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European Signal Processing Conference (EUSIPCO)

作者： Abdulrahman Takiddin Rachad Atat Hayat Mbayed Muhammad Ismail Erchin Serpedin Department of Electrical and Computer Engineering Florida State University Tallahassee FL USA Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Center for Advanced Power Systems Florida State University Tallahassee FL USA Department of Computer Science Tennessee Technological University Cookeville TN USA Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Data communications within the smart power grid components are susceptible to cyberattacks due to the inter-connected nature of the grid and reliance on communication networks. Such cyberattacks can exploit the integrity of the exchanged data and result in operational instability. Existing data-driven cyberattack detection systems (CDSs) are proposed in the literature but their effectiveness is only verified against one type of cyberattacks. In reality, a smart grid system could encounter more than one attack type at once. Thus, in this paper, we investigate the resilience of state-of-the-art data-driven CDSs against replay false data injection, adversarial evasion, and adversarial data poisoning attacks on a realistic IEEE 118-bus system model. It turns out that a convolutional recurrent graph autoencoder-based CDS offers an attack detection rate of 96 – 97.5%, which outperforms other machine learning and deep learning-based data-driven CDSs by 16 – 54% since it captures the recurrent and spatial aspects of the data without being trained on attack data.

关键词： Deep learning Europe Feature extraction Graph neural networks Data models Smart grids Reliability computer crime Unsupervised learning Resilience

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Modelling Handed Shearing Auxetics: Selective Piecewise Constant Strain Kinematics and Dynamic Simulation

Modelling Handed Shearing Auxetics: Selective Piecewise Cons...

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IEEE International Conference on Soft Robotics (RoboSoft)

作者： Maximilian Stölzle Lillian Chin Ryan L. Truby Daniela Rus Cosimo Della Santina Cognitive Robotics department Delft University of Technology Delft CD Netherlands MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology Cambridge MA USA Departments of Materials Science & Engineering and Mechanical Engineering Northwestern University Evanston IL USA Institute of Robotics and Mechatronics German Aerospace Center (DLR) Weßling Germany

Electrically-actuated continuum soft robots based on Handed Shearing Auxetics (HSAs) promise rapid actuation capabilities while preserving structural compliance. However, the foundational models of these novel actuators required for precise control strategies are missing. This paper proposes two key components extending discrete Cosserat rod model (DCM) to allow for modeling HSAs. First, we propose a mechanism for incorporating the auxetic trajectory into DCM dynamical simulations. We also propose an implementation of this extension as a plugin for the Elastica simulator. Second, we introduce a Selective Piecewise Constant Strain (SPCS) kinematic parameterization that can describe an HSA segment's shape with fewer configuration variables. We verify both theoretical contributions experimentally. The simulator is used to replicate experimental data of the mechanical characterization of HSA rods. For the second component, we attach motion capture markers at various points to a parallel HSA robot and find that the shape of the HSAs can be kinematically represented with an average accuracy of 0.3 mm for positions and 0.07 rad for orientations.

关键词： Auxetic materials Shape Motion segmentation Dynamics Kinematics Soft robotics Motion capture

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High-Capacity Double-Layer Data Hiding Technique Based on 3D Magic Cube 25th

High-Capacity Double-Layer Data Hiding Technique Based on 3D...

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25th International computer Symposium on New Trends in computer Technologies and Applications, ICS 2022

作者： Weng, Hao-Yu Tsai, Min-Yi Wang, Wei-Jen Huang, Cheng-Ta Department of Computer Science and Information Engineering National Central University Taoyuan City320 Taiwan Department of Information Management Yuan Ze University Taoyuan City320 Taiwan International Bachelor Program in Informatics Yuan Ze University Taoyuan City320 Taiwan

ISBN: (纸本)9789811995811

Information technology and the Internet have progressed rapidly in people’s lives, the privacy of information has become an important issue due to the accessibility of data. Therefore, to enhance information security and privacy protection, this paper proposes a new high-capacity method for data hiding. The proposed method is based on 3D magic cube with double layers embedding. In the field of data hiding, the reference matrix-based methods attract many researchers because of its high-capacity. Recently, as the needs of the high payloads of the stego-image, the multi-layers-based technique is applied to provide larger embedding capacity. However, the image after data embedding will cause serious distortions, especially on multi-layers embedding, hence, keeping the image at a good visual quality and achieving high-payloads are the main challenges for data hiding. In this scheme, we take advantage of 3D magic cube and 3D reference space with double layers embedding for our data hiding technique to improve the embedding payloads. The experimental results show that our method not only provides good embedding capacity (4.00 bpp) but also keeps the image at acceptable quality (In terms of PSNR > 30). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Embeddings

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A Study on the Development of a Crop Disease Diagnosis Mobile App Service Based on Deep Learning 14th

A Study on the Development of a Crop Disease Diagnosis Mobil...

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14th International Conference on computer science and its Applications, CSA 2022 and the 16th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2022

作者： Lee, Dong In Lee, Ji Hwan Jung, Hoon Seo Jang, Seung Ho Ryu, Seung Gi Yang, Jiann An Oh, Ji Hwan Kim, Jae Won Oh, Se Jong Doo, Ill Chul Computer and Electronic Systems Engineering Hankuk University of Foreign Studies Yongin Korea Republic of Linguistics and Cognitive Science Hankuk University of Foreign Studies Yongin Korea Republic of School of Computer Science and Engineering Soongsil University Seoul Korea Republic of English-Korean Interpretation and Translation Studies Hankuk University of Foreign Studies Yongin Korea Republic of Department of Brazilian Studies Hankuk University of Foreign Studies Yongin Korea Republic of Division of French Language Hankuk University of Foreign Studies Seoul Korea Republic of International Finance Hankuk University of Foreign Studies Yongin Korea Republic of Statistics University of St Andrews St Andrews United Kingdom Artificial Intelligence Education Hankuk University of Foreign Studies Yongin Korea Republic of

ISBN: (纸本)9789819912513

We introduced a crop disease diagnosis mobile application service that improves the weakness of the previous services. We utilized image captioning model with Inception V3-based encoder and transformer-based decoder for detailed explanation. Moreover, object detection model with YOLOv5 was used to display bounding boxes to indicate the damaged region to increase the reliability. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Electronic-photonic quantum systems on-chip

Electronic-photonic quantum systems on-chip

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Quantum 2.0, QUANTUM 2022

作者： Wang, Imbert Ramesh, Anirudh Kramnik, Danielius Fargas Cabanillas, Josep M. Stojanović, Vladimir Kumar, Prem Popović, Miloš A. Department of Electrical and Computer Engineering Photonics Center Boston University BostonMA02215 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Electrical Engineering and Computer Science UC Berkeley BerkeleyCA94720 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

We present progress towards realizing electronic-photonic quantum systems on-chip;particularly, entangled photon-pair sources, placing them in the context of previous work, and outlining our vision for mass-producible... 详细信息

ISBN: (纸本)9781957171111

关键词： Quantum entanglement

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Learning Time-Series Images of Niacin Skin-Flushing Test for the Diagnosis of Schizophrenia and Affective Disorder

Learning Time-Series Images of Niacin Skin-Flushing Test for...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Huang, Yuyang Dong, Shiwen Wang, Dandan Wan, Chunling Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China Bio-X Institutes Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders Ministry of Education Shanghai Jiao Tong University Shanghai200030 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9781665468190

Niacin skin-flushing test is a promising method for fast and objective diagnosis of schizophrenia and affective disorder. Compared to healthy controls, the patients have attenuated flushing extent and reduced flushing rate. The traditional analysis of niacin test relies on manually evaluated flushing extent, while flushing patterns sh own in the sk in test images have not yet been considered. To exploit the potential of raw images for diagnosis and avoid subjective and time-consuming estimation of the flushing levels, we propose a CNN-LSTM hybrid model to learn the time-series of images captured in the niacin skin-flushing test. Moreover, an attention layer is introduced to enhance the model performance and interpretability. We collect a data set of 796 participants, and the experimental results show that the new model outperforms the traditional 4-point scaled measurement and conventional deep learning methods by large margins. Besides, the attention scores yielded by the model reveals key time points during the flushing reaction. © 2022 IEEE.

关键词： Time series

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Economics of Semantic Communication in Metaverse: An Auction Approach

Economics of Semantic Communication in Metaverse: An Auction...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Zi Qin Liew Hongyang Du Wei Yang Bryan Lim Zehui Xiong Dusit Niyato Han Yu Alibaba-NTU Singapore Joint Research Institute Nanyang Technological Univerity Singapore School of Computer Science and Engineering Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

Metaverse provides embodied artificial-reality experience to the users in the virtual spaces. Many innovative and creative services such as virtual conference and tourism have been realized in the digital twins maintained by the virtual service providers (VSP) in Metaverse. Digital twins are digital copies of the physical world constructed virtually by the VSPs using real-world data. For a realistic experience, VSPs need to collect data that is up-to-date and relevant to their services. In this paper, we propose an incentive design framework to support the data trading between VSPs and edge devices. In the auction model, we model the valuation of data by considering data relatedness and data freshness. In our model, the semantic communication model is used to filter the relevant data, and the age of information (AoI) metric is used to assess the data freshness. Results show that by considering the data freshness, our mechanism helps to increase the average update frequency so that the VSPs obtain fresh data for construction of digital twins. Our model ensures the desired properties of individual rationality, incentive compatibility, and budget balance.

关键词： Measurement Metaverse Data integrity Semantics Streaming media Information filters Data models

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WISE: A Semantic and Interoperable Web of Things Architecture for Smart Environments

WISE: A Semantic and Interoperable Web of Things Architectur...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Luca Bedogni Sebastiano Manfredini Francesco Poggi Davide Rossi Department of Physics Informatics and Mathematics University of Modena and Reggio Emilia Modena Italy Institute of Cognitive Science and Technologies National Research Council Rome Italy Department of Computer Science and Engineering University of Bologna Bologna Italy

The rapid proliferation of Internet of Things devices has led to a number of different standards and technologies which offer novel and exciting services. One of the key aspect of the Internet of Things is its ubiquitness, as devices may spontaneously form networks and leave them possibly in short time frames. This is the case of Smart Environments such as Smart Homes, in which users carry a set of devices like wearables and mobile applications to monitor their behavior and provide contextual services. However, the interoperability and seamless interaction of different devices is yet to be fully realized. In this paper we propose WISE, a framework that leverages the Web of Thing architecture and Semantic technologies to overcome technical and conceptual interoperability difficulties and enables the creation of cooperative Smart Environments that self-adapt on the basis of users' preferences. The use of Semantic technologies enables to understand which devices can provide the needed affordances to meet the user preferences, while the WoT architecture is leveraged to access devices in a standardized manner. We also propose a reference implementation based on off-the-shelf devices which demonstrate the feasibility of WISE.

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Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Van Huynh, Nguyen Du, Hongyang Hoang, Dinh Thai Niyato, Dusit Zhu, Kun Kang, Jiawen Xiong, Zehui Jamalipour, Abbas In Kim, Dong The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The Department of Electrical Engineering and Electronics University of Liverpool Liverpool L69 3GJ United Kingdom The School of Electrical and Data Engineering University of Technology Sydney Australia The School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney Australia The College of Information and Communication Engineering Sungkyunkwan University Korea Republic of

With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans. However, learning and coordinating movements and actions for a large number of unmanned vehicles in complex and dynamic environments introduce significant challenges to conventional AI methods. Generative AI (GAI), with its capabilities in complex data feature extraction, transformation, and enhancement, offers great potential in solving these challenges of unmanned vehicle swarms. For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms. Specifically, we first present an overview of unmanned vehicles and unmanned vehicle swarms as well as their use cases and existing issues. Then, an in-depth background of various GAI techniques together with their capabilities in enhancing unmanned vehicle swarms are provided. After that, we present a comprehensive review on the applications and challenges of GAI in unmanned vehicle swarms with various insights and discussions. Finally, we highlight open issues of GAI in unmanned vehicle swarms and discuss potential research directions. © 2024, CC BY.

关键词： Metadata

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Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach

Vision-based Semantic Communications for Metaverse Services:...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Guangyuan Liu Hongyang Du Dusit Niyato Jiawen Kang Zehui Xiong Boon Hee Soong Energy Research Institute@NTU Interdisciplinary Graduate Program School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering Nanyang Technological University Singapore

The popularity of Metaverse as an entertainment, social, and work platform has led to a great need for seamless avatar integration in the virtual world. In Metaverse, avatars must be updated and rendered to reflect users' behaviour. Achieving real-time synchronization between the virtual bilocation and the user is complex, placing high demands on the Metaverse Service Provider (MSP)'s rendering resource allocation scheme. To tackle this issue, we propose a semantic communication framework that leverages contest theory to model the interactions between users and MSPs and determine optimal resource allocation for each user. To reduce the consumption of network resources in wireless transmission, we use the semantic communication technique to reduce the amount of data to be transmitted. Under our simulation settings, the encoded semantic data only contains 51 bytes of skeleton coordinates instead of the image size of 8.243 megabytes. Moreover, we implement Deep Q-Network to optimize reward settings for maximum performance and efficient resource allocation. With the optimal reward setting, users are incentivized to select their respective suitable uploading frequency, reducing down-sampling loss due to rendering resource constraints by 66.076% compared with the traditional average distribution method. The framework provides a novel solution to resource allocation for avatar association in VR environments, ensuring a smooth and immersive experience for all users.

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