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9th IEEE International Conference on Progress in Informatics and Computing, PIC 2024

作者： Luo, Bin Wen, Yang Ji, Jianhua Cao, Wenming Li, Ping Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronic and Information Engineering Shenzhen China College of Electronic and Information Engineering Shenzhen University Shenzhen China The Hong Kong Polytechnic University Department of Computing and School of Design Hong Kong Hong Kong

ISBN: (纸本)9798350363203

Enhancing fundus images is crucial for early diagnosis and monitoring of retinal diseases. Although CNN and Transformer-based methods have made great progress, CNNs struggle with long-range dependencies, and Transformers face challenges with large model sizes and low computational efficiency. To address these issues, Selective Structured State-Space Models (SSMs) offer an efficient solution with linear complexity. However, standard Mamba models still suffer from local pixel information loss and channel redundancy, limiting their performance in fundus image enhancement. We propose MambaFE, which incorporates localized refinement and an advanced channel attention mechanism to resolve challenges such as pixel detail loss and excessive channel information. Considering that retinal structures play a vital role in fundus analysis, retaining these structures during the enhancement process is crucial. As retinal structures are predominantly located within the high-frequency components of fundus images, we introduce a frequency-guided approach to capture reliable representations of these features, ensuring improved retention of essential information during enhancement. Comprehensive experimental findings indicate that the proposed MambaFE outperforms contemporary fundus image enhancement techniques, significantly enhancing the quality of clinically low-quality and synthetically degraded fundus images, while offering robust support for real-world medical applications. © 2024 IEEE.

关键词： Pixels

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A Local Regression-Based SLA-Aware Energy-Efficient Virtual Machine Consolidation Algorithm in Data Centers 9

A Local Regression-Based SLA-Aware Energy-Efficient Virtual ...

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9th International Conference on Big Data Computing and Communications, BigCom 2023

作者： Wu, Xiaodong Wang, Ronghai Lin, Guoxin Quanzhou Normal University School of Mathmatics and Computer Science China Fujian Province University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian China Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

ISBN: (纸本)9798350331240

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms. © 2023 IEEE.

关键词： Virtual machine

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Research on the Influencing Factors and Crime Prediction of Drug Crimes Based on Decision Tree Algorithm 5

Research on the Influencing Factors and Crime Prediction of ...

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5th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Wu, Shaobing Wang, Changmei Institute of Information Security Yunnan Police College Yunnan Police College Key Laboratory of Intelligent Drug Control Kunming China Yunnan Normal University Educ. Ministry Key Laboratory of Advanced Technology and Preparation for Renewable Energy Materials Kunming China

ISBN: (纸本)9798331528911

This research aims to design and develop a decision tree model to achieve drug crime prediction in border ethnic minority areas. This study will build on previous research by examining the extent to which four different types of factors - occupation, permanent population, age, and education level - can explain drug crimes in border ethnic minority areas in 2014 and 2021. The data comes from a sample of 1,856 drug crimes. This study uses the decision tree method to reveal that drug crimes are related to occupation, permanent population, age, and education level, while ethnic identity in border ethnic minority areas is not the only factor related to drug crimes. These results show that the new framework model introduced in this study can better explain drug crimes. The research findings have important practical implications, as they show that targeting these four factors in anti-drug crime policies is crucial. © 2024 IEEE.

关键词： Prediction models

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A New Approach of Intrusion Detection with Command Sequence-To-Sequence Model 6th

A New Approach of Intrusion Detection with Command Sequence-...

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6th Euro-China Conference on intelligent Data Analysis and Applications, ECC 2019

作者： Liu, Wei Mao, Yu Ci, Linlin Zhang, Fuquan School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350117 China

ISBN: (纸本)9789811650352

Traditionally, researchers have focused on network level intrusion detection and program level intrusion detection to improve computer security. However, neither approach is foolproof. We argue that the internal and external security of a computer system are equally important. Typically, a successful attacker manifests in the form of the attacker becoming a user on the host either with elevated or normal user privileges. At this point, user-level intrusion detection attempts to deter and curtail an attacker even after the system has been compromised. In this work, we introduce a new approach of intrusion detection based on recurrent neural networks (RNNs) to solve the long sequential problem. We build a robust user command sequence-to-sequence model by semantic modeling command. Our model implements the prediction of user command sequence and the prophesying of user behaviors. The experimental results on data sets of Purdue University, SEA and self-collected data show that an accurate, effective and efficient detection can be achieved by using the proposed approach. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

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Toward Understanding How the Saturation Effect Impacts the Spread of Computer Viruses 6

Toward Understanding How the Saturation Effect Impacts the S...

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6th International Conference on Electronic Engineering and Informatics, EEI 2024

作者： Zhang, Xianxiu Feng, Yuming Yang, Yi Zhang, Baojie School of Computer Science and Engineering Chongqing Three Gorges University Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Wanzhou404100 China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Chongqing Wanzhou404100 China

ISBN: (纸本)9798350353594

It is well known that people who know the presence of network viruses will defend against them, and the more computer viruses are detected and the more measures are taken. However, most previous computer virus propagation models have completely ignored the saturation effect. This paper investigates how the saturation effect impacts the transmission of electronic virus. For this purpose, a new transmission model of network virus is proposed, in which the virus is not very prevalent. Under certain conditions, there is a global asymptotical stable equilibrium point with no virus in this model. Finally, we give several numerical examples to exhibit that sufficient saturation effect is greatly conducive to containing network viruses. © 2024 IEEE.

关键词： Coronavirus

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Prediction of Heat Load of Thermal Station Based on Neural Network 24

Prediction of Heat Load of Thermal Station Based on Neural N...

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6th International Conference on control and Computer Vision, ICCCV 2024

作者： Zhu, Xinying Geng, Liqing Yang, Genghuang Huo, Yang Xu, Shiyuan Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin University of Technology and Education School of Automation and Electrical Engineering Tianjin University of Technology and Education China Co. Ltd China

ISBN: (纸本)9798400718045

To solve the problem of improving the prediction accuracy of thermal station, multi-strategy fusion Sparrow Search Algorithm to optimize the Neural Network prediction method. First, the acquired data is preprocessed to ensure the continuity of the data;second, the Pearson Correlation Coefficient method is used to determine the input feature variables and construct the data set through the data;and finally, Sparrow Search Algorithm and multi-strategy fusion Sparrow Search Algorithm respectively optimizes Back Propagation Neural Network and Long Short-Term Memory Neural Network models for heat supply prediction. After example verification and comparison, the multi-strategy fusion Sparrow Search Algorithm optimizes the Back Propagation Neural Network model with the minimum prediction error, which effectively improves the prediction accuracy. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Prediction models

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9th International Conference on Fuzzy Systems and Data Mining, FSDM 2023

作者： Xu, Changlin Wang, Kuan School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province North Minzu University Ningxia Yinchuan750021 China

ISBN: (纸本)9781643684703

Uncertainty exists widely in nature and human society. The handling and analysis of these uncertain phenomena have long been a focal point of research in natural sciences, representing a hot and crucial topic. The cloud model uses three numerical characteristics to realize qualitative and quantitative transformation of uncertain knowledge. Normal cloud is one of the more important and common cloud models. The similarity measurement of cloud model is used to judge the similarity of two cloud concepts, and the purpose is to understand the correlation between different cloud concepts, so as to improve the efficiency of classification and clustering. As a deformation extension of f divergence, Hellinger distance has good properties and is suitable for describing the difference between two probability distributions. HECMk and HCCMk algorithms are proposed by combining Hellinger distance and expectation curve cluster with entropy. Compared with previous algorithms, these two algorithms have higher distinguishing ability. Among them, HCCMk algorithm has better comprehensive performance and is feasible in actual similarity measurement. © 2023 The authors and IOS Press.

关键词： Entropy

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Recovering unknown topology in a two-layer multiplex network:One layer infers the other layer

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Science China(Technological Sciences) 2022年第7期65卷 1493-1505页

作者： LIU Hui SHANG ZhiCheng REN ZiYi LI Yan ZENG ZhiGang LU JunAn School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074China School of Mathematics and Statistics Wuhan UniversityWuhan 430072China

There have been increasing interests in studying multiplex dynamical networks *** paper focuses on topology identiflcation of two-layer multiplex networks with peer-to-peer interlayer *** a two-layer network model in which different layers have different coupling patterns,we propose novel methods to recover unknown topological structure of one layer,using the information of the other layer known as a *** proposed methods make full use of the measured evolutional states of the multiplex network itself,and treat the layer with a known structure as an auxiliary layer which is designed to identify the unknown topological *** with the traditional synchronization-based identiflcation method,the proposed methods are in no need of constructing an additional auxiliary network to identify the unknown topological layer,and thus greatly reduce the cost of topology ***,numerical simulations validate the effectiveness of the proposed methods.

关键词： multiplex network synchronization topology identiflcation

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intelligent Waste Sorting Platform Based on Taurus & Pegasus 5

Intelligent Waste Sorting Platform Based on Taurus & Pegasus

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5th International Conference on Mechatronics, Robotics and Automation, ICMRA 2022

作者： Chen, Xiang Tian, Liguo Li, Meng Liu, Jinqi Wang, Yuesong Fang, Donghui Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin China

ISBN: (纸本)9781665472654

Garbage sorting is an action for everyone to participate, but the existing garbage sorting mainly relies on manual discrimination. In this paper, an intelligent garbage sorting platform is designed using Taurus & Pegasus kit, which records the video stream of garbage using the sensor side of Taurus vision development board. The generated video files are then copied to an SD card for dataset creation, data cleaning, and data annotation. ResNetl8 network model is built using Pytorch framework for training (get pytorch), model transformation (model-caffe), quantization and simulation. Do end-to-end modeling and visualization. Network models are combined with HiSilicon NNIE deep learning acceleration engine to implement fast target classification algorithms. Finally, the model is deployed to the Taurus side for image recognition using an SD card. Residents place garbage in front of the camera, and the Taurus side recognizes and displays the garbage classification inference results and probabilities on the screen. At the same time, the result is sent to Pegasus via serial communication. Pegasus receives the classification result and drives the corresponding steering engine to open the garbage can lid and give voice prompt. When no object or background picture is captured in the camera screen, the trash can lid will be closed. © 2022 IEEE.

关键词： Sorting

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Reward Modeling Requires Automatic Adjustment Based on Data Quality

Reward Modeling Requires Automatic Adjustment Based on Data ...

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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Wang, Binghai Zheng, Rui Chen, Lu Xi, Zhiheng Shen, Wei Zhou, Yuhao Yan, Dong Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China Baichuan Inc. Beijing China Institute of Modern Languages and Linguistics Fudan University China Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9798891761681

In Reinforcement Learning from Human Feedback (RLHF), the reward model plays a crucial role in aligning language model outputs with human values. The human preference data used to train the reward model consists of a prompt and a response pair, with humans annotating which response better aligns with human value preferences. Due to the complexity and subjectivity of the annotation task, multiple organizations including OpenAI and Anthropic report significant noise in the human preference datasets, leading to instability and deviation in reward model training from human values. We discover that the difference in scores assigned to response pairs by the reward model effectively indicates the quality of data, and data of varying qualities show significant distinctions in reward model training. We introduce a method that automatically adjusts reward modeling based on data quality, reducing the impact of noise and making full use of dataset. Experiments on multiple human preference datasets demonstrate that our method stabilizes reward model training and significantly enhances the alignment performance of RLHF. © 2024 Association for Computational Linguistics.

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