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SSRN 2024年

作者： Yang, Shuhui Chen, Jian Jie, Jia Guo, Liang Wang, Xingwei School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China

This paper investigates a novel non-orthogonal multiple access (NOMA) assisted ultra-reliable low-latency communications (URLLC) paradigm. By allowing multiple users to occupy the same time or frequency resource blocks with the NOMA technique, the transmission delays and network congestion in multi-user URLLC communication systems are thus mitigated. Furthermore, the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is employed to provide full-space coverage by proactively reconfiguring the electromagnetic propagation environment. The energy consumption optimization problem is formulated by refining blocklength assignment and jointly optimizing the power allocation and the STAR-RIS configuration. Due to highly coupled variables and the non-convexity of constraints, the optimization problem is proven to be NP-hard. To tackle the intractable problem, we propose an alternating optimization (AO)-based solution combining adaptive genetic algorithm (AGA), and successive convex approximation (SCA) methods. Numerical results demonstrate that: i) the proposed refined blocklength assignment scheme outperforms that with fixed blocklength assignment;ii) the proposed STAR-RIS-assisted NOMA system is superior to the conventional RIS-assisted NOMA system. © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation

arXiv

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arXiv 2024年

作者： He, Weizhao Zhang, Yang Zhuo, Wei Shen, Linlin Yang, Jiaqi Deng, Songhe Sun, Liang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China School of Computer Science University of Nottingham China

Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains, and their performances degrade significantly while applied to a distinct domain. To this end, we propose to leverage the cutting-edge foundation model, the Segment Anything Model (SAM), for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data, which primarily comprise natural scene images, and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work, we introduce APSeg, a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS), which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically, we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support prototypes, allowing features to be transformed into a more stable domain-agnostic space. Additionally, a Meta Prompt Generator (MPG) module is introduced to automatically generate prompt embeddings, eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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RAHP: A Redundancy-aware Accelerator for High-performance Hypergraph Neural Network

RAHP: A Redundancy-aware Accelerator for High-performance Hy...

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Hui Yu Yu Zhang Ligang He Yingqi Zhao Xintao Li Ruida Xin Jin Zhao Xiaofei Liao Haikun Liu Bingsheng He Hai Jin National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Warwick United Kingdom National University of Singapore Singapore

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Hypergraph Neural Network (HyperGNN) has emerged as a potent methodology for dissecting intricate multilateral connections among various entities. Current software/hardware solutions leverage a sequential execution model that relies on hyperedge and vertex indices for conducting standard matrix operations for HyperGNN inference. Yet, they are impeded by the dual challenges of redundant computation and irregular memory access overheads. This is primarily due to the frequent and repetitive access and updating of a number of feature vectors corresponding to the same hyperedges and vertices. To address these challenges, we propose the first redundancy-aware accelerator, RAHP, which enables high performance execution of HyperGNN inference. Specifically, we present a redundancy-aware asynchronous execution approach into the accelerator design for HyperGNN to reduce redundant computations and off-chip memory accesses. To unveil opportunities for data reuse and unlock the parallelism that existing HyperGNN solutions fail to capture, it prioritizes vertices with the highest degree as roots, prefetching other vertices along the hypergraph structure to capture the common vertices among multiple hyperedges, and synchronizing the computations of hyperedges and vertices in real-time. By such means, this facilitates the concurrent processing of relevant hyperedge and vertex computations of the common vertices along the hypergraph topology, resulting in smaller redundant computations overhead. Furthermore, by efficiently caching intermediate results of the common vertices, it curtails memory traffic and off-chip communications. To fully harness the performance potential of our proposed approach in the accelerator, RAHP incorporates a topology-driven data loading mechanism to minimize off-chip memory accesses on the fly. It is also endowed with an adaptive data synchronization scheme to mitigate the effects of conflicting updates of both hyperedges and vertices. Moreover, RAHP emplo

关键词： Computational modeling Neural networks Memory management Loading Energy conservation Vectors Software Topology Synchronization Standards

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DUCG-based Spacecraft Fault Diagnosis Methodology 3

DUCG-based Spacecraft Fault Diagnosis Methodology

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3rd International Conference on Communication Technology and Information Technology, ICCTIT 2023

作者： Li, Hongfei Yao, Quanying Liu, Peng Zhang, Zhan Qiu, Rui Zhang, Mingjiang Spacecraft On-orbit Operation Center Beijing Institute of Spacecraft System Engineering Beijing China Technology Department Yutong Intelligence Technology Co. Ltd. Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9798350359664

In order to solve the problems of intelligent fault diagnosis of spacecraft, this paper builds a diagnostic model based on Dynamic Uncertain Causality Graph (DUCG). This model solves the issues with data-driven and model-based methods, such as poor interpretability, high data dependence, and low diagnostic correctness. The DUCG methodology relies on the expertise of the domain experts to demonstrate uncertainties between the spacecraft telemetry parameters and possible faults in a graphic way. Notably, DUCG exhibits high diagnostic accuracy and interpretability, even in the absence of existing fault data. The diagnostic model developed utilizing the DUCG contains a total of 29 faults and 110 telemetry parameters. Empirical findings indicate that the model achieves a remarkable accuracy rate of 100%. The research results in this paper are of great significance for the development and promotion of spacecraft fault diagnosis technology. © 2023 IEEE.

关键词： Failure analysis

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VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting

arXiv

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arXiv 2024年

作者： Yang, Yingnan Zhu, Qingling Chen, Jianyong College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Multivariate time series (MTS) forecasting has been extensively applied across diverse domains, such as weather prediction and energy consumption. However, current studies still rely on the vanilla point-wise self-attention mechanism to capture cross-variable dependencies, which is inadequate in extracting the intricate cross-correlation implied between variables. To fill this gap, we propose Variable Correlation Transformer (VCformer), which utilizes Variable Correlation Attention (VCA) module to mine the correlations among variables. Specifically, based on the stochastic process theory, VCA calculates and integrates the cross-correlation scores corresponding to different lags between queries and keys, thereby enhancing its ability to uncover multivariate relationships. Additionally, inspired by Koopman dynamics theory, we also develop Koopman Temporal Detector (KTD) to better address the non-stationarity in time series. The two key components enable VCformer to extract both multivariate correlations and temporal dependencies. Our extensive experiments on eight real-world datasets demonstrate the effectiveness of VCformer, achieving top-tier performance compared to other state-of-the-art baseline models. Code is available at this repository: https://***/CSyyn/VCformer. Copyright © 2024, The Authors. All rights reserved.

关键词： Random processes

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RPViT: Vision Transformer Based on Region Proposal 2022

RPViT: Vision Transformer Based on Region Proposal

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5th International Conference on Image and Graphics Processing, ICIGP 2022

作者： Ge, Jing Wang, Qianxiang Tong, Jiahui Gao, Guangyu School of Computer Science and Technology Beijing Institute of Technology China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9781450395465

Vision Transformers constantly absorb the characteristics of convolutional neural networks to solve its shortcomings in translational invariance and scale invariance. However, dividing the image by a simple grid often destroys the position and scale features in the image at the beginning of the network. In this paper, we propose a vision transformer based on region proposal, which obtains the inductive bias in a simple way. Specifically, RPViT achieves locality and scale-invariance by extracting regions with locality using a traditional region proposal algorithm and deflating objects of different scales to the same scale by a bilinear interpolation algorithm. In addition, to enable the network to fully utilize and encode diverse candidate objects, a multi-class token approach based on orthogonalization is proposed and applied. Experiments on ImageNet demonstrate that RPViT outperforms baseline converters and related work. © 2022 ACM.

关键词： Convolutional neural networks

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Self-supervised deep clustering for spammer group detection

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Applied Intelligence 2025年第11期55卷 1-19页

作者： Wang, Changwu Feng, Zhongkai Zhang, Kun Zhang, Fuzhi School of Information Science and Engineering Yanshan University Qinhuangdao Hebei Province China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao Hebei Province China

E-commerce platforms face the issue of spammer groups that post many fraudulent reviews within a certain period. This practice misleads consumers and undermines fair competition among merchants. Researchers have proposed various methods to combat these spammers. However, these methods typically handle user feature representation and candidate group division independently, lacking an effective feedback mechanism between the two. Moreover, the vast discrepancy in the number of negative samples compared to positive samples in the data leads to reduced recognition accuracy in detection models, affecting the precision of detection outcomes. Therefore, we propose a spammer group detection method based on self-supervised deep clustering. Initially, the relationship between user review timing and product ratings is extracted from user review data, and user relevance is calculated and used as weights to construct a weighted user relationship graph. Subsequently, we integrate deep learning models with clustering algorithms to propose a self-supervised deep clustering model that jointly optimizes user representation and clustering distribution. This model employs graph and node autoencoders to capture global structural information and local preference information of user nodes, respectively, and designs a linear fusion method to enhance user feature representation. Additionally, we construct a reliable target distribution and introduce Kullback-Leibler(KL) divergence to form a self-supervised mechanism, continuously optimizing feature representation and clustering assignment to refine high-quality candidate groups. Finally, we propose an anomaly detection method based on the Gaussian Mixture Model (GMM), which designs a filtering mechanism to improve the detection efficiency of spammer groups. Experiments indicate that the proposed method outperforms baseline methods on the Amazon, Yelp, and YelpChi datasets.

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A Speed Measurement Method of Rail Transit Based on Millimeter-Wave Radar 1

引用

5th International Symposium for Intelligent Transportation and Smart City, ITASC 2022

作者： Shen, Tuo Xie, Lanxin Luo, Jinnan Zeng, Xiaoqing Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety Shanghai201804 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Institute of Rail Transit Tongji University Shanghai China

ISBN: (数字)9789819922529

ISBN: (纸本)9789819922512

The growing use of millimeter-wave radars in the intelligent transportation field and increasing accuracy provide new possibilities for train speed measurement. This paper proposes a measuring train speed method by using millimeter-wave radar to detect static objects. The millimeter-wave radar was used to obtain and process objects data. Then, using the RANSAC algorithm to extract the static objects group, and calculated the relative speed of the millimeter-wave radar and the static objects group. Finally, we completed the comparative experiment of using millimeter-wave radar and odometer to measure train speed in a real track environment. The results show that the measurement error of this method is less than 1.5 m/s, and it has the characteristics of low cost, fast speed measurement, and high precision. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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A Smart and Privacy-Preserving Logistics system Based on IoT and Blockchain Technologies 1

A Smart and Privacy-Preserving Logistics System Based on IoT...

引用

1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Balfaqih, Mohammed Balfagih, Zain Almohammedi, Akram A. Alfawaz, Khaled M. University of Jeddah Department of Computer and Network Engineering Jeddah Saudi Arabia Effat University Computer of Science Department Jeddah Saudi Arabia King Abdulaziz University Management Information System Department Jeddah Saudi Arabia Sotuh Ural State University Automobile Transportation Department Chelyabinsk Russia Karabiik University Electrical & Electronics Engineering Department Karabük Turkey

ISBN: (纸本)9781665472753

The smart city concept serves the well-being of the urban population in order to improve quality of life in different life aspects including logistic services. However, the continuous growth of logistics volume combined with information opaqueness and process complexity led to challenging issues related to managing the services and information of logistics. Hence, efficient management of logistics activities with traceability and condition monitoring capabilities is required to ensure quality and safe delivery. It is necessary also to ensure the accuracy and dependability of distribution data. In this context, this paper proposes a smart and privacy-preserving logistics system for high-price goods distribution. An intelligent parcel (iParcel) containing piezoresistive sensors is developed to pack delivered goods during shipping process for violation detection such as severe fall or theft. Moreover, smart contracts based on blockchain is also developed for automatic approval and payment with the consideration of distributing the shipping information between legitimate logistics parties only. A zero-knowledge proof is used to conceal blockchain address and prove the authentication. iParcels are automatically tracked and traced in which upon violation occurrence, the contract is cancelled, and the payment is refunded. The transaction fee per party is reasonable for high-price products in the pay of guarantee successful shipment. © 2023 IEEE.

关键词： Smart contract

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Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation

arXiv

引用

arXiv 2024年

作者： Chu, Zhaoyang Wan, Yao Li, Qian Wu, Yang Zhang, Hongyu Sui, Yulei Xu, Guandong Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology China School of Electrical Engineering Computing and Mathematical Sciences Curtin University Australia School of Big Data and Software Engineering Chongqing University China School of Computer Science and Engineering University of New South Wales Australia School of Computer Science University of Technology Sydney Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

Vulnerability detection is crucial for ensuring the security and reliability of software systems. Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding approach for vulnerability detection, owing to their ability to capture the underlying semantic structure of source code. However, GNNs face significant challenges in explainability due to their inherently black-box nature. To this end, several factual reasoning-based explainers have been proposed. These explainers provide explanations for the predictions made by GNNs by analyzing the key features that contribute to the outcomes. We argue that these factual reasoning-based explanations cannot answer critical what-if questions: "What would happen to the GNN's decision if we were to alter the code graph into alternative structures?" Inspired by advancements of counterfactual reasoning in artificial intelligence, we propose CFExplainer, a novel counterfactual explainer for GNN-based vulnerability detection. Unlike factual reasoning-based explainers, CFExplainer seeks the minimal perturbation to the input code graph that leads to a change in the prediction, thereby addressing the what-if questions for vulnerability detection. We term this perturbation a counterfactual explanation, which can pinpoint the root causes of the detected vulnerability and furnish valuable insights for developers to undertake appropriate actions for fixing the vulnerability. Extensive experiments on four GNN-based vulnerability detection models demonstrate the effectiveness of CFExplainer over existing state-of-the-art factual reasoning-based explainers. © 2024, CC BY.

关键词： Graph neural networks

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