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IQABC-Based Hybrid Deployment Algorithm for Mobile Robotic Agents Providing Network Coverage

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Tsinghua science and Technology 2024年第2期29卷 589-604页

作者： Shuang Xu Xiaojie Liu Dengao Li Jumin Zhao College of computer science and Technology(College of Data science) Taiyuan University of TechnologyTaiyuan 030024China Pengcheng Laboratory Shenzhen 518055China College of information and computer Taiyuan University of TechnologyTaiyuan 030024China

Working as aerial base stations,mobile robotic agents can be formed as a wireless robotic network to provide network services for on-ground mobile devices in a target ***,a challenging issue is how to deploy these mobile robotic agents to provide network services with good quality for more users,while considering the mobility of on-ground *** this paper,to solve this issue,we decouple the coverage problem into the vertical dimension and the horizontal dimension without any loss of optimization and introduce the network coverage model with maximum coverage ***,we propose a hybrid deployment algorithm based on the improved quick artificial bee *** algorithm is composed of a centralized deployment algorithm and a distributed *** proposed deployment algorithm deploy a given number of mobile robotic agents to provide network services for the on-ground devices that are independent and identically *** results have demonstrated that the proposed algorithm deploys agents appropriately to cover more ground area and provide better coverage uniformity.

关键词： wireless robotic networks network coverage deployment algorithm improved quick artificial bee colony

Enhancing Log Anomaly Detection with Semantic Embedding and Integrated Neural Network Innovations

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computers, Materials & Continua 2024年第9期80卷 3991-4015页

作者： Zhanyang Xu Zhe Wang Jian Xu Hongyan Shi Hong Zhao School of computer and Software Nanjing University of Information Science and TechnologyNanjing210044China

System logs,serving as a pivotal data source for performance monitoring and anomaly detection,play an indispensable role in assuring service stability and *** this,the majority of existing log-based anomaly detection methodologies predominantly depend on the sequence or quantity attributes of logs,utilizing solely a single Recurrent Neural Network(RNN)and its variant sequence models for *** approaches have not thoroughly exploited the semantic information embedded in logs,exhibit limited adaptability to novel logs,and a single model struggles to fully unearth the potential features within the log *** these challenges,this article proposes a hybrid architecture based on amultiscale convolutional neural network,efficient channel attention and mogrifier gated recurrent unit networks(LogCEM),which amalgamates multiple neural network *** on the superior performance of robustly optimized BERT approach(RoBERTa)in the realm of natural language processing,we employ RoBERTa to extract the original word vectors from each word in the log *** conjunction with the enhanced Smooth Inverse Frequency(SIF)algorithm,we generate more precise log sentence vectors,thereby achieving an in-dept. representation of log ***,these log vector sequences are fed into a hybrid neural network,which fuses 1D Multi-Scale Convolutional Neural Network(MSCNN),Efficient Channel Attention Mechanism(ECA),and Mogrifier Gated Recurrent Unit(GRU).This amalgamation enables themodel to concurrently capture the local and global dependencies of the log sequence and autonomously learn the significance of different log sequences,thereby markedly enhancing the efficacy of log anomaly *** validate the effectiveness of the LogCEM model,we conducted evaluations on two authoritative open-source *** experimental results demonstrate that LogCEM not only exhibits excellent accuracy and robustness,but also outperfo

关键词： Deep learning log analysis anomaly detection natural language processing

Dynamical behaviors in discrete memristor-coupled small-world neuronal networks

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Chinese Physics B 2024年第4期33卷 729-734页

作者：鲁婕妤谢小华卢亚平吴亚联李春来马铭磷 School of Automation and Electronic information Xiangtan UniversityXiangtan 411105China School of computer science School of Cyberspace science Xiangtan UniversityXiangtan 411105China

The brain is a complex network system in which a large number of neurons are widely connected to each other and transmit signals to each *** memory characteristic of memristors makes them suitable for simulating neuronal synapses with *** this paper,a memristor is used to simulate a synapse,a discrete small-world neuronal network is constructed based on Rulkov neurons and its dynamical behavior is *** explore the influence of system parameters on the dynamical behaviors of the discrete small-world network,and the system shows a variety of firing patterns such as spiking firing and triangular burst firing when the neuronal parameterαis *** results of a numerical simulation based on Matlab show that the network topology can affect the synchronous firing behavior of the neuronal network,and the higher the reconnection probability and number of the nearest neurons,the more significant the synchronization state of the *** addition,by increasing the coupling strength of memristor synapses,synchronization performance is *** results of this paper can boost research into complex neuronal networks coupled with memristor synapses and further promote the development of neuroscience.

关键词： small-world networks Rulkov neurons memristor synchronization

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An Effective Power Optimization Approach Based on Whale Optimization Algorithm with Two-Populations and Mutation Strategies

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Chinese Journal of Electronics 2024年第2期33卷 423-435页

作者： Juncai HE Zhenxue HE Jia LIU Yan ZHANG Fan ZHANG Fangfang LIANG Tao WANG Limin XIAO Xiang WANG Hebei Agricultural University Beijing information science and Technology University School of computer science and Engineering Beihang University School of Electronic and information Engineering Beihang University

Power is an issue that must be considered in the design of logic circuits. Power optimization is a combinatorial optimization problem, since it is necessary to search for a logical expression that consumes the least amount of power from a large number of Reed-Muller(RM) logical expressions. The existing approach for optimizing the power of multi-output mixed polarity RM(MPRM) logic circuits suffer from poor optimization results. To solve this problem, a whale optimization algorithm with two-populations strategy and mutation strategy(TMWOA) is proposed in this paper. The two-populations strategy speeds up the convergence of the algorithm by exchanging information about the two-populations. The mutation strategy enhances the ability of the algorithm to jump out of the local optimal solutions by using the information of the current optimal solution. Based on the TMWOA, we propose a multi-output MPRM logic circuits power optimization approach(TMMPOA). Experiments based on the benchmark circuits of the Microelectronics Center of North Carolina(MCNC) validate the effectiveness and superiority of the proposed TMMPOA.

关键词： Logic circuits Benchmark testing Search problems Whale optimization algorithms Microelectronics Optimization Convergence

Modifying Lyapunov exponent of chaotic map by self-cascading

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science China(Technological sciences) 2024年第7期67卷 2203-2214页

作者： YI ChenLong LI ChunBiao LI YongXin XIA Ming HUA ZhongYun School of Electronic and information Engineering Nanjing University of Information Science&TechnologyNanjing 210044China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing 210044China School of computer science and Technology Harbin Institute of Technology Shenzhen Graduate SchoolShenzhen 518055China

The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos ***, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.

关键词： self-cascade controllable self-cascade chaos enhancement chaos control

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Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

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computer Modeling in Engineering & sciences 2024年第10期141卷 443-458页

作者： Prasanalakshmi Balaji Omar Alqahtani Sangita Babu Mousmi Ajay Chaurasia Shanmugapriya Prakasam Department of computer science College of Computer ScienceKing Khalid UniversityAbha61451Saudi Arabia College of science and Arts in Rijal Alma King Khalid UniversityAbha61451Saudi Arabia Department of information Technology Muffakham Jah College of Engineering and TechnologyHyderabad500034India Department of computer science and Business systems Rajalakshmi Engineering CollegeThandalam602105India

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire *** recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining *** cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival *** analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection *** upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and ***,the histopathology biopsy images are taken from standard data ***,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are ***,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer *** efficacy of the model is evaluated using divergent *** compared with other methods,the proposed work reveals that it offers impressive results for detection.

关键词： Bidirectional long short-term memory breast cancer detection feature extraction histopathology biopsy images multi-scale dilated vision transformer

Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

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computers, Materials & Continua 2024年第1期78卷 1095-1113页

作者： Fang Xu Songhao Jiang Yi Ma Manzoor Ahmed Zenggang Xiong Yuanlin Lyu School of computer and information science Hubei Engineering UniversityXiaogan432000China School of computer science and information Engineering Hubei UniversityWuhan430062China Artificial Intelligence Industrial Technology Research Institute Hubei Engineering UniversityXiaogan432000China

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research *** paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this *** proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among *** new metrics are defined:the intensity of node social relationships,node activity,and community *** the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node *** a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between *** proposed algorithm was compared to three existing routing algorithms in simulation *** indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

关键词： SIoT data forwarding social attributes social relations community

Quantum self-attention neural networks for text classification

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science China(information sciences) 2024年第4期67卷 301-313页

作者： Guangxi LI Xuanqiang ZHAO Xin WANG Institute for Quantum Computing Baidu Research Centre for Quantum Software and information University of Technology Sydney QICI Quantum information and Computation Initiative Department of Computer ScienceThe University of Hong Kong Thrust of Artificial Intelligence Information HubHong Kong University of Science and Technology(Guangzhou)

An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial intelligence, including natural language processing(NLP). Although some efforts based on syntactic analysis have opened the door to research in quantum NLP(QNLP), limitations such as heavy syntactic preprocessing and syntax-dependent network architecture make them impracticable on larger and real-world data sets. In this paper, we propose a new simple network architecture, called the quantum selfattention neural network(QSANN), which can compensate for these limitations. Specifically, we introduce the self-attention mechanism into quantum neural networks and then utilize a Gaussian projected quantum self-attention serving as a sensible quantum version of self-attention. As a result, QSANN is effective and scalable on larger data sets and has the desirable property of being implementable on near-term quantum devices. In particular, our QSANN outperforms the best existing QNLP model based on syntactic analysis as well as a simple classical self-attention neural network in numerical experiments of text classification tasks on public data sets. We further show that our method exhibits robustness to low-level quantum noises and showcases resilience to quantum neural network architectures.

关键词： quantum neural networks self-attention natural language processing text classification parameterized quantum circuits

Friendship Inference Based on Interest Trajectory Similarity and Co-occurrence

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Chinese Journal of Electronics 2024年第3期33卷 708-720页

作者： Junfeng TIAN Zhengqi HOU School of Cyber Security and computer Hebei University Hebei Key Laboratory of High Confidence information systems Hebei University

Most of the current research on user friendship speculation in location-based social networks is based on the co-occurrence characteristics of users,however,statistics find that co-occurrence is not common among all users;meanwhile,most of the existing work focuses on mining more features to improve the accuracy but ignoring the time complexity in practical *** this basis,a friendship inference model named ITSIC is proposed based on the similarity of user interest tracks and joint user location *** utilizing MeanShift clustering algorithm,ITSIC clustered and filtered user check-ins and divided the dataset into interesting,abnormal,and noise *** interest trajectories were constructed from user interest check-in data,which allows ITSIC to work efficiently even for users without *** the same time,by application of clustering,the single-moment multi-interest trajectory was further proposed,which increased the richness of the meaning of the trajectory *** extensive experiments on two real online social network datasets show that ITSIC outperforms existing methods in terms of AUC score and time efficiency compared to existing methods.

关键词： Data privacy Social networking (online) Noise Clustering algorithms Prediction algorithms Inference algorithms Data models