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3rd International Conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Zhang, Ge Chen, Yitong Kou, Xuye Zhang, Zhou School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China

ISBN: (纸本)9781510681279

Synthetic lethal interactions are critical genetic interactions that have been discovered for identifying new drug targets and potential cancer drug combination strategies. As a targeted approach to selectively kill cancer cells, it has attracted great attention in the field of cancer treatment. However, with the rapid growth of high-throughput data, effective identification of synthetic lethal interactions remains challenging. Although many graph topology methods focus on predicting SL interactions, heterogeneous graphs that exist in the real world tend to be scale-free, where the number of high-order neighbor nodes of high-degree nodes grows exponentially. The graph embedding method based on Euclidean space may not be able to effectively capture the internal hierarchical structure of the scale-free network, and there is not enough space to accommodate the nodes, making the node embedding highly distorted, which greatly limits the modeling ability. In this paper, we propose a model named HLASL based on the hyperbolic Lorentz attention mechanism, it ensures that the learned node features follow a Lorentz manifold. Specifically, in hyperbolic space, we use Lorentzian manifolds to encode heterogeneous information from KGs and information about synthetic lethality. In addition, we introduce the Lorenz knowledge graph attention mechanism to distinguish the weights of different information from the hyperbolic space to optimize gene embedding. Experiments on two datasets show that HLASL outperforms several state-of-the-art baseline methods in synthetic lethality prediction. © 2024 SPIE.

关键词： Forecasting

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ProgMate: An Intelligent Programming Assistant Based on LLM 54

ProgMate: An Intelligent Programming Assistant Based on LLM

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Li, Ying Yang, Runze Zhang, Xiantao Shi, Peng Yang, Da Huang, Xuefei School of Computer Science and Engineering Beihang University Beijing China International Innovation Institute Beihang University Key Laboratory of Data Science and Intelligent Computing Hangzhou China

ISBN: (纸本)9798350351507

This study addresses the challenges faced in personalized tutoring within large-scale programming courses, such as significant ability gaps among students, limited available resources, among others. For these reasons, we proposed an intelligent programming assistant, ProgMate, based on large language models (LLMs). Benefitting from the robust understanding and learning capabilities of LLM, ProgMate not only comprehensively monitors the learning process, but can also surpass human teaching in aspects such as intelligent assignment grading, identification of knowledge gaps, and assessment of learning abilities. ProgMate embodies a new 4-A digital teaching paradigm, characterized by its ability to provide precise guidance with anything, to anyone, anywhere, at any time, with features like 'omnipresence', 'adaptive guidance' and 'customization'. It facilitates the organic integration of collective teaching and individualized guidance, continuous learning, and long-term development, as well as resource efficiency and precision nurturing, offering a viable path and empirical support for the digital transformation of future education. © 2024 IEEE.

关键词： knowledge graph

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Single cell clustering using ZINB Model and variational graph attention autoencoder 3

Single cell clustering using ZINB Model and variational grap...

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3rd International Conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Zhang, Ge Kou, Xuye Chen, Yitong Zhang, Zhou School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China

ISBN: (纸本)9781510681279

Single-cell RNA sequencing allows to discovery of new cell subtypes based on transcriptomic information. Clustering analysis is an effective approach for exploring single-cell heterogeneity. Nevertheless, current single-cell clustering methodologies are challenged by the high-dimensional, sparse, and dropouts on the one hand, and on the other hand, they neglect the thorough exploration of potential relationships between cells and noise removal. To tackle these challenges, this study introduces a novel self-optimized single-cell clustering algorithm named scZVEA, which combines Zero- Inflated Negative Binomial (ZINB) model and variational graph attention autoencoder. The scZVEA framework comprises three key modules. Firstly, a Deep Count Autoencoder (DCA) is designed to model data distribution and eliminate noise. Then, a variational graph attention autoencoder to extract latent features from the data. Lastly, the self-optimized clustering module enables the two previously independent clustering and feature modules to mutually benefit each other by iteratively updating cluster centers to further enhance clustering performance. Experimental findings based on six authentic scRNAseq datasets illustrate that the proposed clustering algorithm significantly enhances clustering accuracy when compared to alternative methods. © 2024 SPIE.

关键词： Clustering algorithms

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Visualizing Program Behavior: A Study of Enhanced Program Diagrams Using LLM 54

Visualizing Program Behavior: A Study of Enhanced Program Di...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Li, Ying Yang, Runze Gui, ShiJie Shi, Peng Huang, Xuefei Yang, Da Zhang, Xiaozhou Gai, Yiming School of Computer Science and Engineering Beihang University Beijing China International Innovation Institute Beihang University Key Laboratory of Data Science and Intelligent Computing Hangzhou China

ISBN: (纸本)9798350351507

This paper aims to address the difficulties faced by novice programmers in grasping code structure and execution flow, improving programming thinking, and pinpointing code errors with accuracy. It proposes providing students with program behavior diagrams based on large language models (LLMs) and visualization techniques to achieve personalized guidance. Specifically, these program behavior diagrams include programming thinking visualization diagrams and code vulnerability visualization diagrams. A programming thinking visualization diagram employs static code analysis to gather code structure information, combined with the structured chain-of-thought method to collectively optimize the LLM. This enables the LLM to explain each interpretable part of the code from top to bottom, detailing the programming concepts, and displaying them on a modularized code structure diagram. The code vulnerability visualization diagram primarily utilizes the fine-tuned LLM, optimizing it based on program analysis and clustering analysis methods to accurately identify vulnerabilities in student code and display them on a code flow diagram. Its feature is to visually display to students the error location, error information, and the impact of errors on program flow, rather than providing the programming answers. Lastly, through experiments and statistical analysis of actual teaching data, this paper serves a demonstration that the enhanced models used in the visualization diagram generation process have a noticeable effect on mainstream LLMs, and that visualization diagrams hold significant value for students at different stages of learning. © 2024 IEEE.

关键词： Students

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Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 58-73页

作者： Qingsong Zhao Lei Shu Kailiang Li Mohamed Amine Ferrag Ximeng Liu Yanbin Li the College of Artificial Intelligence Nanjing Agricultural UniversityNanjing 210031 the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023 the State Key Laboratory of Cryptology Beijing 100878China IEEE the School of Engineering University of LincolnLincoln LN67TSUK the Artificial Intelligence and Digital Science Research Center Technology Innovation InstituteMasdar City 9639UAE the College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security *** issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

关键词： Challenges Internet of Things(IoT) privacy and security security requirements solar insecticidal lamps(SIL)

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3D Printed Integrated Gradient-Conductive MXene/CNT/Polyimide Aerogel Frames for Electromagnetic Interference Shielding with Ultra-Low Reflection

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Nano-Micro Letters 2023年第3期15卷 332-345页

作者： Tiantian Xue Yi Yang Dingyi Yu Qamar Wali Zhenyu Wang Xuesong Cao Wei Fan Tianxi Liu State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and EngineeringDonghua University2999 North Renmin RoadShanghai 201620People’s Republic of China Key Laboratory of Synthetic and Biological Colloids Ministry of EducationSchool of Chemical and Material EngineeringJiangnan UniversityWuxi 214122People’s Republic of China Institute of Environmental Processes and Pollution Control School of Environment and Civil EngineeringJiangnan UniversityWuxi 214122People’s Republic of China NUTECH School of Applied Sciences&Humanities National University of TechnologyIslamabad 44000Pakistan

Construction of advanced electromagnetic interference(EMI)shielding materials with miniaturized,programmable structure and low reflection are promising but ***,an integrated transition-metal carbides/carbon nanotube/polyimide(gradient-conductive MXene/CNT/PI,GCMCP)aerogel frame with hierarchical porous structure and gradient-conductivity has been constructed to achieve EMI shielding with ultra-low *** gradient-conductive structures are obtained by continuous 3D printing of MXene/CNT/poly(amic acid)inks with different CNT contents,where the slightly conductive top layer serves as EM absorption layer and the highly conductive bottom layer as reflection *** addition,the hierarchical porous structure could extend the EM dissipation path and dissipate EM by multiple ***,the GCMCP aerogel frames exhibit an excellent average EMI shielding efficiency(68.2 dB)and low reflection(R=0.23).Furthermore,the GCMCP aerogel frames with miniaturized and programmable structures can be used as EMI shielding gaskets and effectively block wireless power transmission,which shows a prosperous application prospect in defense industry and aerospace.

关键词： 3D printing MXene/CNT/Polyimide aerogel Gradient-conductive Electromagnetic interference shielding

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An ε-domination based two-archive 2 algorithm for many-objective optimization

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Journal of Systems engineering and Electronics 2022年第1期33卷 156-169页

作者： WU Tianwei AN Siguang HAN Jianqiang SHENTU Nanying College of Mechanical and Electrical Engineering China Jiliang UniversityHangzhou 310018China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jilang UniversityHangzhou 310018China

The two-archive 2 algorithm(Two_Arch2) is a manyobjective evolutionary algorithm for balancing the convergence,diversity,and complexity using diversity archive(DA) and convergence archive(CA).However,the individuals in DA are selected based on the traditional Pareto dominance which decreases the selection pressure in the high-dimensional *** traditional algorithm even cannot converge due to the weak selection ***,Two_Arch2 adopts DA as the output of the algorithm which is hard to maintain diversity and coverage of the final solutions synchronously and increase the complexity of the *** increase the evolutionary pressure of the algorithm and improve distribution and convergence of the final solutions,an ε-domination based Two_Arch2 algorithm(ε-Two_Arch2) for many-objective problems(MaOPs) is proposed in this *** ε-Two_Arch2,to decrease the computational complexity and speed up the convergence,a novel evolutionary framework with a fast update strategy is proposed;to increase the selection pressure,ε-domination is assigned to update the individuals in DA;to guarantee the uniform distribution of the solution,a boundary protection strategy based on I_(ε+) indicator is designated as two steps selection strategies to update individuals in *** evaluate the performance of the proposed algorithm,a series of benchmark functions with different numbers of objectives is *** results demonstrate that the proposed method is competitive with the state-of-the-art multi-objective evolutionary algorithms and the efficiency of the algorithm is significantly improved compared with Two_Arch2.

关键词： many-objective optimization ε-domination boundary protection strategy two-archive algorithm

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Controlled-source electromagnetic noise attenuation via a deep convolutional neural network and high-quality sounding curve screening mechanism

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Geophysics 2025年第3期90卷 WA125-WA140页

作者： Liu, Yecheng Li, Diquan Li, Jin Zhang, Xian Central South University Monitoring Ministry of Education Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Changsha China Hunan Provincial Key Laboratory of Non-ferrous Resources and Geological Hazard Detection Changsha China Central South University School of Geoscience and Info-physics Changsha China Hunan Normal University College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha China Hunan University of Finance and Economics School of Information Technology and Management Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology Changsha China

Strong noise is one of the biggest challenges in controlled-source electromagnetic (CSEM) exploration, which severely affects the quality of the recorded signal. We develop a novel and effective CSEM noise attenuation method that integrates a deep convolutional neural network (DCNN) with a high-quality sounding curve screening mechanism. This method first uses a DCNN to learn the large-scale noise characteristics of CSEM measured data and build a DCNN denoising model for attenuating the noise. Because some impulse noise remains after DCNN denoising, the high-quality sounding curve screening mechanism is innovatively applied to optimize the DCNN denoised data. The core of this method is the smoothness value (SV), which quantitatively evaluates the smoothness of the sounding curve (apparent resistivity curve). In effect, the mechanism segments the overall DCNN denoised data and calculates the corresponding sounding curve and its SV for each segment. The highest-quality sounding curve can be screened out by comparing the SVs of all curves as well as obtaining the highest-quality data segment present in the overall DCNN denoised data. Finally, our method is validated with synthetic and field data, demonstrating its feasibility and effectiveness in attenuating large-scale CSEM noise. The quality of CSEM data, which is heavily affected by noise, is significantly improved after processing by our method. © 2025 Society of Exploration Geophysicists. All rights reserved.

关键词： Deep neural networks

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基于数据挖掘技术的税务风险检测方法评述

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engineering 2024年第3期34卷 43-59页

作者： Qinghua Zheng Yiming Xu Huixiang Liu Bin Shi Jiaxiang Wang Bo Dong School of Computer Science and Technology Xi’an Jiaotong UniversityXi’an 710049China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong UniversityXi’an 710049China School of Distance Education Xi’an Jiaotong UniversityXi’an 710049China

Tax risk behavior causes serious loss of fiscal revenue,damages the country’s public infrastructure,and disturbs the market economic order of fair *** recent years,tax risk detection,driven by information technology such as data mining and artificial intelligence,has received extensive *** promote the high-quality development of tax risk detection methods,this paper provides the first comprehensive overview and summary of existing tax risk detection methods *** specifi-cally,it first discusses the causes and negative impacts of tax risk behaviors,along with the development of tax risk *** then focuses on data-mining-based tax risk detection methods utilized around the *** on the different principles employed by the algorithms,existing risk detection methods can be divided into two categories:relationship-based and non-relationship-based.A total of 14 risk detection methods are identified,and each method is thoroughly explored and ***,four major technical bottlenecks of current data-driven tax risk detection methods are analyzed and discussed,including the difficulty of integrating and using fiscal and tax fragmented knowledge,unexplainable risk detection results,the high cost of risk detection algorithms,and the reliance of existing algorithms on labeled *** investigating these issues,it is concluded that knowledge-guided and datadriven big data knowledge engineering will be the development trend in the field of tax risk in the future;that is,the gradual transition of tax risk detection from informatization to intelligence is the future development direction.

关键词： Tax risk detection data mining knowledge guide Informatization Intellectualization

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Effect of Mn alloying on the hydrogen-assisted cracking behavior in multiphase/duplex stainless steel

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材料科学技术（英文版） 2025年第4期207卷 126-141页

作者： Menghao Liu Cuiwei Du Xiaogang Li Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China Key Laboratory for Corrosion and Protection of the Ministry of Education Institute of Advanced Materials & TechnologyUniversity of Science and Technology BeijingBeijing 100083China National Materials Corrosion and Protection Data Center University of Science and Technology BeijingBeijing 100083China

There remains debate on whether Mn is beneficial or detrimental to hydrogen embrittlement in stainless *** this work,a series of stainless steels were designed to study the change of hydrogen embrit-tlement sensitivity,crack propagation,and hydrogen trapping behaviors upon Mn *** results suggest that adding 4 wt.％Mn increased hydrogen embrittlement susceptibility,whereas adding 8 wt.％Mn decreased hydrogen embrittlement *** banded α'-martensite through austenitic grain is the main reason for the increased hydrogen embrittlement sensitivity when adding 4 wt.％Mn,by ad-sorbing hydrogen,promoting crack initiation,and accelerating crack propagation.

关键词： Hydrogen-assisted cracking Multiphase stainless steel Manganese

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