List of Titles

Deep Reinforcement Learning for Vehicular Edge Computing: An Intelligent Offloading System

- Ning, Zhaolong, Dong, Peiran, Wang, Xiaojie, Rodrigues, Joel, Xia, Feng

Authors: Ning, Zhaolong , Dong, Peiran , Wang, Xiaojie , Rodrigues, Joel , Xia, Feng
Date: 2019
Type: Text , Journal article
Relation: ACM Transactions on Intelligent Systems and Technology Vol. 10, no. 6 (Dec 2019), p. 24
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Description: The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.

The impact of a novel mimicry task for increasing emotion recognition in adults with autism spectrum disorder and alexithymia : protocol for a randomized controlled trial

- Caine, Joshua, Klein, Britt, Edwards, Stephen

Authors: Caine, Joshua , Klein, Britt , Edwards, Stephen
Date: 2021
Type: Text , Journal article
Relation: JMIR Research Protocols Vol. 10, no. 6 (2021), p.
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Description: Background: Impaired facial emotion expression recognition (FEER) has typically been considered a correlate of autism spectrum disorder (ASD). Now, the alexithymia hypothesis is suggesting that this emotion processing problem is instead related to alexithymia, which frequently co-occurs with ASD. By combining predictive coding theories of ASD and simulation theories of emotion recognition, it is suggested that facial mimicry may improve the training of FEER in ASD and alexithymia. Objective: This study aims to evaluate a novel mimicry task to improve FEER in adults with and without ASD and alexithymia. Additionally, this study will aim to determine the contributions of alexithymia and ASD to FEER ability and assess which of these 2 populations benefit from this training task. Methods: Recruitment will primarily take place through an ASD community group with emphasis put on snowball recruiting. Included will be 64 consenting adults equally divided between participants without an ASD and participants with an ASD. Participants will be screened online using the Kessler Psychological Distress Scale (K-10; cut-off score of 22), Autism Spectrum Quotient (AQ-10), and Toronto Alexithymia Scale (TAS-20) followed by a clinical interview with a provisional psychologist at the Federation University psychology clinic. The clinical interview will include assessment of ability, anxiety, and depression as well as discussion of past ASD diagnosis and confirmatory administration of the Autism Mental Status Exam (AMSE). Following the clinical interview, the participant will complete the Bermond-Vorst Alexithymia Questionnaire (BVAQ) and then undertake a baseline assessment of FEER. Consenting participants will then be assigned using a permuted blocked randomization method into either the control task condition or the mimicry task condition. A brief measure of satisfaction of the task and a debriefing session will conclude the study. Results: The study has Federation University Human Research Ethics Committee approval and is registered with the Australian New Zealand Clinical Trials. Participant recruitment is predicted to begin in the third quarter of 2021. Conclusions: This study will be the first to evaluate the use of a novel facial mimicry task condition to increase FEER in adults with ASD and alexithymia. If efficacious, this task could prove useful as a cost-effective adjunct intervention that could be used at home and thus remove barriers to entry. This study will also explore the unique effectiveness of this task in people without an ASD, with an ASD, and with alexithymia. © 2021 JMIR Research Protocols.

PAWN: a payload‐based mutual authentication scheme for wireless sensor networks

- Jan, Mian, Nanda, Priyadarsi, Usman, Muhammad, He, Xiangjian

Authors: Jan, Mian , Nanda, Priyadarsi , Usman, Muhammad , He, Xiangjian
Date: 2017
Type: Text , Journal article
Relation: Concurrency and computation Vol. 29, no. 17 (2017), p. e3986-n/a
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Description: Summary Wireless sensor networks (WSNs) consist of resource‐starving miniature sensor nodes deployed in a remote and hostile environment. These networks operate on small batteries for days, months, and even years depending on the requirements of monitored applications. The battery‐powered operation and inaccessible human terrains make it practically infeasible to recharge the nodes unless some energy‐scavenging techniques are used. These networks experience threats at various layers and, as such, are vulnerable to a wide range of attacks. The resource‐constrained nature of sensor nodes, inaccessible human terrains, and error‐prone communication links make it obligatory to design lightweight but robust and secured schemes for these networks. In view of these limitations, we aim to design an extremely lightweight payload‐based mutual authentication scheme for a cluster‐based hierarchical WSN. The proposed scheme, also known as payload‐based mutual authentication for WSNs, operates in 2 steps. First, an optimal percentage of cluster heads is elected, authenticated, and allowed to communicate with neighboring nodes. Second, each cluster head, in a role of server, authenticates the nearby nodes for cluster formation. We validate our proposed scheme using various simulation metrics that outperform the existing schemes.

How much material on BitTorrent is infringing content? A case study

- Watters, Paul, Layton, Robert, Dazeley, Richard

Authors: Watters, Paul , Layton, Robert , Dazeley, Richard
Date: 2011
Type: Text , Journal article
Relation: Information Security Technical Report Vol. 16, no. 2 (2011), p. 79-87
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Description: BitTorrent is a widely used protocol for peer-to-peer (P2P) file sharing, including material which is often suspected to be infringing content. However, little systematic research has been undertaken to establish to measure the true extent of illegal file sharing. In this paper, we propose a new methodology for measuring the extent of infringing content. Our initial results indicate that at least 89.9% of files shared contain infringing content, with a replication study on another sample finding 97%. We discuss the limitations of the approach in this case study, including sampling biases, and outline proposals to further verify the results. The implications of the work vis - vis the management of piracy at the network level are discussed. Â© 2011 Published by Elsevier Ltd. All rights reserved.

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