Aggregation Scheme Research Articles

Depth-adaptive graph neural architecture search for graph classification

In recent years, graph neural networks (GNNs) based on neighborhood aggregation schemes have become a promising method in various graph-based applications. To solve the expert-dependent and time-consuming problem in human-designed GNN architectures, graph neural architecture search (GNAS) has been popular. However, as the mainstream GNAS methods automatically design GNN architectures with fixed GNN depth, they cannot mine the true potential of GNN architectures for graph classification. Although a few GNAS methods have explored the importance of adaptive GNN depth based on fixed GNN architectures, they have not designed a general search space for graph classification, which limits the discovery of excellent GNN architectures. In this paper, we propose Depth-Adaptive Graph Neural Architecture Search for Graph Classification (DAGC), which systemically constructs and explores the search space for graph classification, rather than studying individual designs. Through decoupling the graph classification process, DAGC proposes a complete and flexible search space, including GNN depth, aggregation function, and pooling operation components. To this end, DAGC adopts a learnable agent based on reinforcement learning to effectively guide the search for depth-adaptive GNN architectures. Extensive experiments on five real-world datasets demonstrate that DAGC outperforms the state-of-the-art human-designed GNN architectures and GNAS methods. The code is available at: https://github.com/Zhen-Peng-Wu/DAGC.

DSC-Net: learning discriminative spatial contextual features for semantic segmentation of large-scale ancient architecture point clouds

Semantic segmentation of point cloud data of architectural cultural heritage is of significant importance for HBIM modeling, disease extraction and analysis, and heritage restoration research fields. In the semantic segmentation task of architectural point cloud data, especially for the protection and analysis of architectural cultural heritage, the previous deep learning methods have poor segmentation effects due to the complexity and unevenness of the data, the high geometric feature similarity between different components, and the large scale changes. To this end, this paper proposes a novel encoder-decoder architecture called DSC-Net. It consists of an encoder-decoder structure based on point random sampling and several fully connected layers for semantic segmentation. To overcome the loss of key features caused by random downsampling, DSC-Net has developed two new feature aggregation schemes: the enhanced dual attention pooling module and the global context feature module, to learn discriminative features for the challenging scenes mentioned above. The former fully considers the topology and semantic similarity of neighboring points, generating attention features that can distinguish categories with similar structures. The latter uses spatial location and neighboring volume ratio to provide an overall view of different types of architectural scenes, helping the network understand the spatial relationships and hierarchical structures between different architectural elements. The proposed modules can be easily embedded into various network architectures for point cloud semantic segmentation. We conducted experiments on multiple datasets, including the ancient architecture dataset, the ArCH architectural cultural heritage dataset, and the publicly available architectural segmentation dataset S3DIS. The results show that the mIoU reached 63.56%, 55.84%, and 71.03% respectively. The experimental results prove that our method has the best segmentation effect in dealing with challenging architectural cultural heritage data and also demonstrates its practicality in a wider range of architectural point cloud segmentation applications.

PFDAM: Privacy-Preserving Fine-Grained Data Aggregation Scheme Supporting Multifunctionality in Smart Grid

PFDAM: Privacy-Preserving Fine-Grained Data Aggregation Scheme Supporting Multifunctionality in Smart Grid

D2D-assisted two-stage model aggregation scheme based on over-the-air computation

D2D-assisted two-stage model aggregation scheme based on over-the-air computation

Multiview Spatial-Temporal Meta-Learning for Multivariate Time Series Forecasting.

Multivariate time series modeling has been essential in sensor-based data mining tasks. However, capturing complex dynamics caused by intra-variable (temporal) and inter-variable (spatial) relationships while simultaneously taking into account evolving data distributions is a non-trivial task, which faces accumulated computational overhead and multiple temporal patterns or distribution modes. Most existing methods focus on the former direction without adaptive task-specific learning ability. To this end, we developed a holistic spatial-temporal meta-learning probabilistic inference framework, entitled ST-MeLaPI, for the efficient and versatile learning of complex dynamics. Specifically, first, a multivariate relationship recognition module is utilized to learn task-specific inter-variable dependencies. Then, a multiview meta-learning and probabilistic inference strategy was designed to learn shared parameters while enabling the fast and flexible learning of task-specific parameters for different batches. At the core are spatial dependency-oriented and temporal pattern-oriented meta-learning approximate probabilistic inference modules, which can quickly adapt to changing environments via stochastic neurons at each timestamp. Finally, a gated aggregation scheme is leveraged to realize appropriate information selection for the generative style prediction. We benchmarked our approach against state-of-the-art methods with real-world data. The experimental results demonstrate the superiority of our approach over the baselines.

A Secure Data Aggregation Algorithm Based on a Trust Mechanism.

Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security.

A privacy-preserving data aggregation system based on blockchain in VANET

In the realm of vehicular ad hoc networks (VANETs), data aggregation plays a pivotal role in bringing together data from multiple vehicles for further processing and sharing. Erroneous data feedback can significantly impact vehicle operations, control, and overall safety, necessitating the assurance of security in vehicular data aggregation. Addressing the security risks and challenges inherent in data aggregation within VANETs, this paper introduces a blockchain-based scheme for secure and anonymous data aggregation. The proposed scheme integrates cloud computing with blockchain technology, presenting a novel blockchain-based data aggregation system that robustly supports efficient and secure data collection in VANETs. Leveraging key escrow resilience mechanisms, the solution ensures the security of system keys, preventing the security problems caused by keys generated by third parties alone in the past. Furthermore, through secondary data aggregation, fine-grained data aggregation is achieved, providing effective support for cloud services in VANETs. The effectiveness of the proposed scheme is confirmed through security analysis and performance evaluations, demonstrating superior computational and communication efficiency compared existing alternatives.

Sensitivity of Polarization to Grain Shape. II. Aggregates

A previous study (Paper I) investigated the polarization properties of a variety of simple convex grain shapes, some of which were found to be consistent with the observed polarization properties of interstellar dust from far-ultraviolet to far-infrared. Here, we study the optical properties of 45 nonconvex shapes, all aggregates of N equal-sized spheres. We consider N = 2, N = 3, and N = 256 random aggregates obtained from three different aggregation schemes. We also consider “trimmed” N = 256 aggregates obtained by systematically trimming initially random aggregates to increase either flattening or elongation. The “macroporosities” of the studied aggregates range from Pmacro=0.18 (for the N = 2 bisphere) to Pmacro≈0.85 (for the N = 256 “BA” aggregates). The only aggregates consistent with observations of starlight polarization and polarized thermal emission are shapes that have been trimmed to increase their asymmetry. If interstellar grains are high-porosity aggregates, there must be processes causing extreme elongation or flattening; if not, interstellar grains must be dominated by fairly compact structures, with at most moderate porosities. The ratio of polarization in the 10 μm silicate feature to starlight polarization in the optical is shown to be insensitive to porosity and shape. X-ray scattering may be the best tool to determine the porosity of interstellar grains. We propose that modest porosities of interstellar grains could be the result of “photolytic densification.” High polarization fractions observed in some Class 0 cores require processes to reduce porosities and/or increase asymmetries of aggregates in dense regions.

Smart Contract Assisted Privacy-Preserving Data Aggregation and Management Scheme for Smart Grid

Smart Contract Assisted Privacy-Preserving Data Aggregation and Management Scheme for Smart Grid

A Privacy-Preserving Aggregation Scheme With Continuous Authentication for Federated Learning in VANETs

A Privacy-Preserving Aggregation Scheme With Continuous Authentication for Federated Learning in VANETs

A comprehensive and secure scheme for privacy-preserving smart meter data aggregation in the smart grid

Smart meters play a crucial role in the functioning of the smart grid by rapidly collecting and transmitting power consumption data to electricity companies. However, the real-time nature of smart meter data poses privacy risks for customers. To address this concern, encrypted aggregation of smart meter power consumption has been widely employed to protect customer privacy. In this paper, we propose an innovative scheme designed specifically for smart grids to fulfill these requirements. Our scheme demonstrates superior performance compared to existing solutions in terms of communication cost, computation, and functionality features. The proposed authentication protocol not only enables the secure sharing of power consumption data but also satisfies various security requirements, including mutual authentication, anonymity, prevention of man-in-the-middle attacks, and more. Furthermore, our framework exhibits significantly lower computing, communication, and storage overhead compared to similar schemes in the context of smart grids. This highlights the comprehensive and secure nature of our suggested framework, surpassing other existing smart grid schemes in terms of overall effectiveness and reliability.

Dynamic-anonymous privacy-preserving authenticated aggregation for safety-warning system for the Internet of Vehicles

Currently, the connectivity of vehicles using the latest communication and computing technologies has grown as the Internet of Vehicles (IoV). IoV has been utilized to foster an easy traffic flow by sharing safety-warning messages. However, authentication and confidentiality of shared information is a challenging issue. To solve the problem, various privacy-preserving authenticated data-aggregation schemes have been devised. However, existing schemes have shortcomings specifically concerning a heavy computational cost, and the need for a secure channel. Besides, static pseudonyms may increase the risk of a vehicle’s privacy leakage. In response to these challenges, this study introduces a new pairing-less and secure privacy-preserving authenticated data aggregation (PLS-PPADA) scheme. Leveraging the pairing-less and certificate-based setting, the PLS-PPADA scheme emerges as a robust, efficient, and effective solution for safety-warning systems in IoV. Further, to resolve the risk of privacy leakage due to static pseudonyms, the paradigm of fuzzy identity has been utilized. Thus, it achieves efficiency and dynamic anonymity and also does not require a secure channel for key sharing. A comprehensive security analysis underscores its effective data protection capabilities and efficiency comparison presents it as a compelling alternative to existing state-of-the-art schemes.

P2AT: Pyramid pooling axial transformer for real-time semantic segmentation

Recently, Transformer-based models have achieved promising results in various vision tasks, due to their ability to model long-range dependencies. However, transformers are computationally expensive, which limits their applications in real-time tasks such as autonomous driving. In addition, efficient local and global feature selection and fusion are vital for accurate dense prediction, especially driving scene understanding tasks. In this paper, we propose a real-time semantic segmentation architecture named Pyramid Pooling Axial Transformer (P2AT). The proposed P2AT takes a coarse feature from the CNN encoder to produce scale-aware contextual features, which are then combined with the multi-level feature aggregation scheme to produce enhanced contextual features. Specifically, we introduce a pyramid pooling axial transformer to capture intricate spatial and channel dependencies, leading to improved performance on semantic segmentation. Then, we design a Bidirectional Fusion module (BiF) to combine semantic information at different levels. Meanwhile, a Global Context Enhancer (GCE) is introduced to compensate for the inadequacy of concatenating different semantic levels. Finally, a decoder block is proposed to help maintain a larger receptive field. We evaluate P2AT variants on three challenging scene-understanding datasets. In particular, our P2AT variants achieve state-of-art results on the Camvid dataset 80.5%, 81.0%, 81.1% for P2AT-S, P2AT-M, and P2AT-L, respectively. Furthermore, our experiments on Cityscapes and Pascal VOC 2012 have demonstrated the efficiency of the proposed architecture. The source code will be available at https://github.com/mohamedac29/P2AT.

An ADP framework for flexibility and cost aggregation: Guarantees and open problems

With the increasing amount of Distributed Energy Resources (DERs), coordination of Distribution Grid Operators (DSOs) and Transmission Grid Operators (TSOs) is of paramount importance. Managing a large number of DERs at the TSO level is, however, challenging. To address this problem, flexibility aggregation is a topic of frequent research activities. Aggregation means to describe the combined flexibility of the DERs at the vertical grid coupling between DSO and TSO. Existing works are often limited with respect to guaranteeing feasibility, with respect to efficient numerical implementation, and in terms of quantification of the cost of DER usage. In the present paper, we investigate aggregation based on Approximate Dynamic Programming (ADP). We propose efficient numerical aggregation schemes using tools from computational geometry thus avoiding the need to solve multiple OPF problems. We rely on different variants of the DistFlow model for radial grids, which are computationally efficient. This allows to model of current and voltage limits and enables the consideration of voltage dependencies in the aggregation. Furthermore, we propose a method for cost aggregation and identify open problems of flexibility aggregation.

More Efficient and Verifiable Privacy-Preserving Aggregation Scheme for Internet of Things-Based Federated Learning

More Efficient and Verifiable Privacy-Preserving Aggregation Scheme for Internet of Things-Based Federated Learning

Developing and validating a knowledge-based AI assessment system for learning clinical core medical knowledge in otolaryngology

BackgroundClinical core medical knowledge (CCMK) learning is essential for medical trainees. Adaptive assessment systems can facilitate self-learning, but extracting experts' CCMK is challenging, especially using modern data-driven artificial intelligence (AI) approaches (e.g., deep learning). ObjectivesThis study aims to develop a multi-expert knowledge–aggregated adaptive assessment scheme (MEKAS) using knowledge-based AI approaches to facilitate the learning of CCMK in otolaryngology (CCMK-OTO) and validate its effectiveness through a one-month training program for CCMK-OTO education at a tertiary referral hospital. MethodsThe MEKAS utilized the repertory grid technique and case-based reasoning to aggregate experts' knowledge to construct a representative CCMK base, thereby enabling adaptive assessment for CCMK-OTO training. The effects of longitudinal training were compared between the experimental group (EG) and the control group (CG). Both groups received a normal training program (routine meeting, outpatient/operation room teaching, and classroom teaching), while EG received MEKAS for self-learning. The EG comprised 22 UPGY trainees (6 postgraduate [PGY] and 16 undergraduate [UGY] trainees) and 8 otolaryngology residents (ENT-R); the CG comprised 24 UPGY trainees (8 PGY and 16 UGY trainees). The training effectiveness was compared through pre- and post-test CCMK-OTO scores, and user experiences were evaluated using a technology acceptance model-based questionnaire. ResultsBoth UPGY (z = −3.976, P < 0.001) and ENT-R (z = −2.038, P = 0.042) groups in EG exhibited significant improvements in their CCMK-OTO scores, while UPGY in CG did not (z = −1.204, P = 0.228). The UPGY group in EG also demonstrated a substantial improvement compared to the UPGY group in CG (z = −4.943, P < 0.001). The EG participants were highly satisfied with the MEKAS system concerning self-learning assistance, adaptive testing, perceived satisfaction, intention to use, perceived usefulness, perceived ease of use, and perceived enjoyment, rating it between an overall average of 3.8 and 4.1 out of 5.0 on all scales. ConclusionsThe MEKAS system facilitates CCMK-OTO learning and provides an efficient knowledge aggregation scheme that can be applied to other medical subjects to efficiently build adaptive assessment systems for CCMK learning. Larger-scale validation across diverse institutions and settings is warranted further to assess MEKAS's scalability, generalizability, and long-term impact.

PFCAS—Paring free certificate less aggregate scheme for ensuring efficient authentication in vehicular Ad-hoc networks

PFCAS—Paring free certificate less aggregate scheme for ensuring efficient authentication in vehicular Ad-hoc networks

An efficient privacy-preserving and verifiable scheme for federated learning

As one of the most important methods of privacy computing, federated learning has attracted much attention as it makes data available but invisible (i.e., uploading gradients instead of raw data). However, adversaries may still recover some private information such as tabs, memberships or even training data, from gradients. Additionally, the malicious server may return the incorrect or forged aggregated result to clients for certain illegal interests. To ensure verifiability and privacy-preservation, in this paper, we present a verifiable secure aggregation scheme under the dual-server federated learning framework. Specifically, we combine the learning with error (LWE) cryptosystem with the secret sharing technique to guarantee the privacy of the aggregated result and each client’s local gradient. Meanwhile, we skillfully design a double-verification protocol, including the server-side and client-side verification, to efficiently verify the correctness of the aggregated result and ensure data availability. Specifically, two servers mutually verify the correctness of the aggregated result through the linear homomorphic hash technique. After passing the server-side mutual verification, the malicious server may still directly broadcast the forged aggregated result to clients. Our client-side verification protocol can ensure data availability to identify the correct aggregation result sent by the semi-trusted server. To the best of our knowledge, existing solutions do not take data availability into account. Extensive experimental comparisons with the state-of-the-art schemes demonstrate the effectiveness and efficiency of the proposed scheme in terms of accuracy, computational cost and communication overhead.

Secure multi-party computation with secret sharing for real-time data aggregation in IIoT

Real-time analytics in Industrial Internet-of-Things (IIoT) has received remarkable attention recently due to its capacity to prevent downtime and manage risks. However, the sensed data in IIoT is considered private. Thus, the sensed data of IIoT nodes cannot be transmitted and utilized directly in the cloud server due to the risk of privacy leakage. Data aggregation can effectively balance the availability of data with privacy concerns, making it particularly well-suited for IIoT systems. Although several privacy-preserving aggregation schemes in IIoT have been proposed, the majority of them can only support a single type of aggregation that limits the application scenarios of data aggregation. To address the problems mentioned above, a real-time aggregation analysis scheme for IIoT is proposed, which is constructed based on secure multi-party computation with secret sharing. Specifically, the multi-party computation with secret sharing is utilized to implement data aggregation process for IIoT that achieves multiple types of data aggregation. In addition, the secret sharing is utilized in the proposed scheme that can significantly improve the efficiency of the proposed scheme compared with similar schemes. Moreover, the proposed scheme does not require the involvement of a trusted authority in the data aggregation. Security and performance analyses show that the proposed scheme can enhance the security of the sensed data while effectively aggregating data for real-time analytics in IIoT.

Smart contract assisted secure aggregation scheme for model update in federated learning

Federated learning (FL) is a cutting-edge machine learning architecture that effectively meets the data and model training requirements of numerous businesses, while ensuring privacy protection, data security, and adherence to legal constraints. However, recent studies have demonstrated that attackers can deduce users’ personal data from their shared model parameters, thus underscoring a substantial security risk for federated learning. Despite considerable efforts by academics to address this issue, current approaches suffer from drawbacks such as high communication and processing costs, insufficient robustness, and heavy reliance on trusted third parties. In this paper, we present the smart contract assisted secure aggregation scheme (SCSA) to overcome the aforementioned problems. First, we creatively offer a three-layer secure aggregation architecture that is particularly suitable for scenarios involving multiple sensors used in model training. Secondly, to ensure the credibility of the identities between participating entities in the model parameters aggregation process of FL, a novel certificateless authentication mechanism relying on certificateless encryption is provided. Moreover, for the sake of enhancing the security of the aggregation process in FL, we employ a combination of smart contracts and secret sharing techniques to distribute security masks to users in a decentralized and highly secure manner. It also combines with secure communication to create a double fault tolerance mechanism that significantly boosts the whole system’s robustness. The effectiveness of our approach in achieving secure aggregation is substantiated through theoretical analysis and simulated trials, while simultaneously ensuring strong security and robustness.