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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Zhang, Lu Peng, Jingliang Lv, Na Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China School of Information Science and Engineering University of Jinan Jinan China

ISBN: (纸本)9783031533075

Cross-modal retrieval between video and motion capture (MoCap) data facilitates efficient reuse of human motion data in either skeletal or video format. For this purpose, we propose a deep cross-modal learning model for cross-modal retrieval between MoCap data and video data. First, we use a graph convolution-based network and a 3D convolution-based network to extract features from MoCap data and video data, respectively. In addition, we propose to use a pre-defined common subspace to maximize the inter-class variation and minimize the intra-class variation. Furthermore, we employ a similarity matrix to achieve the alignment between these two modalities and exploit their underlying correlations. For the purpose of experimental evaluation, due to the small amount of video data corresponding to the MoCap data in the public HDM05 dataset, we recorded a video dataset corresponding to the HDM05 motion capture dataset and performed cross-modal retrieval on it. The experimental results proved the effectiveness of the proposed scheme. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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An EMS based on the FLC of a variable three-phase dump load Composed of PV/Fuel Cell/Battery Systems 20

An EMS based on the FLC of a variable three-phase dump load ...

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20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

作者： Ayat, Yahia Badoud, Abd Essalam Mekhilef, Saad Automatic Laboratory of Setif 1 Electrical Engineering Department Setif Algeria Swinburne University of Technology School of Science Computing and Engineering Technologies Hawthorn Australia

ISBN: (纸本)9798350332568

to perceive, monitor, and regulate the behavior of hybrid energy sources, an energy management system is required. The suggested system discuss in this paper integrates photovoltaics (PVs), fuel cells (FCs), and batteries. The primary goals in hybrid systems for renewable energy used fuel-cells and batteries are to reduce hydrogen consumption and maximize battery state of charge (SOC), to accomplish these goals, a fuzzy control strategy as an energy management strategy (EMS) is presented in this research to operate independently from the grid. During the day, this renewable hybrid power system relies on PV electricity as its primary source. In the dark or during times of shade, it uses the FC to provide support as a backup source. When the load power is high, the battery aids the FC. DC/DC and DC/AC converters are included in the system. Two converters DC/DC make up the power conditioning system: a boost-type unidirectional dc/dc converter for the PV and FC, and a boost-type bidirectional dc/dc converter for the battery. The key system parameters, such as FC current and battery state-of-charge (SOC), are controlled using the fuzzy control approach. It was constructed in MATLAB/Simulink in order to study and monitor the system to demonstrate the effectiveness of the fuzzy control technique. The results revealed that the system functions effectively as a micro-grid. The results reveal that the battery's SOC is maintained at an appropriate level for such batteries. The DC link voltage (VDC) is maintained at a safe level. Furthermore, the use of H2 fuel is 17 grams. There is a lot of room for cost-cutting improvements, which necessitates more study into system optimization and the use of diverse control systems. © 2023 IEEE.

关键词： Energy management systems

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Optimizing Antenna Design through AI Integration: A Concise Review 7

Optimizing Antenna Design through AI Integration: A Concise ...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Liu, Xiaotian Chung, Kwok L. Li, Wenbiao Li, Ruihong Huang, Shuwei Li, Yingsong Huizhou University School of Computer Science and Engineering Huizhou516007 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350384437

The integration of artificial intelligence with antenna design and optimization represents a promising research frontier. This paper provides a concise overview of current research, emphasizing the computational cost challenges associated with implementing machine learning in antenna design and optimization. It explores the advantages and limitations of utilizing the Kriging model and the Surrogate-assisted Differential Evolution Algorithm (SADEA) series to tackle these cost constraints. © 2024 IEEE.

关键词： Smart antennas

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Semi-Asynchronous Federated Split Learning for computing-Limited Devices in Wireless Networks

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IEEE Transactions on Wireless Communications 2025年第6期24卷 5196-5212页

作者： Ao, Huiqing Tian, Hui Ni, Wanli Nie, Gaofeng Niyato, Dusit Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Tsinghua University Department of Electronic Engineering Beijing100084 China Nanyang Technological University College of Computing and Data Science Jurong West 639798 Singapore

The rapid evolution of edge computing and artificial intelligence (AI) paves the way for pervasive intelligence in the next-generation network. As a hybrid training paradigm, federated split learning (FSL) leverages data and model parallelism to enhance training efficiency. However, existing FSL encounters unacceptable waiting latency due to device heterogeneity and synchronous model aggregation. To address this issue, we propose a semi-asynchronous FSL (SAFSL) framework that enables personalized model splitting and aperiodic model aggregation. We derive the convergence upper bound by considering factors such as the number of devices, training iterations, and data heterogeneity. To minimize the long-term average training latency while maintaining high energy efficiency in resource-constrained wireless networks, we formulate a stochastic mixed-integer nonlinear programming problem. By decomposing it into multiple sub-problems in each round, we propose a Lyapunov-based alternating optimization algorithm to solve it in an online manner. Numerical results demonstrate that our SAFSL achieves faster convergence with reduced communication overhead while maintaining high prediction performance under non-independent and identically distributed data, outperforming state-of-the-art benchmarks. Moreover, our algorithm achieves a low training latency, highlighting its superior performance and effectiveness. © 2002-2012 IEEE.

关键词： Federated learning

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Cooperative differential games guidance laws for multiple attackers against an active defense target

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Chinese Journal of Aeronautics 2022年第5期35卷 374-389页

作者： Fei LIU Xiwang DONG Qingdong LI Zhang REN School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang UniversityBeijing 100083China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100083China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of ChinaChongqing UniversityChongqing 400044China

This paper is concerned with a scenario of multiple attackers trying to intercept a target with active *** types of agents are considered in the guidance:The multiple attackers,the target and the defender,where the attackers aim to pursuit the target from different directions and evade from the defender *** guidance engagement is formulated in the framework of a zero-sum two-person differential game between the two opposing teams,such that the measurements on the maneuver of the target or estimations on the defending strategy of the defender can be *** of the attackers resides in two aspects:redundant interception under the threat of the defender and the relative intercept geometry with the *** miss distances,the relative intercept angle errors and the costs of the agents are combined into a single performance index of the *** formulation enables a unitary approach to the design of guidance laws for the *** minimize the control efforts and miss distances for the attackers,an optimization method is proposed to find the best anticipated miss distances to the defender under the constraint that the defender is endowed with a capture *** simulations with two cases are conducted to illustrate the effectiveness of the proposed cooperative guidance law.

关键词： Active defense Cooperative guidance Differential games Minimum effort optimization Relative intercept angle

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FedHPM: Online Federated Learning via Heterogeneous Prediction Model 22

FedHPM: Online Federated Learning via Heterogeneous Predicti...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhang, Zhi Zhang, Shiwen Chen, Shuang Liang, Wei Hunan University of Science and Technology College of Computer Science and Technology Xiangtan China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798331509712

In the era of big data and growing privacy concerns, Federated Learning (FL) has emerged as a promising solution for collaborative model training while preserving user data privacy. However, FL faces challenges such as inconsistent data quality and frequent client dropouts, which hinder its performance. To address these challenges, we propose FedHPM, a novel framework that leverages the predictive capabilities of Long Short-Term Memory (LSTM) networks. FedHPM incorporates an intelligent client selection mechanism that utilizes LSTM-based analysis of historical data to forecast clients' potential data quality and dropout likelihood. This mechanism enables efficient selection of the top best-performing clients for participation in each round of global model training. By mitigating the impact of dropouts and optimizing the composition of participating clients, FedHPM significantly improves both the convergence speed and final performance of the global model. Through extensive empirical evaluations, we demonstrate the superior performance of FedHPM in enhancing FL stability, accelerating the convergence rate, and maintaining model accuracy. The results highlight the effectiveness of our framework in addressing the challenges associated with data quality and dropouts, thereby advancing the field of federated learning. © 2024 IEEE.

关键词： Federated learning

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Hierarchical Transformer for Multi-Label Trailer Genre Classification 48

Hierarchical Transformer for Multi-Label Trailer Genre Class...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cai, Zihui Ding, Hongwei Wu, Xuemeng Xu, Mohan Cui, Xiaohui Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering China

ISBN: (纸本)9781728163277

Determining the genres of a trailer is a challenging multi-label classification task. Previous studies tend to classify by CNN or RNN. Recently, Transformer based on attention mechanism has achieved better results in many research fields than CNN and RNN. Inspired by these, we propose a Hierarchical Transformer (HT). HT can process both the frame sequence (HT-F) and audio (HT-A) of trailers. Besides, a feature compression module is inserted into HT-F, and audio spectrogram segment is processed by HT-A as a whole, which can effectively reduce the data processed by the second Transformer. In order to reduce the training cost and improve the performance, we load the pre-trained weights from other related fields into some parameters of HT, and utilize the limited resources to train the remaining parameters. Experiments show that our best model outperforms state-of-the-art methods on several comprehensive metrics. © 2023 IEEE.

关键词： Hierarchical Transformer multi-label self-attention trailer genre classification transfer learning

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A Framework for Scalable Object Storage and Retrieval Considering Privacy Concerns: A Case Study on the Signature Detection 9

A Framework for Scalable Object Storage and Retrieval Consid...

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9th International Conference on Web Research, ICWR 2023

作者： Khomami, Mohammad Mehdi Daliri Saghiri, Ali Mohammad Rezvanian, Alireza Meybodi, Mohammad Reza Computer Engineering Amirkabir University of Technology Soft Computing Laboratory Department Tehran Iran University of Science and Culture Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350399691

Nowadays, we are faced with a huge amount of private data generated in different ecosystems, including the Internet of Things, social networks, peer-to-peer networks, and e-commerce, to mention a few. The performance of traditional object storage and retrieval systems concerning response time is going to be degraded by a drastic increase in generated data. This problem becomes more challenging when a specific private object should be found. To address the mentioned problem, this paper suggests scalable object storage and retrieval. To do this, three well-known methods, content-addressable networks from peer-to-peer systems, vector space model from information retrieval systems, and siamese neural networks from neural networks theory, are involved in a framework to bring a scalable system to solve the mentioned problems. The suggested framework is unique to the best of our knowledge because there is no fusion among mentioned fields reported so far. A case study on signature detection is also conducted to evaluate the proposed framework. The results show that, compared to a centralized system, the proposed framework significantly decreases the response time for detecting a signature while maintaining the same accuracy. In addition, the proposed mechanism does not require the private objects to be sent to a central entity, which helps to alleviate privacy concerns. © 2023 IEEE.

关键词： Peer to peer networks

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VANet: A New Network for Multi-modal Self-supervised Learning from Video and Audio

VANet: A New Network for Multi-modal Self-supervised Learnin...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2023

作者： Liu, Xingrui Zhang, Chen Feng, Zeming Dong, Jiwen Niu, Sijie Gao, Xizhan School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9789819708543

A new Network for Multi-modal Self-Supervised Learning from Video and Audio (VANet) is proposed for video action recognition. To overcome the input issues of different modalities, we establish auxiliary tasks by exploiting the natural synchronization and correlation between video and audio, and align them at both the segment-level representation and the video/audio-level representation. In this work, we introduce a contrastive loss for joint embedding learning of the two input modalities, which is improved to enhance the proximity of semantically relevant samples and meet the special needs of multi-modal learning. In addition, we identify and remove false negative samples from the negative set. Our method achieves competitive results in video action recognition tasks and demonstrates that joint multi-modal learning is superior to single modality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Supervised learning

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Prompt Learning for Developing Software Exploits 23

Prompt Learning for Developing Software Exploits

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14th Asia-Pacific Symposium on Internetware, Internetware 2023

作者： Ruan, Xiaoming Yu, Yaoxiang Ma, Wenhao Cai, Bo Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9798400708947

A software exploit is a sequence of commands that exploits software vulnerabilities or security flaws, written either by security researchers as a Proof-Of-Concept (POC) threat or by malicious attackers for use in their operations. Writing exploits is a challenging task since it is time-consuming and costly. Pre-trained Language Models (PLMs) for code can benefit automatic exploit generation, achieving state-of-the-art performance. However, the typical paradigm for using these PLMs is fine-tuning, which leads to a significant gap between the language model pre-training and the target task fine-tuning process since they are in different forms. In this paper, we propose a prompt learning approach PT4Exploits based on the PLM, i.e., CodeT5 to automatically generate desired exploits by modifying the original English description inputs. More specifically, PT4Exploits can better elicit knowledge from the PLM by inserting trainable prompt tokens into the original input to construct the same form as the pre-training tasks. Experimental results show that our approach can significantly outperform baseline models on both automatic evaluation and human evaluation. Additionally, we conduct extensive experiments to investigate the performance of prompt learning on few-shot settings and the scenario of different prompt templates, which also show the competitive effectiveness of PT4Exploits. © 2023 ACM.

关键词： Learning systems

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