184 research outputs found
A Bayesian Approach to Block Structure Inference in AV1-based Multi-rate Video Encoding
Due to differences in frame structure, existing multi-rate video encoding
algorithms cannot be directly adapted to encoders utilizing special reference
frames such as AV1 without introducing substantial rate-distortion loss. To
tackle this problem, we propose a novel bayesian block structure inference
model inspired by a modification to an HEVC-based algorithm. It estimates the
posterior probabilistic distributions of block partitioning, and adapts early
terminations in the RDO procedure accordingly. Experimental results show that
the proposed method provides flexibility for controlling the tradeoff between
speed and coding efficiency, and can achieve an average time saving of 36.1%
(up to 50.6%) with negligible bitrate cost.Comment: published in IEEE Data Compression Conference, 201
Forecasting model for short-term wind speed using robust local mean decomposition, deep neural networks, intelligent algorithm, and error correction
Wind power generation has aroused widespread concern worldwide. Accurate prediction of wind speed is very important for the safe and economic operation of the power grid. This paper presents a short-term wind speed prediction model which includes data decomposition, deep learning, intelligent algorithm optimization, and error correction modules. First, the robust local mean decomposition (RLMD) is applied to the original wind speed data to reduce the non-stationarity of the data. Then, the salp swarm algorithm (SSA) is used to determine the optimal parameter combination of the bidirectional gated recurrent unit (BiGRU) to ensure prediction quality. In order to eliminate the predictable components of the error further, a correction module based on the improved salp swarm algorithm (ISSA) and deep extreme learning machine (DELM) is constructed. The exploration and exploitation capability of the original SSA is enhanced by introducing a crazy operator and dynamic learning strategy, and the input weights and thresholds in the DELM are optimized by the ISSA to improve the generalization ability of the model. The actual data of wind farms are used to verify the advancement of the proposed model. Compared with other models, the results show that the proposed model has the best prediction performance. As a powerful tool, the developed forecasting system is expected to be further used in the energy system
From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks
Generative Artificial Intelligence (GAI) possesses the capabilities of
generating realistic data and facilitating advanced decision-making. By
integrating GAI into modern Internet of Things (IoT), Generative Internet of
Things (GIoT) is emerging and holds immense potential to revolutionize various
aspects of society, enabling more efficient and intelligent IoT applications,
such as smart surveillance and voice assistants. In this article, we present
the concept of GIoT and conduct an exploration of its potential prospects.
Specifically, we first overview four GAI techniques and investigate promising
GIoT applications. Then, we elaborate on the main challenges in enabling GIoT
and propose a general GAI-based secure incentive mechanism framework to address
them, in which we adopt Generative Diffusion Models (GDMs) for incentive
mechanism designs and apply blockchain technologies for secure GIoT management.
Moreover, we conduct a case study on modern Internet of Vehicle traffic
monitoring, which utilizes GDMs to generate effective contracts for
incentivizing users to contribute sensing data with high quality. Finally, we
suggest several open directions worth investigating for the future popularity
of GIoT
Blockchain-assisted Twin Migration for Vehicular Metaverses: A Game Theory Approach
As the fusion of automotive industry and metaverse, vehicular metaverses
establish a bridge between the physical space and virtual space, providing
intelligent transportation services through the integration of various
technologies, such as extended reality and real-time rendering technologies, to
offer immersive metaverse services for Vehicular Metaverse Users (VMUs). In
vehicular metaverses, VMUs update vehicle twins (VTs) deployed in RoadSide
Units (RSUs) to obtain metaverse services. However, due to the mobility of
vehicles and the limited service coverage of RSUs, VT migration is necessary to
ensure continuous immersive experiences for VMUs. This process requires RSUs to
contribute resources for enabling efficient migration, which leads to a
resource trading problem between RSUs and VMUs. Moreover, a single RSU cannot
support large-scale VT migration. To this end, we propose a blockchain-assisted
game approach framework for reliable VT migration in vehicular metaverses.
Based on the subject logic model, we first calculate the reputation values of
RSUs considering the freshness of interaction between RSUs and VMUs. Then, a
coalition game based on the reputation values of RSUs is formulated, and RSU
coalitions are formed to jointly provide bandwidth resources for reliable and
large-scale VT migration. Subsequently, the RSU coalition with the highest
utility is selected. Finally, to incentivize VMUs to participate in VT
migration, we propose a Stackelberg model between the selected coalition and
VMUs. Numerical results demonstrate the reliability and effectiveness of the
proposed schemes.Comment: Transactions on Emerging Telecommunications Technologies (ISSN:
2161-3915
Privacy Attacks and Defenses for Digital Twin Migrations in Vehicular Metaverses
The gradual fusion of intelligent transportation systems with metaverse
technologies is giving rise to vehicular metaverses, which blend virtual spaces
with physical space. As indispensable components for vehicular metaverses,
Vehicular Twins (VTs) are digital replicas of Vehicular Metaverse Users (VMUs)
and facilitate customized metaverse services to VMUs. VTs are established and
maintained in RoadSide Units (RSUs) with sufficient computing and storage
resources. Due to the limited communication coverage of RSUs and the high
mobility of VMUs, VTs need to be migrated among RSUs to ensure real-time and
seamless services for VMUs. However, during VT migrations, physical-virtual
synchronization and massive communications among VTs may cause identity and
location privacy disclosures of VMUs and VTs. In this article, we study privacy
issues and the corresponding defenses for VT migrations in vehicular
metaverses. We first present four kinds of specific privacy attacks during VT
migrations. Then, we propose a VMU-VT dual pseudonym scheme and a synchronous
pseudonym change framework to defend against these attacks. Additionally, we
evaluate average privacy entropy for pseudonym changes and optimize the number
of pseudonym distribution based on inventory theory. Numerical results show
that the average utility of VMUs under our proposed schemes is 33.8% higher
than that under the equal distribution scheme, demonstrating the superiority of
our schemes.Comment: 8 pages, 6 figure
DADFNet: Dual Attention and Dual Frequency-Guided Dehazing Network for Video-Empowered Intelligent Transportation
Visual surveillance technology is an indispensable functional component of
advanced traffic management systems. It has been applied to perform traffic
supervision tasks, such as object detection, tracking and recognition. However,
adverse weather conditions, e.g., fog, haze and mist, pose severe challenges
for video-based transportation surveillance. To eliminate the influences of
adverse weather conditions, we propose a dual attention and dual
frequency-guided dehazing network (termed DADFNet) for real-time visibility
enhancement. It consists of a dual attention module (DAM) and a high-low
frequency-guided sub-net (HLFN) to jointly consider the attention and frequency
mapping to guide haze-free scene reconstruction. Extensive experiments on both
synthetic and real-world images demonstrate the superiority of DADFNet over
state-of-the-art methods in terms of visibility enhancement and improvement in
detection accuracy. Furthermore, DADFNet only takes ms to process a 1,920
* 1,080 image on the 2080 Ti GPU, making it highly efficient for deployment in
intelligent transportation systems.Comment: This paper is accepted by AAAI 2022 Workshop: AI for Transportatio
Privacy-preserving Anomaly Detection in Cloud Manufacturing via Federated Transformer
With the rapid development of cloud manufacturing, industrial production with
edge computing as the core architecture has been greatly developed. However,
edge devices often suffer from abnormalities and failures in industrial
production. Therefore, detecting these abnormal situations timely and
accurately is crucial for cloud manufacturing. As such, a straightforward
solution is that the edge device uploads the data to the cloud for anomaly
detection. However, Industry 4.0 puts forward higher requirements for data
privacy and security so that it is unrealistic to upload data from edge devices
directly to the cloud. Considering the above-mentioned severe challenges, this
paper customizes a weakly-supervised edge computing anomaly detection
framework, i.e., Federated Learning-based Transformer framework
(\textit{FedAnomaly}), to deal with the anomaly detection problem in cloud
manufacturing. Specifically, we introduce federated learning (FL) framework
that allows edge devices to train an anomaly detection model in collaboration
with the cloud without compromising privacy. To boost the privacy performance
of the framework, we add differential privacy noise to the uploaded features.
To further improve the ability of edge devices to extract abnormal features, we
use the Transformer to extract the feature representation of abnormal data. In
this context, we design a novel collaborative learning protocol to promote
efficient collaboration between FL and Transformer. Furthermore, extensive case
studies on four benchmark data sets verify the effectiveness of the proposed
framework. To the best of our knowledge, this is the first time integrating FL
and Transformer to deal with anomaly detection problems in cloud manufacturing
Resource-efficient Generative Mobile Edge Networks in 6G Era: Fundamentals, Framework and Case Study
As the next-generation wireless communication system, Sixth-Generation (6G)
technologies are emerging, enabling various mobile edge networks that can
revolutionize wireless communication and connectivity. By integrating
Generative Artificial Intelligence (GAI) with mobile edge networks, generative
mobile edge networks possess immense potential to enhance the intelligence and
efficiency of wireless communication networks. In this article, we propose the
concept of generative mobile edge networks and overview widely adopted GAI
technologies and their applications in mobile edge networks. We then discuss
the potential challenges faced by generative mobile edge networks in
resource-constrained scenarios. To address these challenges, we develop a
universal resource-efficient generative incentive mechanism framework, in which
we design resource-efficient methods for network overhead reduction, formulate
appropriate incentive mechanisms for the resource allocation problem, and
utilize Generative Diffusion Models (GDMs) to find the optimal incentive
mechanism solutions. Furthermore, we conduct a case study on
resource-constrained mobile edge networks, employing model partition for
efficient AI task offloading and proposing a GDM-based Stackelberg model to
motivate edge devices to contribute computing resources for mobile edge
intelligence. Finally, we propose several open directions that could contribute
to the future popularity of generative mobile edge networks
Sub-5 nm nanobowl gaps electrochemically templated by SiO2-coated Au nanoparticles as surface-enhanced Raman scattering hot spots
National Natural Science Foundation of China [20873037, 91027037, J1103312, J1210040, 21173171, 11074210]Large-area submonolayer and monolayer Au nanoparticle (NP) arrays with sub-5 nm nanobowl gaps for giant electromagnetic enhancement were created by partially embedding SiO2-coated Au NP arrays in an electrochemically deposited Au film, followed by the removal of the SiO2 shells
Bilirubin Restrains the Anticancer Effect of Vemurafenib on BRAF-Mutant Melanoma Cells Through ERK-MNK1 Signaling
Melanoma, the most threatening cancer in the skin, has been considered to be driven by the carcinogenic RAF-MEK1/2-ERK1/2 signaling pathway. This signaling pathway is usually mainly dysregulated by mutations in BRAF or RAS in skin melanomas. Although inhibitors targeting mutant BRAF, such as vemurafenib, have improved the clinical outcome of melanoma patients with BRAF mutations, the efficiency of vemurafenib is limited in many patients. Here, we show that blood bilirubin in patients with BRAF-mutant melanoma treated with vemurafenib is negatively correlated with clinical outcomes. In vitro and animal experiments show that bilirubin can abrogate vemurafenib-induced growth suppression of BRAF-mutant melanoma cells. Moreover, bilirubin can remarkably rescue vemurafenib-induced apoptosis. Mechanically, the activation of ERK-MNK1 axis is required for bilirubin-induced reversal effects post vemurafenib treatment. Our findings not only demonstrate that bilirubin is an unfavorable for patients with BRAF-mutant melanoma who received vemurafenib treatment, but also uncover the underlying mechanism by which bilirubin restrains the anticancer effect of vemurafenib on BRAF-mutant melanoma cells
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