A recipe for success: Crowdsourcing, online social networks, and their impact on organizational performance

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.

Technological opportunities for sensing of the health effects of weather and climate change: a state-of-the-art-review

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.

Social Signal-Driven Knowledge Automation: A Focus on Social Transportation

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.

People as sensors: mass media and local temperature influence climate change discussion on Twitter

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.

Cyber-physical system with IoT-based smart vehicles

https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.85

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Evaluating the performance of meteorological service not only relies on physical measurements but also requires public feedback. As a significant part of the evaluation, assessing the sentiment of these feedbacks gives an insightful view which can help to establish reliable decision support in policy making and emergency responding. However, climate and weather are always accompanying phenomena in the evolutionary process of human beings, making the climate and weather-related words themselves contain a large number of extend meanings in people's daily expression. These polysemy phenomena have led to a decline in the performance of traditional general sentiment calculations in the vertical field of meteorology. As far as we know, there is no Chinese sentiment calculation tool in the meteorological field, due to the different expressions between different languages. Facing with the above problems and issues, this paper proposes and establishes a sentiment analysis based on lexicon-based framework of Chinese text in the meteorological field. The framework fuses two major sentiment assessment method consists of two approaches, namely machine learning-based and knowledge based one. First, word embedding similarity and word co-occurrence methods are utilized to construct the domain sentiment dictionary which includes 8,796 negative words and 4,751 positive words. Then, by using the extend sentiment dictionary to select theme words from original text as feature, machine learning models are introduced to assess the sentiment tendency. Experiment showed that the performance of the proposed framework was improved up to 85.79% in terms of overall accuracy compared with traditional methods.

Yinan Li

Papers

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Chinese Lexical Based Sentiment Analysis Framework in Meteorology

Attribute granulation in fuzzy formal contexts based on L-fuzzy concepts