229 research outputs found
Beyond mobile apps: a survey of technologies for mental well-being
Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties, which facilitate the delivery of state-of-the-art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy, and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health toolkits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the data these ubiquitous devices can record, state of the art machine learning algorithms can lead to the development of robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time
COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers
Monitoring of prevalent airborne diseases such as COVID-19 characteristically
involves respiratory assessments. While auscultation is a mainstream method for
preliminary screening of disease symptoms, its utility is hampered by the need
for dedicated hospital visits. Remote monitoring based on recordings of
respiratory sounds on portable devices is a promising alternative, which can
assist in early assessment of COVID-19 that primarily affects the lower
respiratory tract. In this study, we introduce a novel deep learning approach
to distinguish patients with COVID-19 from healthy controls given audio
recordings of cough or breathing sounds. The proposed approach leverages a
novel hierarchical spectrogram transformer (HST) on spectrogram representations
of respiratory sounds. HST embodies self-attention mechanisms over local
windows in spectrograms, and window size is progressively grown over model
stages to capture local to global context. HST is compared against
state-of-the-art conventional and deep-learning baselines. Demonstrations on
crowd-sourced multi-national datasets indicate that HST outperforms competing
methods, achieving over 83% area under the receiver operating characteristic
curve (AUC) in detecting COVID-19 cases
Mobile Wound Assessment and 3D Modeling from a Single Image
The prevalence of camera-enabled mobile phones have made mobile wound assessment a viable treatment option for millions of previously difficult to reach patients. We have designed a complete mobile wound assessment platform to ameliorate the many challenges related to chronic wound care. Chronic wounds and infections are the most severe, costly and fatal types of wounds, placing them at the center of mobile wound assessment. Wound physicians assess thousands of single-view wound images from all over the world, and it may be difficult to determine the location of the wound on the body, for example, if the wound is taken at close range. In our solution, end-users capture an image of the wound by taking a picture with their mobile camera. The wound image is segmented and classified using modern convolution neural networks, and is stored securely in the cloud for remote tracking. We use an interactive semi-automated approach to allow users to specify the location of the wound on the body. To accomplish this we have created, to the best our knowledge, the first 3D human surface anatomy labeling system, based off the current NYU and Anatomy Mapper labeling systems. To interactively view wounds in 3D, we have presented an efficient projective texture mapping algorithm for texturing wounds onto a 3D human anatomy model. In so doing, we have demonstrated an approach to 3D wound reconstruction that works even for a single wound image
Tikuna: An Ethereum Blockchain Network Security Monitoring System
Blockchain security is becoming increasingly relevant in today's cyberspace
as it extends its influence in many industries. This paper focuses on
protecting the lowest level layer in the blockchain, particularly the P2P
network that allows the nodes to communicate and share information. The P2P
network layer may be vulnerable to several families of attacks, such as
Distributed Denial of Service (DDoS), eclipse attacks, or Sybil attacks. This
layer is prone to threats inherited from traditional P2P networks, and it must
be analyzed and understood by collecting data and extracting insights from the
network behavior to reduce those risks. We introduce Tikuna, an open-source
tool for monitoring and detecting potential attacks on the Ethereum blockchain
P2P network, at an early stage. Tikuna employs an unsupervised Long Short-Term
Memory (LSTM) method based on Recurrent Neural Network (RNN) to detect attacks
and alert users. Empirical results indicate that the proposed approach
significantly improves detection performance, with the ability to detect and
classify attacks, including eclipse attacks, Covert Flash attacks, and others
that target the Ethereum blockchain P2P network layer, with high accuracy. Our
research findings demonstrate that Tikuna is a valuable security tool for
assisting operators to efficiently monitor and safeguard the status of Ethereum
validators and the wider P2P networkComment: 15 pages, 2 figures, submitted to ISPEC 2023 Conferenc
Participative Urban Health and Healthy Aging in the Age of AI
This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2022, held in Paris, France, in June 2022. The 15 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 33 submissions. They cover topics such as design, development, deployment, and evaluation of AI for health, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems
Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions
Sixth-generation (6G) networks anticipate intelligently supporting a wide
range of smart services and innovative applications. Such a context urges a
heavy usage of Machine Learning (ML) techniques, particularly Deep Learning
(DL), to foster innovation and ease the deployment of intelligent network
functions/operations, which are able to fulfill the various requirements of the
envisioned 6G services. Specifically, collaborative ML/DL consists of deploying
a set of distributed agents that collaboratively train learning models without
sharing their data, thus improving data privacy and reducing the
time/communication overhead. This work provides a comprehensive study on how
collaborative learning can be effectively deployed over 6G wireless networks.
In particular, our study focuses on Split Federated Learning (SFL), a technique
recently emerged promising better performance compared with existing
collaborative learning approaches. We first provide an overview of three
emerging collaborative learning paradigms, including federated learning, split
learning, and split federated learning, as well as of 6G networks along with
their main vision and timeline of key developments. We then highlight the need
for split federated learning towards the upcoming 6G networks in every aspect,
including 6G technologies (e.g., intelligent physical layer, intelligent edge
computing, zero-touch network management, intelligent resource management) and
6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous
systems). Furthermore, we review existing datasets along with frameworks that
can help in implementing SFL for 6G networks. We finally identify key technical
challenges, open issues, and future research directions related to SFL-enabled
6G networks
Cyber Security and Critical Infrastructures
This book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles: an editorial explaining current challenges, innovative solutions, real-world experiences including critical infrastructure, 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems, and a review of cloud, edge computing, and fog's security and privacy issues
Enhancing Clinical Decision Support Systems with Public Knowledge Bases
With vast amount of biomedical literature available online, doctors have the benefits of consulting the literature before making clinical decisions, but they are facing the daunting task of finding needles in haystacks. In this situation, it would help doctors if an effective clinical decision support system could generate accurate queries and return a manageable size of highly useful articles. Existing studies showed the useful-ness of patients’ diagnosis information in such scenario, but diagnosis is often missing in most cases. Furthermore, existing diagnosis prediction systems mainly focus on predicting a small range of diseases with well-formatted features, and it is still a great challenge to perform large-scale automatic diagnosis predictions based on noisy pa-tient medical records. In this paper, we propose automatic diagnosis prediction meth-ods for enhancing the retrieval in a clinical decision support system, where the predic-tion is based on evidences automatically collected from publicly accessible online knowledge bases such as Wikipedia and Semantic MEDLINE Database (SemMedDB). The assumption is that relevant diseases and their corresponding symptoms co-occur more frequently in these knowledge bases. Our methods perfor-mance was evaluated using test collections from the Clinical Decision Support (CDS) track in TREC 2014, 2015 and 2016. The results show that our best method can au-tomatically predict diagnosis with about 65.56% usefulness, and such predictions can significantly improve the biomedical literatures retrieval. Our methods can generate comparable retrieval results to the state-of-art methods, which utilize much more complicated methods and some manually crafted medical knowledge. One possible future work is to apply these methods in collaboration with real doctors
Improving Access and Mental Health for Youth Through Virtual Models of Care
The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial
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