Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace
01 Jun 2020-Vol. 7, Iss: 1, pp 61-79
TL;DR: Previous studies show that the use of crowdsourcing in social space can expand the coverage as well as enhance the performance of meteorological service and are contributing towards a systemic and intelligent knowledge service to establish a better bridge among academic, industrial and individual community.
About: The article was published on 2020-06-01 and is currently open access. It has received 17 citations till now. The article focuses on the topics: Crowdsourcing & Cyberspace.
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TL;DR: In this paper, the importance of online social networks and crowdsourcing as elements that favor organizational learning processes to promote organizational development is investigated. But the interrelationships between these mechanisms are poorly understood and need further testing.
22 citations
University of Toronto1, University of Twente2, University of Novi Sad3, Instituto Superior Técnico4, Indian Institute of Technology Roorkee5, University of Manchester6, Singapore Management University7, University of Nottingham8, Universidad de San Carlos de Guatemala9, University of the Witwatersrand10, Federal University of Paraíba11, University of Brasília12
TL;DR: A review of the opportunities and challenges for studying the health effects of weather and climate change on humans, animals, and plants can be found in this paper, focusing on existing and emerging technologies.
Abstract: Sensing and measuring meteorological and physiological parameters of humans, animals, and plants are necessary to understand the complex interactions that occur between atmospheric processes and the health of the living organisms. Advanced sensing technologies have provided both meteorological and biological data across increasingly vast spatial, spectral, temporal, and thematic scales. Information and communication technologies have reduced barriers to data dissemination, enabling the circulation of information across different jurisdictions and disciplines. Due to the advancement and rapid dissemination of these technologies, a review of the opportunities for sensing the health effects of weather and climate change is necessary. This paper provides such an overview by focusing on existing and emerging technologies and their opportunities and challenges for studying the health effects of weather and climate change on humans, animals, and plants.
13 citations
TL;DR: The current research into a multisource heterogeneous social signal-based traffic decision knowledge automation framework is summarized and the computational paradigm and applications scenarios of this framework are summarized.
Abstract: Urban transportation systems are shaped by factors that include people, vehicles, roads, and the environment, forming a complex and giant system with dynamics, diversity, and uncertainty. Physical signal-driven intelligent transportation systems (ITSs) typically lack the ability to capture social behaviors or crowd willingness, and they achieve only information automation for transportation decision support. The crowdsourcing social signals consist of timely, extensive, comprehensive, and rich intelligence that concern urban dynamics, social behaviors, and traffic environments. Such social signals provide a new paradigm for operating ITS with unstructured semantic data, making knowledge automation for decision intelligence a possibility. This article reviews the knowledge automation paradigms for cyber–physical–social systems (CPSSs) compared with traditional information automation paradigms for cyber–physical systems (CPSs) in ITS, from the perspective of data-driven, modeling space, analytical methodologies, and decision support services. To investigate the key methodology in social spaces that enhance information automation into knowledge automation, we summarize the current research into a multisource heterogeneous social signal-based traffic decision knowledge automation framework and further exploit the computational paradigm and applications scenarios of this framework. Finally, we discuss future challenges for designing and realizing knowledge automation on CPSS in transportation.
12 citations
Cites background from "Social weather: A review of crowdso..."
...turing [21], meteorological monitoring [22], [23], and smart education [24]....
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01 Dec 2014
TL;DR: This article examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media influences the process and found that no convincing evidence was found that the media acts as a mediator in the relationship between local weather and climate change discourse.
Abstract: This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media influences the process. We used the volume of Twitter messages containing words “climate change” and “global warming” as the indicator of attention that public pays to the issue. Specifically, the goals were: (1) to investigate whether people immediately notice substantial local weather anomalies such as deviations from long-term mean temperatures and connect them to climate change by contributing to climate change discourse on Twitter and (2) to examine the role of mass media in this process. Over 2 million tweets were collected for a two-year period (2012–2013) and were assigned to 157 urban areas in the continental US. The rate of tweeting on climate change was regressed on the time variables, number of climate change publications in the mass media, and a number of temperature variables. The analysis was conducted at the two levels of aggregation – national and local. The high significance of the mass media and temperature variables in the majority of regression models suggests that both the weather and mass media coverage control public interest to the topic. However, no convincing evidence was found that the media acts as a mediator in the relationship between local weather and climate change discourse. Overall, the findings confirmed that the public recognize extreme temperature anomalies and connect these anomalies to climate change.
6 citations
16 Jun 2021
TL;DR: IoT-based Advanced Electronic Cyber-Physical System (AE-CPS) has been proposed to minimize the cyber-attack and enhance security for the smart vehicle and shows better results when compared to other methods.
Abstract: Nowadays, smart vehicles can exchange data through various communication protocols in response to technological developments in smart transport systems. Smart vehicles show the perfect model for a cyber-physical system when electronic components and physical devices are embedded in them. The incorporation of computer, network and physical methods are the cyber-physical systems (CPS). Integrated Systems and Network control and manage physical processes, feedback mechanisms that influence physical computation processes and vice versa. Cyber-devices, including hardware, software applications, are protected from cyber-attacks through cybersecurity. Individuals and businesses use this strategy to shield themselves from unauthorized access to data centers and other computerized networks. As the IoT and data remain fundamentally related, virtual vehicle hijacking is possible in the transport network's evolving design. Hence in this paper, IoT-based Advanced Electronic Cyber-Physical System (AE-CPS) has been proposed to minimize the cyber-attack and enhance security for the smart vehicle. Cyber-assault involves cyber-criminals launching a single computer or several computers or networks with one or more computers. A cyber-attack can maliciously deactivate computers, stolen data, or use a broken computer for other attacks. A cyber-attack happens when a hacker attempt to obtain unauthorized access to a computer or network data. It happens when information without permission is accessed. Personal details, such as social security numbers, passwords, and financial account numbers, may be included. First, a state-space structure reflects the driverless, smart vehicle with the board viewing system. The IoT embedded sensors monitor the device states since the smart vehicles and control center has been far from everyone. The vehicle's sensing data is sent through an insecure communication channel to the control center, where attacks occur. The optimal status estimate algorithm is derived from the mean square error theory for the vehicle conditions' information and visualization. The optimization algorithm focused on the partial definite programming approach is designed to govern the vehicle states. The experimental results show that there are less delay and a high-performance rate of 98.97%. high security rate (95.23%), time stages (8.12%), state reaction rate (90.9%), error rate (6.45%), evaluation metrics (93.6%), reaction time (1.44%), feedback period (96.88%) when compared to other methods.
6 citations
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TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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30,558 citations
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...…pretrained language representation models based on prior knowledge (e.g. BERT (Devlin et al., 2018), Word2vec (Mikolov et al., 2013) and Glove vector (Pennington et al., 2014)) better organize and represent the semantic information of text by learning from large texts in the general domain....
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..., 2013) and Glove vector (Pennington et al., 2014)) better organize and represent the semantic information of text by learning from large texts in the general domain....
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Abstract: We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.
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TL;DR: In this article, the authors analyzed important new research from across the social sciences and found that climate change threatens important cultural dimensions of people's lives and livelihoods, including material and lived aspects of culture, identity, community cohesion and sense of place.
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...Weather not only directly affects the physical activities of human but can influence human society in an imperceptible way (Adger et al., 2013)....
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