Towards a Taxonomy for Neighborhood Volunteering Management Platforms

The management and organization of volunteering in the social sector have been strongly influenced by technological progress over the last two decades. New proposals for IT-based volunteering management platforms that draw on many elements of social media are appearing with increasing frequency. In this article, we analyzed the current state of the art and use a methodological approach to develop a taxonomy for classifying existing and emerging developments in the field. The taxonomy is intended to assist practitioners in selecting appropriate systems for their respective purposes as well as support researchers in identifying research gaps. The resulting research artifact has undergone an initial evaluation and can support maintaining a better overview in a growing subject area

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

The role of agriculture in marine plastic pollution

The world consumption of plastics in agriculture amounts yearly to approximately 7.4 million tons and forecasts expect it to increase to 9.5 million tons in 2030, but this data is still limited. Conventional and selective polymers such as PE, PVC, EVA and nets are used to optimize crop production efficiency in the Mediterranean coast. The major drawback starts when the material has reached its useful time and is abandoned and dumped near dry river bed channels where it accumulates as it waits for runoff to wash them towards the sea. Since there is a lot of data missing on the amounts, composition and environmental fate, this research aims to fill the above mentioned substantial gap by performing a research on the Agriculture Plastic Waste life cycle and current management. Once the main issues were identified, a proposal for monitoring sources and fluxes was studied using Unmanned Aerial Vehicles (UAVs) imagery combined with GIS systems as a tool for plastic litter detection, and fluxes on hotspots as they represent a key source of plastic litter accumulation before it reaches the marine systems if actions are to be taken. For the latter, imagery data acquired by UAVs and combined with in situ surveillance to detect mismanaged macroplastics location due to illegal dumping on dry riverbeds in Castell de Ferro, a town located in the tropical coast of Granada in Spain which is as famous for its tourism as for the plastic greenhouses. The study area, was considered suitable for these purposes because it involves a dry riverbed constantly impacted by APW dumped or abandoned by farmers nearby. The image data acquired was then processed and validated with in situ identification of the macroplastics. As a result, the GIS tool was considered to deliver the necessary data for accurate plastic litter assessment and detection. This study was able to detect agriculture macro-plastics showing success performance over 95%. As for the management measures, producers must design and manufacture reusable and recyclable agriculture plastics. To further the process, economic and financial incentives on RDI programs on APW need to be developed in order to avoid or reduce to the extent hazardous substances use on plastic manufacture.I would like to express my gratitude to the Erasmus Mundus Masters global scholarship programme, funded by the UN for letting me be part of this incredible leaders’ network. My sincere thanks to the WACOMA organization team for all the hard work coordinating this master's programme

xxAI - Beyond Explainable AI

This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.https://digitalcommons.unomaha.edu/isqafacbooks/1000/thumbnail.jp

xxAI - Beyond Explainable AI

This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science