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Inherent-attribute-aware dual-graph autoencoder for rating prediction

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Journal of Information and Intelligence 2024年第1期2卷 82-97页

作者： Yangtao Zhou Qingshan Li Hua Chu Jianan Li Lejia Yang Biaobiao Wei Luqiao Wang Wanqiang Yang School of Computer Science and technology Xidian UniversityXi'an 710071China intelligent financial software engineering new technology joint laboratory Xidian UniversityXi'an 710071China Shanghai Fairyland software Corp. Ltd.Shanghai 200233China

Autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users'***,existing methods still have two significant limitations:i)External attributes are often unavailable in the real world due to privacy issues,leading to low quality of representations;and ii)existing methods lack considering complex associations in users'rating behaviors during the encoding *** meet these challenges,this paper innovatively proposes an inherent-attribute-aware dual-graph autoencoder,named IADGAE,for rating *** address the low quality of representations due to the unavailability of external attributes,we propose an inherent attribute perception module that mines inductive user active patterns and item popularity patterns from users'rating behaviors to strengthen user and item *** exploit the complex associations hidden in users’rating behaviors,we design an encoder on the item-item co-occurrence graph to capture the co-occurrence frequency features among ***,we propose a dual-graph feature encoder framework to simultaneously encode and fuse the high-order representations learned from the user-item rating graph and item-item co-occurrence *** experiments on three real datasets demonstrate that IADGAE is effective and outperforms existing rating prediction methods,which achieves a significant improvement of 4.51%~41.63%in the RMSE metric.

关键词： Rating prediction Graph convolutional network Autoencoder Inherent attribute aware

Multi-layer network embedding on scc-based network with motif

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Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key laboratory of Big Data intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key laboratory for Ubiquitous Network and Service software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication engineering Hainan UniversityHaikou 570228China State Key laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

An intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

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Chinese Journal of Electronics 2024年第4期33卷 910-919页

作者： Liang YAO Xiaolong XU Wanchun DOU Muhammad Bilal School of software Nanjing University of Information Science and Technology State Key laboratory for Novel software technology Nanjing University Department of Computer and Electronics Systems engineering Hankuk University of Foreign Studies

As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.

关键词： Industries Privacy Energy consumption Transportation Computational efficiency Encryption Protection

XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

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Science China(Information Sciences) 2024年第7期67卷 46-71页

作者： Xiaolong XU Fan WU Muhammad BILAL Xiaoyu XIA Wanchun DOU Lina YAO Weiyi ZHONG School of software Nanjing University of Information Science and Technology School of Computing and Communications Lancaster University School of Computing Technologies Royal Melbourne Institute of Technology State Key laboratory for Novel software technology Nanjing University School of Computer Science and engineering University of New South Wales Data 61 Commonwealth Scientific and Industrial Research Organization School of Computer Science Qufu Normal University

Content delivery networks(CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning(DRL) offers a promising solution for intelligent zero-touch network governance. However, the blackbox nature of DRL models poses challenges in understanding and making trusting decisions. In this paper,we propose an explainable reinforcement learning(XRL)-based intelligent edge service caching approach,namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence(XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent's Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental *** results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service(QoS),a

关键词： deep reinforcement learning (DRL) explainable artificial intelligence (XAI) multi-level cache content delivery network (CDN) D3QN algorithm Deep-SHAP

Adaptive fusion of structure and attribute guided polarized communities search

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Frontiers of Computer Science 2024年第1期18卷 255-257页

作者： Fanyi YANG Huifang MA Wentao WANG Zhixin LI Liang CHANG College of Computer Science and engineering Northwest Normal UniversityLanzhou 730070China Key Lab of Education Blockchain and intelligent technology(Ministry of Education) Guangxi Normal UniversityGuilin 541004China Guangxi Key laboratory of Trusted software Guilin University of Electronic TechnologyGuilin 541004China

1 Introduction and main contributions The exploration of polarized communities,which consist of two antagonistic subgraphs and include a set of query nodes,is a crucial task in community search on signed *** existing methods either predominantly rely on topological structure while disregarding node attributes or tend to prioritize the global identification of all polarized ***,they fail to consider two crucial ***,integrating node attributes with network structure can enhance the search quality for polarized communities in attributed signed networks by leveraging complementary ***,global criteria-based polarized community detection aims to identify all polarized communities,neglecting personalized analyses centered around individual users.

关键词： polarized structure integrating

Insight into Lithium–sulfur batteries performance enhancement: from metal nanoparticles to metal nanoclusters to single metal atoms

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Tungsten 2024年第3期6卷 504-521页

作者： Shuang Ma Qing-Ling Ruan Xue-Cheng Liu Gui-Jun Zhu Die Yuan Ling-Qiao Hu Yu-Mei Huang Xing-Xing Gu Chongqing Key laboratory of Catalysis and new Environmental Materials College of Environment and Resources Chongqing Technology and Business University National Research Base of intelligent Manufacturing Service Chongqing Technology and Business University

Lithium-sulfur(Li-S) batteries have attracted much attention and developed rapidly in recent years due to their high energy density,low cost,and ***,its commercialization process still encounters various *** them,the sulfur cathode is easy to dissolve and shuttle,resulting in the loss of active substances and the severe growth of lithium anode dendrites,which is one of the biggest *** the redox conversion kinetics of sulfur cathode is expected to fundamentally alleviate the shuttle phenomenon of poly ***,this paper reviews the effects of metal nanoparticles to metal nanoclusters to metal single-atom catalysts on improving the redox of Li-S batteries,to let researchers better understand the size effect on the kinetics of sulfur cathodes in Li-S batteries,which will provide important guidance for the future large-scale commercialization of Li-S batteries.

关键词： Lithium–sulfur batteries Metal nanoparticles Metal nanoclusters Metal single atoms Size effects

Boosting Adversarial Training with Learnable Distribution

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Computers, Materials & Continua 2024年第3期78卷 3247-3265页

作者： Kai Chen Jinwei Wang James Msughter Adeke Guangjie Liu Yuewei Dai School of Electronics and Information engineering Nanjing University of Information Science and TechnologyNanjing210044China Key laboratory of intelligent Support technology for Complex Environments Ministry of EducationNanjing210044China School of Computer and software Nanjing University of Information Science and TechnologyNanjing210044China Nanjing Center for Applied Mathematics Nanjing211135China

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural *** training is one of the most potent methods to defend against adversarial ***,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial *** paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture *** distribution centroid is built to classify samples and constrain the distribution of the sample *** natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the *** proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial *** algorithm gradually increases the accuracy and robustness of the model by scaling ***,the proposed method outputs the predicted labels and the distance between the sample and the distribution *** distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model *** effectiveness of the proposed method is demonstrated through comprehensive experiments.

关键词： Adversarial training feature space learnable distribution distribution centroid

Comparative analysis of thermodynamic and mechanical responses between underground hydrogen storage and compressed air energy storage in lined rock caverns

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International Journal of Mining Science and technology 2024年第4期34卷 531-543页

作者： Bowen Hu Liyuan Yu Xianzhen Mi Fei Xu Shuchen Li Wei Li Chao Wei Tao Zhang State Key laboratory of intelligent Construction and Healthy Operation and Maintenance of Deep Underground engineering China University of Mining and TechnologyXuzhou 221116China Key laboratory of Large Structure Health Monitoring and Control Shijiazhuang Tiedao UniversityShijiazhuang 050043China School of Mechanics and Civil engineering China University of Mining and TechnologyXuzhou 221116China

Underground hydrogen storage(UHS)and compressed air energy storage(CAES)are two viable largescale energy storage technologies for mitigating the intermittency of wind and solar ***,it is meaningful to compare the properties of hydrogen and air with typical thermodynamic storage *** study employs a multi-physical coupling model to compare the operations of CAES and UHS,integrating gas thermodynamics within caverns,thermal conduction,and mechanical deformation around rock *** thermodynamic responses are validated using additional simulations and the field test *** and pressure variations of air and hydrogen within rock caverns exhibit similarities under both adiabatic and diabatic simulation *** reaches higher temperature and pressure following gas charging stage compared to air,and the ideal gas assumption may lead to overestimation of gas temperature and *** steel lining of CAES,the sealing layer(fibre-reinforced plastic FRP)in UHS is prone to deformation but can effectively mitigates stress in the sealing *** CAES,the first principal stress on the surface of the sealing layer and concrete lining is tensile stress,whereas UHS exhibits compressive stress in the same *** present research can provide references for the selection of energy storage methods.

关键词： Underground hydrogen storage Compressed air energy storage Mechanical response Thermodynamic response Lined rock caverns

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A Novel Locomotion Rule Rmbedding Long Short-Term Memory Network with Attention for Human Locomotor Intent Classification Using Multi-Sensors Signals

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Computers, Materials & Continua 2024年第6期79卷 4349-4370页

作者： Jiajie Shen Yan Wang Dongxu Zhang Key laboratory of Symbol Computation and Knowledge engineering Ministry of EducationColleague of Computer Science and TechnologyJilin UniversityChangchun130012China College of software and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun130012China

Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable *** work have achieved impressive performance in classifying steady locomotion ***,it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion *** to the similarities between the information of the transitions and their adjacent steady ***,most of these methods rely solely on data and overlook the objective laws between physical activities,resulting in lower accuracy,particularly when encountering complex locomotion modes such as *** address the existing deficiencies,we propose the locomotion rule embedding long short-term memory(LSTM)network with Attention(LREAL)for human locomotor intent classification,with a particular focus on transitions,using data from fewer sensors(two inertial measurement units and four goniometers).The LREAL network consists of two levels:One responsible for distinguishing between steady states and transitions,and the other for the accurate identification of locomotor *** classifier in these levels is composed of multiple-LSTM layers and an attention *** introduce real-world motion rules and apply constraints to the network,a prior knowledge was added to the network via a rule-modulating *** method was tested on the ENABL3S dataset,which contains continuous locomotion date for seven steady and twelve transitions *** results showed that the LREAL network could recognize locomotor intents with an average accuracy of 99.03%and 96.52%for the steady and transitions states,*** is worth noting that the LREAL network accuracy for transition-state recognition improved by 0.18%compared to other state-of-the-art network,while using data from fewer sensors.

关键词： Lower-limb prosthetics deep neural networks motion classification