128,958 research outputs found
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
Trust in social machines: the challenges
The World Wide Web has ushered in a new generation of applications constructively linking people and computers to create what have been called âsocial machines.â The âcomponentsâ of these machines are people and technologies. It has long been recognised that for people to participate in social machines, they have to trust the processes. However, the notions of trust often used tend to be imported from agent-based computing, and may be too formal, objective and selective to describe human trust accurately. This paper applies a theory of human trust to social machines research, and sets out some of the challenges to system designers
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Disruptive Innovations and Disruptive Assurance: Assuring Machine Learning and Autonomy
Autonomous and machine learning-based systems are disruptive innovations and thus require a corresponding disruptive assurance strategy. We offer an overview of a framework based on claims, arguments, and evidence aimed at addressing these systems and use it to identify specific gaps, challenges, and potential solutions
Trust on the Web: Some Web Science Research Challenges
Web Science is the interdisciplinary study of the World Wide Web as a first-order object in order to understand its relationship with the wider societies in which it is embedded, and in order to facilitate its future engineering as a beneficial object. In this paper, research issues and challenges relating to the vital topic of trust are reviewed, showing how the Web Science agenda requires trust to be addressed, and how addressing the challenges requires a range of disciplinary skills applied in an integrated manner
Large Process Models: Business Process Management in the Age of Generative AI
The continued success of Large Language Models (LLMs) and other generative
artificial intelligence approaches highlights the advantages that large
information corpora can have over rigidly defined symbolic models, but also
serves as a proof-point of the challenges that purely statistics-based
approaches have in terms of safety and trustworthiness. As a framework for
contextualizing the potential, as well as the limitations of LLMs and other
foundation model-based technologies, we propose the concept of a Large Process
Model (LPM) that combines the correlation power of LLMs with the analytical
precision and reliability of knowledge-based systems and automated reasoning
approaches. LPMs are envisioned to directly utilize the wealth of process
management experience that experts have accumulated, as well as process
performance data of organizations with diverse characteristics, e.g., regarding
size, region, or industry. In this vision, the proposed LPM would allow
organizations to receive context-specific (tailored) process and other business
models, analytical deep-dives, and improvement recommendations. As such, they
would allow to substantially decrease the time and effort required for business
transformation, while also allowing for deeper, more impactful, and more
actionable insights than previously possible. We argue that implementing an LPM
is feasible, but also highlight limitations and research challenges that need
to be solved to implement particular aspects of the LPM vision
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