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In today’s surveillance systems, a multitude of sensors are used. Thus, the data volume is clearly increasing and the human decision maker has to be supported in analyzing this data in an intelligent way. This contribution deals with the process of situation assessment, which is analyzing real-time data with respect to pre-modeled situations of interest with a dynamic Bayesian network. The quality of the recognition is evaluated with a maritime dataset.
data fusion --- dynamic Bayesian networks --- SituationsbewusstseinSituation assessment --- maritime surveillance --- maritime Überwachung --- Situationsanalyse --- Datenfusion --- situation awareness --- dynamische Bayes’sche Netze
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A concept for time-related forecasts of lane change maneuvers in highway scenarios is presented within the present work. Automated driving systems rely on understanding the driving environment to fulfill their driving task transparently and safely. This involves the perception of the driving environment as well as its interpretation to detect and predict driving maneuvers of road users.
Fahrstreifenwechsel --- Automatisches Fahren --- Maschinelles Lernen --- dynamic Bayesian networks --- Dynamische Bayes'sche Netzwerke --- lane change --- machine learning --- automated driving
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We live in a world of big data: the amount of information collected on human behavior each day is staggering, and exponentially greater than at any time in the past. Additionally, powerful algorithms are capable of churning through seas of data to uncover patterns. Providing a simple and accessible introduction to data mining, Paul Attewell and David B. Monaghan discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists. The authors also empower social scientists to tap into these new resources and incorporate data mining methodologies in their analytical toolkits. Data Mining for the Social Sciences demystifies the process by describing the diverse set of techniques available, discussing the strengths and weaknesses of various approaches, and giving practical demonstrations of how to carry out analyses using tools in various statistical software packages.
Social sciences --- Data mining. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Data processing. --- Statistical methods. --- analyzing data. --- bayesian networks. --- big data. --- bootstrapping. --- business analytics. --- chaid. --- classification and regression trees. --- classification trees. --- confusion matrix. --- data analysis. --- data mining. --- data processing. --- data scholarship. --- data science. --- hardware for data mining. --- heteroscedasticity. --- naive bayes. --- partition trees. --- permutation tests. --- scholarly data. --- social science. --- social scientists. --- software for data mining. --- statistical methods. --- statistical modeling. --- studying data. --- text mining. --- vif regression. --- weka.
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It has been confirmed that the number of cases and the death toll of COVID-19 are continuing to rise in many countries around the globe. Governments around the world have been struggling with containing and reducing the socioeconomic impacts of COVID-19; however, their respective responses have not been consistent. Aggressive measures imposed by some governments have resulted in a complete lockdown that has disrupted all facets of life and poses massive health, social, and financial impacts. Other countries, however, are taking a more wait-and-see approach in an attempt to maintain business as usual. Collectively, these challenges reflect a super wicked problem that places immense pressure on economies and societies and requires the strategic management of health systems to avoid overwhelming them—this has been linked to the public mantra of ‘flattening the curve’, which acknowledges that while the pandemic cannot be stopped, its impact can be regulated so that the number of cases at any given time is not beyond the capacity of the health system. Dynamic simulation modelling is a framework that facilitates the understanding/exploring of complex problems, of searching for and finding the best option(s) from all practical solutions where time dynamics are essential. The papers in this book provide research insights into this super wicked problem and case studies exploring the interactions between social, economic, environmental, and health factors through the use of a systems approach.
Technology: general issues --- History of engineering & technology --- Environmental science, engineering & technology --- COVID-19 --- pandemic --- wicked problem --- systems approach --- leverage points --- Bayesian Networks --- system thinking --- mathematical epidemiology --- SIR-type model --- model parameter estimation --- non-pharmaceutical intervention --- dynamical systems --- COVID-19/SARS-CoV2 --- computational cognitive science --- semantic networks --- text mining --- social media mining --- emotions --- tour and traveling --- digitalization shift --- change readiness --- expanded TOPSIS --- UK --- vaccination --- immunity --- policy --- system dynamics --- modelling --- uncertainty --- branded content --- marketing --- total interpretive structural modelling --- decision-making Trial and Evaluation Laboratory --- causal loop diagram --- systems thinking --- network theory --- complexity economics --- economic crisis --- agent-based model --- information theory --- global value chains --- megaprojects --- housing markets --- economic networks --- n/a
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It has been confirmed that the number of cases and the death toll of COVID-19 are continuing to rise in many countries around the globe. Governments around the world have been struggling with containing and reducing the socioeconomic impacts of COVID-19; however, their respective responses have not been consistent. Aggressive measures imposed by some governments have resulted in a complete lockdown that has disrupted all facets of life and poses massive health, social, and financial impacts. Other countries, however, are taking a more wait-and-see approach in an attempt to maintain business as usual. Collectively, these challenges reflect a super wicked problem that places immense pressure on economies and societies and requires the strategic management of health systems to avoid overwhelming them—this has been linked to the public mantra of ‘flattening the curve’, which acknowledges that while the pandemic cannot be stopped, its impact can be regulated so that the number of cases at any given time is not beyond the capacity of the health system. Dynamic simulation modelling is a framework that facilitates the understanding/exploring of complex problems, of searching for and finding the best option(s) from all practical solutions where time dynamics are essential. The papers in this book provide research insights into this super wicked problem and case studies exploring the interactions between social, economic, environmental, and health factors through the use of a systems approach.
COVID-19 --- pandemic --- wicked problem --- systems approach --- leverage points --- Bayesian Networks --- system thinking --- mathematical epidemiology --- SIR-type model --- model parameter estimation --- non-pharmaceutical intervention --- dynamical systems --- COVID-19/SARS-CoV2 --- computational cognitive science --- semantic networks --- text mining --- social media mining --- emotions --- tour and traveling --- digitalization shift --- change readiness --- expanded TOPSIS --- UK --- vaccination --- immunity --- policy --- system dynamics --- modelling --- uncertainty --- branded content --- marketing --- total interpretive structural modelling --- decision-making Trial and Evaluation Laboratory --- causal loop diagram --- systems thinking --- network theory --- complexity economics --- economic crisis --- agent-based model --- information theory --- global value chains --- megaprojects --- housing markets --- economic networks --- n/a
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It has been confirmed that the number of cases and the death toll of COVID-19 are continuing to rise in many countries around the globe. Governments around the world have been struggling with containing and reducing the socioeconomic impacts of COVID-19; however, their respective responses have not been consistent. Aggressive measures imposed by some governments have resulted in a complete lockdown that has disrupted all facets of life and poses massive health, social, and financial impacts. Other countries, however, are taking a more wait-and-see approach in an attempt to maintain business as usual. Collectively, these challenges reflect a super wicked problem that places immense pressure on economies and societies and requires the strategic management of health systems to avoid overwhelming them—this has been linked to the public mantra of ‘flattening the curve’, which acknowledges that while the pandemic cannot be stopped, its impact can be regulated so that the number of cases at any given time is not beyond the capacity of the health system. Dynamic simulation modelling is a framework that facilitates the understanding/exploring of complex problems, of searching for and finding the best option(s) from all practical solutions where time dynamics are essential. The papers in this book provide research insights into this super wicked problem and case studies exploring the interactions between social, economic, environmental, and health factors through the use of a systems approach.
Technology: general issues --- History of engineering & technology --- Environmental science, engineering & technology --- COVID-19 --- pandemic --- wicked problem --- systems approach --- leverage points --- Bayesian Networks --- system thinking --- mathematical epidemiology --- SIR-type model --- model parameter estimation --- non-pharmaceutical intervention --- dynamical systems --- COVID-19/SARS-CoV2 --- computational cognitive science --- semantic networks --- text mining --- social media mining --- emotions --- tour and traveling --- digitalization shift --- change readiness --- expanded TOPSIS --- UK --- vaccination --- immunity --- policy --- system dynamics --- modelling --- uncertainty --- branded content --- marketing --- total interpretive structural modelling --- decision-making Trial and Evaluation Laboratory --- causal loop diagram --- systems thinking --- network theory --- complexity economics --- economic crisis --- agent-based model --- information theory --- global value chains --- megaprojects --- housing markets --- economic networks --- COVID-19 --- pandemic --- wicked problem --- systems approach --- leverage points --- Bayesian Networks --- system thinking --- mathematical epidemiology --- SIR-type model --- model parameter estimation --- non-pharmaceutical intervention --- dynamical systems --- COVID-19/SARS-CoV2 --- computational cognitive science --- semantic networks --- text mining --- social media mining --- emotions --- tour and traveling --- digitalization shift --- change readiness --- expanded TOPSIS --- UK --- vaccination --- immunity --- policy --- system dynamics --- modelling --- uncertainty --- branded content --- marketing --- total interpretive structural modelling --- decision-making Trial and Evaluation Laboratory --- causal loop diagram --- systems thinking --- network theory --- complexity economics --- economic crisis --- agent-based model --- information theory --- global value chains --- megaprojects --- housing markets --- economic networks
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Attention in the AI safety community has increasingly started to include strategic considerations of coordination between relevant actors in the field of AI and AI safety, in addition to the steadily growing work on the technical considerations of building safe AI systems. This shift has several reasons: Multiplier effects, pragmatism, and urgency. Given the benefits of coordination between those working towards safe superintelligence, this book surveys promising research in this emerging field regarding AI safety. On a meta-level, the hope is that this book can serve as a map to inform those working in the field of AI coordination about other promising efforts. While this book focuses on AI safety coordination, coordination is important to most other known existential risks (e.g., biotechnology risks), and future, human-made existential risks. Thus, while most coordination strategies in this book are specific to superintelligence, we hope that some insights yield “collateral benefits” for the reduction of other existential risks, by creating an overall civilizational framework that increases robustness, resiliency, and antifragility.
strategic oversight --- multi-agent systems --- autonomous distributed system --- artificial superintelligence --- safe for design --- adaptive learning systems --- explainable AI --- ethics --- scenario mapping --- typologies of AI policy --- artificial intelligence --- design for values --- distributed goals management --- scenario analysis --- Goodhart’s Law --- specification gaming --- AI Thinking --- VSD --- AI --- human-in-the-loop --- value sensitive design --- future-ready --- forecasting AI behavior --- AI arms race --- AI alignment --- blockchain --- artilects --- policy making on AI --- distributed ledger --- AI risk --- Bayesian networks --- artificial intelligence safety --- conflict --- AI welfare science --- moral and ethical behavior --- scenario network mapping --- policymaking process --- human-centric reasoning --- antispeciesism --- AI forecasting --- transformative AI --- ASILOMAR --- judgmental distillation mapping --- terraforming --- pedagogical motif --- AI welfare policies --- superintelligence --- artificial general intelligence --- supermorality --- AI value alignment --- AGI --- predictive optimization --- AI safety --- technological singularity --- machine learning --- holistic forecasting framework --- simulations --- existential risk --- technology forecasting --- AI governance --- sentiocentrism --- AI containment
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The concept of sustainability has been intensively used over the last decades since Brundtland´s report was published in 1987. This concept, due to its transversal, horizontal and interdisciplinary nature, can be used in many disciplines, scenarios, spatio-temporal dimensions and different circumstances. The intensive development in recent years of analytical techniques and tools based on disciplines such as artificial intelligence, machine learning, data mining, information theory and the Internet of Things, among others, has meant we are very well-placed for analysing the sustainability of water systems in a multiperspective way. Water systems management requires the most advanced approaches and tools for rigorously addressing all the dimensions involved in the sustainability of its development. Consequently, addressing the sustainability of water systems management may comprise physical (natural processes), chemical, socioeconomic, legal, institutional, infrastructure (engineering), political and cultural aspects, among others. This Special Issue welcomes general and specific contributions that address the sustainability of water systems management considering its development. Special interest will be given to those contributions that consider tradeoffs and/or integration between some of the aspects or disciplines that drive the sustainability of water systems in the context of their management and development.
History of engineering & technology --- suspended solids --- unmanned aerial vehicle --- spectral imaging --- artificial neural networks --- water resource --- South Korean urban industry --- green use efficiency of industrial water (GUEIW) --- global non-radial directional distance function model (GNDDF) --- economic efficiency of industrial water use (ECEIW) --- environmental efficiency of industrial water use (ENEIW) --- water quality --- climate change --- Bayesian networks --- uncertainty --- multi-models --- prioritization --- geomorphometric parameters --- compound parameter --- geospatial distribution --- GIS --- GHGs --- aquatic factors --- random forest --- water temperature --- nitrogen --- sulfate --- concrete arch-dams --- stability scenarios --- deformation scenarios --- safety management --- sustainability assessment --- runoff --- temporal dependence --- rivers’ sustainability --- predictive methods --- causal reasoning --- runoff fractions --- water management --- contamination --- integrated water resources management --- groundwater --- pollution --- Sub-Saharan Africa --- transition management --- water safety plan --- aquifer management --- water governance --- irrigation --- unauthorized use --- barbate river basin --- biocalcarenites --- remote sensing --- citizen surveys --- artificial neural network (ANN) --- chemical oxygen demand (COD) --- wastewater treatment plant (WWTP)
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The concept of sustainability has been intensively used over the last decades since Brundtland´s report was published in 1987. This concept, due to its transversal, horizontal and interdisciplinary nature, can be used in many disciplines, scenarios, spatio-temporal dimensions and different circumstances. The intensive development in recent years of analytical techniques and tools based on disciplines such as artificial intelligence, machine learning, data mining, information theory and the Internet of Things, among others, has meant we are very well-placed for analysing the sustainability of water systems in a multiperspective way. Water systems management requires the most advanced approaches and tools for rigorously addressing all the dimensions involved in the sustainability of its development. Consequently, addressing the sustainability of water systems management may comprise physical (natural processes), chemical, socioeconomic, legal, institutional, infrastructure (engineering), political and cultural aspects, among others. This Special Issue welcomes general and specific contributions that address the sustainability of water systems management considering its development. Special interest will be given to those contributions that consider tradeoffs and/or integration between some of the aspects or disciplines that drive the sustainability of water systems in the context of their management and development.
suspended solids --- unmanned aerial vehicle --- spectral imaging --- artificial neural networks --- water resource --- South Korean urban industry --- green use efficiency of industrial water (GUEIW) --- global non-radial directional distance function model (GNDDF) --- economic efficiency of industrial water use (ECEIW) --- environmental efficiency of industrial water use (ENEIW) --- water quality --- climate change --- Bayesian networks --- uncertainty --- multi-models --- prioritization --- geomorphometric parameters --- compound parameter --- geospatial distribution --- GIS --- GHGs --- aquatic factors --- random forest --- water temperature --- nitrogen --- sulfate --- concrete arch-dams --- stability scenarios --- deformation scenarios --- safety management --- sustainability assessment --- runoff --- temporal dependence --- rivers’ sustainability --- predictive methods --- causal reasoning --- runoff fractions --- water management --- contamination --- integrated water resources management --- groundwater --- pollution --- Sub-Saharan Africa --- transition management --- water safety plan --- aquifer management --- water governance --- irrigation --- unauthorized use --- barbate river basin --- biocalcarenites --- remote sensing --- citizen surveys --- artificial neural network (ANN) --- chemical oxygen demand (COD) --- wastewater treatment plant (WWTP)
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The concept of sustainability has been intensively used over the last decades since Brundtland´s report was published in 1987. This concept, due to its transversal, horizontal and interdisciplinary nature, can be used in many disciplines, scenarios, spatio-temporal dimensions and different circumstances. The intensive development in recent years of analytical techniques and tools based on disciplines such as artificial intelligence, machine learning, data mining, information theory and the Internet of Things, among others, has meant we are very well-placed for analysing the sustainability of water systems in a multiperspective way. Water systems management requires the most advanced approaches and tools for rigorously addressing all the dimensions involved in the sustainability of its development. Consequently, addressing the sustainability of water systems management may comprise physical (natural processes), chemical, socioeconomic, legal, institutional, infrastructure (engineering), political and cultural aspects, among others. This Special Issue welcomes general and specific contributions that address the sustainability of water systems management considering its development. Special interest will be given to those contributions that consider tradeoffs and/or integration between some of the aspects or disciplines that drive the sustainability of water systems in the context of their management and development.
History of engineering & technology --- suspended solids --- unmanned aerial vehicle --- spectral imaging --- artificial neural networks --- water resource --- South Korean urban industry --- green use efficiency of industrial water (GUEIW) --- global non-radial directional distance function model (GNDDF) --- economic efficiency of industrial water use (ECEIW) --- environmental efficiency of industrial water use (ENEIW) --- water quality --- climate change --- Bayesian networks --- uncertainty --- multi-models --- prioritization --- geomorphometric parameters --- compound parameter --- geospatial distribution --- GIS --- GHGs --- aquatic factors --- random forest --- water temperature --- nitrogen --- sulfate --- concrete arch-dams --- stability scenarios --- deformation scenarios --- safety management --- sustainability assessment --- runoff --- temporal dependence --- rivers’ sustainability --- predictive methods --- causal reasoning --- runoff fractions --- water management --- contamination --- integrated water resources management --- groundwater --- pollution --- Sub-Saharan Africa --- transition management --- water safety plan --- aquifer management --- water governance --- irrigation --- unauthorized use --- barbate river basin --- biocalcarenites --- remote sensing --- citizen surveys --- artificial neural network (ANN) --- chemical oxygen demand (COD) --- wastewater treatment plant (WWTP) --- suspended solids --- unmanned aerial vehicle --- spectral imaging --- artificial neural networks --- water resource --- South Korean urban industry --- green use efficiency of industrial water (GUEIW) --- global non-radial directional distance function model (GNDDF) --- economic efficiency of industrial water use (ECEIW) --- environmental efficiency of industrial water use (ENEIW) --- water quality --- climate change --- Bayesian networks --- uncertainty --- multi-models --- prioritization --- geomorphometric parameters --- compound parameter --- geospatial distribution --- GIS --- GHGs --- aquatic factors --- random forest --- water temperature --- nitrogen --- sulfate --- concrete arch-dams --- stability scenarios --- deformation scenarios --- safety management --- sustainability assessment --- runoff --- temporal dependence --- rivers’ sustainability --- predictive methods --- causal reasoning --- runoff fractions --- water management --- contamination --- integrated water resources management --- groundwater --- pollution --- Sub-Saharan Africa --- transition management --- water safety plan --- aquifer management --- water governance --- irrigation --- unauthorized use --- barbate river basin --- biocalcarenites --- remote sensing --- citizen surveys --- artificial neural network (ANN) --- chemical oxygen demand (COD) --- wastewater treatment plant (WWTP)
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