pinecone/core-2020-05-10-deduplication huggingface dataset:
Adaptive Join Operator for Federated Queries over Linked Data Endpoints

Traditional static query optimization is not adequate for query federation over linked data endpoints due to unpredictable data arrival rates and missing statistics. In this paper, we propose an adaptive join operator for federated query processing which can change the join method during the execution. Our approach always begins with symmetric hash join in order to produce the first result tuple as soon as possible and changes the join method as bind join when it estimates that bind join is more efficient than symmetric hash join for the rest of the process. We compare our approach with symmetric hash join and bind join. Performance evaluation shows that our approach provides optimal response time and has the adaptation ability to the different data arrival rates
H-word: Supporting job scheduling in Hadoop with workload-driven data redistribution

The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-44039-2_21Today’s distributed data processing systems typically follow a query shipping approach and exploit data locality for reducing network traffic. In such systems the distribution of data over the cluster resources plays a significant role, and when skewed, it can harm the performance of executing applications. In this paper, we addressthe challenges of automatically adapting the distribution of data in a cluster to the workload imposed by the input applications. We propose a generic algorithm, named H-WorD, which, based on the estimated workload over resources, suggests alternative execution scenarios of tasks, and hence identifies required transfers of input data a priori, for timely bringing data close to the execution. We exemplify our algorithm in the context of MapReduce jobs in a Hadoop ecosystem. Finally, we evaluate our approach and demonstrate the performance gains of automatic data redistribution.Peer ReviewedPostprint (author's final draft
Heavy-tailed distributions for agent-based economic modelling

This chapter is devoted to the parametric statistical distributions of economic size phenomena of various types. Probability distributions of size variables are usually taken as the first quantitative characterization of complex systems, allowing one to detect the possible occurrence of regularities and to identify the underlying mechanisms at their origin - and thus at the origin of the behaviour of the complex system under study. A rapid survey covers the class of "heavy-tailed" distributions decreasing slower than exponentially at infinity. The fascination for "power laws" is then explained, starting from the statistical approaches for quantifying and testing a power-law distribution from your data, and ending with a (not exhaustive) list of mechanisms leading to power-law distributions. The description of distributions is ultimately enlarged by proposing the Laplace distribution, which has both tails - the upper and the lower - heavier than a standard Gaussian
BAC: A bagged associative classifier for big data frameworks

Big Data frameworks allow powerful distributed computations extending the results achievable on a single machine. In this work, we present a novel distributed associative classifier, named BAC, based on ensemble techniques. Ensembles are a popular approach that builds several models on different subsets of the original dataset, eventually voting to provide a unique classification outcome. Experiments on Apache Spark and preliminary results showed the capability of the proposed ensemble classifier to obtain a quality comparable with the single-machine version on popular real-world datasets, and overcome their scalability limits on large synthetic dataset
Computing the safe working zone of a 3-RRS parallel manipulator

Determination of the safe working zone (SWZ) of a parallel manipulator is a one-time computational task with several permanent benefits. As this subspace of the workspace of the manipulator is free of both the loss- and gain-type singularities, link interference, as well as physical joint limits, the manipulator can move freely in this space. Moreover, if the natural choice of a convex-shaped SWZ is adhered to, then point-to-point path planning inside the SWZ always has a trivial solution, namely, a segment joining the two points, which is guaranteed to be inside the workspace. In this paper, the SWZ of the 3-RRS existing in the _Izmir Institute of Technology has been computed. Starting with the geometry of the manipulator, the loop-closure constraint equations have been derived. The singularity conditions are obtained based on the singularity of certain Jacobian matrices associated with the constraint functions. The interference between the links are detected by first encapsulating the links in rectangular parallelepipeds, which are then discretized into triangles, and subjected to collision tests between the relevant pairs of triangles. Using these theoretical developments, the SWZ is computed. The numerical results are depicted graphically
3D formulation for revolute clearance joints

In this work, a general and comprehensive methodology for the modeling and analysis of spatial multibody systems with revolute clearance joints is presented. The proposed formulation considers the journal and the bearing elements as two cylindrical colliding bodies. All the potential contact scenarios and their kinematics are determined from the relative positions of two cylinders and from the kinematics of the multibody systems. It must be highlighted that both radial and axial clearances are modeled under the new proposed approach. The methodology presented here is demonstrated by using a spatial slider-crank mechanism that incorporates a spatial revolute clearance joint. The results show that the system’s response is considerably influenced by the existence of a clearance joint.The first author thanks to the Portuguese Foundation for Sci-ence and Technology through the PhD grant (PD/BD/114154/2016). This work has been supported by the Portuguese Foundation for Science and Technology with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 – Programa Operacional Competitividade e Internacionaliza-ção (POCI) with the reference project POCI-01-0145-FEDER-006941
Recent developments on cylindrical contact force models with realistic properties

In this work a numerical study was performed involving a spatial revolute joint formed by a journal and a bearing with the purpose to evaluate the effect of the bearing thickness, misalignment and impact velocity on the force generated in the contact process. These analyses were run in a developed three-dimensional finite element model that simulates the referred aspects in a realistic way. This numerical tool allows defining both radial and axial clearances, which are important aspects in the analysis of operating conditions of actual journal-bearing joints. Resulting forces and stress profiles were calculated in different regions of the model, contributing to
the accurate definition of design parameters. The generated data was ultimately compared and validated with the one obtained from the models that are currently used in the context of multibody dynamics and predict kinematic aspects of mechanical systems involving cylindrical contact interaction.The second author expresses his deepest gratitude to the Portuguese Foundation
for Science and Technology through the PhD grant (PD/BD/114154/2016). This work is
supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the
COMPETE 2020 Programa Operacional Competitividade e Internacionalização (POCI) with the
reference project POCI-01-0145-FEDER-006941
Alternating error effects on decomposition method in function generation synthesis

In approximate function generation synthesis methods, error between the desired function’s output and designed mechanism’s output oscillate about zero error while crossing the zero error margin at precision points. The common goal of these methods is to minimize the error within the selected working region of the mechanism. For mechanisms like Bennett overconstrained six-revolute jointed linkages that have relatively large number of construction parameters, it is a difficult task to solve for them at once. Decomposition method enables to divide such linkages into two loops and independently solve for each loop with less construction parameters. Although some approximation methods are proven to produce
smaller errors than others for a single-loop synthesis, in this work, it is shown that smaller errors are not guaranteed for a certain method when used along with decomposition method. Numerical examples indicate that in decomposition method, more attention should be given to the alternation of the error of each decomposed mechanism, rather than the approximation method used
Design of a cable-driven four-bar mechanism for arm rehabilitation

This paper presents the design of an assistive mechanism to be used for the rehabilitation of human arm. First the motions of two types of rehabilitation exercises are described. The motions are planar motions, so a planar four-bar mechanism is designed. The synthesis problem is formulated as a three-position synthesis problem. Next, the actuation issue is addressed. Actuation via cables is preferred for better force transmission. The connection point of the cable to the mechanism is optimized considering the curvature of the coupler point curve. Finally, multiple pulleys are designed for enhancing force transmission
Modeling relevant legal information for consumer disputes

Accessing relevant legal information found in text excerpts from heterogeneous sources is essential to the decision making process in consumer disputes. The Ontology of Relevant Legal Information in Consumer Disputes (ric) is the domain-independent ontology modeling this relevant legal information comprising rights, their requisites, exceptions, constraints, enforcement procedures, legal sources. Its use is exemplified with one extension thereof, the Air Transport Passenger Incidents Ontology (ric-atpi), representing both the possible incidents triggered by a complaint in the air transport passenger domain and the related legal information that might be applicable.The Ontology models the key provisions found in the hard law, and those in softlaw, comprising heterogeneous sources in a structured manner.An ontology-based system provides the knowledge embedded in the legal sources and their relation to the specific scenario
Deep active learning for autonomous navigation.

Imitation learning refers to an agent's ability to mimic a desired behavior by learning from bservations. A major challenge facing learning from demonstrations is to represent the demonstrations in a manner that is adequate for learning and efficient for real time decisions. Creating feature representations is especially challenging when extracted from high dimensional visual data. In this paper, we present a method for imitation learning from raw visual data. The proposed method is applied to a popular imitation learning domain that is relevant to a variety of real life applications; namely navigation. To create a training set, a teacher uses an optimal policy to perform a navigation task, and the actions taken are recorded along with visual footage from the first person perspective. Features are automatically extracted and used to learn a policy that mimics the teacher via a deep convolutional neural network. A trained agent can then predict an action to perform based on the scene it finds itself in. This method is generic, and the network is trained without knowledge of the task, targets or environment in which it is acting. Another common challenge in imitation learning is generalizing a policy over unseen situation in training data. To address this challenge, the learned policy is subsequently improved by employing active learning. While the agent is executing a task, it can query the teacher for the correct action to take in situations where it has low confidence. The active samples are added to the training set and used to update the initial policy. The proposed approach is demonstrated on 4 different tasks in a 3D simulated environment. The experiments show that an agent can effectively perform imitation learning from raw visual data for navigation tasks and that active learning can significantly improve the initial policy using a small number of samples. The simulated test bed facilitates reproduction of these results and comparison with other approaches
Intelligent measurement in unmanned aerial cyber physical systems for traffic surveillance.

An adaptive framework for building intelligent measurement systems has been proposed in the paper and tested on simulated traffic surveillance data. The use of the framework enables making intelligent decisions related to the presence of anomalies in the surveillance data with the help of statistical analysis, computational intelligent and machine learning. Computational intelligence can also be effectively utilised for identifying the main contributing features in detecting anomalous data points within the surveillance data. The experimental results have demonstrated that a reasonable performance is achieved in terms of inferential accuracy and data processing speed
Accelerated Optimal Topology Search for Two-hidden-layer Neural Networks

Two-hidden-layer feedforward neural networks are investigated for the existence of an optimal hidden node ratio. In the experiments, the heuristic n1 =int (0.5nh + 1), where n1 is the number of nodes in the first hidden layer and nh is the total number of hidden nodes, found networks with generalisation errors, on average, just 0.023%-0.056% greater than those found by exhaustive search. This reduced the complexity of an exhaustive search from quadratic, to linear in , with very little penalty. Further reductions in search complexity to logarithmic could be possible using existing methods developed by the Author
An outlier ranking tree selection approach to extreme pruning of random forests.

Random Forest (RF) is an ensemble classification technique that was developed by Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is still room for enhancing and improving its performance in terms of predictive accuracy. This explains why, over the past decade, there have been many extensions of RF where each extension employed a variety of techniques and strategies to improve certain aspect (s) of RF. Since it has been proven empirically that ensembles tend to yield better results when there is a significant diversity among the constituent models, the objective of this paper is twofold. First, it investigates how an unsupervised learning technique, namely, Local Outlier Factor (LOF) can be used to identify diverse trees in the RF. Second, trees with the highest LOF scores are then used to create a new RF termed LOFB-DRF that is much smaller in size than RF, and yet performs at least as good as RF, but mostly exhibits higher performance in terms of accuracy. The latter refers to a known technique called ensemble pruning. Experimental results on 10 real datasets prove the superiority of our proposed method over the traditional RF. Unprecedented pruning levels reaching as high as 99% have been achieved at the time of boosting the predictive accuracy of the ensemble. The notably extreme pruning level makes the technique a good candidate for real-time applications
A hybrid deep learning neural approach for emotion recognition from facial expressions for socially assistive robots

© 2018, The Natural Computing Applications Forum. We have recently seen significant advancements in the development of robotic machines that are designed to assist people with their daily lives. Socially assistive robots are now able to perform a number of tasks autonomously and without human supervision. However, if these robots are to be accepted by human users, there is a need to focus on the form of human–robot interaction that is seen as acceptable by such users. In this paper, we extend our previous work, originally presented in Ruiz-Garcia et al. (in: Engineering applications of neural networks: 17th international conference, EANN 2016, Aberdeen, UK, September 2–5, 2016, proceedings, pp 79–93, 2016. https://doi.org/10.1007/978-3-319-44188-7_6), to provide emotion recognition from human facial expressions for application on a real-time robot. We expand on previous work by presenting a new hybrid deep learning emotion recognition model and preliminary results using this model on real-time emotion recognition performed by our humanoid robot. The hybrid emotion recognition model combines a Deep Convolutional Neural Network (CNN) for self-learnt feature extraction and a Support Vector Machine (SVM) for emotion classification. Compared to more complex approaches that use more layers in the convolutional model, this hybrid deep learning model produces state-of-the-art classification rate of 96.26 % , when tested on the Karolinska Directed Emotional Faces dataset (Lundqvist et al. in The Karolinska Directed Emotional Faces—KDEF, 1998), and offers similar performance on unseen data when tested on the Extended Cohn–Kanade dataset (Lucey et al. in: Proceedings of the third international workshop on CVPR for human communicative behaviour analysis (CVPR4HB 2010), San Francisco, USA, pp 94–101, 2010). This architecture also takes advantage of batch normalisation (Ioffe and Szegedy in Batch normalization: accelerating deep network training by reducing internal covariate shift. http://arxiv.org/abs/1502.03167, 2015) for fast learning from a smaller number of training samples. A comparison between Gabor filters and CNN for feature extraction, and between SVM and multilayer perceptron for classification is also provided
Mobile soundscape mixer : Ready for action

Today, cultural organizations such as museums are seeking new ways to attract and engage audience. Augmented reality based applications are seen very promising. The target is to provide more interactive experiences for an audience with high familiarity of digital interaction. So far, visual presentation has been dominant in augmented reality systems. In contrast to this trend, we have chosen to concentrate on audio augmentation as user generated soundscapes. This paper discusses our approach, focusing on how to design and develop an easyto- use and smoothly working Android application, which increases user interaction by developing soundscapes from building blocks stored in audio digital asset management system. We have successfully implemented applications for Android platform and evaluated their performance
Water Resources Systems Planning and Management: An Introduction to Methods, Models and Applications

This revised, updated textbook presents a systems approach to the planning, management, and operation of water resources infrastructure in the environment.  Previously published in 2005 by UNESCO and Deltares (Delft Hydraulics at the time), this new edition, written again with contributions from Jery R. Stedinger, Jozef P. M. Dijkman, and Monique T. Villars, is aimed equally at students and professionals.  It introduces readers to the concept of viewing issues involving water resources as a system of multiple interacting components and scales. It offers guidelines for initiating and carrying out water resource system planning and management projects. 
It introduces alternative optimization, simulation, and statistical methods useful for project identification, design, siting, operation and evaluation and for studying post-planning issues. The authors cover both basin-wide and urban water issues and present ways of identifying and evaluating alternatives for addressing multiple-purpose and multi-objective water quantity and quality management challenges. Reinforced with cases studies, exercises, and media supplements throughout, the text is ideal for upper-level undergraduate and graduate courses in water resource planning and management as well as for practicing planners and engineers in the field.Deltares, UNESCO-IHE Institute for Water Educatio
Toward a Boundary Regional Control Problem for Boolean Cellular Automata

An important question to be addressed regarding system control on a time
interval $[0, T]$ is whether some particular target state in the configuration
space is reachable from a given initial state. When the target of interest
refers only to a portion of the spatial domain, we speak about regional
analysis. Cellular Automata (CA) approach have been recently promoted for the
study of control problems on spatially extended systems for which the classical
approaches cannot be used. An interesting problem concerns the situation where
the subregion of interest is not interior to the domain but a portion of its
boundary. In this paper we address the problem of regional controllability of
cellular automata via boundary actions, i.e., we investigate the
characteristics of a cellular automaton so that it can be controlled inside a
given region only acting on the value of sites at its boundaries.Comment: Version 1, with the name "Regional Control of Boolean Cellular
  Automata" has been published in ACRI 2016, LNCS 9863, pp. 101-112, 2016,
  doi:10.1007/978-3-319-44365-2_1
Asynchronous simulation of Boolean networks by monotone Boolean networks

International audienceWe prove that the fully asynchronous dynamics of a Boolean network f : {0, 1}^n → {0, 1}^n without negative loop can be simulated, in a very specific way, by a monotone Boolean network with 2n components. We then use this result to prove that, for every even n, there exists a monotone Boolean network f : {0, 1}^n → {0, 1}^n , an initial configuration x and a fixed point y of f such that: (i) y can be reached from x with a fully asynchronous updating strategy, and (ii) all such strategies contains at least 2^{n/2} updates. This contrasts with the following known property: if f : {0, 1}^n → {0, 1}^n is monotone, then, for every initial configuration x, there exists a fixed point y such that y can be reached from x with a fully asynchronous strategy that contains at most n updates
The Garden of Eden Theorem for Cellular Automata on Group Sets

We prove the Garden of Eden theorem for cellular automata with finite set of
states and finite neighbourhood on right amenable left homogeneous spaces with
finite stabilisers. It states that the global transition function of such an
automaton is surjective if and only if it is pre-injective. Pre-Injectivity
means that two global configurations that differ at most on a finite subset and
have the same image under the global transition function must be identical.Comment: arXiv admin note: text overlap with arXiv:1603.0646
A spatial sensitivity analysis of a spatially explicit model for myxomatosis in Belgium

Motivated by their ability to mimic complex biological and natural processes, many spatially explicit models (SEMs) have been proposed during the last two decades for simulating such processes. Yet, a sensitivity analysis (SA) of such models is typically not performed, or model sensitivity is only studied over time on the basis of aggregated quantities, due to the lack of an appropriate framework. Taking a SEM for myxomatosis among European rabbits in Belgium as a model SEM, we conduct a spatial SA and investigate to what extent the sensitivity of this model varies spatially and whether or not this should become common practice when developing a SEM