interpretability and insight in the decision process. In this paper we aim to overcome this issue of comprehensibility by learning a single decision tree that approximates an ensemble of decision trees. The new model is obtained by exploiting the class distributions predicted by the ensemble. These are employed to compute heuristics for deciding which tests are to be used in the new tree. As such we acquire a model that is able to give insight in the decision process, while being more accurate than the single model directly learned on the data. The proposed method is experimentally evaluated on a large number of UCI data sets, and compared to an existing approach that makes use of artificially generated data.status: publishe
Learning directed probabilistic logical models: Ordering-search versus structure-search

Acceptance rate = 24%We discuss how to learn non-recursive directed probabilistic logical models from relational data. This problem has been tackled before by upgrading the structure-search algorithm initially proposed for Bayesian networks. In this paper we propose to upgrade another algorithm, namely ordering-search, since for Bayesian networks this was found to work better than structure-search. We experimentally compare the two upgraded algorithms on two relational domains. We conclude that there is no signifcant difference between the two algorithms in terms of quality of the learnt models while ordering-search is signifcantly faster.status: publishe
Ensembles of multi-objective decision trees

Ensemble methods are able to improve the predictive performance of many base classifiers. Up till now, they have been applied to classifiers that predict a single target attribute. Given the non-trivial interactions that may occur among the different targets in multi-objective prediction tasks, it is unclear whether ensemble methods also improve the performance in this setting. In this paper, we consider two ensemble learning techniques, bagging and random forests, and apply them to multi-objective decision trees (MODTs), which are decision trees that predict multiple target attributes at once. We empirically investigate the performance of ensembles of MODTs. Our most important conclusions are: (1) ensembles of MODTs yield better predictive performance than MODTs, and (2) ensembles of MODTs are equally good, or better than ensembles of single-objective decision trees, i.e., a set of ensembles for each target. Moreover, ensembles of MODTs have smaller model size and are faster to learn than ensembles of single-objective decision trees.status: publishe
Transfer learning in reinforcement learning problems through partial policy recycling

Acceptance rate = 23%In this paper we investigate the relation between transfer learning in reinforcement learning with function approximation and supervised learning with concept drift. We present a new incremental relational regression tree algorithm that is capable of dealing with concept drift through tree restructuring and show that it enables a reinforcement learner, more precisely a Q-learner, to transfer knowledge from one task to another by recycling those parts of the generalized Q-function that still hold interesting information for the new task. We illustrate the performance of the algorithm in experiments with both supervised learning tasks with concept drift and reinforcement learning tasks that allow the transfer of knowledge from easier, related tasks.status: publishe