pinecone/core-2020-05-10-deduplication huggingface dataset:
Path planning for automatic recharging system for steep-slope vineyard robots

Develop cost-effective ground robots for crop monitoring in steep slope vineyards is a complex challenge. The terrain presents harsh conditions for mobile robots and most of the time there is no one available to give support to the robots. So, a fully autonomous steep-slope robot requires a robust automatic recharging system. This work proposes a multilevel system that monitors a vineyard robot autonomy, to plan off-line the trajectory to the nearest recharging point and dock the robot on that recharging point considering visual tags. The proposed system called VineRecharge was developed to be deployed into a cost-effective robot with low computational power. Besides, this paper benchmarks several visual tags and detectors and integrates the best one into the VineRecharge system.This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project “POCI-01-0145-FEDER-006961”, and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013.info:eu-repo/semantics/publishedVersio
Low resolution lidar-based multi object tracking for driving applications

The final publication is available at link.springer.comVehicle detection and tracking in real scenarios are key com- ponents to develop assisted and autonomous driving systems. Lidar sen- sors are specially suitable for this task, as they bring robustness to harsh weather conditions while providing accurate spatial information. How- ever, the resolution provided by point cloud data is very scarce in com- parison to camera images. In this work we explore the possibilities of Deep Learning (DL) methodologies applied to low resolution 3D lidar sensors such as the Velodyne VLP-16 (PUCK), in the context of vehicle detection and tracking. For this purpose we developed a lidar-based sys- tem that uses a Convolutional Neural Network (CNN), to perform point- wise vehicle detection using PUCK data, and Multi-Hypothesis Extended Kalman Filters (MH-EKF), to estimate the actual position and veloci- ties of the detected vehicles. Comparative studies between the proposed lower resolution (VLP-16) tracking system and a high-end system, using Velodyne HDL-64, were carried out on the Kitti Tracking Benchmark dataset. Moreover, to analyze the influence of the CNN-based vehicle detection approach, comparisons were also performed with respect to the geometric-only detector. The results demonstrate that the proposed low resolution Deep Learning architecture is able to successfully accom- plish the vehicle detection task, outperforming the geometric baseline approach. Moreover, it has been observed that our system achieves a similar tracking performance to the high-end HDL-64 sensor at close range. On the other hand, at long range, detection is limited to half the distance of the higher-end sensor.Peer ReviewedPostprint (author's final draft
Knowledge and tasks representation for an industrial robotic application

The paper presents an implementation of knowledge representation and task representation, based on ontologies for an Industrial Robotic Application. The industrial application is to insert up to 56 small pins, e.g., sealants, in a harness box terminal for the automotive industry. The number of sealants and their insertion pattern vary significantly with the production requests. Based on the knowledge representation of the robot and also based on the tasks to be performed, plans are built and then sent to the robot controller based on the seal pattern production order. Moreover, the robotic system is capable to perform re-planning when an insertion error is reported by a machine vision system. The ontology-based approach was used to define the robot, the machine vision system, and the tasks that were needed to be performed by the robotic system. The robotic system was validated experimentally by showing its capability to correct seal insertion errors, while re-planning.info:eu-repo/semantics/publishedVersio
Ultra-wideband time of flight based localization system and odometry fusion for a scanning 3 DoF magnetic field autonomous robot

Solving the robot localization problem is one of the most necessary requirements for autonomous robots. Several methodologies can be used to determine its location as accurately as possible. What makes this difficult is the existence of uncertainty in the sensing of the robot. The uncertain information needs to be combined in an optimal way. This paper stresses a Kalman filter to combine information from the odometry and Ultra Wide Band Time of Flight distance modules, which lacks the orientation. The proposed system validated in a real developed platform performs the fusion task which outputs position and orientation of the robot. It is used to localize the robot and make a 3 DoF scanning of magnetic field in a room. Other examples can be pointed out with the same localization techniques in service and industrial autonomous robots.Project “TEC4Growth - Pervasive Intelligence, Enhancers and
Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020” is
financed by the North Portugal Regional Operational. Programme (NORTE 2020),
under the PORTUGAL 2020 Partnership Agreement, and through the European
Regional Development Fund (ERDF). This work is also financed by the ERDF European
Regional Development Fund through the Operational Programme for Competitiveness
and Internationalisation - COMPETE 2020 Programme, and by National
Funds through the FCT Funda¸cao para a Ciˆencia e a Tecnologia (Portuguese Foundation
for Science and Technology) within project POCI-01-0145-FEDER-006961.info:eu-repo/semantics/publishedVersio
Dynamic collision avoidance system for a manipulator based on RGB-D data

The new paradigms of Industry 4.0 demand the collabora-
tion between robot and humans. They could help and collaborate each
other without any additional safety unlike other manipulators. The robot
should have the ability of acquire the environment and plan (or re-plan)
on-the-
y the movement avoiding the obstacles and people. This paper
proposes a system that acquires the environment space, based on a kinect
sensor, performs the path planning of a UR5 manipulator for pick and
place tasks while avoiding the objects, based on the point cloud from
kinect. Results allow to validate the proposed system.Project ”TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020” is financed by the North Portugal Regional Operational. Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). This work is also financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation -COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013.info:eu-repo/semantics/publishedVersio
Development of a dynamic path for a toxic substances mapping mobile robot in industry environment

Some industries have critical areas (dangerous or hazardous) where the presence of a human must be reduced or avoided. In some cases, there are areas where humans should be replaced by robots. The present work uses a robot with differential drive to scan an environment with known and unknown obstacles, defined in 3D simulation. It is important that the robot be able to make the right decisions about its way without the need of an operator. A solution to this challenge will be presented in this paper. The control application and its communication module with a simulator or a real robot are proposed. The robot can perform the scan, passing through all the waypoints arranged in a grid. The results are presented, showcasing the robot’s capacity to perform a viable trajectory without human intervention.Project ”TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020” is financed by the North Portugal Regional Operational. Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).
This work is also financed by the ERDF European Regional Development
Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013.info:eu-repo/semantics/publishedVersio
New Generation Metadata Vocabulary for Ontology Description and Publication

Scientific communities are using an increasing number of ontologies and vocabularies. Currently, the problem lies in the difficulty to find and select them for a specific knowledge engineering task. Thus, there is a real need to precisely describe these ontologies with adapted metadata, but none of the existing metadata vocabularies can completely meet this need if taken independently. In this paper, we present a new version of Metadata vocabulary for Ontology Description and publication, referred as MOD 1.2 which succeeds previous work published in 2015. It has been designed by reviewing in total 23 standard existing metadata vocabularies (e.g., Dublin Core, OMV, DCAT, VoID) and selecting relevant properties for describing ontologies. Then, we studied metadata usage analytics within ontologies and ontology repositories. MOD 1.2 proposes in total 88 properties to serve both as (i) a vocabulary to be used by ontology developers to annotate and describe their ontologies, or (ii) an explicit OWL vocabulary to be used by ontology libraries to offer semantic descriptions of ontologies as linked data. The experimental results show that MOD 1.2 supports a new set of queries for ontology libraries. Because MOD is still in early stage, we also pitch the plan for a collaborative design and adoption of future versions within an international working group
Introduction to Random Matrices - Theory and Practice

This is a book for absolute beginners. If you have heard about random matrix
theory, commonly denoted RMT, but you do not know what that is, then welcome!,
this is the place for you. Our aim is to provide a truly accessible
introductory account of RMT for physicists and mathematicians at the beginning
of their research career. We tried to write the sort of text we would have
loved to read when we were beginning Ph.D. students ourselves. Our book is
structured with light and short chapters, and the style is informal. The
calculations we found most instructive are spelt out in full. Particular
attention is paid to the numerical verification of most analytical results. Our
book covers standard material - classical ensembles, orthogonal polynomial
techniques, spectral densities and spacings - but also more advanced and modern
topics - replica approach and free probability - that are not normally included
in elementary accounts on RMT. This book is dedicated to the fond memory of
Oriol Bohigas
A system for the management of clinical tasks throughout the clinical process with notification features

Computer-Interpretable Guidelines have been associated with a higher integration of standard practices in the daily context of health care institutions. The Clinical Decision Support Systems that deliver these machine-interpretable recommendations usually follow a Q & A style of communication, retrieving information from the user or a clinical repository and performing reasoning upon it, based on the rules from Clinical Practice Guidelines. However, these systems are limited in the reach they are capable of achieving as they were initially conceived for use in very specific moments of the clinical process, namely in physician appointments. The purpose of this work is thus to present a system that, in addition to Q & A reasoning, is equipped with other functionalities such as the scheduling and temporal management of clinical tasks, the mapping of these tasks onto an agenda of activities to allow an easy consultation by health care professionals, and notifications that let health care professionals know of task enactment times and information collection times. In this way, the system ensures the delivery of procedures. The main components of the system, which reflect a different perspective on the delivery of CIG advice that we call guideline as a service, are disclosed, and they include a health care Personal Assistant Web Application, a health care assistant mobile application, and the integration with the private calendar services of the user.(POCI-01-0145-)info:eu-repo/semantics/publishedVersio
Two linearities for quantum computing in the lambda calculus

We propose a way to unify two approaches of non-cloning in quantum
lambda-calculi. The first approach is to forbid duplicating variables, while
the second is to consider all lambda-terms as algebraic-linear functions. We
illustrate this idea by defining a quantum extension of first-order
simply-typed lambda-calculus, where the type is linear on superposition, while
allows cloning base vectors. In addition, we provide an interpretation of the
calculus where superposed types are interpreted as vector spaces and
non-superposed types as their basis.Comment: Long version of TPNC'17 paper (doi:10.1007/978-3-319-71069-3_22)
  including third author's "Licenciatura"'s thesi
Simulating Perceptions of Security

This document is the Accepted Manuscript of a conference paper published in Proceedings of the Cambridge International Workshop on Security Protocols 2017. Under embargo until 29 November 2018. The final publication is available at Springer via: https://doi.org/10.1007/978-3-319-71075-4_7Systems complicated enough to have ongoing security issues are difficult to understand, and hard to model. The models are hard to understand, even when they are right (another reason they are usually wrong), and too complicated to use to make decisions. Instead attackers, developers, and users make security decisions based on their {\em perceptions} of the system, and not on properties that the system actually has. These perceptions differ between communities, causing decisions made by one community to appear irrational to another. Attempting to predict such irrational behaviour by basing a model of perception on a model of the system is even more complicated than the original modelling problem we can't solve. Ockham's razor says to model the perceptions directly, since these will be simpler than the system itself
Towards a Deep Reinforcement Learning Approach for Tower Line Wars

There have been numerous breakthroughs with reinforcement learning in the
recent years, perhaps most notably on Deep Reinforcement Learning successfully
playing and winning relatively advanced computer games. There is undoubtedly an
anticipation that Deep Reinforcement Learning will play a major role when the
first AI masters the complicated game plays needed to beat a professional
Real-Time Strategy game player. For this to be possible, there needs to be a
game environment that targets and fosters AI research, and specifically Deep
Reinforcement Learning. Some game environments already exist, however, these
are either overly simplistic such as Atari 2600 or complex such as Starcraft II
from Blizzard Entertainment. We propose a game environment in between Atari
2600 and Starcraft II, particularly targeting Deep Reinforcement Learning
algorithm research. The environment is a variant of Tower Line Wars from
Warcraft III, Blizzard Entertainment. Further, as a proof of concept that the
environment can harbor Deep Reinforcement algorithms, we propose and apply a
Deep Q-Reinforcement architecture. The architecture simplifies the state space
so that it is applicable to Q-learning, and in turn improves performance
compared to current state-of-the-art methods. Our experiments show that the
proposed architecture can learn to play the environment well, and score 33%
better than standard Deep Q-learning which in turn proves the usefulness of the
game environment.Comment: Proceedings of the 37th SGAI International Conference on Artificial
  Intelligence, Cambridge, UK, 2017, Artificial Intelligence XXXIV, 201
An Ontology to support Knowledge Management in Behaviour-based Healthcare

A Do Something Different (DSD) behaviour change programme consists of a series of digitally delivered behavioural prompts, or "Dos", for targeted behaviour change designed to aid people to achieve personal goals or to break bad habits. "Dos" may address for example behaviour connected with exercise, smoking, diet, sleep or diabetes self-management, or personal development objectives such as leadership. DSD's current database contains thousands of such "Dos", developed by behaviour change experts. We have developed an ontology to organise the large database of behavioural prompts in order to help DSD with its knowledge management. We began by developing an ontology of function, bottom-up; then we expanded this into a multi-perspective ontology covering WHO, WHAT, HOW, WHEN, WHERE and WHY perspectives. The expected benefits from our ontology are: 1. response data from users can be aggregated by ontology category to get insights into users' behaviour, 2. the ontology enables the implementation of smarter algorithms for selecting which "Dos" to suggest to which users, and 3. the ontology assists in the process of generating new "Dos", either by domain experts or by crowd-sourcing. Finally we follow an ontology-theoretic approach to argue that our ontology should be re-usable across other behaviour change applications, but maybe not across other application types
Feature Level Ensemble Method for Classifying Multi-Media Data

Multimedia data consists of several different types of data, such as numbers, text, images, audio etc. and they usually need to be fused or integrated before analysis. This study investigates a feature-level aggregation approach to combine multimedia datasets for building heterogeneous ensembles for classification. It firstly aggregates multimedia datasets at feature level to form a normalised big dataset, then uses some parts of it to generate classifiers with different learning algorithms. Finally, it applies three rules to select appropriate classifiers based on their accuracy and/or diversity to build heterogeneous ensembles. The method is tested on a multimedia dataset and the results show that the heterogeneous ensembles outperform the individual classifiers as well as homogeneous ensembles. However, it should be noted that, it is possible in some cases that the combined dataset does not produce better results than using single media data
Measuring Relations Between Concepts In Conceptual Spaces

The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
high-dimensional space and concepts are represented by regions in this space.
Our recent mathematical formalization of this framework is capable of
representing correlations between different domains in a geometric way. In this
paper, we extend our formalization by providing quantitative mathematical
definitions for the notions of concept size, subsethood, implication,
similarity, and betweenness. This considerably increases the representational
power of our formalization by introducing measurable ways of describing
relations between concepts.Comment: Accepted at SGAI 2017 (http://www.bcs-sgai.org/ai2017/). The final
  publication is available at Springer via
  https://doi.org/10.1007/978-3-319-71078-5_7. arXiv admin note: substantial
  text overlap with arXiv:1707.05165, arXiv:1706.0636
A lambda calculus for density matrices with classical and probabilistic
  controls

In this paper we present two flavors of a quantum extension to the lambda
calculus. The first one, $\lambda_\rho$, follows the approach of classical
control/quantum data, where the quantum data is represented by density
matrices. We provide an interpretation for programs as density matrices and
functions upon them. The second one, $\lambda_\rho^\circ$, take advantage of
the density matrices presentation in order to follow the mixed trace of
programs in a kind of generalised density matrix. Such a control can be seen as
a weaker form of the quantum control and data approach.Comment: This version includes a 11-pages appendix with proofs, and a small
  fix in the definition of property P(b,A
Non-Redundant Spectral Dimensionality Reduction

Spectral dimensionality reduction algorithms are widely used in numerous
domains, including for recognition, segmentation, tracking and visualization.
However, despite their popularity, these algorithms suffer from a major
limitation known as the "repeated Eigen-directions" phenomenon. That is, many
of the embedding coordinates they produce typically capture the same direction
along the data manifold. This leads to redundant and inefficient
representations that do not reveal the true intrinsic dimensionality of the
data. In this paper, we propose a general method for avoiding redundancy in
spectral algorithms. Our approach relies on replacing the orthogonality
constraints underlying those methods by unpredictability constraints.
Specifically, we require that each embedding coordinate be unpredictable (in
the statistical sense) from all previous ones. We prove that these constraints
necessarily prevent redundancy, and provide a simple technique to incorporate
them into existing methods. As we illustrate on challenging high-dimensional
scenarios, our approach produces significantly more informative and compact
representations, which improve visualization and classification tasks
Scatteract: Automated extraction of data from scatter plots

Charts are an excellent way to convey patterns and trends in data, but they
do not facilitate further modeling of the data or close inspection of
individual data points. We present a fully automated system for extracting the
numerical values of data points from images of scatter plots. We use deep
learning techniques to identify the key components of the chart, and optical
character recognition together with robust regression to map from pixels to the
coordinate system of the chart. We focus on scatter plots with linear scales,
which already have several interesting challenges. Previous work has done fully
automatic extraction for other types of charts, but to our knowledge this is
the first approach that is fully automatic for scatter plots. Our method
performs well, achieving successful data extraction on 89% of the plots in our
test set.Comment: Submitted to ECML PKDD 2017 proceedings, 16 page