1,828 research outputs found
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There are many different ways to rate mobile content in the form of various explicit
user feedback e.g. like buttons, thumbs up and thumbs down, star ratings as well
as there are ways to analyse usage statistics of applications on using mobile analytics
tools. Implicit feedback enables to collect more data for getting better insight of content
usage and user behaviour. In recent years many works have been conducted in order
to classify activities using smartphones. Previous works have shown that sensor-based
activity recognition on smartphones is feasible. Yet previous works have not classified
reading activity on smartphones. This work proposes one possible way to classify
this activity with high accuracy. Classifying reading activity provides possibility to
have more precise estimates on mobile content usage statistics, by utilizing sensorand
visual-based activity recognition techniques. A set of mobile applications was
developed to facilitate data collection and labelling. Accelerometer and gyroscope data
was collected from 35 different subjects, after cleaning data 4438 sample readings were left. A neural network was trained on 80% of data and 94% accuracy was reached
on classifying reading activity using a smartphone. The results show that classifying
reading activity using accelerometer and gyroscope data is possible with high degree
of accuracy. We provide Android application source code along with neural network
training implementation accompanied by training data in a Git repositor
Review on smartphone sensing technology for structural health monitoring
Sensing is a critical and inevitable sector of structural health monitoring (SHM). Recently, smartphone sensing technology has become an emerging, affordable, and effective system for SHM and other engineering fields. This is because a modern smartphone is equipped with various built-in sensors and technologies, especially a triaxial accelerometer, gyroscope, global positioning system, high-resolution cameras, and wireless data communications under the internet-of-things paradigm, which are suitable for vibration- and vision-based SHM applications. This article presents a state-of-the-art review on recent research progress of smartphone-based SHM. Although there are some short reviews on this topic, the major contribution of this article is to exclusively present a compre- hensive survey of recent practices of smartphone sensors to health monitoring of civil structures from the per- spectives of measurement techniques, third-party apps developed in Android and iOS, and various application domains. Findings of this article provide thorough understanding of the main ideas and recent SHM studies on smartphone sensing technology
The Future of Cybercrime: AI and Emerging Technologies Are Creating a Cybercrime Tsunami
This paper reviews the impact of AI and emerging technologies on the future of cybercrime and the necessary strategies to combat it effectively. Society faces a pressing challenge as cybercrime proliferates through AI and emerging technologies. At the same time, law enforcement and regulators struggle to keep it up. Our primary challenge is raising awareness as cybercrime operates within a distinct criminal ecosystem. We explore the hijacking of emerging technologies by criminals (CrimeTech) and their use in illicit activities, along with the tools and processes (InfoSec) to protect against future cybercrime. We also explore the role of AI and emerging technologies (DeepTech) in supporting law enforcement, regulation, and legal services (LawTech)
A review of the role of sensors in mobile context-aware recommendation systems
Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics
With the rise of complex cyber devices Cyber Forensics (CF) is facing many
new challenges. For example, there are dozens of systems running on
smartphones, each with more than millions of downloadable applications. Sifting
through this large amount of data and making sense requires new techniques,
such as from the field of Artificial Intelligence (AI). To apply these
techniques successfully in CF, we need to justify and explain the results to
the stakeholders of CF, such as forensic analysts and members of the court, for
them to make an informed decision. If we want to apply AI successfully in CF,
there is a need to develop trust in AI systems. Some other factors in accepting
the use of AI in CF are to make AI authentic, interpretable, understandable,
and interactive. This way, AI systems will be more acceptable to the public and
ensure alignment with legal standards. An explainable AI (XAI) system can play
this role in CF, and we call such a system XAI-CF. XAI-CF is indispensable and
is still in its infancy. In this paper, we explore and make a case for the
significance and advantages of XAI-CF. We strongly emphasize the need to build
a successful and practical XAI-CF system and discuss some of the main
requirements and prerequisites of such a system. We present a formal definition
of the terms CF and XAI-CF and a comprehensive literature review of previous
works that apply and utilize XAI to build and increase trust in CF. We discuss
some challenges facing XAI-CF. We also provide some concrete solutions to these
challenges. We identify key insights and future research directions for
building XAI applications for CF. This paper is an effort to explore and
familiarize the readers with the role of XAI applications in CF, and we believe
that our work provides a promising basis for future researchers interested in
XAI-CF
The Role of Intelligent Transportation Systems and Artificial Intelligence in Energy Efficiency and Emission Reduction
Despite the technological advancements in the transportation sector, the
industry continues to grapple with increasing energy consumption and vehicular
emissions, which intensify environmental degradation and climate change. The
inefficient management of traffic flow, the underutilization of transport
network interconnectivity, and the limited implementation of artificial
intelligence (AI)-driven predictive models pose significant challenges to
achieving energy efficiency and emission reduction. Thus, there is a timely and
critical need for an integrated, sophisticated approach that leverages
intelligent transportation systems (ITSs) and AI for energy conservation and
emission reduction. In this paper, we explore the role of ITSs and AI in future
enhanced energy and emission reduction (EER). More specifically, we discuss the
impact of sensors at different levels of ITS on improving EER. We also
investigate the potential networking connections in ITSs and provide an
illustration of how they improve EER. Finally, we discuss potential AI services
for improved EER in the future. The findings discussed in this paper will
contribute to the ongoing discussion about the vital role of ITSs and AI
applications in addressing the challenges associated with achieving energy
savings and emission reductions in the transportation sector. Additionally, it
will provide insights for policymakers and industry professionals to enable
them to develop policies and implementation plans for the integration of ITSs
and AI technologies in the transportation sector.Comment: 25 pages, 4 figure
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
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