89 research outputs found
An Improved Masking Strategy for Self-supervised Masked Reconstruction in Human Activity Recognition
Masked reconstruction serves as a fundamental pretext task for
self-supervised learning, enabling the model to enhance its feature extraction
capabilities by reconstructing the masked segments from extensive unlabeled
data. In human activity recognition, this pretext task employed a masking
strategy centered on the time dimension. However, this masking strategy fails
to fully exploit the inherent characteristics of wearable sensor data and
overlooks the inter-channel information coupling, thereby limiting its
potential as a powerful pretext task. To address these limitations, we propose
a novel masking strategy called Channel Masking. It involves masking the sensor
data along the channel dimension, thereby compelling the encoder to extract
channel-related features while performing the masked reconstruction task.
Moreover, Channel Masking can be seamlessly integrated with masking strategies
along the time dimension, thereby motivating the self-supervised model to
undertake the masked reconstruction task in both the time and channel
dimensions. Integrated masking strategies are named Time-Channel Masking and
Span-Channel Masking. Finally, we optimize the reconstruction loss function to
incorporate the reconstruction loss in both the time and channel dimensions. We
evaluate proposed masking strategies on three public datasets, and experimental
results show that the proposed strategies outperform prior strategies in both
self-supervised and semi-supervised scenarios
Cyber-Syndrome: Concept, Theoretical Characterization, and Control Mechanism
The prevalence of social media and mobile computing has led to intensive user engagement in the emergent Cyber-Physical-Social-Thinking (CPST) space. However, the easy access, the lack of governance, and excessive use has generated a raft of new behaviors within CPST, which affects users' physical, social, and mental states. In this paper, we conceive the Cyber-Syndrome concept to denote the collection of cyber disorders due to excessive or problematic Cyberspace interactions based on CPST theories. Then we characterize the Cyber-Syndrome concept in terms of Maslow's theory of Needs, from which we establish an in-depth theoretical understanding of Cyber-Syndrome from its etiology, formation, symptoms, and manifestations. Finally, we propose an entropy-based Cyber-Syndrome control mechanism for its computation and management. The goal of this study is to give new insights into this rising phenomenon and offer guidance for further research and development.<br/
Artificial Intelligence for Suicide Assessment using Audiovisual Cues: A Review
Death by suicide is the seventh leading death cause worldwide. The recent
advancement in Artificial Intelligence (AI), specifically AI applications in
image and voice processing, has created a promising opportunity to
revolutionize suicide risk assessment. Subsequently, we have witnessed
fast-growing literature of research that applies AI to extract audiovisual
non-verbal cues for mental illness assessment. However, the majority of the
recent works focus on depression, despite the evident difference between
depression symptoms and suicidal behavior and non-verbal cues. This paper
reviews recent works that study suicide ideation and suicide behavior detection
through audiovisual feature analysis, mainly suicidal voice/speech acoustic
features analysis and suicidal visual cues. Automatic suicide assessment is a
promising research direction that is still in the early stages. Accordingly,
there is a lack of large datasets that can be used to train machine learning
and deep learning models proven to be effective in other, similar tasks.Comment: Manuscript submitted to Arificial Intelligence Reviews (2022
Emergency message dissemination schemes based on congestion avoidance in VANET and vehicular FoG computing
With the rapid growth in connected vehicles, FoG-assisted vehicular ad hoc network (VANET) is an emerging and novel field of research. For information sharing, a number of messages are exchanged in various applications, including traffic monitoring and area-specific live weather and social aspects monitoring. It is quite challenging where vehicles' speed, direction, and density of neighbors on the move are not consistent. In this scenario, congestion avoidance is also quite challenging to avoid communication loss during busy hours or in emergency cases. This paper presents emergency message dissemination schemes that are based on congestion avoidance scenario in VANET and vehicular FoG computing. In the similar vein, FoG-assisted VANET architecture is explored that can efficiently manage the message congestion scenarios. We present a taxonomy of schemes that address message congestion avoidance. Next, we have included a discussion about comparison of congestion avoidance schemes to highlight the strengths and weaknesses. We have also identified that FoG servers help to reduce the accessibility delays and congestion as compared to directly approaching cloud for all requests in linkage with big data repositories. For the dependable applicability of FoG in VANET, we have identified a number of open research challenges. © 2013 IEEE
An Open Internet of Things System Architecture Based on Software-Defined Device
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The Internet of Things(IoT) connects more and more devices and supports an ever-growing diversity of applications. The heterogeneity of the cross-industry and cross-platform device resources is one of the main challenges to realize the unified management and information sharing, ultimately the large-scale uptake of the IoT. Inspired by software-defined networking(SDN), we propose the concept of software-defined device(SDD) and further elaborate its definition and operational mechanism from the perspective of cyber-physical mapping. Based on the device-as-a-software concept, we develop an open Internet of Things system architecture which decouples upper-level applications from the underlying physical devices through the SDD mechanism. A logically centralized controller is designed to conveniently manage physical devices and flexibly provide the device discovery service and the device control interfaces for various application requests. We also describe an application use scenario which illustrates that the SDD-based system architecture can implement the unified management, sharing, reusing, recombining and modular customization of device resources in multiple applications, and the ubiquitous IoT applications can be interconnected and intercommunicated on the shared physical devices
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