检索结果-内蒙古大学图书馆

IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Chen, Shiyu Li, Qianmu Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350351637

Social influence is an individual's ability to change the thoughts or behaviors of others due to factors such as social status, social connections, and social wealth. Studying social influence, especially modeling and measuring the influence between people on large-scale social network data, is very important for product promotion, friend recommendation, expert discovery and user behavior prediction in social applications. In addition, it plays an important role in promoting the virtual business market and national public opinion monitoring. From a computational point of view, the research on social influence includes detection, modeling, measurement, and application of social influence. First, a training dataset is generated through independent cascade and linear threshold models. The influence of each node in the network is then calculated through graph convolutional network and reinforcement learning techniques. Finally, the proposed model is tested on three real-world large-scale datasets. Experimental results show that the proposed method can help predict the influence of nodes in social networks. © 2024 IEEE.

关键词： deep learning graph convolutional network graph embedding reinforcement learning social influence social networks

来源：评论

学校读者我要写书评

暂无评论

Q2Cypher: Converting Natural Language Questions to Cypher with Fine-Tuned Large Language Models 5

Q2Cypher: Converting Natural Language Questions to Cypher wi...

引用

5th International Conference on Artificial Intelligence and computer engineering, ICAICE 2024

作者： Zou, Yunqi Wang, Yongli Liu, Dongmei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331528911

Knowledge Base Question Answering (KBQA) intends to obtain credible answers to natural language questions based on knowledge bases. Knowledge graphs, also known as graph databases, explicitly present a large amount of factual knowledge and the associations among them. Neo4j, one of the most popular graph databases, uses the Cypher Query Language (CQL) for efficient querying. Due to its complexity, converting natural language questions into CQL, i.e., Text-to-CQL, is in demand, but the performance is undesirable for existing models. To address the issue, we propose Q2Cypher, a Text-to-CQL framework that is able to generate corresponding CQL based on natural language questions by fine-tuning open-source large language models (LLMs). Q2Cypher employs an unsupervised joint retrieval method to further align entity and relation names in the CQL, thus improving generation from the models more directly. We conduct experiments on open-source LLMs such as ChatGLM2, Baichuan2 and Llama-2, using different fine-tuning methods such as LoRA, Freeze and QLoRA for comparison. The experimental results demonstrate that Q2Cypher is effective and achieves better performance than existing methods on the SpCQL dataset. © 2024 IEEE.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Method for Relation Extraction Based on Entity Boundary Features 24

Method for Relation Extraction Based on Entity Boundary Feat...

引用

2024 7th International Conference on computer Information science and Artificial Intelligence, CISAI 2024

作者： Wang, Zining Ge, Jike Yang, Xiaolu Tan, Jie Xiang, Yu School of Computer Science and Engineering Chongqing University of Science and Technology Chongqing China

ISBN: (纸本)9798400707254

As a key task in natural language processing, the current knowledge extraction methods mostly involve joint extraction, simultaneously extracting named entities and relationships. When conducting relationship extraction, entities containing multiple words are usually represented using pooling or weighted sum methods, leading to the neglect of entity boundary information. To address the issue of reduced accuracy in relationship classification due to the introduction of intermediate noise information based on entity segment features, a relationship extraction method focusing on entity boundary features is proposed. Firstly, the semantic representation of the text is obtained using the pre-trained language model DeBERTa. Then, features of the head and tail positions of entities in the text are extracted and concatenated into a composite embedding vector containing information from these two key positions, used to enhance entity representation. By concatenating the composite embedding vectors of the head and tail entities, the final embedding vector of the entity pair is obtained, representing the information of each entity and enhancing the model’s understanding of entity boundaries and semantic information. Finally, the existence of relationships between entity pairs is determined by the relationship discriminator and relationship classifier, simultaneously classifying the relationship types of the entity pairs for relationship classification. Experimental results show that the proposed method achieves F1 scores of 89.3%, 94.4%, 94.2%, and 94.9% on the NYT, NYT*, WebNLG, and WebNLG* datasets, respectively, significantly outperforming the baseline models. Additionally, ablation experiments demonstrate the importance of the relationship discriminator, further confirming the superiority of the relationship extraction method focusing on entity boundary features. © 2024 Copyright held by the owner/author(s).

关键词： Boundary features

来源：评论

学校读者我要写书评

暂无评论

Reasonable Gradients for Online Training Algorithms in Spiking Neural Networks 27

Reasonable Gradients for Online Training Algorithms in Spiki...

引用

27th European Conference on Artificial Intelligence, ECAI 2024

作者： Xue, Lang Liu, HanWen Wang, Jing Qu, Hong School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9781643685489

Spiking neural networks (SNNs) have the potential to simulate sparse and spatio-temporal dynamics observed in biological neurons, making them promising for achieving energy-efficient artificial general intelligence. While backpropagation through time (BPTT) ensures reliable precision for training SNNs, it is hampered by high computation and storage complexity and does not conform to the instantaneous learning mechanism in brains. On the contrary, online training algorithms, which are biologically interpretable, offer low latency and memory efficiency, and are well-suited for on-chip learning applications. However, recent research exhibit a deficiency in the scientific comprehension of online gradients, which leads to certain limitations. To address this issue, we conduct an in-depth analysis of the calculation deviation in chain derivations induced by weight update and find two pivotal factors that affect the accuracy of online gradients: completeness and timeliness. To further enhance the performance of online training leveraging these findings, we propose spatio-temporal online learning (STOL), which substantially ameliorates the accuracy of the online gradients and demonstrates superior computation and memory efficiency. Our experiments on CIFAR-10, CIFAR-100, ImageNet, CIFAR10-DVS, and DVS128-Gesture datasets demonstrate that our method achieves state-of-the-art performance across most of these tasks. Besides, it shows a great improvement compared with existing online training algorithms. © 2024 The Authors.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Multi-Modal Knowledge Graph Completion Enhanced by Muti-Level Attention Mechanisms 3

Multi-Modal Knowledge Graph Completion Enhanced by Muti-Leve...

引用

3rd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2024

作者： Zhang, Tingting Wang, Yongli Liu, Dongmei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331506582

Multimodel knowledge graph completion (MKGC) aims to predict missing entities in MKGs. Differing from prior Single-model knowledge graph completion efforts, MKGC leverages rich textual descriptions, images, and other multi-model information associated with entities. Traditional MKGC methods often use simple concatennation, weighted fusion, or tensor-based approaches for multi-modal information fusion, but they fail to capture the in-tra-modality and inter-modality dynamics. Moreover, traditional models like TransE and TransH, based on translational operations, struggle with complex relationships such as 1-N, N-N. To address these issue, this paper proposes a multi-modal information fusion enhanced by multi-level attention mechanism and relation-specific graph attention network method for MKGC. Firstly, obtaining text, attribute, and image embeddings of entities by leveraging pre-trained modals such as BERT and Vision Transformer. Secondly, using low-rank tensors for multimodal fusion and introducing self-attention and cross-attention blocks to capture complementary information within and across modalities respectively. Finally, constructing a multi-source node graph and a relation graph attention network is employed to capture the structural information of the graph. The ConvE modal is used as a decoder, and the performance is evaluated through link prediction tasks on real datasets. Experimental results show that new method out-performs other baselines, such as TransE, R-GCN, RSME, KG-BERT. © 2024 IEEE.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

引用

computers, Materials & Continua 2024年第8期80卷 2557-2578页

作者： Prasanna Kumar Kannughatta Ranganna Siddesh Gaddadevara Matt Chin-Ling Chen Ananda Babu Jayachandra Yong-Yuan Deng Department of Computer Science&Engineering Ramaiah Institute of TechnologyBangalore560054India Department of Artificial Intelligence&Data Science Ramaiah Institute of TechnologyBangalore560054India School of Information Engineering Changchun Sci-Tech UniversityChangchun130600China Department of Computer Science and Information Engineering Chaoyang University of TechnologyTaichung41349Taiwan Department of Information Science and Engineering Malnad College of EngineeringHassan573202India

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

关键词： Fog computing fractional selectivity approach particle swarm optimization algorithm task scheduling virtual machine allocation

来源：评论

学校读者我要写书评

暂无评论

A Few-Shot and Anti-Forgetting Network Intrusion Detection System based on Online Meta Learning

A Few-Shot and Anti-Forgetting Network Intrusion Detection S...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Zhen Lu, Yifei Wu, Wenxin Lu, Yun Wang, Hongxiang Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350351255

In the actual Internet of Things (IoT) environment, the proportion of abnormal behavior is much lower than that of normal behavior, and abnormal samples are often scarce, so it is a significant challenge to train efficient network intrusion detection systems using limited labeled samples. Meanwhile, intrusion detection systems based on online learning are prone to catastrophic forgetting, which greatly reduces the performance of online models. Previous research has not comprehensively addressed these two issues. Therefore, this paper proposes a few-shot and anti-forgetting network intrusion detection system based on online meta-learning. The system uses meta-learning as the basic algorithm to efficiently utilize data to train well-performing models with very few samples, thereby addressing the problem of insufficient samples;meanwhile, by saving previous models and invoking them when necessary to combat catastrophic forgetting. The experiments on the CIC-IDS2017 and CIC-IDS2018 datasets show that our proposed detection system can maintain good performance over the long term without forgetting. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

A Hessian Recovery Based Linear Finite Element Method for Molecular Beam Epitaxy Growth Model with Slope Selection

引用

Advances in Applied Mathematics and Mechanics 2024年第1期16卷 1-23页

作者： Minqiang Xu Qingsong Zou College of Science Zhejiang University of TechnologyHangzhouZhejiang 310023China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhouGuangdong 510275China School of Computer Science and Engineering and Guangdong Province Key Laboratory of Computational ScienceSun Yat-Sen UniversityGuangzhouGuangdong 510275China

In this paper,we present a Hessian recovery based linear finite element method to simulate the molecular beam epitaxy growth model with slope *** the time discretization,we apply a first-order convex splitting method and secondorder Crank-Nicolson *** the space discretization,we utilize the Hessian recovery operator to approximate second-order derivatives of a C^(0)linear finite element function and hence the weak formulation of the fourth-order differential operator can be discretized in the linear finite element *** energy-decay property of our proposed fully discrete schemes is rigorously *** robustness and the optimal-order convergence of the proposed algorithm are numerically *** a large spatial domain for a long period,we simulate coarsening dynamics,where 1/3-power-law is observed.

关键词： Molecular beam epitaxy Hessian recovery linear finite element method superconvergence.

来源：评论

学校读者我要写书评

暂无评论

A Holistic Secure Communication Mechanism Using a Multilayered Cryptographic Protocol to Enhanced Security

引用

computers, Materials & Continua 2024年第3期78卷 4417-4452页

作者： Fauziyah Zhaoshun Wang Mujahid Tabassum School of Computer and Communication Engineering University of Science and TechnologyBeijing100000China Noroff School of Technology and Digital Media(Noroff Accelerate) Noroff University CollegeKristiansand4612Norway

In an era characterized by digital pervasiveness and rapidly expanding datasets,ensuring the integrity and reliability of information is *** cyber threats evolve in complexity,traditional cryptographic methods face increasingly sophisticated *** article initiates an exploration into these challenges,focusing on key exchanges(encompassing their variety and subtleties),scalability,and the time metrics associated with various cryptographic *** propose a novel cryptographic approach underpinned by theoretical frameworks and practical *** to this approach is a thorough analysis of the interplay between Confidentiality and Integrity,foundational pillars of information *** method employs a phased strategy,beginning with a detailed examination of traditional cryptographic processes,including Elliptic Curve Diffie-Hellman(ECDH)key *** also delve into encrypt/decrypt paradigms,signature generation modes,and the hashes used for Message Authentication Codes(MACs).Each process is rigorously evaluated for performance and *** gain a comprehensive understanding,a meticulously designed simulation was conducted,revealing the strengths and potential improvement areas of various ***,our cryptographic protocol achieved a confidentiality metric of 9.13 in comprehensive simulation runs,marking a significant advancement over existing ***,with integrity metrics at 9.35,the protocol’s resilience is further *** metrics,derived from stringent testing,underscore the protocol’s efficacy in enhancing data security.

关键词： Cryptographic security privacy preservation decryption integrity

来源：评论

学校读者我要写书评

暂无评论

Forecasting Cloud Workload through Reinforcement Learning-Based Ensemble Model 4

Forecasting Cloud Workload through Reinforcement Learning-Ba...

引用

4th International Conference on Electronic Information engineering and computer Communication, EIECC 2024

作者： Kang, Yuliang Li, Yongke Zhang, Gongxuan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798331534622

The escalating demand for cloud computing has intensified the need for accurate workload forecasting to optimize resource allocation and maintain service quality. This paper presents a novel approach employing a reinforcement learning-based ensemble model to enhance the accuracy of workload predictions in cloud environments. The study addresses the challenges posed by complex and dynamic workload patterns, leveraging deep learning techniques to improve prediction outcomes. The proposed model dynamically selects the optimal subset of predictors, demonstrating superior performance on the Google Cluster dataset. The research aims to contribute to more efficient and intelligent resource management in cloud computing. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：