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Multi-trial Vector-based Whale Optimization Algorithm

Journal of Bionic Engineering 2024年第3期21卷 1465-1495页

作者： Mohammad H.Nadimi-Shahraki Hajar Farhanginasab Shokooh Taghian Ali Safaa Sadiq Seyedali Mirjalili Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran department of Computer science Nottingham Trent UniversityNottinghamNG118NSUK Centre for artificial intelligence Research and Optimisation Torrens UniversityBrisbane4006Australia

The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback *** spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex *** address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of ***-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and *** the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test ***,MTV-WOA is compared with well-stablished,recent,and WOA variant *** experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence ***,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative ***,we solved five engineering design problems to demonstrate the practicality of *** results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms.

关键词： Swarm intelligence algorithms Metaheuristic algorithms Optimization Engineering design problems Whale optimization algorithm

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Aspect-Guided Multi-Graph Convolutional Networks for Aspect-based Sentiment Analysis

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data intelligence 2024年第3期6卷 771-791页

作者： Yong Wang Ningchuang Yang Duoqian Miao Qiuyi Chen Schoole of artificial intelligence Chongqing University of Technology401135ChongqingChina department of Computer science and Technology Tongji University201804ShanghaiChina

The Aspect-Based Sentiment Analysis(ABSA)task is designed to judge the sentiment polarity of a particular aspect in a *** studies have proved that GCN can capture syntactic and semantic features from dependency graphs generated by dependency trees and semantic graphs generated by Multi-headed self-attention(MHSA).However,these approaches do not highlight the sentiment information associated with aspect in the syntactic and semantic *** propose the Aspect-Guided Multi-Graph Convolutional Networks(AGGCN)for Aspect-Based Sentiment ***,we reconstruct two kinds of graphs,changing the weight of the dependency graph by distance from aspect and improving the semantic graph by Aspect-guided *** interactive learning of syntax and semantics,we dynamically fuse syntactic and semantic diagrams to generate syntactic-semantic graphs to learn emotional features *** addition,Multi-dropout is added to solve the overftting of AGGCN in *** experimental results on extensive datasets show that our model AGGCN achieves particularly advanced results and validates the effectiveness of the model.

关键词： Graph convolutional networks Aspect-based sentiment analysis Multi-headed attention BERT encoder

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Video Summarization Approach Based on Binary Robust Invariant Scalable Keypoints and Bisecting K-Means

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Computers, Materials & Continua 2024年第3期78卷 3565-3583页

作者： Sameh Zarif Eman Morad Khalid Amin Abdullah Alharbi Wail S.Elkilani Shouze Tang department of Information Technology Faculty of Computers and InformationMenoufia UniversityMenoufia32511Egypt department of artificial intelligence Faculty of Artificial IntelligenceEgyptian Russian UniversityBader11829Egypt department of Computer science Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia College of Applied Computer science King Saud UniversityAl-Muzahmiya19676Saudi Arabia School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China

Due to the exponential growth of video data,aided by rapid advancements in multimedia *** became difficult for the user to obtain information from a large video *** process of providing an abstract of the entire video that includes the most representative frames is known as static video *** method resulted in rapid exploration,indexing,and retrieval of massive video *** propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint(BRISK)and bisecting K-means clustering *** current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video *** video frames’BRISK features are clustered using a bisecting K-means,and the keyframe is determined by selecting the frame that is most near the cluster *** applying any clustering parameters,the appropriate clusters number is determined using the silhouette *** were carried out on a publicly available open video project(OVP)dataset that contained videos of different *** proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics,and the proposed method achieves a trade-off between computational cost and quality.

关键词： BRISK bisecting K-mean video summarization keyframe extraction shot detection

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GATiT:An Intelligent Diagnosis Model Based on Graph Attention Network Incorporating Text Representation in Knowledge Reasoning

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Computers, Materials & Continua 2024年第9期80卷 4767-4790页

作者： Yu Song Pengcheng Wu Dongming Dai Mingyu Gui Kunli Zhang School of Computer and artificial intelligence Zhengzhou UniversityZhengzhou450001China

The growing prevalence of knowledge reasoning using knowledge graphs(KGs)has substantially improved the accuracy and efficiency of intelligent medical ***,current models primarily integrate electronic medical records(EMRs)and KGs into the knowledge reasoning process,ignoring the differing significance of various types of knowledge in EMRs and the diverse data types present in the *** better integrate EMR text information,we propose a novel intelligent diagnostic model named the Graph ATtention network incorporating Text representation in knowledge reasoning(GATiT),which comprises text representation,subgraph construction,knowledge reasoning,and diagnostic *** the text representation process,GATiT uses a pre-trained model to obtain text representations of the EMRs and additionally enhances embeddings by including chief complaint information and numerical information in the *** the subgraph construction process,GATiT constructs text subgraphs and disease subgraphs from the KG,utilizing EMR text and the disease to be *** differentiate the varying importance of nodes within the subgraphs features such as node categories,relevance scores,and other relevant factors are introduced into the text ***-passing strategy and attention weight calculation of the graph attention network are adjusted to learn these features in the knowledge reasoning ***,in the diagnostic classification process,the interactive attention-based fusion method integrates the results of knowledge reasoning with text representations to produce the final diagnosis *** results on multi-label and single-label EMR datasets demonstrate the model’s superiority over several state-of-theart methods.

关键词： Intelligent diagnosis knowledge graph graph attention network knowledge reasoning

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SinGRAV: Learning a Generative Radiance Volume from a Single Natural Scene

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Journal of Computer science & Technology 2024年第2期39卷 305-319页

作者：王玉洁陈学霖陈宝权 School of Computer science and Technology Shandong UniversityQingdao 266237China State Key Laboratory of General artificial intelligence Beijing 100871China School of intelligence science and Technology Peking UniversityBeijing 100871China Tencent AI Lab Tencent Holdings LimitedShenzhen 518057China

We present SinGRAV, an attempt to learn a generative radiance volume from multi-view observations of a single natural scene, in stark contrast to existing category-level 3D generative models that learn from images of many object-centric scenes. Inspired by SinGAN, we also learn the internal distribution of the input scene, which necessitates our key designs w.r.t. the scene representation and network architecture. Unlike popular multi-layer perceptrons (MLP)-based architectures, we particularly employ convolutional generators and discriminators, which inherently possess spatial locality bias, to operate over voxelized volumes for learning the internal distribution over a plethora of overlapping regions. On the other hand, localizing the adversarial generators and discriminators over confined areas with limited receptive fields easily leads to highly implausible geometric structures in the spatial. Our remedy is to use spatial inductive bias and joint discrimination on geometric clues in the form of 2D depth maps. This strategy is effective in improving spatial arrangement while incurring negligible additional computational cost. Experimental results demonstrate the ability of SinGRAV in generating plausible and diverse variations from a single scene, the merits of SinGRAV over state-of-the-art generative neural scene models, and the versatility of SinGRAV by its use in a variety of applications. Code and data will be released to facilitate further research.

关键词： generative model neural radiance field 3D scene generation

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A study on dynamic group signature scheme with threshold traceability for blockchain

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High-Confidence Computing 2024年第2期4卷 73-81页

作者： Hyo-jin Song Teahoon Kim Yong-Woon Hwang Daehee Seo Im-Yeong Lee department of Software Convergence Soonchunhyang UniversityAsan 31538Republic of Korea Faculty of artificial intelligence and data Engineering Sangmyung UniversitySeoul 03016Republic of Korea department of Computer Software Engineering Soonchunhyang UniversityAsan 31538Republic of Korea

Blockchain technology provides transparency and reliability by sharing transactions and maintaining the same information through consensus among all ***,single-signature applications in transactions can lead to user identification issues due to the reuse of public *** address this issue,group signatures can be used,where the same group public key is used to verify signatures from group members to provide anonymity to ***,in dynamic groups where membership may change,an attack can occur where a user who has left the group can disguise themselves as a group member by leaking a partial *** problem cannot be traced back to the partial key *** this paper,we propose assigning different partial keys to group members to trace partial key leakers and partially alleviate the damage caused by partial key *** schemes have shown that arbitrary tracing issues occurred when a single administrator had exclusive key generation and tracing *** paper proposes a group signature scheme that solves the synchronization problem by involving a threshold number of TMs while preventing arbitrary tracing by distributing authority among multiple TMs.

关键词： Blockchain Group signature Privacy Anonymity Traceability

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High-Speed Railway 2024年第2期2卷 101-109页

作者： Jiaxu Guo Ding Ding Peihan Yang Qi Zou Yaping Huang School of Computer science and Technology Beijing Jiaotong UniversityBeijing 100044China Key Laboratory of Big data&artificial intelligence in Transportation Ministry of EducationBeijing 100044China

It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining ***,high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale *** the context of such needs,we propose a related degree-based frequent pattern mining algorithm,named Related High Utility Quantitative Item set Mining(RHUQI-Miner),to enable the effective mining of railway fault *** algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees,reducing redundancy and invalid frequent ***,it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the *** experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process,thus providing data support for differentiated and precise maintenance strategies.

关键词： High utility Quantitative Frequent pattern mining Related degree pruning Fixed pattern length

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Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality data

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Computer Modeling in Engineering & sciences 2024年第4期139卷 319-336页

作者： SangSeok Lee HaeWon Moon Lee Sael department of artificial intelligence Ajou UniversitySuwon-si16499Korea department of Software and Computer Engineering Ajou UniversitySuwon-si16499Korea

How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing *** example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long *** data,accumulated over time,is redundant and consumes a lot ofmemory in its raw *** need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time *** this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal *** that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save *** modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker *** factor matrices and core tensor of the decomposed results can then be used for further data *** compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker *** show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction *** applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to *** were able to model the spatial and temporal air quality *** were also able to verify unusual events,such as chronic ozone alerts and large fire events.

关键词： Dynamic decomposition tucker tensor tensor factorization spatiotemporal data tensor analysis air quality

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Fog-computing based mobility and resource management for resilient mobile networks

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High-Confidence Computing 2024年第2期4卷 145-151页

作者： Hang Zhao Shengling Wang Hongwei Shi School of artificial intelligence Beijing Normal UniversityBeijing 100875China

Mobile networks are facing unprecedented challenges due to the traits of large scale,heterogeneity,and high ***,the emergence of fog computing offers surprisingly perfect solutions considering the features of consumer proximity,wide-spread geographical distribution,and elastic resource *** this paper,we propose a novel mobile networking framework based on fog computing which outperforms others in *** scheme is constituted of two parts:the personalized customization mobility management(MM)and the market-driven resource management(RM).The former provides a dynamically customized MM framework for any specific mobile node to optimize the handoff performance according to its traffic and mobility traits;the latter makes room for economic tussles to find out the competitive service providers offering a high level of service quality at sound ***,our proposed MM and RM schemes can holistically support a full-fledged resilient mobile network,which has been practically corroborated by numerical experiments。

关键词： Resilient mobile networking Fog computing Mobility management Resource management

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Audio Mixing Inversion via Embodied Self-supervised Learning

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Machine intelligence Research 2024年第1期21卷 55-62页

作者： Haotian Zhou Feng Yu Xihong Wu department of AI Music and Music Information Technology Central Conservatory of MusicBeijing 100031China Laboratory of Music artificial intelligence Laboratory of Philosophy and Social SciencesMinistry of EducationBeijing 100031China School of intelligence science and Technology Peking UniversityBeijing 100871China

Audio mixing is a crucial part of music *** analyzing or recreating audio mixing,it is of great importance to conduct research on estimating mixing parameters used to create mixdowns from music recordings,i.e.,audio mixing ***,approaches of audio mixing inversion are rarely explored.A method of estimating mixing parameters from raw tracks and a stereo mixdown via embodied self-supervised learning is *** this work,several commonly used audio effects including gain,pan,equalization,reverb,and compression,are taken into *** method is able to learn an inference neural network that takes a stereo mixdown and the raw audio sources as input and estimate mixing parameters used to create the mixdown by iteratively sampling and *** the sampling step,the inference network predicts a set of mixing parameters,which is sampled and fed to an audio-processing framework to generate audio data for the training *** the training step,the same network used in the sampling step is optimized with the sampled data generated from the sampling *** method is able to explicitly model the mixing process in an interpretable way instead of using a black-box neural network model.A set of objective measures are used for *** experimental results show that this method has better performance than current state-of-the-art methods.

关键词： Audio mixing inversion intelligent audio mixing self-supervised learning audio signal processing deep learning

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