Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure ...Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.展开更多
Destination prediction has attracted widespread attention because it can help vehicle-aid systems recommend related services in advance to improve user driving experience.However,the relevant research is mainly based ...Destination prediction has attracted widespread attention because it can help vehicle-aid systems recommend related services in advance to improve user driving experience.However,the relevant research is mainly based on driving trajectory of vehicles to predict the destinations,which is challenging to achieve the early destination prediction.To this end,we propose a model of early destination prediction,DP-BPR,to predict the destinations by users’travel time and locations.There are three challenges to accomplish the model:1)the extremely sparse historical data make it challenge to predict destinations directly from raw historical data;2)the destinations are related to not only departure points but also departure time so that both of them should be taken into consideration in prediction;3)how to learn destination preferences from historical data.To deal with these challenges,we map sparse high-dimensional data to a dense low-dimensional space through embedding learning using deep neural networks.We learn the embeddings not only for users but also for locations and time under the supervision of historical data,and then use Bayesian personalized ranking(BPR)to learn to rank destinations.Experimental results on the Zebra dataset show the effectiveness of DP-BPR.展开更多
在6LoWPAN(IPv6 over Low-power Wireless Personal Area Network)的基础上,该文提出应用于物联网的寻址策略,实现基于IEEE 802.15.4协议的底层异构网络与互联网的实时通信。寻址策略包括IPv6地址自动配置和报头压缩。采用的分层地址自...在6LoWPAN(IPv6 over Low-power Wireless Personal Area Network)的基础上,该文提出应用于物联网的寻址策略,实现基于IEEE 802.15.4协议的底层异构网络与互联网的实时通信。寻址策略包括IPv6地址自动配置和报头压缩。采用的分层地址自动配置策略,首先在底层网络内部允许节点使用16位短地址导出的链路本地地址进行数据分组传输,该链路本地地址需通过执行基于分簇的重复地址检测机制保证唯一性;其次,每个底层网络中的Sink节点通过上层IP路由器获取全球路由前缀,并与接口标识符相结合,形成Sink节点的全球地址,实现底层网络与互联网的数据交换。同时,通过在报头压缩编码中植入链路本地地址和全球地址控制位,提出了一种适用于物联网应用的报头压缩方案IIPHC(IoTs IPv6 Header Compression)。如果地址类型为链路本地地址,则采用简单灵活的IIPHC1方案,如果地址类型为全球地址,则采用相对复杂但有效的IIPHC2方案。仿真及测试结果表明,基于6LoWPAN的物联网寻址策略在网络开销、时延、吞吐量、能耗等性能方面存在一定的优越性。展开更多
苯扎贝特（Bezafibrate）作为新兴污染物-药品和个人护理品（Pharmaceutical and personal care products,PPCPs）的一种,被广泛关注.其在污水处理厂的污水、地表水甚至是饮用水中常被检出[1].最新研究表明它对人体有类雌激素作用[2].目...苯扎贝特（Bezafibrate）作为新兴污染物-药品和个人护理品（Pharmaceutical and personal care products,PPCPs）的一种,被广泛关注.其在污水处理厂的污水、地表水甚至是饮用水中常被检出[1].最新研究表明它对人体有类雌激素作用[2].目前,有人研究过光催化降解[1]和活性污泥转化[3]苯扎贝特的产物,但苯扎贝特在水环境中的生物降解途径及其中间产物的生态毒理效应尚不明确。展开更多
The Snows of Kilimanjaro,written by Ernest Hemingway,embodies his writing style typically.The paper focuses on elaborating it from narrative person so that we can grasp and master the theme—beginning is the end,the e...The Snows of Kilimanjaro,written by Ernest Hemingway,embodies his writing style typically.The paper focuses on elaborating it from narrative person so that we can grasp and master the theme—beginning is the end,the end is the beginning.展开更多
The purpose of the paper is to find out the individual differences among language learners from the following aspects: motivation;personality factors;the role of age in SLA.The author concludes that language learners ...The purpose of the paper is to find out the individual differences among language learners from the following aspects: motivation;personality factors;the role of age in SLA.The author concludes that language learners have a lot of individual differences which are of great importance to their proficiency of language. Teachers should apply different methods to motivate and teach students in order to make them well aquire language.展开更多
Although real-world experiences show that preparing one image per person is more convenient, most of the appearance-based face recognition methods degrade or fail to work if there is only a single sample per person(SS...Although real-world experiences show that preparing one image per person is more convenient, most of the appearance-based face recognition methods degrade or fail to work if there is only a single sample per person(SSPP). In this work, we introduce a novel supervised learning method called supervised locality preserving multimanifold(SLPMM) for face recognition with SSPP. In SLPMM, two graphs: within-manifold graph and between-manifold graph are made to represent the information inside every manifold and the information among different manifolds, respectively. SLPMM simultaneously maximizes the between-manifold scatter and minimizes the within-manifold scatter which leads to discriminant space by adopting locality preserving projection(LPP) concept. Experimental results on two widely used face databases FERET and AR face database are presented to prove the efficacy of the proposed approach.展开更多
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B 187)。
文摘Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.
基金Project(2018YFF0214706)supported by the National Key Research and Development Program of ChinaProject(cstc2020jcyj-msxmX0690)supported by the Natural Science Foundation of Chongqing,China+1 种基金Project(2020CDJ-LHZZ-039)supported by the Fundamental Research Funds for the Central Universities of Chongqing,ChinaProject(cstc2019jscx-fxydX0012)supported by the Key Research Program of Chongqing Technology Innovation and Application Development,China。
文摘Destination prediction has attracted widespread attention because it can help vehicle-aid systems recommend related services in advance to improve user driving experience.However,the relevant research is mainly based on driving trajectory of vehicles to predict the destinations,which is challenging to achieve the early destination prediction.To this end,we propose a model of early destination prediction,DP-BPR,to predict the destinations by users’travel time and locations.There are three challenges to accomplish the model:1)the extremely sparse historical data make it challenge to predict destinations directly from raw historical data;2)the destinations are related to not only departure points but also departure time so that both of them should be taken into consideration in prediction;3)how to learn destination preferences from historical data.To deal with these challenges,we map sparse high-dimensional data to a dense low-dimensional space through embedding learning using deep neural networks.We learn the embeddings not only for users but also for locations and time under the supervision of historical data,and then use Bayesian personalized ranking(BPR)to learn to rank destinations.Experimental results on the Zebra dataset show the effectiveness of DP-BPR.
文摘在6LoWPAN(IPv6 over Low-power Wireless Personal Area Network)的基础上,该文提出应用于物联网的寻址策略,实现基于IEEE 802.15.4协议的底层异构网络与互联网的实时通信。寻址策略包括IPv6地址自动配置和报头压缩。采用的分层地址自动配置策略,首先在底层网络内部允许节点使用16位短地址导出的链路本地地址进行数据分组传输,该链路本地地址需通过执行基于分簇的重复地址检测机制保证唯一性;其次,每个底层网络中的Sink节点通过上层IP路由器获取全球路由前缀,并与接口标识符相结合,形成Sink节点的全球地址,实现底层网络与互联网的数据交换。同时,通过在报头压缩编码中植入链路本地地址和全球地址控制位,提出了一种适用于物联网应用的报头压缩方案IIPHC(IoTs IPv6 Header Compression)。如果地址类型为链路本地地址,则采用简单灵活的IIPHC1方案,如果地址类型为全球地址,则采用相对复杂但有效的IIPHC2方案。仿真及测试结果表明,基于6LoWPAN的物联网寻址策略在网络开销、时延、吞吐量、能耗等性能方面存在一定的优越性。
文摘苯扎贝特（Bezafibrate）作为新兴污染物-药品和个人护理品（Pharmaceutical and personal care products,PPCPs）的一种,被广泛关注.其在污水处理厂的污水、地表水甚至是饮用水中常被检出[1].最新研究表明它对人体有类雌激素作用[2].目前,有人研究过光催化降解[1]和活性污泥转化[3]苯扎贝特的产物,但苯扎贝特在水环境中的生物降解途径及其中间产物的生态毒理效应尚不明确。
文摘The Snows of Kilimanjaro,written by Ernest Hemingway,embodies his writing style typically.The paper focuses on elaborating it from narrative person so that we can grasp and master the theme—beginning is the end,the end is the beginning.
文摘The purpose of the paper is to find out the individual differences among language learners from the following aspects: motivation;personality factors;the role of age in SLA.The author concludes that language learners have a lot of individual differences which are of great importance to their proficiency of language. Teachers should apply different methods to motivate and teach students in order to make them well aquire language.
文摘Although real-world experiences show that preparing one image per person is more convenient, most of the appearance-based face recognition methods degrade or fail to work if there is only a single sample per person(SSPP). In this work, we introduce a novel supervised learning method called supervised locality preserving multimanifold(SLPMM) for face recognition with SSPP. In SLPMM, two graphs: within-manifold graph and between-manifold graph are made to represent the information inside every manifold and the information among different manifolds, respectively. SLPMM simultaneously maximizes the between-manifold scatter and minimizes the within-manifold scatter which leads to discriminant space by adopting locality preserving projection(LPP) concept. Experimental results on two widely used face databases FERET and AR face database are presented to prove the efficacy of the proposed approach.
