pinecone/core-2020-05-10-deduplication huggingface dataset:
Improving the Accuracy of Action Classification Using View-Dependent Context Information

Proceedings of: 6th International Conference, HAIS 2011, Wroclaw,
Poland, May 23-25, 2011This paper presents a human action recognition system that decomposes the task in two subtasks. First, a view-independent classifier, shared between the multiple views to analyze, is applied to obtain an initial guess of the posterior distribution of the performed action. Then, this posterior distribution is combined with view based knowledge to improve the action classification. This allows to reuse the view-independent component when a new view has to be analyzed, needing to only specify the view dependent knowledge. An example of the application of the system into an smart home domain is discussed.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/
TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/
TIC-1485) and DPS2008-07029-C02-02.Publicad
A General Purpose Context Reasoning Environment to Deal with Tracking Problems: An Ontology-based Prototype

Proceedings of: 6th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2011). Wroclaw, Poland, May 23-25, 2011The high complexity of semantics extraction with automatic video analysis has forced the researchers to the generalization of mixed approaches based on perceptual and context data. These mixed approaches do not usually take in account the advantages and benefits of the data fusion discipline. This paper presents a context reasoning environment to deal with general and specific tracking problems. The cornerstone of the environment is a symbolic architecture based on the Joint Directors of Laboratories fusion model. This architecture may build a symbolic data representation from any source, check the data consistency, create new knowledge and refine it through inference obtaining a higher understanding level of the scene and providing feedback to autocorrect the tracking errors. An ontology-based prototype has been developed to carry out experimental tests. The prototype has been proved against tracking analysis occlusion problems.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI,
CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/ TIC-1485) and
DPS2008-07029-C02-02.Publicad
Using Composition Trees to Model and Compare Software Process

Software processes described by natural languages are frequently ambiguous and it is usually difficult to compare the similarity and difference between one process defined in one standard and its counterpart defined in another standard. This paper proposes Composition Tree (CT) as a graphic language to model software process based on its purpose and expected outcomes. CT is a formal graphic notation originally designed for modeling component based software system. This paper demonstrates that CT can be a powerful notation to give a clear and unambiguous description of a software process as well. This paper also investigates an algorithm which can compare two CT-modeled processes and provide an intuitive view called a Comparison Composition Tree (CCT) to highlight the differences and similarities between the two processesGriffith Sciences, School of Information and Communication TechnologyFull Tex
Feature selection in regression tasks using conditional mutual information

This paper presents a supervised feature selection method applied to regression problems. The selectionmethod uses a Dissimilarity matrix originally developed for classification problems, whose applicability is extended here to regression and built using the conditional mutual information between features with respect to a continuous relevant variable that represents the regression function. Applying an agglomerative hierarchical clustering technique, the algorithm selects a subset of the original set offeatures. The proposed technique is compared with other three methods. Experiments on four data-sets of different nature are presented to show the importance of the features selected from the point of view of the regression estimation error (using Support Vector Regression) considering the Root Mean Squared Error (RMSE)This work was supported by the Spanish Ministry of Science and Innovation under the projects Consolider Ingenio 2010 CSD2007 − 00018, and EODIX AYA2008 − 05965 − C04 − 04/ESP and by Fundació Caixa-Castelló through the project P1 1B2007 − 4
Interactive structured output prediction: application to chromosome classification

Interactive Pattern Recognition concepts and techniques are applied to problems with structured output; i.e., problems in which the result is not just a simple class label, but a suitable structure of labels. For illustration purposes (a simplification of) the problem of Human Karyotyping is considered. Results show that a) taking into account label dependencies in a karyogram significantly reduces the classical (non-interactive) chromosome label prediction error rate and b) they are further improved when interactive processing is adopted.Work supported by the MIPRCV Spanish MICINN “Consolider Ingenio 2010” program (CSD2007-00018). Work supported by the Spanish CICyT through project TIN2009-14205-C04-01. This work was supported in part by the IST Programme of the European Community, under the PASCAL2 Network of Excellence, IST-2007-216886
Human recognition based on gait poses

This paper introduces a new approach for gait analysis based on the Gait Energy Image (GEI). The main idea is to segment the gait cycle into some biomechanical poses, and to compute a particular GEI for eachpose. Pose-based GEIs can better represent body parts and dynamics descriptors with respect to the usually blurred depiction provided by a general GEI. Gait classification is carried out by fusing separatedpose-based decisions. Experiments on human identification prove the benefits of this new approach when compared to the original GEI method.Partially supported by projects CSD2007-00018 and CICYT TIN2009-14205-C04-04 from the Spanish Ministry of Innovation and Science, P1-1B2009-04 from Fundació Bancaixa and PREDOC/2008/04 grant from Universitat Jaume I. Portions of the research in this paper use the CASIA Gait Database collected by Institute of Automation, Chinese Academy of Science
On-line classification of data streams with missing values based on reinforcement learning

In some applications, data arrive sequentially and they are not available in batch form, what makes difficult the use of traditional classification systems. In addition, some attributes may lack due to some real-world conditions. For this problem, a number of decisions have to be made regarding how to proceedwith the incomplete and unlabeled incoming objects, how to guess its missing attributes values, how to classify it, whether to include it in the training set, or when to ask for the class label to an expert. Unfortunately, no decision works well for all data sets. This data dependency motivates our formulation of the problem in terms of elements of reinforcement learning. The application of this learning paradigm for this problem is, to the best of our knowledge, novel. The empirical results are encouraging since the proposed framework behaves better and more generally than many strategies used isolatedly, and makes an efficient use of human effort (requests for the class label to an expert) and computer memory (the increase of size of the training set)This work has been supported in part by the Spanish Ministry of Education and Science under grants CSD2007–00018 (Consolider Ingenio 2010) and TIN2009–14205, and by Bancaixa under grant P1–1B2009–0
On the influence of spatial information for hyper-spectral satellite imaging characterization

Land-use classification for hyper-spectral satellite images requires a previous step of pixel characterization. In the easiest case, each pixel is characterized by its spectral curve. The improvementof the spectral and spatial resolution in hyper-spectral sensors has led to very large data sets. Some researches have focused on better classifiers that can handle big amounts of data. Others have faced the problem of band selection to reduce the dimensionality of the feature space. However, thanks to the improvement in the spatial resolution of the sensors, spatial information may also provide new featuresfor hyper-spectral satellite data. Here, an study on the influence of spectral-spatial features combined with an unsupervised band selection method is presented. The results show that it is possible to reduce very significantly the number of spectral bands required while having an adequate description of the spectral-spatial characteristics of the image for pixel classification tasksThis work has been partly supported by grant FPI PREDOC/2007/20 from Fundació Caixa Castelló-Bancaixa and projects CSD2007-00018 (Consolider Ingenio 2010) and AYA2008-05965-C04-04 from the Spanish Ministry of Science and Innovatio
There Is More Than One Way to Get Out of a Car: Automatic Mode Finding for Action Recognition in the Wild

Actions in the wild” is the term given to examples of human motion that are performed in natural settings, such as those harvested from movies [10] or the Internet [9]. State-of-the-art approaches in this domain are orders of magnitude lower than in more contrived settings. One of the primary reasons being the huge variability within each action class. We propose to tackle recognition in the wild by automatically breaking complex action categories into multiple modes/group, and training a separate classifier for each mode. This is achieved using RANSAC which identifies and separates the modes while rejecting outliers. We employ a novel reweighting scheme within the RANSAC procedure to iteratively reweight training examples, ensuring their inclusion in the final classification model. Our results demonstrate the validity of the approach, and for classes which exhibit multi-modality, we achieve in excess of double the performance over approaches that assume single modality
A distance for partially labeled trees

In a number of practical situations, data have structure and the relations among its component parts need to be coded with suitable data models. Trees are usually utilized for representing data for which hierarchical relations can be defined. This is the case in a number of fields like image analysis, natural language processing, protein structure, or music retrieval, to name a few. In those cases, procedures for comparing trees are very relevant. An approximate tree edit distance algorithm has been introduced for working with trees labeled only at the leaves. In this paper, it has been applied to handwritten character recognition, providing accuracies comparable to those by the most comprehensive search method, being as efficient as the fastest.This work is supported by the Spanish Ministry projects DRIMS (TIN2009-14247-C02), and Consolider Ingenio 2010 (MIPRCV, CSD2007-00018), partially supported by EU ERDF and the Pascal Network of Excellence
Classifying melodies using tree grammars

Similarity computation is a difficult issue in music information retrieval, because it tries to emulate the special ability that humans show for pattern recognition in general, and particularly in the presence of noisy data. A number of works have addressed the problem of what is the best representation for symbolic music in this context. The tree representation, using rhythm for defining the tree structure and pitch information for leaf and node labeling has proven to be effective in melodic similarity computation. In this paper we propose a solution when we have melodies represented by trees for the training but the duration information is not available for the input data. For that, we infer a probabilistic context-free grammar using the information in the trees (duration and pitch) and classify new melodies represented by strings using only the pitch. The case study in this paper is to identify a snippet query among a set of songs stored in symbolic format. For it, the utilized method must be able to deal with inexact queries and efficient for scalability issues.This work is supported by the Spanish Ministry project TIN2009-14247-C02-02, TIN2009-14205-C04-C1, the Pascal Network of Excellence, and the program Consolider Ingenio 2010 (CSD2007-00018)
Classification of high dimensional and imbalanced hyperspectral imagery data

The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA is applied to reduce the number of spectral bands. This is a preliminary study that pursues to investigate the benefits of using together these two techniques, and also to evaluate the application order that leads to the best classification performance. Experimental results demonstrate the significance of combining these preprocessing tools to improve the performance of hyperspectral imagery classification. Although it seems that the most effective order of application corresponds to first a resampling algorithm and then PCA, this is a question that still needs a much more thorough investigationPartially supported by the Spanish Ministry of Education and Science under grants CSD2007–00018, AYA2008–05965–0596–C04–04/ESP and TIN2009–14205–C04–04, and by Fundació Caixa Castelló–Bancaixa under grant P1–1B2009–0
Detection performance evaluation of boosted random Ferns

We present an experimental evaluation of Boosted Random Ferns in terms of the detection performance and the training data. We show that adding an iterative bootstrapping phase during the learning of the object classifier, it increases its detection rates given that additional positive and negative samples are collected (bootstrapped) for retraining the boosted classifier. After each bootstrapping iteration, the learning algorithm is concentrated on computing more discriminative and robust features (Random Ferns), since the bootstrapped samples extend the training data with more difficult images.\ud
The resulting classifier has been validated in two different object datasets, yielding successful detections rates in spite of challenging image conditions such as lighting changes, mild occlusions and cluttered background.Postprint (author’s final draft
Norm refinement and design through inductive learning

In the physical world, the rules governing behaviour are debugged by observing an outcome that was not intended and the addition of new constraints to prevent the attainment of that outcome. We propose a similar approach to support the incremental development of normative frameworks (also called institutions) and demonstrate how this works through the validation and synthesis of normative rules using model generation and inductive learning. This is achieved by the designer providing a set of use cases, comprising collections of event traces that describe how the system is used along with the desired outcome with respect to the normative framework. The model generator encodes the description of the current behaviour of the system. The current specification and the traces for which current behaviour and expected behaviour do not match are given to the learning framework to propose new rules that revise the existing norm set in order to inhibit the unwanted behaviour. The elaboration of a normative system can then be viewed as a semi-automatic, iterative process for the detection of incompleteness or incorrectness of the existing normative rules, with respect to desired properties, and the construction of potential additional rules for the normative system
Using a normative framework to explore the prototyping of wireless grids

The capacity for normative frameworks to capture the essential features of interactions between components in open architectures suggests they might also be of assistance in an early, rapid prototyping phase of system development, helping to refine concepts, identify actors, explore policies and evaluate feasibility. As an exercise to examine this thesis, we investigate the concept of the wireless grid. Wireless grids have been proposed to address the energy issues arising from a new generation of mobile phones, the idea being that local communication with other mobile phones, being cheaper, can be used in combination with network communication to achieve common goals while at the same time extending the battery duty cycle. This results in a social dilemma, as it is advantageous for rational users to benefit from the energy savings without any contribution to the cooperation, as every commitment has its price. We present a necessarily simplified model, whose purpose is to provide us with the foundation to explore issues in the management of such a framework, policies to encourage collaborative behaviour, and the means to evaluate the effects on energy consumption
On the dynamics of active categorisation of different objects shape through tactile sensors

Tuci E., Massera G., Nolfi S. On the dynamics of active categorisation of different objects shape through tactile sensors. Proceedings of the 10th European Conference of Artificial Life (ECAL 2009), Vol. LNCS/LNAI 5777, 5778, Springer Verlag, 2009Active perception refers to a theoretical approach to thestudy of perception grounded on the idea that perceiving is a way ofacting, rather than a process whereby the brain constructs an internalrepresentation of the world. In this paper, we complement previousstudies by illustrating the operational principles of an active categorisationprocess in which a neuro-controlled anthropomorphic robotic arm,equipped with coarse-grained tactile sensors, is required to perceptuallycategorise spherical and ellipsoid objects
Input from the External Environment and Input from within the Body

Behaviour responds to both input from the external environment and input from within the organism\u27s body. Input from the external environment has mainly the function to regulate the execution of the organism\u27s activities while input from the body is used to decide which activity to execute. We evolve artificial organisms which to survive and reproduce have to both eat food and drink water in equivalent quantities and therefore at any given time they have to decide whether to look for food or water. We show that in some environments the appropriate behaviour can evolve with no need for the organism\u27s brain to know the current level of energy and water in the body while in other environments the brain needs this information from the body in the form of hunger and thirst. We discuss how the body and the body\u27s interactions with the brain are part of the overall adaptive pattern of an organism and must co-evolve with brain and behaviour
To grip, or not to grip: Evolving coordination in autonomous robots

Ampatzis C., Santos F.C., Trianni V., Tuci E. To grip, or not to grip: Evolving coordination in autonomous robots. Proceedings of the 10th European Conference of Artificial Life (ECAL 2009), Vol. LNCS/LNAI 5777, 5778, Springer Verlag, 2009In evolutionary robotics, as in the animal world, performing a task which is beneficial to the entire group demands the coordination of different individuals. Whenever time-dependent dynamic allocation of roles is needed and individual roles are not pre-defined, coordination can often be hard to achieve. In this paper, we study the evolution of role allocation and self-assembling strategies in a group of two homogeneous robots.We show how robot coordination and individual choices (who will grip whom) can be successfully restated in terms of anti-coordination problems, showing how conventional game theoretical tools can be used in the interpretation and design of evolutionary outcomes in collective robotics. Moreover, we highlight and discuss striking similarities between the way our physical robots allocate roles and the way animals solve conflicts. Arguably, these similarities suggest that evolutionary robotics may offer apart from automatic controller design for autonomous robots a viable alternative for the study of biological phenomena.;In evolutionary robotics, as in the animal world, performing a task which is beneficial to the entire group demands the coordination of different individuals. Whenever time-dependent dynamic allocation of roles is needed and individual roles are not pre-defined, coordination can often be hard to achieve. In this paper, we study the evolution of role allocation and self-assembling strategies in a group of two homogeneous robots.We show how robot coordination and individual choices (who will grip whom) can be successfully restated in terms of anti-coordination problems, showing how conventional game theoretical tools can be used in the interpretation and design of evolutionary outcomes in collective robotics. Moreover, we highlight and discuss striking similarities between the way our physical robots allocate roles and the way animals solve conflicts. Arguably, these similarities suggest that evolutionary robotics may offer apart from automatic controller design for autonomous robots a viable alternative for the study of biological phenomena
The effect of proprioceptive feedback on the distribution of sensory information in a model of an undulatory organism

In an animal, a crucial factor concerning the arrival of information at the sensors and subsequent transmission to the effectors, is how it is distributed. At the same time, higher animals also employ proprioceptive feedback so that their respective neural circuits have information regarding the state of the animal body. In order to disseminate what this practically means for the distribution of sensory information, we have modeled a segmented swimming organism (animat) coevolving its nervous system and body plan morphology. In a simulated aquatic environment, we find that animats artificially endowed with proprioceptive feedback are able to evolve completely decoupled central pattern generators (CPGs) meaning that they emerge without any connections made to neural circuits in adjacent body segments. Without such feedback however, we also find that the distribution of sensory information from the head of the animat becomes far more important, with adjacent CPG circuits becoming interconnected. Crucially, this demonstrates that where proprioceptive mechanisms are lacking, more effective delivery of sensory input is essential
Emerged coupling of motor control and morphological development in evolution of multi-cellular animats

A model for co-evolving behavior control and morphological development is presented in this paper. The development of the morphology starts with a single cell that is able to divide or die, which is controlled by a gene regulatory network. The cells are connected by springs and form the morphology of the grown individuals. The movements of animats are resulted from the shrinking and relaxation of the springs connecting the lateral cells on the body morphology. The gene regulatory network, together with the frequency and phase shifts of the spring movements are evolved to maximize the distance that the animats can swim in a given time interval. To facilitate the evolution of swimming animats, a term that awards an elongated morphology is also included in the fitness function. We show that animats with different body-plans emerge in the evolutionary runs and that the evolved movement control strategy is coupled with the body plan
Probabilistic rule learning

Traditionally, rule learners have learned deterministic rules from deterministic data, that is,  the rules have been expressed as logical statements and also the examples and their classification have been purely logical. We upgrade rule learning to a probabilistic setting, in which both the examples themselves as well as their classification can be probabilistic. The setting is incorporated in the probabilistic rule learner ProbFOIL, which combines  the principles of the relational rule learner FOIL with the probabilistic Prolog, ProbLog.  We report also on some experiments that demonstrate the utility of the approach.status: publishe