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Dynamic Individual Selection and Crossover Boosted Forensic-based Investigation Algorithm for Global Optimization and Feature Selection

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Journal of Bionic Engineering 2023年第5期20卷 2416-2442页

作者： Hanyu Hu Weifeng Shan Jun Chen Lili Xing Ali Asghar Heidari Huiling Chen Xinxin He Maofa Wang School of Emergency Management Institute of Disaster PreventionSanhe065201China Earthquake Administration of Anhui Province Hefei230031China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China

The advent of Big Data has rendered Machine Learning tasks more intricate as they frequently involve higher-dimensional *** Selection(FS)methods can abate the complexity of the data and enhance the accuracy,generalizability,and interpretability of ***-heuristic algorithms are often utilized for FS tasks due to their low requirements and efficient *** paper introduces an augmented Forensic-Based Investigation algorithm(DCFBI)that incorporates a Dynamic Individual Selection(DIS)and crisscross(CC)mechanism to improve the pursuit phase of the ***,a binary version of DCFBI(BDCFBI)is applied to *** conducted on IEEE CEC 2017 with other metaheuristics demonstrate that DCFBI surpasses them in search *** influence of different mechanisms on the original FBI is analyzed on benchmark functions,while its scalability is verified by comparing it with the original FBI on benchmarks with varied *** is then applied to 18 real datasets from the UCI machine learning database and the Wieslaw dataset to select near-optimal features,which are then compared with six renowned binary *** results show that BDCFBI can be more competitive than similar methods and acquire a subset of features with superior classification accuracy.

关键词： Feature selection Forensic-based investigation algorithm Crisscross mechanism Global optimization Metaheuristic algorithms Bionic algorithm

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DRS: A deep reinforcement learning enhanced Kubernetes scheduler for microservice-based system

DRS: A deep reinforcement learning enhanced Kubernetes sched...

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作者： Jian, Zhaolong Xie, Xueshuo Fang, Yaozheng Jiang, Yibing Lu, Ye Dash, Ankan Li, Tao Wang, Guiling College of Computer Science Nankai University Tianjin China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China College of Cyber Science Nankai University Tianjin China Department of Computer Science New Jersey Institute of Technology NewarkNJ United States

Recently, Kubernetes is widely used to manage and schedule the resources of microservices in cloud-native distributed applications, as the most famous container orchestration framework. However, Kubernetes preferentially schedules microservices to nodes with rich and balanced CPU and memory resources on a single node. The native scheduler of Kubernetes, called Kube-scheduler, may cause resource fragmentation and decrease resource utilization. In this paper, we propose a deep reinforcement learning enhanced Kubernetes scheduler named DRS. We initially frame the Kubernetes scheduling problem as a Markov decision process with intricately designed state, action, and reward structures in an effort to increase resource usage and decrease load imbalance. Then, we design and implement DRS mointor to perceive six parameters concerning resource utilization and create a thorough picture of all available resources globally. Finally, DRS can automatically learn the scheduling policy through interaction with the Kubernetes cluster, without relying on expert knowledge about workload and cluster status. We implement a prototype of DRS in a Kubernetes cluster with five nodes and evaluate its performance. Experimental results highlight that DRS overcomes the shortcomings of Kube-scheduler and achieves the expected scheduling target with three workloads. With only 3.27% CPU overhead and 0.648% communication delay, DRS outperforms Kube-scheduler by 27.29% in terms of resource utilization and reduces load imbalance by 2.90 times on average. © 2023 John Wiley & Sons, Ltd.

关键词： Reinforcement learning

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Buffered asynchronous SGD for byzantine learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 9761-9822页

作者： Yi-Rui Yang Wu-Jun Li National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Distributed learning has become a hot research topic due to its wide application in cluster-based large-scale learning, federated learning, edge computing, and so on. Most traditional distributed learning methods typically assume no failure or attack. However, many unexpected cases, such as communication failure and even malicious attack, may happen in real applications. Hence, Byzantine learning (BL), which refers to distributed learning with failure or attack, has recently attracted much attention. Most existing BL methods are synchronous, which are impractical in some applications due to heterogeneous or offline workers. In these cases, asynchronous BL (ABL) is usually preferred. In this paper, we propose a novel method, called buffered asynchronous stochastic gradient descent (BASGD), for ABL. To the best of our knowledge, BASGD is the first ABL method that can resist non-omniscient attacks without storing any instances on the server. Furthermore, we also propose an improved variant of BASGD, called BASGD with momentum (BASGDm), by introducing local momentum into BASGD. Compared with those methods which need to store instances on server, BASGD and BASGDm have a wider scope of application. Both BASGD and BASGDm are compatible with various aggregation rules. Moreover, both BASGD and BASGDm are proved to be convergent and able to resist failure or attack. Empirical results show that our methods significantly outperform existing ABL baselines when there exists failure or attack on workers.

关键词： distributed machine learning momentum asynchronous Byzantine learning buffer stochastic gradient descent

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Literature Review of Circulating Current of Parallel Voltage Source Converters

Literature Review of Circulating Current of Parallel Voltage...

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2023 IEEE PELS Students and Young Professionals Symposium, SYPS 2023

作者： Zhang, Haoyu Wong, Man-Chung University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering Faculty of Science and Technology China

ISBN: (纸本)9798350307832

To increase the power density of voltage source converters (VSC) usually use parallel structures. However, parallel VSC will easily introduce circulating current, which can cause a power efficiency decline. This paper gives a review of the circulating current and the solutions of it. Through the analysis of the principle of circulating current generation and the detail of the solutions, this paper gives an overview of the current research stage of circulating current. The technology can be divided into hardware solutions and software solutions are then analyzed and evaluated. © 2023 IEEE.

关键词： Power converters

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AdvTTS: Adversarial Text-to-Speech Synthesis Attack on Speaker Identification Systems

AdvTTS: Adversarial Text-to-Speech Synthesis Attack on Speak...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chu-Xiao Zuo Zhi-Jun Jia Wu-Jun Li Department of Computer Science and Technology National Key Laboratory for Novel Software Technology Nanjing University China

Speaker identification (SI) systems have been widely employed in real-world applications. However, recent research has demonstrated that SI systems are vulnerable to two prevalent attacks even without providing feedback to the attacker: the transfer-based adversarial attack and the speech synthesis spoofing attack. The transfer-based adversarial attack faces the challenges of collecting natural speech for specific content and timbre. In contrast, the speech synthesis spoofing attack can synthesize speech for any content and timbre but can be detected by audio deepfake detectors (ADD). In this paper, we propose a novel method, called adversarial text-to-speech synthesis (AdvTTS), for attacking SI systems. AdvTTS combines the strengths of transfer-based adversarial attacks and speech synthesis spoofing attacks, by synthesizing transferable attack speech with local surrogate models. AdvTTS is the first attack method that can conduct both adversarial and spoofing attacks with any speech content and timbre. AdvTTS can deceive SI systems with high-quality speech while evading ADD detection. Experiments show that AdvTTS can outperform other baselines for spoofing attacks, and can outperform the baselines for adversarial attacks with the combination of projected gradient descent (PGD).

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InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning

arXiv

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arXiv 2024年

作者： Liang, Yan-Shuo Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

Continual learning requires the model to learn multiple tasks sequentially. In continual learning, the model should possess the ability to maintain its performance on old tasks (stability) and the ability to adapt to new tasks continuously (plasticity). Recently, parameter-efficient fine-tuning (PEFT), which involves freezing a pre-trained model and injecting a small number of learnable parameters to adapt to downstream tasks, has gained increasing popularity in continual learning. Although existing continual learning methods based on PEFT have demonstrated superior performance compared to those not based on PEFT, most of them do not consider how to eliminate the interference of the new task on the old tasks, which inhibits the model from making a good trade-off between stability and plasticity. In this work, we propose a new PEFT method, called interference-free low-rank adaptation (InfLoRA), for continual learning. InfLoRA injects a small number of parameters to reparameterize the pre-trained weights and shows that fine-tuning these injected parameters is equivalent to fine-tuning the pre-trained weights within a subspace. Furthermore, InfLoRA designs this subspace to eliminate the interference of the new task on the old tasks, making a good trade-off between stability and plasticity. Experimental results show that InfLoRA outperforms existing state-of-the-art continual learning methods on multiple datasets. Code is available at https://***/liangyanshuo/InfLoRA. Copyright © 2024, The Authors. All rights reserved.

关键词： Economic and social effects

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Deep Semantic and Attentive Network for Unsupervised Video Summarization

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ACM Transactions on Multimedia Computing, Communications and Applications 2022年第2期18卷 1-21页

作者： Zhong, Sheng-Hua Lin, Jingxu Lu, Jianglin Fares, Ahmed Ren, Tongwei College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical Engineering Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University Cairo Egypt State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

With the rapid growth of video data, video summarization is a promising approach to shorten a lengthy video into a compact version. Although supervised summarization approaches have achieved state-of-the-art performance, they require frame-level annotated labels. Such an annotation process is time-consuming and tedious. In this article, we propose a novel deep summarization framework named Deep Semantic and Attentive Network for Video Summarization (DSAVS) that can select the most semantically representative summary by minimizing the distance between video representation and text representation without any frame-level labels. Another challenge associated with video summarization tasks mainly originates from the difficulty of considering temporal information over a long time. Long Short-Term Memory (LSTM) performs well for temporal dependencies modeling but does not work well with long video clips. Therefore, we introduce a self-attention mechanism into our summarization framework to capture the long-range temporal dependencies among the frames. Extensive experiments on two popular benchmark datasets, i.e., SumMe and TVSum, show that our proposed framework outperforms other state-of-the-art unsupervised approaches and even most supervised methods. © 2022 Association for Computing Machinery.

关键词： Semantics

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Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning

arXiv

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arXiv 2024年

作者： Wang, Kun Yang, Yi-Rui Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

Asynchronous federated learning (AFL) is an effective method to address the challenge of device heterogeneity in cross-device federated learning. However, AFL is usually incompatible with existing secure aggregation protocols used to protect user privacy in federated learning because most existing secure aggregation protocols are based on synchronous aggregation. To address this problem, we propose a novel secure aggregation protocol named buffered asynchronous secure aggregation (BASA) in this paper. Compared with existing protocols, BASA is fully compatible with AFL and provides secure aggregation under the condition that each user only needs one round of communication with the server without relying on any synchronous interaction among users. Based on BASA, we propose the first AFL method which achieves secure aggregation without extra requirements on hardware. We empirically demonstrate that BASA outperforms existing secure aggregation protocols for cross-device federated learning in terms of training efficiency and scalability. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis and Treatment through Efficient and Meaningful Segmentation of 3D Medical Images

Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Chenhang Jin, Yujian Liang, Jiayi Yang, YuHan Nie, Shanling Hai, Yang East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China The University of Sydney Faculty of Engineering Sydney Australia

ISBN: (纸本)9798350337488

Hepatocellular Carcinoma (HCC) holds a record of high incidence and severe global harm. In tasks of liver cancer segmentation based on 3D medical images, the majority of methods have endeavored to enhance the 3D U-net by integrating the latest modules from the field of computer Vision (such as the transformer), often overlooking the distinct characteristics of liver components. We introduced a novel deep learning architecture, Modif-SegUnet, to circumvent this limitation. This architecture extends the U-net model by incorporating a 3D-modifiable attention module, thereby fostering a heightened focus on distinguishing between normal liver sections and lesions. Furthermore, Modif-SegUnet ingeniously amalgamates the 3D-modifiable attention module with the Modif-transformer block, enabling efficient capture of relevant and valuable full-text information in CT images that contain liver or tumor regions. We subjected the proposed Modif-SegUnet to evaluation on the Liver Tumor Segmentation benchmark dataset. Experimental outcomes indicate that our methodology surpasses state-of-the-art approaches in liver tumor segmentation, suggesting potential pathways for advancing the diagnosis and treatment of liver cancer. © 2023 IEEE.

关键词： 3D medical image attention CT Liver cancer Segmentation transformer U-Net

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Optimal Policies for Quantum Markov Decision Processes

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International Journal of Automation and computing 2021年第3期18卷 410-421页

作者： Ming-Sheng Ying Yuan Feng Sheng-Gang Ying Centre for Quantum Software and Information University of Technology SydneyNSW 2007Australia State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

Markov decision process(MDP)offers a general framework for modelling sequential decision making where outcomes are *** particular,it serves as a mathematical framework for reinforcement *** paper introduces an extension of MDP,namely quantum MDP(q MDP),that can serve as a mathematical model of decision making about quantum *** develop dynamic programming algorithms for policy evaluation and finding optimal policies for q MDPs in the case of *** results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world.

关键词： Quantum Markov decision processes quantum machine learning reinforcement learning dynamic programming decision making

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