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Adaptive Multi-strategy Rabbit Optimizer for Large-scale Optimization

Journal of Bionic engineering 2025年第1期22卷 398-416页

作者： Baowei Xiang Yixin Xiang Department of Computing School of Electronic and Information Engineering Taizhou UniversityTaizhou318000China Department of Software Engineering College of Software Nankai UniversityTianjin300350China

As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly ***,the challenge lies in identifying the right parameters and strategies for these *** this paper,we introduce the adaptive multi-strategy Rabbit Algorithm(RA).RA is inspired by the social interactions of rabbits,incorporating elements such as exploration,exploitation,and adaptation to address optimization *** employs three distinct subgroups,comprising male,female,and child rabbits,to execute a multi-strategy *** parameters,including distance factor,balance factor,and learning factor,strike a balance between precision and computational *** offer practical recommendations for fine-tuning five essential RA parameters,making them versatile and *** is capable of autonomously selecting adaptive parameter settings and mutation strategies,enabling it to successfully tackle a range of 17 CEC05 benchmark functions with dimensions scaling up to *** results underscore RA’s superior performance in large-scale optimization tasks,surpassing other state-of-the-art metaheuristics in convergence speed,computational precision,and ***,RA has demonstrated its proficiency in solving complicated optimization problems in real-world engineering by completing 10 problems in CEC2020.

关键词： Adaptive parameter Large scale optimization Rabbit algorithm Swarm intelligence engineering optimization

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Taming diffusion model for exemplar-based image translation

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Computational Visual Media 2024年第6期10卷 1031-1043页

作者： Ma, Hao Yang, Jingyuan Huang, Hui Visual Computing Research Center College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Exemplar-based image translation involves converting semantic masks into photorealistic images that adopt the style of a given ***,most existing GAN-based translation methods fail to produce photorealistic *** this study,we propose a new diffusion model-based approach for generating high-quality images that are semantically aligned with the input mask and resemble an exemplar in *** proposed method trains a conditional denoising diffusion probabilistic model(DDPM)with a SPADE module to integrate the semantic *** then used a novel contextual loss and auxiliary color loss to guide the optimization process,resulting in images that were visually pleasing and semantically *** demonstrate that our method outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.

关键词： exemplar image translation denoising diffusion probabilistic model(DDPM)

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Classification of Privacy Preserved Medical Data with Fractional Tuna Sailfish Optimization Based Deep Residual Network in Cloud

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Annals of Data Science 2025年第3期12卷 829-854页

作者： Shikalgar, Shabanam K. Kumar, N. V. S. Pavan Singh, Gavendra Rashid, Faizur Sharad Institute of Technology College of Engineering Yadrav India Koneru Lakshmiah Education Foundation Vijayawada India Department of Software Engineering College of Computing and Informatics Haramaya University Dire Dawa138 Ethiopia College of Computing and Informatics Haramaya University Dire Dawa138 Ethiopia

Nowadays, with the growth of emerging technologies, increased attention has been paid to the classification of privacy-preserved medical data and development of various privacy-preserving models for the promotion of online medical pre-diagnosis systems. Medical data is highly sensitive and it is essential to ensure privacy of medical records from third-party users to increase service quality, satisfy patients and earn trust. The classification of medical preserved data is helpful to build a clinical decision system by classifying patients based on their disease and symptoms. In this article, a hybrid optimization-based deep learning model named Fractional Tuna Sailfish Optimization–Deep Residual Network (FractionalTSFO-DRN) is designed to precisely classify the privacy preserved medical data. A privacy utility coefficient matrix is used to ensure the privacy of medical data by generating a key matrix using Tuna Sailfish Optimization (TSFO) algorithmic technique. The privacy-preserved medical data is allowed for the classification process using DRN and the introduced Fractional TSFO is used to optimize and enhance the classification in DRN. The assessment followed by using heart disease prediction databases proved that the employed classification technique recorded an accuracy of 94.67%, a True Positive Rate of 93.56%, and a True Negative Rate of 89.68% respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Classification (of information)

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Activation Sparsity Opportunities for Compressing General Large Language Models

Activation Sparsity Opportunities for Compressing General La...

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2024 IEEE International Performance, computing, and Communications Conference, IPCCC 2024

作者： Dhar, Nobel Deng, Bobin Islam, Md Romyull Ahmad Nasif, Kazi Fahim Zhao, Liang Suo, Kun Kennesaw State University College of Computing and Software Engineering United States

ISBN: (纸本)9798350367942

Deploying local AI models, such as Large Language Models (LLMs), to edge devices can substantially enhance devices' independent capabilities, alleviate the server's burden, and lower the response time. Owing to these tremendous potentials, many big tech companies have been actively promoting edge LLM evolution and released several lightweight Small Language Models (SLMs) to bridge this gap. However, SLMs currently only work well on limited real-world applications. We still have huge motivations to deploy more powerful (larger-scale) AI models on edge devices and enhance their smartness level. Unlike the conventional approaches for AI model compression, we investigate from activation sparsity. The activation sparsity method is orthogonal and combinable with existing techniques to maximize compression rate while maintaining great accuracy. According to statistics of open-source LLMs, their Feed-Forward Network (FFN) components typically comprise a large proportion of parameters (around 2/3). This internal feature ensures that our FFN optimizations would have a better chance of achieving effective compression. Moreover, our findings are beneficial to general LLMs and are not restricted to ReLU-based *** work systematically investigates the tradeoff between enforcing activation sparsity and perplexity (accuracy) on state-of-the-art LLMs. Our empirical analysis demonstrates that we can obtain around 50% of main memory and computing reductions for critical FFN components with negligible accuracy degradation. This extra 50% sparsity does not naturally exist in the current LLMs, which require tuning LLMs' activation outputs by injecting zero-enforcing thresholds. To obtain the benefits of activation sparsity, we provide a guideline for the system architect for LLM prediction and prefetching. Moreover, we further verified the predictability of activation patterns in recent LLMs. The success prediction allows the system to prefetch the necessary weights while omitting

关键词： Cache memory

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BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation

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Frontiers of Computer Science 2023年第5期17卷 103-118页

作者： Jinwei LUO Mingkai HE Weike PAN Zhong MING College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen 518060China

Session-based recommendation(SBR)and multibehavior recommendation(MBR)are both important problems and have attracted the attention of many researchers and *** from SBR that solely uses one single type of behavior sequences and MBR that neglects sequential dynamics,heterogeneous SBR(HSBR)that exploits different types of behavioral information(e.g.,examinations like clicks or browses,purchases,adds-to-carts and adds-to-favorites)in sequences is more consistent with real-world recommendation scenarios,but it is rarely *** efforts towards HSBR focus on distinguishing different types of behaviors or exploiting homogeneous behavior transitions in a sequence with the same type of ***,all the existing solutions for HSBR do not exploit the rich heterogeneous behavior transitions in an explicit way and thus may fail to capture the semantic relations between different types of ***,all the existing solutions for HSBR do not model the rich heterogeneous behavior transitions in the form of graphs and thus may fail to capture the semantic relations between different types of *** limitation hinders the development of HSBR and results in unsatisfactory *** a response,we propose a novel behavior-aware graph neural network(BGNN)for *** BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a ***,our BGNN integrates the information of both homogeneous behavior transitions and heterogeneous behavior transitions in a unified *** then conduct extensive empirical studies on three real-world datasets,and find that our BGNN outperforms the best baseline by 21.87%,18.49%,and 37.16%on average correspondingly.A series of further experiments and visualization studies demonstrate the rationality and effectiveness of our *** exploratory study on extending our BGNN to handle more than two types of behaviors show that our BGNN can e

关键词： session-based recommendation graph neural network heterogeneous behaviors

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Evolution Analysis of Network Attack and Defense Situation Based on Game Theory

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Computers, Materials & Continua 2025年第4期83卷 1475-1494页

作者： Haiyan Sun Chenglong Shao Jianwei Zhang Kun Wang Wanwei Huang College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450007China Intelligent Computing Power Research Department Zhengzhou Xinda Advanced Technology Research InstituteZhengzhou450007China

To address the problem that existing studies lack analysis of the relationship between attack-defense game behaviors and situation evolution from the game perspective after constructing an attack-defense model,this paper proposes a network attack-defense game model(ADGM).Firstly,based on the assumption of incomplete information between the two sides of the game,the ADGM model is established,and methods of payoff quantification,equilibrium solution,and determination of strategy confrontation results are ***,drawing on infectious disease dynamics,the network attack-defense situation is defined based on the density of nodes in various security states,and the transition paths of network node security states are ***,the network zero-day virus attack-defense behaviors are analyzed,and comparative experiments on the attack-defense evolution trends under the scenarios of different strategy combinations,interference methods,and initial numbers are conducted using the NetLogo simulation *** experimental results indicate that this model can effectively analyze the evolution of the macro-level network attack-defense situation from the micro-level attack-defense *** instance,in the strategy selection experiment,when the attack success rate decreases from 0.49 to 0.29,the network destruction rate drops by 11.3%,in the active defense experiment,when the interference coefficient is reduced from 1 to 0.7,the network destruction rate decreases by 7%,and in the initial node number experiment,when the number of initially infected nodes increases from 10 to 30,the network destruction rate rises by 3%.

关键词： Network attack-defense situation evolution zero-day virus NetLogo

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An Information Cascade Prediction Algorithm Based on Time Series 6

An Information Cascade Prediction Algorithm Based on Time Se...

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6th ACM International Conference on Multimedia in Asia, MMAsia 2024

作者： Chen, Dongming Nie, Mingshuo Sun, Zhengping Chen, Huilin Wang, Dongqi Software College Northeastern University Shenyang China College of Engineering Computing and Cybernetics Australian National University Canberra Australia

ISBN: (纸本)9798400712739

The prediction of information cascades is a crucial task in data mining, which aims to understand the patterns of information diffusion at macroscopic and microscopic levels. The objective of the macro level is to predict the popularity of information cascades in the future. However, the current macro information cascade prediction algorithms produce a fixed popularity prediction value for a specific future time, which lacks flexibility. In this paper, we propose a novel information cascade prediction algorithm, named CasInformer. This algorithm views information cascades as a sequence of cascaded graph snapshots and employs time series prediction to make inferences. CasInformer enhances the selection method of cascaded snapshots and utilizes diverse information to encode supplementary data, which significantly enhances prediction accuracy compared to existing algorithms. CasInformer can predict the cascading popularity at multiple different times to obtain the future trend of information cascading popularity, which is more practical in real scenarios. Experimental results on real datasets show that CasInformer has achieved significant improvement in both prediction accuracy and prediction ability compared to existing research. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Prediction models

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Interactive Programming Tutorials in Automated Programming Assessment System Edgar 47

Interactive Programming Tutorials in Automated Programming A...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Mekterović, I. Brkić, Lj. Fertalj, M. Fabijanić, M. University Of Zagreb Faculty Of Electrical Engineering And Computing Zagreb Croatia Algebra University College Software Engineering Zagreb Croatia

ISBN: (纸本)9798350382495

Automated programming assessment tools are software systems widely used in education to assess programming code without manual intervention. Beyond exam scenarios, these tools are increasingly applied in e-learning contexts. In this realm, interactive programming tutorials have gained prominence for their effectiveness in teaching programming concepts. These tutorials blend theoretical knowledge with hands-on exercises, providing real-time feedback on code errors to facilitate prompt identification and correction by learners. The interactive nature engages learners actively, enhancing their understanding, and adaptability accommodates individualized progress. The accessibility and scalability of interactive programming tutorials suit learners of diverse skill levels. Integrated with automated assessment systems, interactive tutorials not only provide a dynamic and personalized learning experience but also alleviate the burden on instructors by enabling interactive content creation and offering valuable learning analytics. This paper introduces an evolution of the Edgar system, now equipped with an integrated interactive tutorial module. This module can evaluate embedded questions and code playgrounds in various programming languages, including SQL, Java, C, Python, etc., as well as multiple-choice questions. The integration represents a practical shift in programming education, offering learners a versatile and personalized approach to acquiring essential skills. © 2024 IEEE.

关键词： Python

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Cyber Security in Blockchain 6

Cyber Security in Blockchain

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6th IEEE Conference on Dependable and Secure computing, DSC 2023

作者： Cooper, Eric Weese, Eric Fortson, Alex Lo, Dan Shi, Yong Kennesaw State University College of Computing and Software Engineering Marietta United States

ISBN: (纸本)9798350382112

Blockchain is a growing technology that utilizes a decentralized public record of transactions. It is not only used for cryptocurrency, but also IoT, financial systems, healthcare, supply chain, etc. Although blockchain is quite robust, it is not invulnerable. This project will explain and demonstrate several weaknesses of blockchain and how to prevent them. We will demonstrate the following attacks and how to prevent them using Solidity: Transaction Order Dependence, Denial of Service, Replay Attack, Writing of Arbitrary Storage Address Attack, Weak Randomness, and Honeypot *** Concepts:Security and Privacy $\to$ software and Application Security $\to$ software Security engineering © 2023 IEEE.

关键词： Cybersecurity

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Design and Implementation of an ERC-20 Smart Contract on the Ethereum Blockchain

Design and Implementation of an ERC-20 Smart Contract on the...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Priest, Joshua Cooper, Cameron Lovell, Savvy Shi, Yong Lo, Dan Kennesaw State University College of Computing and Software Engineering Marietta United States

ISBN: (纸本)9798350324457

As technology continues to develop, there is a growing need to find sustainable solutions in all industries, including cryptocurrency. Due to the high energy consumption that cryptocurrencies are known for, there have been efforts to reduce waste consumption and in turn minimize the carbon footprint. We support the trends for creating an environment-friendly crypto token using the ERC-20 standard on the Ethereum blockchain. We outline the various aspects that make a token more sustainable and highlight the potential benefits of such tokens. Our proposal involves the design of a smart contract that incorporates eco-friendly features such as lower energy consumption, carbon offsetting, and more efficient methods or algorithms. We also discuss the importance of transparency and accountability in the design and implementation of such tokens. This paper discusses not only the practical tools and steps necessary in creating a crypto token but also highlights the challenges associated with creating a more sustainable token. © 2023 IEEE.

关键词： ERC-20 [Ethereum Request for Comment] Ethereum Gas Fee MetaMask Remix IDE Smart Contract Solidity

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