检索结果-内蒙古大学图书馆

MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Liu, Kang Xue, Feng Guo, Dan Wu, Le Li, Shujie Hong, Richang Hefei University of Technology School of Computer Science and Information Engineering Key Laboratory of Knowledge Engineering with Big Data Intelligent Interconnected Systems Laboratory of Anhui Province 485 Danxia Road Anhui Province Hefei230601 China Hefei University of Technology School of Software Key Laboratory of Knowledge Engineering with Big Data Intelligent Interconnected Systems Laboratory of Anhui Province 485 Danxia Road Anhui Province Hefei230601 China

In most E-commerce platforms, whether the displayed items trigger the user's interest largely depends on their most eye-catching multimodal content. Consequently, increasing efforts focus on modeling multimodal user preference, and the pressing paradigm is to incorporate complete multimodal deep features of the items into the recommendation module. However, the existing studies ignore the mismatch problem between multimodal feature extraction (MFE) and user interest modeling (UIM). That is, MFE and UIM have different emphases. Specifically, MFE is migrated from and adapted to upstream tasks such as image classification. In addition, it is mainly a content-oriented and non-personalized process, while UIM, with its greater focus on understanding user interaction, is essentially a user-oriented and personalized process. Therefore, the direct incorporation of MFE into UIM for purely user-oriented tasks, tends to introduce a large number of preference-independent multimodal noise and contaminate the embedding representations in UIM. This paper aims at solving the mismatch problem between MFE and UIM, so as to generate high-quality embedding representations and better model multimodal user preferences. Towards this end, we develop a novel model, multimodal entity graph collaborative filtering, short for MEGCF. The UIM of the proposed model captures the semantic correlation between interactions and the features obtained from MFE, thus making a better match between MFE and UIM. More precisely, semantic-rich entities are first extracted from the multimodal data, since they are more relevant to user preferences than other multimodal information. These entities are then integrated into the user-item interaction graph. Afterwards, a symmetric linear Graph Convolution Network (GCN) module is constructed to perform message propagation over the graph, in order to capture both high-order semantic correlation and collaborative filtering signals. Finally, the sentiment information fr

关键词： Sentiment analysis

Connecting latent relationships over heterogeneous attributed network for recommendation

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Duan, Ziheng Wang, Yueyang Ye, Weihao Feng, Zixuan Fan, Qilin Li, Xiuhua School of Big Data and Software Engineering Chongqing University Chongqing401331 China Department of Control Science and Technology Zhejiang University Hangzhou310027 China

Recently, deep neural network models for graph-structured data have been demonstrating to be influential in recommendation systems. Graph Neural Network (GNN), which can generate high-quality embeddings by capturing graph-structured information, is convenient for the recommendation. However, most existing GNN models mainly focus on the homogeneous graph. They cannot characterize heterogeneous and complex data in the recommendation system. Meanwhile, it is challenging to develop effective methods to mine the heterogeneity and latent correlations in the graph. In this paper, we adopt Heterogeneous Attributed Network (HAN), which involves different node types as well as rich node attributes, to model data in the recommendation system. Furthermore, we propose a novel graph neural network-based model to deal with HAN for Recommendation, called HANRec. In particular, we design a component connecting potential neighbors to explore the influence between neighbors and provide two different strategies with the attention mechanism to aggregate neighbors’ information. The experimental results on two real-world datasets prove that HANRec outperforms other state-of-the-arts methods1 Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

ROD: Reception-aware online distillation for sparse graphs

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zhang, Wentao Jiang, Yuezihan Li, Yang Sheng, Zeang Shen, Yu Miao, Xupeng Wang, Liang Yang, Zhi Cui, Bin School of EECS Key Laboratory of High Confidence Software Technologies Peking University Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications Alibaba Group

Graph neural networks (GNNs) have been widely used in many graph-based tasks such as node classification, link prediction, and node clustering. However, GNNs gain their performance benefits mainly from performing the feature propagation and smoothing across the edges of the graph, thus requiring sufficient connectivity and label information for effective propagation. Unfortunately, many real-world networks are sparse in terms of both edges and labels, leading to sub-optimal performance of GNNs. Recent interest in this sparse problem has focused on the self-training approach, which expands supervised signals with pseudo labels. Nevertheless, the self-training approach inherently cannot realize the full potential of refining the learning performance on sparse graphs due to the unsatisfactory quality and quantity of pseudo labels. In this paper, we propose ROD, a novel reception-aware online knowledge distillation approach for sparse graph learning. We design three supervision signals for ROD: multi-scale reception-aware graph knowledge, task-based supervision, and rich distilled knowledge, allowing online knowledge transfer in a peer-teaching manner. To extract knowledge concealed in the multi-scale reception fields, ROD explicitly requires individual student models to preserve different levels of locality information. For a given task, each student would predict based on its reception-scale knowledge, while simultaneously a strong teacher is established on-the-fly by combining multi-scale knowledge. Our approach has been extensively evaluated on 9 datasets and a variety of graph-based tasks, including node classification, link prediction, and node clustering. The result demonstrates that ROD achieves state-of-art performance and is more robust for the graph sparsity. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

Grain: Improving data efficiency of graph neural networks via diversified influence maximization

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zhang, Wentao Yang, Zhi Wang, Yexin Shen, Yu Li, Yang Wang, Liang Cui, Bin School of EECS Key Laboratory of High Confidence Software Technologies Peking University Tencent Inc Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications

data selection methods, such as active learning and core-set selection, are useful tools for improving the data efficiency of deep learning models on large-scale datasets. However, recent deep learning models have moved forward from independent and identically distributed data to graph-structured data, such as social networks, e-commerce user-item graphs, and knowledge graphs. This evolution has led to the emergence of Graph Neural Networks (GNNs) that go beyond the models existing data selection methods are designed for. Therefore, we present Grain, an efficient framework that opens up a new perspective through connecting data selection in GNNs with social influence maximization. By exploiting the common patterns of GNNs, Grain introduces a novel feature propagation concept, a diversified influence maximization objective with novel influence and diversity functions, and a greedy algorithm with an approximation guarantee into a unified framework. Empirical studies on public datasets demonstrate that Grain significantly improves both the performance and efficiency of data selection (including active learning and core-set selection) for GNNs. To the best of our knowledge, this is the first attempt to bridge two largely parallel threads of research, data selection, and social influence maximization, in the setting of GNNs, paving new ways for improving data efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： data reduction

Detection of ARP Spoofing Attack in Healthcare Networks Using a LSTM Model

学校读者我要写书评

暂无评论

Detection of ARP Spoofing Attack in Healthcare Networks Usin...

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Salam Al-E'mari Yousef Sanjalawe Ghader Kurdi Budoor Allehyani Information Security Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (UJ) Amman Jordan Department of Data Science College of Computing Umm Al-Qura University (UQU) Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University (UQU) Makkah Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Healthcare networks face significant cybersecurity threats due to their extensive interconnectivity, with Address Resolution Protocol (ARP) spoofing posing a major risk to the confidentiality and integrity of medical data. ARP spoofing is a Man-in-the-Middle (MITM) attack where an attacker sends falsified ARP messages to associate their MAC address with the IP address of a legitimate device, enabling unauthorized interception or manipulation of network traffic. In this paper, we propose an LSTM-based detection model designed to effectively identify ARP spoofing attacks in healthcare environments. Unlike traditional methods that rely on static markers at fixed time intervals, the LSTM model leverages temporal dependencies in network traffic, enhancing detection accuracy and adaptability to evolving attack patterns. Experimental results based on the CICIoMT dataset 2024 demonstrate that the proposed model achieves a detection accuracy of 98.5%, significantly outperforming conventional approaches. These findings highlight the effectiveness of LSTM networks in mitigating ARP spoofing threats, offering a scalable solution for securing healthcare networks.

关键词： Adaptation models Protocols Accuracy Computational modeling Intrusion detection Telecommunication traffic Internet of Medical Things IP networks Long short term memory Faces

Hyperbolic geometric latent diffusion model for graph generation 24

学校读者我要写书评

暂无评论

Hyperbolic geometric latent diffusion model for graph genera...

Proceedings of the 41st International Conference on Machine Learning

作者： Xingcheng Fu Yisen Gao Yuecen Wei Qingyun Sun Hao Peng Jianxin Li Xianxian Li Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China and Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies.

关键词：

Is larger always better? Evaluating and prompting large language models for non-generative medical tasks

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhu, Yinghao Gao, Junyi Wang, Zixiang Liao, Weibin Zheng, Xiaochen Liang, Lifang Wang, Yasha Pan, Chengwei Harrison, Ewen M. Ma, Liantao Institute of Artificial Intelligence Beihang University Beijing100191 China Centre for Medical Informatics University of Edinburgh EdinburghEH8 9YL United Kingdom Health Data Research UK United Kingdom School of Computer Science Peking University Beijing100871 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China ETH Zürich Zürich8092 Switzerland

Background: The deployment of Large Language Models (LLMs) in medical fields is increasing, but few research evaluate their ability to manage both structured Electronic Health Record (EHR) data and unstructured clinical notes. This study provides a comprehensive benchmark of various models, including GPT-based LLMs, BERT-based language models, and conventional clinical predictive models to determine their efficacy in non-generative medical tasks using widely recognized datasets. Methods: We evaluated 14 language models (9 GPT-based and 5 BERT-based) alongside 7 conventional clinical predictive models on the MIMIC dataset, which encompasses comprehensive ICU patient records, and the TJH dataset, which includes EHR data from early COVID-19 patients. The performance of these models was assessed through a series of both supervised and unsupervised tasks, including mortality and readmission predictions, disease hierarchy reconstruction, and biomedical sentence matching. The study focused on comparing zero-shot prediction capabilities of LLMs with conventional models and the effectiveness of finetuned BERT-based models in handling clinical texts. Results: LLMs demonstrated promising zero-shot predictive abilities on structured EHR data when equipped with well-designed prompting strategies, often surpassing traditional models trained with limited datasets. However, in tasks involving unstructured medical texts, LLMs did not outperform existing BERT-based models. Both in supervised and unsupervised scenarios, the performance of finetuned BERT models was superior, suggesting that the additional cost and complexity of implementing LLMs might not be justifiable for non-generative tasks. Conclusion: While LLMs show significant potential in interpreting structured EHR data through zero-shot learning, their application in processing unstructured medical texts is less advantageous compared to finetuned BERT models. The findings underscore the importance of selecting appropriate mo

关键词： Clinical research

data Analytics and Machine Learning for Reliable Energy Management: A Case Study

学校读者我要写书评

暂无评论

Data Analytics and Machine Learning for Reliable Energy Mana...

International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Melak Ayenew Hang Lei Xiaoyu Li Kulla Kekeba Maregu Assefa Abebe Tamrat Tegene Seid Belay Muhammed Habte Lejebo Leka School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu Center of Big Data Analytics and HPC Addis Ababa Science and Technology University Addis Ababa Ethiopia Institute of Electronics and Information Industry Technology of Kash Add Kash P.R. China

ISBN: (纸本)9781665493901

Renewable electric energy with reliable supply contributes to society, the economy, and the environment. Careful management of electric power from the consumers’ side is crucial on top of stable production, transmission, and distribution systems for reliable consumption. Electric power supply and consumption have been problematic in urban areas of Ethiopia, where frequent power interruptions come from overloaded transmission and distribution systems. In this paper, we proposed a focused Demand Side Management approach for improving reliable consumption in Addis Ababa. We used data analytics and machine learning (K-mean and long and short-term memory) approaches to understand the data, identify potential customers, and predict the aggregate substation load. We identified intermediate and supper-peak demand hours and potential customers for price-based demand load shifting management. Further, the analysis shows that an increase in electric prices at peak hours causes a reduction in electric demand. Consequently, it reduces distribution load and improves reliability.

关键词： Electric potential Renewable energy sources Substations data analysis Aggregates Urban areas Machine learning

A Lightweight Experimental Platform for Big data Based on Docker Containers 2

学校读者我要写书评

暂无评论

A Lightweight Experimental Platform for Big Data Based on Do...

2019 2nd International Symposium on Big data and Applied Statistics, ISBDAS 2019

作者： Ruijun, Gu Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of Technology Nanjing211169 China School of Computer Engineering Jinling Institute of Technology Nanjing211169 China

In recent years, many colleges and universities have set up the major of big data. The biggest problem in teaching is that there is no supporting basic experimental environment, and it is difficult to deploy and configure the big data environment at the same time. In addition, the lack of experimental data, experimental teaching plans and experimental manuals in the experimental process makes it difficult to carry out relevant teaching. In order to reduce the cost of laboratory construction and the difficulty of learning big data, a lightweight big data experimental platform was constructed based on virtualized container technology. Through this platform, we can create a big data cluster, provide various suitable experimental environments, focus on the technology itself, and greatly improve the learning efficiency. © Published under licence by IOP Publishing Ltd.

关键词： Big data