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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Wang, Xubin Jia, Weijia Institute of Artificial Intelligence and Future Networks Beijing Normal University Guangdong Key Lab of Ai and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9781665468190

The discovery of biomarker genes from gene expression data is a hot topic for understanding the mechanisms underlying disease etiology. However, while the collection of high-dimensional gene expression data has been made possible by the adoption of technologies such as DNA microarray, it also poses challenges for the identification of key disease-causing genes due to its high-dimensional nature. To address this problem, we propose a feature weighting particle swarm optimization method (FWPSO) for efficiently identifying biomarker genes from high-dimensional microarray data. Specifically, there are two significant phases in FWPSO: 1) Feature Weighting Phase: Features will be discriminated into relevant and irrelevant based on the evolutionary performance of individuals in the PSO population in each generation, and features will be assigned weights based on this. 2) Feature Selection Phase: By focusing the search on a feature set that have been determined to be relevant based on the results of the previous phase, the PSO population will improve the efficiency of removing redundant features and discovering the most related genes. Both phases work together and operate in synergy to achieve the optimized results. The experimental results on four microarray datasets shows that FWPSO not only reduces the number of feature dimensions to a large extent, but also achieves higher classification accuracy compared to other methods, demonstrating the effectiveness of our method. Our implementation of FWPSO is available at https://***/wangxb96/FWPSO. © 2022 IEEE.

关键词： Classification (of information)

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Boundary-Aware Feature Fusion With Dual-Stream Attention for Remote Sensing Small Object Detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Song, Jingnan Zhou, Mingliang Luo, Jun Pu, Huayan Feng, Yong Wei, Xuekai Jia, Weijia Chongqing Univ Sch Comp Sci Chongqing 400044 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Heavy Rainfall Res Ctr China Wuhan 430074 Peoples R China Beijing Normal Univ Zhuhai BNU UIC Inst Artificial Intelligence & Future Netw Zhuhai 519087 Peoples R China BNU HKBU United Int Coll Guangdong Key Lab AI & Multi Modal Data Proc Zhuhai 519087 Guangdong Peoples R China

Detecting small objects in remote sensing images poses significant challenges to the field of computer vision, primarily stemming from the complexity of backgrounds, limitations in pixel resolution, and information loss during the feature fusion process. While general object detection has significantly advanced in recent years, remote sensing small object detection remains an unsolved problem, with existing frameworks struggling to achieve high performance at small scales. In this article, we propose a novel framework called the boundary-aware feature fusion network (BAFNet), which significantly enhances the model's ability to represent and locate small objects precisely within complex remote sensing scenarios. First, a dual-stream attention fusion module captures complementary foreground and background cues through bidirectional context modeling. Jointly attending to objects and their surroundings enhances discriminative power for distinguishing small objects. Additionally, we incorporate a boundary-aware branch to better preserve crucial detailed information vital for small-scale objects. This auxiliary component supervises the fusion of contextual semantics and spatial information, aiding in retaining critical boundary details that are prone to loss during cross-layer feature fusion. We conducted experiments on the challenging ai-TOD, VisDrone, DIOR, and LEVIR-Ship datasets. The results demonstrate the superiority of our approach over other state-of-the-art (SOTA) object detection methods, particularly in terms of precisely identifying small objects within remote sensing images. The code is available at https://***/ooo1128/BAFNet.

关键词： Feature extraction Object detection Remote sensing Detectors Accuracy Semantics Training Neck Attention mechanisms Artificial intelligence Boundary-aware feature fusion small object detection

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Stabilization of switched systems with unstable modes via mode-partition-dependent ADT

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Journal of the Franklin Institute 2023年第2期360卷 1308-1326页

作者： Jin, Cui-Li Wang, Qing-Guo Wang, Rui Wu, Di Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology Dalian116024 China Institute of Artificial Intelligence and Future Networks Beijing Normal University at ZhuhaiGuangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China Key Laboratory of Advanced Technology for Aerospace Vehicles Liaoning Province and School of Aeronautics and Astronautics Dalian University of Technology Dalian116024 China

This paper concerns the stability of switched systems orchestrating between unstable modes. First, a mode partition is applied to select stabilizing switching from the switching between modes from different subsets. Second, a piecewise Lyapunov-like function is applied to decline at stabilizing switching times. Then, the stability criteria build on mode-partition-dependent average dwell time by collecting enough stabilizing switching behavior to stabilize the destabilizing switching behavior and unstable modes. Finally, the simulation verification shows the feasibility and effectiveness of the method. © 2022 The Franklin Institute

关键词： Stability criteria

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A Fast Task Offloading Optimization Framework for IRS-Assisted multi-Access Edge Computing System

arXiv

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arXiv 2023年

作者： Wu, Jianqiu Yu, Zhongyi Guo, Jianxiong Tang, Zhiqing Wang, Tian Jia, Weijia The Guangdong Key Lab of Ai and Multi-Modal Data Processing Department of Computer Science Bnuhkbu United International College 519087 Zhuhai China The Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China The Guangdong Key Lab of Ai and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China

Terahertz communication networks and intelligent reflecting surfaces exhibit significant potential in advancing wireless networks, particularly within the domain of aerial-based multi-access edge computing systems. These technologies enable efficient offloading of computational tasks from user electronic devices to Unmanned Aerial Vehicles or local execution. For the generation of high-quality task-offloading allocations, conventional numerical optimization methods often struggle to solve challenging combinatorial optimization problems within the limited channel coherence time, thereby failing to respond quickly to dynamic changes in system conditions. To address this challenge, we propose a deep learning-based optimization framework called Iterative Order-Preserving policy Optimization (IOPO), which enables the generation of energy-efficient task-offloading decisions within milliseconds. Unlike exhaustive search methods, IOPO provides continuous updates to the offloading decisions without resorting to exhaustive search, resulting in accelerated convergence and reduced computational complexity, particularly when dealing with complex problems characterized by extensive solution spaces. Experimental results demonstrate that the proposed framework can generate energy-efficient task-offloading decisions within a very short time period, outperforming other benchmark methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Critical Density for K-Coverage Under Border Effects in Camera Sensor Networks With Irregular Obstacles Existence

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IEEE INTERNET OF THINGS JOURNAL 2024年第4期11卷 6426-6437页

作者： Liu, Zhimin Jiang, Guiyan Jia, Weijia Wang, Tian Wu, Youke Hunan First Normal Univ Sch Comp Sci Changsha 410205 Peoples R China Wuhan Univ Technol Sch Econ Wuhan 430070 Peoples R China Beijing Normal Univ Inst Artificial Intelligence & Future Networks Zhuhai 519000 Peoples R China BNU HKBU United Int Coll Guangdong Key Lab AI & Multi Modal Data Proc Zhuhai 519000 Peoples R China Wuyi Univ Sch Econ & Management Jiangmen 529020 Guangdong Peoples R China

From the perspective of saving energy, it is important to maintain a desired coverage ratio with a minimum sensor density in camera sensor networks (CSNs). In some actual applications, the Field of Interest (FoI) is often an irregular bounded area with obstacles existence. The existence of obstacles would have an adverse effect on quality of coverage in CSNs. Besides, due to the camera sensor may be located in the boundary of the FoI, it also has influence on the coverage contribution of the camera sensor. In this article, we assume that heterogeneous camera sensors are randomly deployed in a convex polygon FoI with irregular obstacles existence and consider the border effects to derive the critical density (CD) of camera sensors for a desired coverage ratio in CSNs. We propose the concept of occlusion K-coverage, and present the expected effective possible sensing region to evaluate the coverage contribution of the camera sensor. Next, a series of simulation experiments are conducted to demonstrate the impact of border effects and parameters of camera sensor on the occlusion K-coverage ratio. The results show that our method can effectively estimate the CD for a desired occlusion K-coverage ratio.

关键词： Camera sensor networks (CSNs) convex area K-coverage minimum sensor density obstacles existence

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Sequence-Aware Online Container Scheduling with Reinforcement Learning in Parked Vehicle Edge Computing

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IEEE Transactions on Vehicular Technology 2025年

作者： Wu, Jianqiu Guo, Jianxiong Tang, Zhiqing Luo, Chuanwen Wang, Tian Jia, Weijia Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Department of Computer Science Zhuhai519087 China Beijing Normal University Advanced Institute of Natural Sciences Zhuhai519087 China Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Zhuhai519087 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China

Intelligent vehicles, often parked for long periods, are ideally suited to serve as computational nodes to expand the Mobile Edge Computing (MEC) infrastructure, with containerization significantly enhancing the system's load balancing, self-healing, resource isolation, and security. However, fluctuations in task demand and frequent container image downloads during peak hours create high loads on containerized nodes, as multiple mobile devices offload tasks simultaneously, leading to significant processing delays. Many existing studies make the simplified assumption of predefined patterns of task arrivals, which overlooks this issue and makes suboptimal decisions. In this paper, we consider a Parked Vehicles (PVs)-extended MEC scenario, where multiple devices request services on PVs functioning as edge servers, all controlled by a central base station. Task arrivals follow observed patterns based on long-term trends, such as peak and off-peak periods, resembling realistic arrival patterns rather than predefined ones. To optimize task offloading by identifying these patterns, we propose the Sequence-Aware Task Scheduling (SATS) algorithm, which is a policy gradient-based deep reinforcement learning approach that integrates Transformer and LSTM architectures to capture patterns in time-series task arrivals and relationships between nodes in a collaborative and containerized environment, thereby enhancing the efficiency of online task scheduling. The primary objective of SATS is to optimize the task offloading policy and minimize delay and energy consumption for all devices and PVs. Extensive numerical comparisons against baselines demonstrate the effectiveness and advantages of our algorithm. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Profit Maximization for multiple Products in Community-Based Social Networks 16th

Profit Maximization for Multiple Products in Community-Bas...

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16th International Conference on Algorithmic Aspects in Information and Management, AaiM 2022

作者： Ni, Qiufen Guo, Jianxiong School of Computers Guangdong University of Technology Guangzhou510006 China Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China

ISBN: (纸本)9783031160806

In this paper, we studies the profit maximization problem for multiple kinds of products in social networks. It is formulated as a Profit Maximization Problem for multiple Products (PMPMP), which aims at selecting a set of seed users within the total budget B such that the total profit for k kinds of products is maximized. We introduce the community structure and assume that different kinds of products are adopted by different groups of people, and different product information spread in different communities under the IC information propagation model. We prove that the objective function satisfies the k-submodularity, and then use the multilinear extension to relax the objective function. A continuous greedy algorithm is put forward for the relaxed function, which can obtain an 12 approximation performance guarantee, respectively. The experimental results on two real world social network datasets show the effectiveness of the proposed continuous greedy algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Profitability

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Efficient Serverless Function Scheduling at the Network Edge

arXiv

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arXiv 2023年

作者： Lou, Jiong Tang, Zhiqing Yuan, Shijing Li, Jie Wu, Chengtao Department of Computer Science and Engineering Shanghai Jiao Tong University China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China

Serverless computing is a promising approach for edge computing since its inherent features, e.g., lightweight virtualization, rapid scalability, and economic efficiency. However, previous studies have not studied well the issues of significant cold start latency and highly dynamic workloads in serverless function scheduling, which are exacerbated at the resource-limited network edge. In this paper, we formulate the Serverless Function Scheduling (SFS) problem for resource-limited edge computing, aiming to minimize the average response time. To efficiently solve this intractable scheduling problem, we first consider a simplified offline form of the problem and design a polynomial-time optimal scheduling algorithm based on each function’s weight. Furthermore, we propose an Enhanced Shortest Function First (ESFF) algorithm, in which the function weight represents the scheduling urgency. To avoid frequent cold starts, ESFF selectively decides the initialization of new function instances when receiving requests. To deal with dynamic workloads, ESFF judiciously replaces serverless functions based on the function weight at the completion time of requests. Extensive simulations based on real-world serverless request traces are conducted, and the results show that ESFF consistently and substantially outperforms existing baselines under different settings. © 2023, CC BY.

关键词： Polynomial approximation

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Online Container Scheduling for Low-Latency IoT Services in Edge Cluster Upgrade: A Reinforcement Learning Approach

Online Container Scheduling for Low-Latency IoT Services in ...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Cui, Hanshuai Tang, Zhiqing Lou, Jiong Jia, Weijia Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai519087 China Beijing Normal University School of Artificial Intelligence Beijing100875 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China BNU-HKBU United International College Guangdong Key Lab of Ai and Multi-Modal Data Processing Zhuhai519087 China

ISBN: (纸本)9798350345384

In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Frequent up-grades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers and upgrade edge clusters to minimize the impact on running tasks. In this paper, we propose a low-latency container scheduling algorithm for edge cluster upgrades. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm to address this problem, considering the unique characteristics of MEC. 3) Experimental results demonstrate that our algorithm reduces total task latency by approximately 27% compared to baseline algorithms. © 2023 IEEE.

关键词： Reinforcement learning

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An Online Resource Scheduling for Maximizing Quality-of-Experience in Meta Computing

arXiv

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arXiv 2023年

作者： Li, Yandi Guo, Jianxiong Li, Yupeng Wang, Tian Jia, Weijia The Guangdong Key Lab of AI and Multi-Modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai519087 China The Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China The Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China The Department of Interactive Media Hong Kong Baptist University Hong Kong

Meta Computing is a new computing paradigm, which aims to solve the problem of computing islands in current edge computing paradigms and integrate all the resources on a network by incorporating cloud, edge, and particularly terminal-end devices. It throws light on solving the problem of lacking computing power. However, at this stage, due to technical limitations, it is impossible to integrate the resources of the whole network. Thus, we create a new meta computing architecture composed of multiple meta computers, each of which integrates the resources in a small-scale network. To make meta computing widely applied in society, the service quality and user experience of meta computing cannot be ignored. Consider a meta computing system providing services for users by scheduling meta computers, how to choose from multiple meta computers to achieve maximum Quality-of-Experience (QoE) with limited budgets especially when the true expected QoE of each meta computer is not known as a priori? The existing studies, however, usually ignore the costs and budgets and barely consider the ubiquitous law of diminishing marginal utility. In this paper, we formulate a resource scheduling problem from the perspective of the multi-armed bandit (MAB). To determine a scheduling strategy that can maximize the total QoE utility under a limited budget, we propose an upper confidence bound (UCB) based algorithm and model the utility of service by using a concave function of total QoE to characterize the marginal utility in the real world. We theoretically upper bound the regret of our proposed algorithm with sublinear growth to the budget. Finally, extensive experiments are conducted, and the results indicate the correctness and effectiveness of our algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Budget control

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