163 research outputs found
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
PROCEEDINGS 5th PLATE Conference
The 5th international PLATE conference (Product Lifetimes and the Environment) addressed product lifetimes in the context of sustainability. The PLATE conference, which has been running since 2015, has successfully been able to establish a solid network of researchers around its core theme. The topic has come to the forefront of current (political, scientific & societal) debates due to its interconnectedness with a number of recent prominent movements, such as the circular economy, eco-design and collaborative consumption. For the 2023 edition of the conference, we encouraged researchers to propose how to extend, widen or critically re-construct thematic sessions for the PLATE conference, and the paper call was constructed based on these proposals. In this 5th PLATE conference, we had 171 paper presentations and 238 participants from 14 different countries. Beside of paper sessions we organized workshops and REPAIR exhibitions
Advances in Artificial Intelligence: Models, Optimization, and Machine Learning
The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications
Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture
Big streams of Earth images from satellites or other platforms (e.g., drones
and mobile phones) are becoming increasingly available at low or no cost and
with enhanced spatial and temporal resolution. This thesis recognizes the
unprecedented opportunities offered by the high quality and open access Earth
observation data of our times and introduces novel machine learning and big
data methods to properly exploit them towards developing applications for
sustainable and resilient agriculture. The thesis addresses three distinct
thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP),
the monitoring of food security and applications for smart and resilient
agriculture. The methodological innovations of the developments related to the
three thematic areas address the following issues: i) the processing of big
Earth Observation (EO) data, ii) the scarcity of annotated data for machine
learning model training and iii) the gap between machine learning outputs and
actionable advice.
This thesis demonstrated how big data technologies such as data cubes,
distributed learning, linked open data and semantic enrichment can be used to
exploit the data deluge and extract knowledge to address real user needs.
Furthermore, this thesis argues for the importance of semi-supervised and
unsupervised machine learning models that circumvent the ever-present challenge
of scarce annotations and thus allow for model generalization in space and
time. Specifically, it is shown how merely few ground truth data are needed to
generate high quality crop type maps and crop phenology estimations. Finally,
this thesis argues there is considerable distance in value between model
inferences and decision making in real-world scenarios and thereby showcases
the power of causal and interpretable machine learning in bridging this gap.Comment: Phd thesi
Localization, Mapping and SLAM in Marine and Underwater Environments
The use of robots in marine and underwater applications is growing rapidly. These applications share the common requirement of modeling the environment and estimating the robots’ pose. Although there are several mapping, SLAM, target detection and localization methods, marine and underwater environments have several challenging characteristics, such as poor visibility, water currents, communication issues, sonar inaccuracies or unstructured environments, that have to be considered. The purpose of this Special Issue is to present the current research trends in the topics of underwater localization, mapping, SLAM, and target detection and localization. To this end, we have collected seven articles from leading researchers in the field, and present the different approaches and methods currently being investigated to improve the performance of underwater robots
Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022
The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing
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