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Resource allocation for wireless networks based on enhanced Harris hawks optimisation algorithm

International Journal of Security and Networks 2024年第4期19卷 178-187页

作者： Meng, Xianmeng Cai, Cuicui Tan, Linglong Wang, Qijian School of Electronics Engineering Anhui Xinhua University Hefei230088 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China School of Electronics and Information Engineering West Anhui University Lu’an237012 China

Efficient resource allocation is critical to improve the quality of service in wireless networks. The problem of resource allocation is usually non-convex and non-deterministic polynomial-hard. Meta-heuristic algorithms are widely used to solve non-convex optimisation problems due to their efficient execution and high iteration accuracy. The Harris hawk optimisation (HHO) algorithm has recently received attention as an effective approach for addressing various optimisation problems. However, the HHO algorithm is employed for resource allocation issues in WNs and suffers from early convergence and local optimal problems. To overcome these issues, we develop a novel enhanced HHO (EHHO) algorithm with low computational complexity and explore the performance of the EHHO in resource allocation. Firstly, we introduce the power allocation for secrecy rate maximisation and spectrum efficiency trade-off in wireless networks. Then, the fundamental of the basic HHO algorithm is described, and the EHHO algorithm is proposed with nonlinear control update and local search strategies. Finally, we demonstrate the remarkable capabilities of the EHHO in terms of the benchmark functions and resource allocation in wireless networks. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： energy efficiency enhanced Harris hawks optimisation algorithm meta-heuristic optimisation resource allocation secrecy rate spectral efficiency

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Controllable Preparation of Branched Polyolefins with Various Microstructural Units via Chain-walking Ethylene and Pentene Polymerizations

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Chinese Journal of Polymer science 2023年第6期41卷 905-914,I0008页

作者： Hong-Ju Liu Wan-Lu Tian Xiao-Yue Wang Tong Lei Pei Li Guo-Yong Xu Chao Li Shao-Jie Zhang Fu-Zhou Wang Institutes of Physical Science and Information Technology School of Computer Science and TechnologyAnhui UniversityHefei 230601China

Branched polyolefins with controllable topology structures were generated from the chain-walking(co)polymerizations of ethylene,1-pentene(1P)and 2-pentene(2P)using Brookhart-typeα-diimine Ni(II)-based catalysts possessing different para-substituted groups,{[(4-R-2-Et-6-Me-C6H2N=C)2Nap]NiBr2,Nap:1,8-naphthdiyl;R=CHMePh,Ni1;R=Ph,Ni2;R=H,Ni3}.The X-ray diffraction analysis demonstrated that the crystalline structure of Ni1′is in centrosymmetric dimer structure mode with the bimetallic Ni center connected by two bromide *** para-sec-phenethyl moiety in the catalyst Ni1 obviously improved its catalytic performance and thermal stability in the ethylene *** Ni1/Et2AlCl system showed great catalytic activities(up to 7.73×106 g·mol-1·h-1)and achieved polyethylene(PE)with alkyl chains,including Me,Et,n-Pr,n-Bu,sec-Bu branches and longer chains(Lg).Compared with the 1-pentene polymerization,this catalyst system successfully mediated the polymerization of 2P to give highly branched polymers with approximately 195 branches/1000C possessing Me,Et,and n-Pr branches and a long methylene sequence due to the monomer *** Et branches derived from 2,3-insertion is slightly less than the sum of Me and n-Pr branches derived from 3,2-insertion,indicating that the 3,2-insertion mode is a slightly favorable pathway in the polymerization of 2P.

关键词： polymerization insertion chains

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A Novel CAPTCHA Recognition System Based on Refined Visual Attention

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computers, Materials & Continua 2025年第4期83卷 115-136页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Monir Abdullah Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.

关键词： Text-based CAPTCHA recognition refined visual attention web security computer vision

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PEAR: phrase-based hand-object interaction anticipation

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science China(information sciences) 2025年第5期68卷 133-152页

作者： Zichen ZHANG Hongchen LUO Wei ZHAI Yang CAO Yu KANG School of Information Science and Technology University of Science and Technology of China College of Information Science and Engineering Northeastern University Institute of Artificial Intelligence Hefei Comprehensive National Science Center

First-person hand-object interaction anticipation aims to predict the interaction process over a forthcoming period based on current scenes and prompts. This capability is crucial for embodied intelligence and human-robot *** complete interaction process involves both pre-contact interaction intention(i.e., hand motion trends and interaction hotspots) and post-contact interaction manipulation(i.e., manipulation trajectories and hand pose with contact). Existing research typically anticipates only interaction intention while neglecting manipulation, resulting in incomplete predictions and an increased likelihood of intention errors due to the lack of manipulation constraints. To address this issue, we propose a novel model, PEAR(phrase-based hand-object interaction anticipation), which jointly anticipates interaction intention and manipulation. To handle interaction uncertainty, we employ a twofold approach. Firstly, we perform cross-alignment of verbs, nouns, and images to reduce the diversity of hand movement patterns and object functional attributes, thereby mitigating intention uncertainty. Secondly, we establish bidirectional constraints between intention and manipulation using dynamic integration and residual connections, ensuring consistency among elements and thus overcoming manipulation uncertainty. To rigorously evaluate the performance of the proposed model, we collect a new task-relevant dataset, EGO-HOIP,with comprehensive annotations. Extensive experimental results demonstrate the superiority of our method.

关键词： hand-object interaction first-person perspective trajectory forecasting hand pose prediction embodied intelligence

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Additive Noise Model Structure Learning Based on Spatial Coordinates

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 3858-3871页

作者： Yang, Jing Lu, Ting Zhu, Youjie Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230009 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China

Discovering causal relationships from a large amount of observational data is an important research direction in data mining. To address the challenges of discovering and constructing causal networks on nonlinear and high-dimensional data, this article proposes a new structural learning algorithm called spatial coordinates based (SCB). The SCB algorithm effectively discovers causal networks from large scale nonlinear data. In this article, we make three main contributions. Firstly, based on the Hilbert-Schmidt independence criterion (HSIC), we propose new independence test coefficients: spatial coordinate (SC) coefficient and conditional spatial coordinate (CSC) coefficient. We also prove that the statistical distribution of the CSC coefficient follows a standard normal distribution. Secondly, using the statistical distribution of the CSC coefficient, we redefine the correlation of variables and combine it with local learning to propose the SCB algorithm. Finally, we demonstrate the effectiveness of the algorithm through experiments on data generated from various nonlinear function dependencies. Compared to existing algorithms, the causal network models constructed using the SCB algorithm exhibit significant improvements in accuracy and time performance. The effectiveness of the proposed method is further validated on real power plant data. © 2020 IEEE.

关键词： Additive noise

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Metarelation2vec:A Metapath-Free Scalable Representation Learning Model for Heterogeneous Networks

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Tsinghua science and technology 2024年第2期29卷 553-575页

作者： Lei Chen Yuan Li Yong Lei Xingye Deng School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan 411201China

Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks(HNs)for most of the existing representation learning ***,any metapaths consisting of multiple,simple metarelations must be driven by domain *** sensitive,expensive,and limited metapaths severely reduce the flexibility and scalability of the existing models.A metapath-free,scalable representation learning model,called Metarelation2vec,is proposed for HNs with biased joint learning of all metarelations in a bid to address this ***,a metarelation-aware,biased walk strategy is first designed to obtain better training samples by using autogenerating cooperation probabilities for all metarelations rather than using expert-given ***,grouped nodes by the type,a common and shallow skip-gram model is used to separately learn structural proximity for each node ***,grouped links by the type,a novel and shallow model is used to separately learn the semantic proximity for each link ***,supervised by the cooperation probabilities of all meta-words,the biased training samples are thrown into the shallow models to jointly learn the structural and semantic information in the HNs,ensuring the accuracy and scalability of the *** experimental results on three tasks and four open datasets demonstrate the advantages of our proposed model.

关键词： metarelation random walk heterogeneous network metapath representation learning

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NeurDB: an AI-powered autonomous data system

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science China(information sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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How graph convolutions amplify popularity bias for recommendation?

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Frontiers of computer science 2024年第5期18卷 121-132页

作者： Jiajia CHEN Jiancan WU Jiawei CHEN Xin XIN Yong LI Xiangnan HE School of Information Science and Technology University of Science and Technology of ChinaHefei 230026China School of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computer Science and Technology Shandong UniversityQingdao 250100China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China

Graphconvolutional networks(GCNs)have become prevalent in recommender system(RS)due to their superiority in modeling collaborative *** improving the overall accuracy,GCNs unfortunately amplify popularity bias-tail items are less likely to be *** effect prevents the GCN-based RS from making precise and fair recommendations,decreasing the effectiveness of recommender systems in the long *** this paper,we investigate how graph convolutions amplify the popularity bias in *** theoretical analyses,we identify two fundamental factors:(1)with graph convolution(i.e.,neighborhood aggregation),popular items exert larger influence than tail items on neighbor users,making the users move towards popular items in the representation space;(2)after multiple times of graph convolution,popular items would affect more high-order neighbors and become more *** two points make popular items get closer to almost users and thus being recommended more *** rectify this,we propose to estimate the amplified effect of popular nodes on each node's representation,and intervene the effect after each graph ***,we adopt clustering to discover highly-influential nodes and estimate the amplification effect of each node,then remove the effect from the node embeddings at each graph convolution *** method is simple and generic-it can be used in the inference stage to correct existing models rather than training a new model from scratch,and can be applied to various GCN *** demonstrate our method on two representative GCN backbones LightGCN and UltraGCN,verifying its ability in improving the recommendations of tail items without sacrificing the performance of popular *** are open-sourced^(1)).

关键词： recommendation graph convolution networks popularity bias

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Align is not Enough: Multimodal Universal Jailbreak Attack against Multimodal Large Language Models

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5475-5488页

作者： Wang, Youze Hu, Wenbo Dong, Yinpeng Liu, Jing Zhang, Hanwang Hong, Richang Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China Tsinghua University Beijing100084 China Chinese academy of science Institute of automation Beijing100190 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

Large Language Models (LLMs) have evolved into Multimodal Large Language Models (MLLMs), significantly enhancing their capabilities by integrating visual information and other types, thus aligning more closely with the nature of human intelligence, which processes a variety of data forms beyond just text. Despite advancements, the undesirable generation of these models remains a critical concern, particularly due to vulnerabilities exposed by text-based jailbreak attacks, which have represented a significant threat by challenging existing safety protocols. Motivated by the unique security risks posed by the integration of new and old modalities for MLLMs, we propose a unified multimodal universal jailbreak attack framework that leverages iterative image-text interactions and transfer-based strategy to generate a universal adversarial suffix and image. Our work not only highlights the interaction of image-text modalities can be used as a critical vulnerability but also validates that multimodal universal jailbreak attacks can bring higher-quality undesirable generations across different MLLMs. We evaluate the undesirable context generation of MLLMs like LLaVA, Yi-VL, MiniGPT4, MiniGPT-v2, and InstructBLIP, and reveal significant multimodal safety alignment issues, highlighting the inadequacy of current safety mechanisms against sophisticated multimodal attacks. This study underscores the urgent need for robust safety measures in MLLMs, advocating for a comprehensive review and enhancement of security protocols to mitigate potential risks associated with multimodal capabilities. © 1991-2012 IEEE.

关键词： Human form models

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Porous FeP/FeO Deposited on Carbon Cloth with Accelerated Kinetics for the Hydrogen Evolution Reaction

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Journal of Wuhan university of technology(Materials science) 2024年第6期39卷 1434-1439页

作者： YU Hai ZHAO Yujia LIU Su ZUO Lei LEI Qianqian HU Ruiling LU Jianguo ZHAO Min WANG Congrong WANG Cunyong ZHANG Miao YANG Lei ZHONG Jin CAO Chunbin School of Physics and Materials Engineering and Key Laboratory for Photoelectric Detection Science and Technology of Education Department of Anhui Province Hefei Normal UniversityHefei 230601China School of Physics and Material Science Anhui UniversityHefei 230039China Department of Chemistry and Materials Engineering Hefei UniversityHefei 230601China School of Computer Science and Technology Hefei Normal UniversityHefei 230601China School of Science Anhui Agricultural UniversityHefei 230036China

FeP/FeO was prepared on carbon cloth(CC)via hydrothermal method,heat treatment in air,and phosphorization in ***/FeO/CC presents a porous and loose morphology which is conducive to the exposure of active sites and the transfer of ***/FeO/CC requires the low overpotentials of 257 and 117 mV(*** hydrogen electrode(RHE))to achieve the current density of 10 mA_·cm^(-2)for oxygen evolution reaction(OER)and hydrogen evolution reaction(HER)in alkaline KOH solution,*** small Tafel slope values of 36.1 mV_·dec^(-1)(for OER)and 96.2 mV_·dec^(-1)(for HER)indicate that FeP/FeO/CC exhibits the fast electrocatalytic reactive kinetics for OER and *** particular,the reaction kinetics of FeP/FeO/CC accelerated with the progress of *** charge-transfer resistance(R_(ct))of FeP/FeO/CC is only 11Ω.Excellent bifunctional electrocatalytic performances of FeP/FeO/CC should be attributed to the porous morphology and the lower charge-transfer resistance.

关键词： FeP/FeO/CC OER HER water splitting

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