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A BiLSTM cardinality estimator in complex database systems based on attention mechanism

CAAI Transactions on Intelligence Technology 2022年第3期7卷 537-546页

作者： Qiang Zhou Guoping Yang Haiquan Song Jin Guo Yadong Zhang Shengjie Wei Lulu Qu Louis Alberto Gutierrez Shaojie Qiao School of Information Science and Technology Southwest Jiaotong UniversityChengduChina School of Software Engineering Chengdu University of Information TechnologyChengduChina School of Energy Power and Mechanical Engineering North China Electric Power UniversityBaodingChina Digital Media Art Key Laboratory of Sichuan ProvinceSichuan Conservatory of MusicChengduChina Department of Computer Science Rensselaer Polytechnic InstituteNew YorkNew YorkUSA

An excellent cardinality estimation can make the query optimiser produce a good execution *** there are some studies on cardinality estimation,the prediction results of existing cardinality estimators are inaccurate and the query efficiency cannot be guaranteed as *** particular,they are difficult to accurately obtain the complex relationships between multiple tables in complex database *** dealing with complex queries,the existing cardinality estimators cannot achieve good *** this study,a novel cardinality estimator is *** uses the core techniques with the BiLSTM network structure and adds the attention ***,the columns involved in the query statements in the training set are sampled and compressed into ***,the Word2vec model is used to embed the word vectors about the query ***,the BiLSTM network and attention mechanism are employed to deal with word *** proposed model takes into consideration not only the correlation between tables but also the processing of complex *** experiments and the evaluation of BiLSTM-Attention Cardinality Estimator(BACE)on the IMDB datasets are *** results show that the deep learning model can significantly improve the quality of cardinality estimation,which is a vital role in query optimisation for complex databases.

关键词： attention BiLSTM cardinality estimation complex database systems query optimiser Word2vec

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Overbooking-enabled Virtual Machine Deployment Approach in Mobile Edge Computing

Overbooking-enabled Virtual Machine Deployment Approach in M...

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2022 CCF International Conference on Service science, CCF ICSS 2022

作者： Hu, Bingyi Gao, Jixun Huang, Quanzhen Wang, Huaichen Hu, Yanxin Liu, Jialei Ge, Yanmin Henan Polytechnic University School of Computer Science and Technology Jiaozuo454000 China Henan University of Engineering School of Computer Zhengzhou451191 China Anyang Normal University School of Software Engineering Anyang455000 China Anyang Science and Technology Innovation Service Center 455000 China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

Mobile Edge Computing (MEC) integrates computing, storage and other resources on the edge of the network and constructs a unified user service platform. Then, according to the principle of nearest service, MEC responds to the task requests of the edge nodes in time and effectively processes them. In MEC, edge servers are virtualized into several slots so that resources can be shared among different mobile users. However, there are many unpredictable risks in MEC, these risks can cause edge servers to fail, the virtual machine deployed in the server slot fails and the task cannot be executed normally. The introduction of primary-backup virtual machines solves this problem well. However, when the primary virtual machine is working normally, its backup virtual machine is idle, this will result in a waste of resources. In order to improve the resource utilization of the system, this paper firstly overbooks the backup virtual machine reasonably, and then formulates the virtual machine deployment problem as a combinatorial optimization problem. Finally, Virtual Machine Deployment Algorithm (VMDA) is proposed based on genetic algorithm. With the increase of the number of algorithm iterations and the population size of the virtual machine deployment scheme, there may be more optimal virtual machine deployment scheme individuals in the population. Therefore, the algorithm can obtain the approximate optimal value of resource utilization within the risk range allowed by the system, and the algorithm is compared with two other typical bin packing algorithms. The results confirm that VMDA outperforms the other two algorithms. © 2022 IEEE.

关键词： Virtual machine

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Hierarchical Crowdsourcing for Data Labeling with Heterogeneous Crowd 39

Hierarchical Crowdsourcing for Data Labeling with Heterogene...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Zhang, Haodi Huang, Wenxi Su, Zhenhan Chen, Junyang Jiang, Di Fan, Lixin Zhang, Chen Lian, Defu Wu, Kaishun Shenzhen University College of Computer Science and Software Engineering China Shenzhen University WeBank Institute of Financial Technology China Webank Co. Ltd. China Hong Kong Polytechnic University Department of Computing Hong Kong University of Science and Technology of China School of Data Science China

ISBN: (纸本)9798350322279

With the rapid and continuous development of data-driven technologies such as supervised learning, high-quality labeled data sets are commonly required by many applications. Due to the easiness of crowdsourcing small tasks with low cost, a straightforward solution for label quality improvement is to collect multiple labels from a crowd, and then aggregate the answers. The aggregation strategies include majority voting and its many variants, EM-based approaches, Graph Neural Nets and so on. However, due to the uncertainty information loss and commonly existing task correlations, the aggregated labels usually contain errors and may damnify the downstream model *** address the above problem, we propose a hierarchical crowdsourcing framework1 for data labeling with noisy answers about correlated data. We make use of the heterogeneity of the labeling crowd and form an initialization-checking-update loop to improve the quality of labeled data. We formalize and successfully solve the core optimization problem, namely, selecting a proper set of checking tasks for each round. We prove that maximizing the expected quality improvement is equivalent to minimizing the conditional entropy of the observations given the crowdsourced answer families for the selected task set, which is NP-hard to solve. Therefore, we design an efficient approximation algorithm and conduct a series of experiments on real data. The experimental results show that the proposed method effectively improves the quality of the labeled data sets as well as the SOTA performance, yet without extra human labor costs. © 2023 IEEE.

关键词： Approximation algorithms

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Propagation-Based Code Clone Analysis for Detecting Smart Contract Vulnerability

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IEICE Transactions on Information and Systems 2025年第6期E108.D卷 634-639页

作者： Zhang, Zhuo Li, Donghui Jiang, Kun Li, Ya Wang, Junhu Meng, Xiankai School of Electrical and Information Engineering Tianjin University Tianjin30072 China School of Computer Science and Engineering Xian University of Technolog Xian710000 China Ningbo Artificial Intelligence Institute Shanghai Jiaotong University Ningbo315000 China School of Information Technology and Engineering Guangzhou College of Commerce Guangzhou510000 China College of Computer and Information Engineering Polytechnic University Shanghai200127 China

Smart contracts are self-executing programs that operate on a blockchain. Once deployed, they cannot be altered, which introduces distinct maintenance challenges unlike those found in traditional software systems. Bugs and vulnerabilities in smart contracts have led to significant economic losses, drawing increased attention to their security. The immutability of smart contracts has made thorough security checks prior to deployment a priority. In this paper, we introduce a smart contract timestamp vulnerability detection technique PropaDT with propagation-based code clone analysis. The core idea of this technique involves using dataflow analysis based on an Abstract Syntax Tree (AST) to extract propagation chains that reveal how variables interact, potentially leading to vulnerabilities. Next, we extract code snippets based on the propagation chains and compare them with known vulnerability patterns in a database. This allows us to determine whether the tested smart contract contains a timestamp vulnerability, facilitating the detection of potential timestamp vulnerabilities in the code. Copyright © 2025 The Institute of Electronics, Information and Communication Engineers.

关键词： Data flow analysis

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Evaluating the spike in the symptomatic proportion of SARS-CoV-2 in China in 2022 with variolation effects:a modeling analysis

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Infectious Disease Modelling 2024年第2期9卷 601-617页

作者： Salihu S.Musa Shi Zhao Ismail Abdulrashid Sania Qureshi Andrés Colubri Daihai He Department of Genomics and Computational Biology University of Massachusetts Chan Medical SchoolWorcesterMA01605USA Department of Applied Mathematics Hong Kong Polytechnic UniversityHong Kong SARChina Department of Mathematics Aliko Dangote University of Science and TechnologyKanoNigeria School of Public Health Tianjin Medical UniversityTianjin300070China School of Finance and Operations Management The University of Tulsa800 South Tucker Dr.TulsaOK74104USA Department of Basic Sciences and Related Studies Mehran University of Engineering and Tech.JamshoroPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

Despite most COVID-19 infections being asymptomatic,China's Mainland had a high increase in symptomatic cases at the end of *** this study,we examine China's sudden COVID-19 symptomatic surge using a conceptual SIR-based *** model considers the epidemiological characteristics of SARS-CoV-2,particularly variolation,from non-pharmaceutical intervention(facial masking and social distance),demography,and disease mortality in China's *** increase in symptomatic proportions in China may be attributable to(1)higher sensitivity and vulnerability during winter and(2)enhanced viral inhalation due to spikes in SARS-CoV-2 infections(high transmissibility).These two reasons could explain China's high symptomatic proportion of COVID-19 in December *** study,therefore,can serve as a decision-support tool to enhance SARS-CoV-2 prevention and control ***,we highlight that facemask-induced variolation could potentially reduces transmissibility rather than severity in infected ***,further investigation is required to understand the variolation effect on disease severity.

关键词： SARS-CoV-2 Epidemic Epidemiological modeling Reproduction number Variolation

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Intelligent Cybersecurity Defense Strategies Based on Large Language Model Agents

Intelligent Cybersecurity Defense Strategies Based on Large ...

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2025 2nd International Conference on Algorithms, software engineering and Network Security (ASENS)

作者： Deyang Ye Zihao Wang Yuyan Song Jingtian Liang School of Cyber Science and Engineering Xi'an Jiaotong University Xi’an China School of Computer Science China West Normal University Nanchong China School of Software Engineering Xi'an Jiaotong University Xi’an China School of Computer and Information Engineering Shanghai Second Polytechnic University China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

As the complexity of cyberattacks continues to increase, traditional static defense strategies have shown significant limitations. This study proposes an intelligent cybersecurity defense framework based on large language model Agents, and conducts a comparative analysis of the effectiveness of various leading models in the cybersecurity defense domain. By developing an attack behavior model, designing an Agent decision-making mechanism, and constructing a defense strategy execution and evaluation system, we assess the defense performance of models such as GPT-4.5, Claude 3.7, LLaMA 3.3, and Gemini 2.0 under different attack scenarios. The experimental results demonstrate that defense strategies based on LLM Agents outperform traditional methods in terms of attack detection rate, response time, and false alarm rate. Furthermore, each model exhibits unique advantages in specific security contexts. This research provides new insights for the development of more adaptive and efficient cybersecurity defense systems.

关键词： Analytical models Large language models software algorithms Decision making Network security Complexity theory Time factors computer crime software engineering Context modeling

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Improving Confidence of Uncertain Knowledge Graphs by Crowdsourcing with Limited Budget

Improving Confidence of Uncertain Knowledge Graphs by Crowds...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Haodi Zhang Wenxi Huang Chenyu Xu Zhengbang Yang Hongxin Zhou Fengtian Qi Chen Zhang Kaishun Wu School of Computer Science and Software Engineering Shenzhen University Department of Computing Hong Kong Polytechnic University

Knowledge graphs (KGs), either constructed automatically from texts or collected manually from crowdsourcing workers, may contain uncertainty. The uncertainty may propagate into the knowledge graph embedding and downstream tasks, which is potentially harmful, especially for those confidencesensitive applications such as medical diagnostic suggestion. Crowdsourcing workers with domain knowledge can help improve the data quality of knowledge graphs, by knowledge checking. However, due to the large scale of knowledge graphs and the limitation of adequate crowdsourcing workers, it is unrealistic to check all triplets in a knowledge graph to improve the data quality. Therefore, in this paper, we propose a crowdsourcing framework that efficiently improves the confidence of knowledge graphs with limited budget. We instantiate the framework in the medical domain and conduct a series of experiments with realworld medical data. We deploy the framework for knowledge graph embedding UKGE and corresponding downstream tasks. The experimental results show that the proposed method efficiently improves the quality of the knowledge graphs, and hence improves the performance of probabilistic knowledge graph embedding in the downstream tasks.

关键词： Crowdsourcing Uncertainty Data integrity Distributed databases Knowledge graphs Probabilistic logic Medical diagnosis

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Spike-Visnet: A Novel Framework for Visual Recognition with Focuslayer-Stdp Learning

SSRN

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SSRN 2024年

作者： Liu, Ying Luo, Xiaoling Zhang, Ya Zhang, Yun Zhang, Wei Qu, Hong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer Science and Engineering Sichuan University of Science and Engineering Yibin644000 China School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China School of Electronic and Information Engineering Hebei University of Technology Tianjin300401 China

Current vision-inspired spiking neural networks (SNNs) face key challenges due to their model structures typically focusing on single mechanisms and neglecting the integration of multiple biological features. These limitations, coupled with limited synaptic plasticity, hinder their ability to implement biologically realistic visual processing. To address these issues, we propose Spike-VisNet, a novel retina-inspired framework designed to enhance visual recognition capabilities. This framework simulates both the functional and layered structure of the retina. To further enhance this architecture, we integrate the FocusLayer-STDP learning rule, allowing Spike-VisNet to dynamically adjust synaptic weights in response to varying visual stimuli. This rule combines channel attention, inhibition mechanisms, and competitive mechanisms with spike-timing-dependent plasticity (STDP), significantly improving synaptic adaptability and visual recognition performance. Comprehensive evaluations on benchmark datasets demonstrate that Spike-VisNet outperforms other STDP-based SNNs, achieving precision scores of 98.6% on MNIST, 93.29% on ETH-80, and 86.27% on CIFAR-10. These results highlight its effectiveness and robustness, showcasing Spike-VisNet's potential to simulate human visual processing and its applicability to complex real-world visual challenges. © 2024, The Authors. All rights reserved.

关键词： Neural networks

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Collaborative DNNs Inference with Joint Model Partition and Compression in Mobile Edge-Cloud Computing Networks

Collaborative DNNs Inference with Joint Model Partition and ...

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IEEE Conference on Wireless Communications and Networking

作者： Yaxin Tang Xiuhua Li Hui Li Zhengyi Yang Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Departrnent of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Mobile edge-cloud computing utilizes the computing resources of edge devices and cloud servers to execute complex deep neural networks (DNNs) for collaborative inference. However, many existing collaborative inference methods do not fully consider the limited resources of edge devices, resulting in high inference latency. In this paper, we design an integrated computational framework that combines model partition and compression to reduce inference latency. Specifically, we partition a DNN model at the middle layer and deploy the previous layer on the edge device and the subsequent layer on the cloud server respectively. We propose a collaborative dual-agent reinforcement learning algorithm called CPCDRL to determine partition point and compression ratios. It enables adaptive adjustments of compression ratios based on various partition points, with the overarching goal of minimizing the inference latency across the entire DNN model. The proposed algorithm can significantly reduce computational latency while minimizing accuracy loss compared to the baseline schemes.

关键词： Performance evaluation Adaptation models Computational modeling Simulation Collaboration Artificial neural networks Reinforcement learning

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Competitive and Cooperative Computation Offloading for Intensive Heterogeneous Tasks in Vehicular Edge Computing Networks

Competitive and Cooperative Computation Offloading for Inten...

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IEEE International Conference on Communications (ICC)

作者： Yuan Chen Xiuhua Li Guozeng Xu Ling Liu Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Computation offloading is widely used in vehicular edge computing (VEC) networks to satisfy the computational intensity and latency sensitivity requirements. However, many existing offloading algorithms do not comprehensively consider the dynamically changing characteristics of heterogeneous tasks within a roadside unit (RSU), resulting in tasks timeout and being dropped. In this paper, we design a competitive and cooperative computation offloading (C3O) model to reduce task execution latency. Specifically, when intensive heterogeneous tasks are generated, these vehicles jointly compete for the computing resource of a RSU, or they can also offload tasks to the task vehicle (TaV) with free computing resource. Meanwhile, We analyze the latency model of local execution and offloading to RSU or TaV execution and formulate a sequential task offloading decision problem, NP-hard. To address it, we propose a multi-agent reinforcement learning algorithm based on C3O (MARC3O) to intelligently determine the computation offloading policy for each vehicle according to the state of VEC networks. Simulation results demonstrate that the proposed algorithm can significantly reduce task execution latency and improve task completion rates compared with baseline schemes.

关键词： Analytical models Sensitivity Costs Computational modeling Simulation Heuristic algorithms Collaboration

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