In the latest years,researchers from the industry and academia extensively applied machine learning algorithms in a broad range of domains.The goal of this special issue is to illustrate the most recent applications o...In the latest years,researchers from the industry and academia extensively applied machine learning algorithms in a broad range of domains.The goal of this special issue is to illustrate the most recent applications of deep learning methods in a range of real-life domains and to show the practical utility of these techniques.A particular attention goes towards methods to process network data that is capable of modelling complex artificial and natural systems as the interactions of a multitude of simpler entities.展开更多
Recent text generation methods frequently learn node representations from graph‐based data via global or local aggregation,such as knowledge graphs.Since all nodes are connected directly,node global representation en...Recent text generation methods frequently learn node representations from graph‐based data via global or local aggregation,such as knowledge graphs.Since all nodes are connected directly,node global representation encoding enables direct communication between two distant nodes while disregarding graph topology.Node local representation encoding,which captures the graph structure,considers the connections between nearby nodes but misses out onlong‐range relations.A quantum‐like approach to learning bettercontextualised node embeddings is proposed using a fusion model that combines both encoding strategies.Our methods significantly improve on two graph‐to‐text datasets compared to state‐of‐the‐art models in various experiments.展开更多
In this paper we propose X-MAQoS, a novel XML-based multi-agent system for the QoS management in telecommunications networks. This system is characterized by the following features: (i) it handles a user profile and e...In this paper we propose X-MAQoS, a novel XML-based multi-agent system for the QoS management in telecommunications networks. This system is characterized by the following features: (i) it handles a user profile and exploits it jointly with suitable network resource management techniques to maximize user satisfaction; (ii) it is capable of operating in a large variety of telecommunications networks; (iii) it is semi-automatic; (iv) it exploits XML for guaranteeing a light, versatile and standard mechanism for information representation, storing and exchange. In this paper the basic features of the system are discussed in details. Furthermore, the main results of a performance evaluation study in UMTS environment, aiming at comparing X-MAQoS with alternative agent-based approaches for handling user access to telecommunications networks, are reported.展开更多
文摘In the latest years,researchers from the industry and academia extensively applied machine learning algorithms in a broad range of domains.The goal of this special issue is to illustrate the most recent applications of deep learning methods in a range of real-life domains and to show the practical utility of these techniques.A particular attention goes towards methods to process network data that is capable of modelling complex artificial and natural systems as the interactions of a multitude of simpler entities.
基金supported by the National Natural Science Foundation of China under Grant(62077015)the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province,Zhejiang Normal University,Zhejiang,China,the Key Research and Development Program of Zhejiang Province(No.2021C03141)the National Key R&D Program of China under Grant(2022YFC3303600).
文摘Recent text generation methods frequently learn node representations from graph‐based data via global or local aggregation,such as knowledge graphs.Since all nodes are connected directly,node global representation encoding enables direct communication between two distant nodes while disregarding graph topology.Node local representation encoding,which captures the graph structure,considers the connections between nearby nodes but misses out onlong‐range relations.A quantum‐like approach to learning bettercontextualised node embeddings is proposed using a fusion model that combines both encoding strategies.Our methods significantly improve on two graph‐to‐text datasets compared to state‐of‐the‐art models in various experiments.
文摘In this paper we propose X-MAQoS, a novel XML-based multi-agent system for the QoS management in telecommunications networks. This system is characterized by the following features: (i) it handles a user profile and exploits it jointly with suitable network resource management techniques to maximize user satisfaction; (ii) it is capable of operating in a large variety of telecommunications networks; (iii) it is semi-automatic; (iv) it exploits XML for guaranteeing a light, versatile and standard mechanism for information representation, storing and exchange. In this paper the basic features of the system are discussed in details. Furthermore, the main results of a performance evaluation study in UMTS environment, aiming at comparing X-MAQoS with alternative agent-based approaches for handling user access to telecommunications networks, are reported.
