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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Yannan Chen Licheng Zhao Kaiming Shen School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) China Shenzhen Research Institute of Big Data China

There is considerable interest in the use of fractional programming (FP) for the communication system design because many problems in this area are fractionally structured. Notably, max-FP and min-FP are not interchangeable in general if there are multiple ratios, so the two types of FP are often dealt with separately in the existing literature. As a result, an FP method for maximizing the signal-to-interference-plus-noise ratios (SINRs) typically cannot be used for minimizing the Cramér-Rao bounds (CRBs). In contrast, this work proposes a unified approach that bridges the gap between max-FP and min-FP. Particularly, we examine the theoretical basis of this unified approach from a minorization-maximization (MM) perspective, and in return obtain a matrix extension of this new FP technique. Moreover, this work presents two application cases: (i) joint radar sensing and (ii) multi-cell secure transmission, neither of which can be efficiently addressed by the existing FP tools.

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PresRecCD: A Novel Herbal Prescription Recommendation Framework with Cross-Domain Learning and Neural Collaborative Filtering

PresRecCD: A Novel Herbal Prescription Recommendation Framew...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Wansong Dong, Xin Yang, Kuo Zhang, Lei Wang, Xinyu Huang, Rouye Zhang, Runshun Zhou, Xuezhong Beijing Key Lab of Traffic Data Analysis and Mining School of Computer Science & Technology Beijing Jiaotong University Institute of Medical Intelligence Department of Artificial Intelligence Beijing100044 China China Academy of Chinese Medical Sciences National Data Center of Traditional Chinese Medicine Beijing China Guang'Anmen Hospital China Academy of Chinese Medical Sciences Beijing China

ISBN: (纸本)9798350386226

Herbal prescriptions hold significant importance in Traditional Chinese Medicine (TCM) diagnosis and treatment, embodying millennia of clinical case summaries and wisdom. Despite numerous proposed methods for herbal prescription recommendation (HPR), significant challenges persist due to the lack of comprehensive clinical data, particularly regarding the relationships between symptoms and herbs. This scarcity poses considerable hurdles for effective HPR modeling. In this study, we introduced a novel herbal prescription recommendation framework with cross-domain learning and neural collaborative filtering (termed PresRecCD). The cross-domain learning mechanism is introduced to learn the noise-reduced cross-domain features of herbs and symptoms in the unified space, alleviating the sparsity of data, and neural collaborative filtering is utilized to carry out prescription recommendations. Comprehensive experiments demonstrate the superiority of the proposed PresRecCD model over the SOTA model. This study contributes to enhancing the performance of the HPR model, ultimately benefiting the efficiency and precision of clinical treatment. © 2024 IEEE.

关键词： Medicinal chemistry

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False data Injection Attack Detection of Cyber-Physical Charging Systems Based on Time-Frequency Analysis

False Data Injection Attack Detection of Cyber-Physical Char...

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2023 International Conference on Smart Electrical Grid and Renewable Energy, SEGRE 2023

作者： Hu, Chao Fan, Peishun Li, Yuxin Chiu, I-Ju Wang, Yibin Zhou, Yiquan Li, Yi Li, Heng Central South University Big Data Institute Changsha China Central South University School of Computer Science and Engineering Changsha China University College London Department of Information Studies London United Kingdom Hunan Post and Telecommunication College Changsha China

ISBN: (纸本)9798350323269

Distributed charging systems play a critical role in the operation of supercapacitor electric vehicles. During the charging process, multiple charging modules rely on a communication network to achieve information sharing and interaction. If an external attack occurs on the network, the system will be in danger. To solve this problem, this paper proposes an anomaly detection mechanism based on real-time feature extraction to process measurement data. By analyzing the delayed effect of communication signal propagation, a method for identifying component faults and false data injection attacks (FDIA) is proposed. Moreover, the effectiveness and feasibility of the proposed distributed detection method are verified through experiments. Multiple experiments have consistently demonstrated that the proposed method outperforms existing methods FDIA detection. © 2023 IEEE.

关键词： Anomaly detection

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Implementation of CBDC System for the Customised Development of Finance Service System

Implementation of CBDC System for the Customised Development...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Devansh Mehra A Sri Vamsi Prabhu Kavin Department of Data Science and Business Systems School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India

CBDC–as-a-problem looks like a mania of interest from central banks and financial institutions all over the world. It may describe CBDC technology and CBDC technology development, the positive aspects of CBDC and its future perspectives as follows: The CBDC is linked to the existing banking systems in the past which was based on the past findings of the blockchain, DLT, etc., that possessed the qualities the previous ones did not have – security, trust, and transparency. Innovation, design and CBDC safety that falls below this paper are discussed in this section below. The paper proceeds to show several paths that central banks are taking to CBDC development given the regulation and changing dynamic of the financial world. However, the studies do not end there; information on the potential of CBDC for financial stability, monetary policy formulation trajectories, transaction privacy, and the role of banks is included. Given that in field of finance the CBDCs are becoming more and more important, this research paper shall ensure to provide consistent background to policymakers, financial institutions and society at large on CBDC and its structural implications. This guarantees that everyone is in the know so it will be possible for them to come up with compliant plans and decisions informed by the knowledge in this changing landscape that simply does not stop evolving.

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Football player identification based on YOLOv5 backbone and SPD-Conv 8

Football player identification based on YOLOv5 backbone and ...

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8th International Conference on Electronic technology and Information science, ICETIS 2023

作者： Liu, Jiwei Li, Yanchao Ning, Tao Song, Jinmiao Duan, Xiaodong SEAC key Laboratory of Big Data Applied Technology Dalian Minzu University Dalian116000 China Dalian Key Lab of Digital Technology for National Culture Dalian Minzu University Dalian116600 China College of Computer Science and Engineering Dalian Minzu University Dalian116600 China

ISBN: (纸本)9781510666535

With the rapid development of computer technology and Internet technology, the information age has come. The combination of computer technology and sports is one of the most popular research fields. This paper mainly completes the construction of football player number dataset and identification of football players through the jersey number. Firstly, the dataset is constructed based on player detection and number region detection. Then in the player identification task, we uses part of the backbone network of YOLOv5 model as the feature extraction module of the player identification network. Moreover, SPD-Conv module is added to improve the network recognition performance under the condition of small size target and low resolution. A series of experiments were also done to verify the performance of our proposed model. Finally, the recognition accuracy of our proposed model reached 92.75%. © 2023 SPIE.

关键词： Sports

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Exploring the Potential of Large Vision-Language Models for Unsupervised Text-Based Person Retrieval 39

Exploring the Potential of Large Vision-Language Models for ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Zongyi Jianbo, Li Shi, Yuxuan Chen, Jiazhong Huang, Shijuan Tu, Linnan Shen, Fei Ling, Hefei Department of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center of Educational Big Data The Faculty of Artificial Intelligence in Education Central China Normal University China Nanjing University of Science and Technology China

ISBN: (纸本)157735897X

The aim of text-based person retrieval is to identify pedestrians using natural language descriptions within a large-scale image gallery. Traditional methods rely heavily on manually annotated image-text pairs, which are resource-intensive to obtain. With the emergence of Large Vision-Language Models (LVLMs), the advanced capabilities of contemporary models in image understanding have led to the generation of highly accurate captions. Therefore, this paper explores the potential of employing Large Vision-Language Models for unsupervised text-based pedestrian image retrieval and proposes a Multi-grained Uncertainty Modeling and Alignment framework (MUMA). Initially, multiple Large Vision-Language Models are employed to generate diverse and hierarchically structured pedestrian descriptions across different styles and granularities. However, the generated captions inevitably introduce noise. To address this issue, an uncertainty-guided sample filtration module is proposed to estimate and filter out unreliable image-text pairs. Additionally, to simulate the diversity of styles and granularities in captions, a multi-grained uncertainty modeling approach is applied to model the distributions of captions, with each caption represented as a multivariate Gaussian distribution. Finally, a multi-level consistency distillation loss is employed to integrate and align the multi-grained captions, aiming to transfer knowledge across different granularities. Experimental evaluations conducted on three widely-used datasets demonstrate the significant advancements achieved by our approach. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Gaussian distribution

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Sensitivity Analysis of CoVaR

Sensitivity Analysis of CoVaR

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IEEE International Conference on Automation science and Engineering (CASE)

作者： Han Fu L. Jeff Hong Guangxin Jiang School of Data Science Fudan University Shanghai China School of Management and School of Data Science Fudan University Shanghai China School of Management Harbin Institute of Technology Harbin Heilongjiang China

CoVaR is an important measure for assessing the systemic risk of a network composed of many systems. To optimize and control the systemic risk of the network, we need to know the sensitivity of CoVaR. In this paper, we derive closed-form expressions of the CoVaR sensitivities and design batched estimators using the infinitesimal perturbation analysis (IPA) and finite-difference methods. We establish the consistency and asymptotic normality of the proposed estimators and show that the convergence rate of the estimators is strictly slower than $n^{-1/6}$ . Numerical experiments show the effectiveness of our estimator and support the theoretical results.

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Stability and Generalization of p-Regularized Stochastic Learning for GCN 32

Stability and Generalization of p-Regularized Stochastic Lea...

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32nd International Joint Conference on Artificial Intelligence, IJCAI 2023

作者： Liu, Shiyu Wei, Linsen Lv, Shaogao Li, Ming University of Electronic Science and Technology of China China School of Astronautics Northwestern Polytechnical University China Department of Statistics and Data Science Nanjing Audit University China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province Zhejiang Normal University China

ISBN: (纸本)9781956792034

Graph convolutional networks (GCN) are viewed as one of the most popular representations among the variants of graph neural networks over graph data and have shown powerful performance in empirical experiments. That 2-based graph smoothing enforces the global smoothness of GCN, while (soft) 1-based sparse graph learning tends to promote signal sparsity to trade for discontinuity. This paper aims to quantify the trade-off of GCN between smoothness and sparsity, with the help of a general p-regularized (1 p-regularized learning scheme does not satisfy such a smooth condition. To tackle this issue, we propose a novel SGD proximal algorithm for GCNs with an inexact operator. For a single-layer GCN, we establish an explicit theoretical understanding of GCN with the p-regularized stochastic learning by analyzing the stability of our SGD proximal algorithm. We conduct multiple empirical experiments to validate our theoretical findings. © 2023 International Joint Conferences on Artificial Intelligence. All rights reserved.

关键词： Graph neural networks

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It Takes Two to Tango: Serverless Workflow Serving via Bilaterally Engaged Resource Adaptation

arXiv

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arXiv 2025年

作者： Wu, Jing Wang, Lin Deng, Quanfeng Yu, Chen Zhang, Dong Yan, Bingheng Liu, Fangming 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 China Paderborn University Germany Inspur Data Co. Ltd. China Peng Cheng Laboratory China

Serverless platforms typically adopt an early-binding approach for function sizing, requiring developers to specify an immutable size for each function within a workflow beforehand. Accounting for potential runtime variability, developers must size functions for worst-case scenarios to ensure service-level objectives (SLOs), resulting in significant resource inefficiency. To address this issue, we propose Janus, a novel resource adaptation framework for serverless platforms. Janus employs a late-binding approach, allowing function sizes to be dynamically adapted based on runtime conditions. The main challenge lies in the information barrier between the developer and the provider: developers lack access to runtime information, while providers lack domain knowledge about the workflow. To bridge this gap, Janus allows developers to provide hints containing rules and options for resource adaptation. Providers then follow these hints to dynamically adjust resource allocation at runtime based on real-time function execution information, ensuring compliance with SLOs. We implement Janus and conduct extensive experiments with real-world serverless workflows. Our results demonstrate that Janus enhances resource efficiency by up to 34.7% compared to the state-of-the-art. © 2025, CC BY-NC-ND.

关键词： Resource allocation

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Towards Feature Selection for Detecting LDDoS in SD-IoT of Smart Grids: A Multi-correlation Information EA-based Method 2

Towards Feature Selection for Detecting LDDoS in SD-IoT of S...

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2nd International Symposium on Computing and Artificial Intelligence, ISCAI 2023

作者： Huan, Xuanyu Zhao, Jianpeng Chen, Guolin Li, Xianchao Cui, Yunhe Yang, Hongzhen Guizhou Xiangming Technology Co. Ltd. Guiyang China State Key Laboratory of Big Data College of Computer Science and Technology Guizhou University Guiyang China State Grid Zhejiang Electric Power Co. ltd. Information and Telecommunication Branch Hangzhou China

ISBN: (纸本)9798400708954

Abstract. Internet of Things (IoT) plays an important role in smart grids. Software-Defined Internet of Things (SD-IoT) employs the Software-Defined Networking (SDN) as the network architecture of IoT in smart grids. Hence, in smart grids, SD-IoT can achieve flexible network management using the network programmability. Nevertheless, IoT devices in smart grids face a number of security issues. The Low-rate Distributed Denial of Service (DDoS) is one of such a security issue. Detecting LDDoS is one of the most important steps in defending LDDoS. When detecting LDDoS in SD-IoT, a large number of network features are useless or even have side effects on the detection of LDDoS. Therefore, how to choose the feature subset from all features to improve the LDDoS detection performance is a key problem. In order to solve these problems, in this work, we propose ENTER, a multi-correlation information EA-based feature selection method, used to detect LDDoS for SD-IoT in smart grids. ENTER calculates the multi-correlation information of different individuals and adjusts the partial variation of the population through the multi-correlation information. ENTER is consisted of a novel multi-correlation information-based gene mutation strategy and a novel local optimum bounce strategy. The evaluation results indicate that ENTER can achieve high feature compression ratio while improve the detection performance of the LDDoS detector, in terms of detection precision, accuracy, recall, f-score and detection time. © 2023 ACM.

关键词： Internet of things

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