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Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 23

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

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23rd international conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Wu, Xiaoqiang Zhu, Qingling Lin, Qiuzhen Chen, Weineng Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method. © 2024 international Foundation for Autonomous Agents and Multiagent Systems.

关键词： Reinforcement learning

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Revisiting Pseudo-Label for Single-Positive Multi-Label Learning 40

Revisiting Pseudo-Label for Single-Positive Multi-Label Lear...

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40th international conference on Machine Learning

作者： Liu, Biao Xu, Ning Lv, Jiaqi Geng, Xin Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Peoples R China RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan

To deal with the challenge of high cost of annotating all relevant labels for each example in multilabel learning, single-positive multi-label learning (SPMLL) has been studied in recent years, where each example is annotated with only one positive label. By adopting pseudo-label generation, i.e., assigning pseudo-label to each example by various strategies, existing methods have empirically validated that SPMLL would significantly reduce the amount of supervision with a tolerable damage in classification performance. However, there is no existing method that can provide a theoretical guarantee for learning from pseudo-label on SPMLL. In this paper, the conditions of the effectiveness of learning from pseudo-label for SPMLL are shown and the learnability of pseudo-label-based methods is proven. Furthermore, based on the theoretical guarantee of pseudo-label for SPMLL, we propose a novel SPMLL method named MIME, i.e., Mutual label enhancement for sIngle-positive Multi-label lEarning and prove that the generated pseudo-label by MIME approximately converges to the fully-supervised case. Experiments on four image datasets and five MLL datasets show the effectiveness of our methods over several existing SPMLL approaches.

关键词： Machine learning

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Flow Characteristics Estimation Based On Multilayer Virtual Active Counter Sharing in data Center Network 9

Flow Characteristics Estimation Based On Multilayer Virtual ...

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9th international conference on Advanced Cloud and big data, CBD 2021

作者： Wang, Shiqi Dong, Fang Shen, Dian Zhao, Wei School of Cyber Science and Engineering Sounthest University Nanjing China School of Computer Science and Engineering Sounthest University Nanjing China Institute for Artficial Intelligence Hefei Comprehensive National Science Center Hefei China School of Computer Science and Technology Anhui University of Technology Hefei China

ISBN: (纸本)9781665407458

Per flow characteristics estimation has a variety of applications in the fields of traffic engineering and routing distribution, etc. Some recent works have adopted different methods to estimate the flow size, and the counter architecture used can be suitable for compact SRAM. However, the existing counter architecture still has limitations. Either it still requires a considerable amount of SRAM space, or the processing speed is reduced in order to ensure accuracy in a tight space. At the same time, the scalability is insufficient when performing multi-characteristic estimation. It is not suitable for flow characteristics other than the flow size. In this paper, we design a scalable active counter architecture called Multilayer-VAC, which can not only achieve better counting efficiency, but also can be used to count multiple characteristics of traffic. Experiments conducted with real network trajectories show that this counter architecture can produce good estimation of various sizes of traffic characteristic values in a compact memory space. © 2022 IEEE.

关键词： Static random access storage

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th international conference on Advances in Artificial intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for computing Machinery.

关键词： Ethanol

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Coded Distributed computing Schemes with Fewer Output Functions 6

Coded Distributed Computing Schemes with Fewer Output Functi...

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6th international conference on Computer Science and Artificial intelligence, CSAI 2022

作者： Jiang, Jing Ji, Shengpeng Guangxi Key Laboratory of Multi-Source Information Mining and Security Guangxi Normal University China

ISBN: (纸本)9781450397773

Li et al. proposed coded distributed computing (CDC) to reduce the communication load between servers by increasing the computational load of each server. They have shown that this scheme achieves the fundamental trade-off between computational and communication load. However, with the increase of the number of servers in this scheme, the output function and input file also increase exponentially. In this paper, we propose a new scheme to solve this problem. We show that when the number of servers increases, 1) the number of output functions of the proposed scheme is much smaller than that of Li et al., and the number of output functions required decreases exponentially;2) the ratio of the communication load of our new scheme to that of Li et al. is less than 1.9981. © 2022 ACM.

关键词： Economic and social effects

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Dynamic Behavior Pattern: Mining the Fraudsters in Telecom Network 24

Dynamic Behavior Pattern: Mining the Fraudsters in Telecom N...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Li, Dengshi Zeng, Lu Hu, Ruimin Huang, Zijun Liang, Xiaocong Zang, Yilong School of Artificial Intelligence Jianghan University Wuhan China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798350319934

In telecom network fraud, the fraud method of the fraudsters varies significantly in different periods. However, the traditional method of telecom fraud is to train the telecom fraud mining model through the user's long-term telephone data. So they can not detect the new fraud method of fraudsters and can not identify those with only have short-term telephone data. To increase the identification precision of telecom fraudsters, the dynamic changes in social structure and behavior pattern features of fraudsters were studied in this paper. Specifically, from the perspective of social structure change, the social structure of fraudsters changes rapidly over time. In contrast, the social structure of normal users is stable over time. From the perspective of user behavior pattern features, we found that the behavior patterns of fraudsters and normal users were very different. The behavior pattern of fraudsters in the different periods also changes significantly. Therefore, a telecom network fraud detection framework based on the social relationship evo-lution model was proposed. We designed the feature extraction method of dynamic telephone social behavior patterns based on time slices. The method combines the features of social structure and behavior patterns of users. And the Telephone Network Fraud Detection(TNFD) model is built using LSTM. We set up a sliding time window to enable the model to identify users with only short-term telephone data. This time window dynamically takes the features of the user's telephone social behavior pattern as the input of the TNFD model. Experiments on real dataset show that the precision of TNFD is 7% higher than that of the latest method. Our research may reference public security organs in telecom fraud cases. © 2022 IEEE.

关键词： Crime

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The Semi-definite Programming Model for Optimizing Information Propagation Rate on Social Network Based on the Spectral Graph Theory 22

The Semi-definite Programming Model for Optimizing Informati...

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7th international conference on big data and computing, ICBDC 2022

作者： Chen, Hui Wang, Bo Yang, Ke Hunan Seefore Information Technology Co. Ltd. China College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (纸本)9781450396097

In the study of online collaboration, one of the important aspects to optimize information propagation among users is to improve the rate of the propagation besides extending the propagation range, e.g., to deliver the advertisements to given target customers in shortest time or to inform a community in limited time. Due to the global optimization requirement, it is often challenging to optimize the propagation rate on a social network efficiently. Concerning the transition probability on each interpersonal relationship, we define information propagation as a random walk on a weighted directed graph, and indicate the propagation rate with the convergence rate of the random walk. Based on spectral graph theory, we further connect the convergence rate with the second smallest eigenvalue of the graph. Finally, by increasing the , a semi-definite programming model is proposed to globally optimize the information propagation rate more easily. Experimental results on real social network shows that the proposed solution can optimize the information propagation rate and further benefit the effectiveness of different typical influence diffusion models. © 2022 ACM.

关键词： Global optimization

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Text-Region Matching for Multi-Label Image Recognition with Missing Labels 24

Text-Region Matching for Multi-Label Image Recognition with ...

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32nd ACM international conference on Multimedia, MM 2024

作者： Ma, Leilei Xie, Hongxing Wang, Lei Fu, Yanping Sun, Dengdi Zhao, Haifeng School of Computer Science and Technology Anhui University Anhui Hefei China School of Artificial Intelligence Anhui University Anhui Hefei China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400706868

Recently, large-scale visual language pre-trained (VLP) models have demonstrated impressive performance across various downstream tasks. Motivated by these advancements, pioneering efforts have emerged in multi-label image recognition with missing labels, leveraging VLP prompt-tuning technology. However, they usually cannot match text and vision features well, due to complicated semantics gaps and missing labels in a multi-label image. To tackle this challenge, we propose Text-Region Matching for optimizing Multi-Label prompt tuning, namely TRM-ML, a novel method for enhancing meaningful cross-modal matching. Compared to existing methods, we advocate exploring the information of category-aware regions rather than the entire image or pixels, which contributes to bridging the semantic gap between textual and visual representations in a one-to-one matching manner. Concurrently, we further introduce multimodal contrastive learning to narrow the semantic gap between textual and visual modalities and establish intra-class and inter-class relationships. Additionally, to deal with missing labels, we propose a multimodal category prototype that leverages intra- and inter-category semantic relationships to estimate unknown labels, facilitating pseudo-label generation. Extensive experiments on the MS-COCO, PASCAL VOC, Visual Genome, NUS-WIDE, and CUB-200-211 benchmark datasets demonstrate that our proposed framework outperforms the state-of-the-art methods by a significant margin. Our code is available here. © 2024 ACM.

关键词： Semantics

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A new type of knowledge distance in approximation space 3

A new type of knowledge distance in approximation space

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3rd international conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Tang, Jianguo Wang, Jianghua Li, Xu Hu, Chunxia School of Artificial Intelligence and Big Data Chongqing College of Electronic Engineering Chongqing401331 China

ISBN: (纸本)9781510668522

Rough sets is a granular computing model mainly used for knowledge discovery. How to quantify the uncertainty, information content, and relationships between knowledge is an important aspect of rough set research. Although many scholars have proposed various knowledge measure models, it is still meaningful to study this issue from different perspectives. This paper constructs a new knowledge measure model. Firstly, we propose a new concept of knowledge distance. Unlike existing knowledge distance models, it no longer requires intersection and union operations on sets in knowledge, the calculation process of computing knowledge distance was simplified. Then, we studied its properties and found that it can effectively characterize the essential features of knowledge in approximate spaces. Finally, we discuss its relationship with information measure and knowledge granularity, proving that it is both an information measure and a knowledge granularity measure. These research results will help people more conveniently and intuitively measure and understand the uncertainty and information content of knowledge in approximate space. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Rough set theory

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Predicting of the physique performance and accuracy of college students based on multiple linear regression and BP neural network 3

Predicting of the physique performance and accuracy of colle...

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3rd Guangdong-Hong Kong-Macao Greater Bay Area Artificial intelligence and big data Forum, AIBDF 2023

作者： Huang, Xinxin Sulaiman, Mohd Suffian Bin Rosli, Marshima Mohd Binchun, Jiang College of Computing Informatics & Mathematics Universiti Teknologi MARA SHAH ALAM Malaysia College of Mechanical & Electrical Engineering Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9798400716362

A multiple linear regression model and a BP neural network model are established to predict the physique performance of college students and analyze its accuracy based on the physical fitness test data of college students. The results of the study show that the average error of the multiple linear regression model and the BP neural network model for predicting the physique performance of college students are 1.23 and 0.51 points, respectively, and the average error percentage is 1.84% and 0.74%, respectively. Both models can effectively predict the physique performance of college students, but the BP neural network model has higher prediction accuracy than the multiple linear regression model. © 2023 Copyright held by the owner/author(s).

关键词： Multiple linear regression

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