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
The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have ...The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.展开更多
Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent...Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.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
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supported in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B186 and No.2022D01B05)。
文摘The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.
文摘Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.
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.
