云应用提供商收益驱动的最佳云资源配置策略

为解决云资源配置中存在服务质量(QoS)约束违反频繁、资源利用率不充分、云应用提供商收益低的问题,提出面向云应用提供商收益的最佳云资源配置策略。在该策略中,云应用提供商根据经验得出一定时间内用户访问量的概率分布;借鉴报童模型思想,采用损失期望值最小原则计算最佳云资源配置量;固定价格、分段价格、动态价格是较为常见的云资源计费模式,进而基于上述方法设计出固定价格的云资源配置策略、分段价格云资源配置策略、动态价格云资源配置策略。当用户访问量随机时,通过该策略求得的最佳云资源配置量能够使软件即服务提供商收益最大,能充分利用云资源,在基础设施即服务(IaaS)、平台即服务(PaaS)层面不存在违反服务质量约束。

算法时代网络服务提供商“避风港”规则的司法考察及路径选择

算法技术是数字时代发展的新兴力量,进入人工智能时代,侵权内容算法通知、个性化算法推荐、版权内容算法过滤对“避风港”规则的适用带来了系统性冲击。在国际层面,美国暂不全面修改“避风港”规则,欧盟则立法引入信息过滤条款进行回应。知识产权制度的本土生成,或可从制度完善与制度展望两个维度出发。在制度完善中,借助“自治规则”优化“避风港”规则;在制度展望中,通过适当提高网络服务提供商的注意义务和合理限缩版权内容过滤义务主体范围,衔接版权内容过滤义务与“避风港”规则,从而构建起以“内容过滤+必要措施+异议救济”为内容的版权责任体系。

探索元宇宙:2024年服务提供商的举措

OMDIA由Informa Tech的研究部门(OVUM、Heavy Reading和Tractica)与收购的IHS Markit技术研究部门合并而成,是一家全球领先的技术研究机构。OMDIA拥有超过400位分析师,研究范围包括所有技术领域,覆盖150个市场,每年出版3000多部研究报告,涉及数千家科技、媒体和电信企业,为超过14000个客户提供服务。我们以详尽的情报和深厚的技术背景为依托,利用可操作性研究帮助客户在当今不断变化的技术环境中梳理关键环节,改善业务成果。

北奔重汽:全力打造绿色低碳运输方案解决提供商

北奔重型汽车集团有限公司积极“构建完善北奔重卡特色专业技术体系”,围绕新能源整车设计技术、电驱动构型及控制技术、整车轻量化技术、整车能量管理及控制技术、车辆智能网联技术,实现新能源技术+(电驱动、轻量化、能量管理、智能化),加快构建北奔特有的专业技术体系。

诺力智能装备股份有限公司 打造一流智能内部物流系统综合解决方案提供商

2023年,诺力智能装备股份有限公司(以下简称“诺力股份”)凭借其在科技创新和信息化工业化融合方面取得的突破性进展而获得多项殊荣:“一种基于二维码定位的货物托盘存取系统及其存取方法”获得第二十四届中国专利优秀奖;入选工信部2023年5G工厂名录;进口供应链平台获授浙江省重点进口平台;荣获“两化融合管理体系AAA级评定证书”……

苏州新施诺:顺时聚势,成为全球领先的AMHS解决方案提供商——访苏州新施诺半导体设备有限公司总经理王宏玉

苏州新施诺公司以“成为全球AMHS技术第一”为愿景,依托韩国Synus Tech公司与新松机器人公司在洁净物流系统领域的核心技术,通过国内外协同研发及供应链整合,不断升级AMHS系统解决方案,为客户提供稳定可靠高效的洁净物流系统设备与控制软件,以及优质快捷的服务,助力中国半导体行业发展。

LSSC内物流服务集成商管理策略对提供商安全生产行为的影响研究

考虑由“功能型物流服务提供商—物流服务集成商—客户”构成的物流服务供应链(Logistics Service Supply Chain, LSSC),针对链内提供商安全生产问题的动态性与复杂性,探究集成商不同管理策略对提供商安全生产行为的影响。采用计算实验方法,抽象模拟出二者在选择与合作阶段的交互活动,分别进行集成商订单配置决策中安全因素权重、安全生产考评标准、安全风险抵押金对提供商安全生产行为的影响实验。研究结果表明:在客户具有安全偏好情况下,集成商通过合理的订单配置决策可实现提供商安全生产水平与自身收益的协同提升;当安全生产考评标准超过提供商可接受阈值时,其安全生产水平反而将会降低;收取安全风险抵押金可使提供商安全生产水平有所提高,但效果有限。

论网络服务提供者对侵权通知的审查义务

“通知-删除”规则在我国网络侵权治理中发挥着重要作用,随着网络侵权形势日趋复杂,亟须确立网络服务提供者对侵权通知的审查义务以应对频发的错误通知。对比分析不同法域“通知-删除”规则的差异,尊重网络服务提供者对侵权通知进行审查的事实,从法解释论视角,明确网络服务提供者对侵权通知的审查义务。我国法语境下的“通知-删除”规则应为归责规范,适用过错责任原则,网络服务提供者的审查义务是其注意义务的组成部分。确定网络服务提供者对侵权通知审查义务的内容应当同步考量权利人的维权成本和网络服务提供者的审查负担,以“盖然性优势”为标准对通知的不同要素进行审查,是网络服务提供者对侵权通知的审查义务的一般性内容。针对不同类型权利的权属证据和侵权证据,网络服务提供者的审查逻辑和判断标准可进行适当调整。

低碳背景下政府、企业与低碳服务提供商的演化博弈

基于企业低碳发展的需要和消费者存在低碳偏好的现实背景下,本文构建了政府、企业及低碳服务提供商三方演化博弈模型,运用演化博弈理论分析三方稳定策略的博弈过程,最后利用MATLAB软件进行数值仿真,分析了政府、企业及低碳服务提供商三方的演化路径。结果表明:政府监管成本对政府选择监管策略起负向作用;相对于补贴力度,企业对惩罚力度更加敏感;通过降低政府监管成本、加大对不实行低碳生产的企业的处罚及降低低碳服务提供商的服务成本等途径能够有效的推动三方演化博弈模型向稳定策略发展,以期推动我国低碳经济的发展。

医疗服务提供者和接受者对“互联网+”缓和医疗服务认知、态度、意愿及需求现状调查

目的:了解天津市医疗服务提供者和接受者对“互联网+”缓和医疗服务认知、态度、意愿及需求情况,并分析影响意愿的因素。方法:采用便利抽样法,于2023年3月至5月使用自行编制的“医疗服务提供者问卷”和“医疗服务接受者问卷”对天津市2所三级甲等医院的282名医疗服务提供者和100名医疗服务接受者进行调查。结果:医疗服务提供者认知、态度、意愿得分分别为(4.28±1.61)分、(33.56±9.55)分和(9.79±3.40)分。医疗服务接受者认知、态度、意愿得分分别为(5.10±2.36)分、(2.70±1.21)分和(2.09±0.87)分。工作年限和态度是医疗服务提供者实践意愿的影响因素(P<0.05);认知是医疗服务接受者使用意愿的影响因素(P<0.05)。结论:医疗服务提供者和接受者对于“互联网+”缓和医疗服务的认知水平偏低,二者对其持有支持态度和不同的需求,医疗服务接受者和年资高的医疗服务提供者的意愿相对较低。今后需继续加强对移动医疗和缓和医疗的科普教育,并根据需求设计人性化、方便、实用、安全性高的移动医疗平台,助力缓和医疗发展。

基于组合赋权的冷链物流服务提供商选择

为科学合理地选择冷链物流服务提供商,基于冷链物流的特点,从企业素质、物流信息技术、物流作业能力和物流服务质量等4个方面构建评价指标体系。针对指标的不确定性和难以量化的特点,采用定性分析与定量分析相结合的方式,并引入精确数、区间数和语义模糊数等多种量化处理方法。通过直觉模糊层次分析法和熵权法组合赋权,客观、合理地计算指标权重,计算结果表明服务成本、生鲜品损耗率和冷藏车保有量等指标是生鲜企业比较关注的影响因素。对6个冷链物流服务提供商运用逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)进行评估,筛选出最优的冷链物流服务提供商,并且通过比较分析验证了本文方法的合理性和有效性,为生鲜企业的决策提供科学参考。

数字经济时代网络服务提供商责任认定——基于欧盟《数字服务法案》横纵分类的思考

欧盟《数字服务法案》(DSA)在《电子商务指令》与《数字化单一市场版权指令》的基础上对责任主体进行了整合。横向上,DSA在沿袭《电子商务指令》主体规定的同时,对当局下达命令的要求作了详细规定,同时为托管服务提供商增加了额外义务;纵向上,DSA将在线平台划分为三档并设定相应的责任,同时对超大型网络平台设立了非对称的勤勉义务。其对网络服务提供商的责任设置与我国2021年公布的《互联网平台分类分级指南》《互联网平台落实主体责任指南》两份征求意见稿思路类似,吸纳其经验有助于对网络平台责任作出更体系性的划分,对算法推荐系统作出清晰阐释。

The impact of Internet access on household dietary quality:Evidence from rural China

Over the past few decades,the Internet has rapidly diffused across China.The spread of the Internet has had a profound economic and social impact on Chinese rural areas.Existing research shows that Internet access significantly impacts agricultural production and improves smallholder farmers’income.Beyond these,the Internet can affect other dimensions of social welfare.However,research about the impact of Internet access on dietary quality in rural China remains scarce.This study utilizes multi-period panel data from Fixed Observation Point in rural China from 2009 to 2015 to estimate the impact of Internet access on dietary quality and food consumption of rural households and conducts a causal analysis.Regression models with time and household fixed effects allow robust estimation while reducing potential issues of unobserved heterogeneity.The estimates show that Internet access has significantly increased rural household dietary quality(measured by the Chinese Diet Balance Index).Further research finds that Internet access has increased the consumption of animal products,such as aquatic and dairy products.We also examine the underlying mechanisms.Internet access improves dietary quality and food consumption mainly through increasing household income and food expenditure.These results encourage the promotion of Internet access as a valuable tool for nutritional improvements,especially in rural areas.

A credibility-aware swarm-federated deep learning framework in internet of vehicles

Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead and data privacy risks.The recently proposed Swarm Learning(SL)provides a decentralized machine learning approach for unit edge computing and blockchain-based coordination.A Swarm-Federated Deep Learning framework in the IoV system(IoV-SFDL)that integrates SL into the FDL framework is proposed in this paper.The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL,then aggregates the global FDL model among different SL groups with a credibility weights prediction algorithm.Extensive experimental results show that compared with the baseline frameworks,the proposed IoV-SFDL framework reduces the overhead of client-to-server communication by 16.72%,while the model performance improves by about 5.02%for the same training iterations.

云服务器提供商著作权责任认定的困境与出路

云计算服务模式在互联网产业中的广泛应用,推动着网络服务提供者侵权责任规则作出适应性调整。云服务器提供商作为该服务体系中的关键主体,其在服务内容、技术能力、行业伦理方面的特殊性导致其在司法实践中面对责任主体定位不清、责任认定依据不明、责任内容界定不当等诸多问题,亟需在法律规范层面对其著作权侵权责任认定规则予以廓清。基于对云服务器提供商基本定位的释明与对既有立法规则适用关系的厘定,应当明确以《民法典》规定之“通知-必要措施”规则作为其著作权侵权责任的认定依据,并以比例原则为价值指引,通过扩张解释“必要措施”之基本内涵,对云服务器提供商的著作权侵权责任认定路径予以细化完善。在维护法律规范自身稳定性的同时,实现版权产业与互联网产业的利益均衡。

通用人工智能提供者内容审查注意义务的证成

通用人工智能可自发或受用户操控被动生成有害信息,它的提供者作为新型的网络内容提供者,负有数字内容“守门人”职责,同时负有网络空间的安全保护义务,应承担内容审查的注意义务,从而预防有害信息的侵权风险。“以数据为中心”的内容生产方式和算法黑箱效应降低了通用人工智能提供者对内容的管控力,导致对生成内容的普遍审查难以被实现。应针对有害信息,结合内容生产场景、提供者风险预见能力差异化构建内容审查的注意义务。该注意义务以技术性审查为标准,提供者采取符合技术特点的内容审查机制,便应当认定履行了相关义务。

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

ResNeSt-biGRU: An Intrusion Detection Model Based on Internet of Things

The rapid expansion of Internet of Things (IoT) devices across various sectors is driven by steadily increasingdemands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device hascaught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. Thishas resulted in a myriad of security challenges, including information leakage, malware propagation, and financialloss, among others. Consequently, developing an intrusion detection system to identify both active and potentialintrusion traffic in IoT networks is of paramount importance. In this paper, we propose ResNeSt-biGRU, a practicalintrusion detection model that combines the strengths of ResNeSt, a variant of Residual Neural Network, andbidirectionalGated RecurrentUnitNetwork (biGRU).Our ResNeSt-biGRUframework diverges fromconventionalintrusion detection systems (IDS) by employing this dual-layeredmechanism that exploits the temporal continuityand spatial feature within network data streams, a methodological innovation that enhances detection accuracy.In conjunction with this, we introduce the PreIoT dataset, a compilation of prevalent IoT network behaviors, totrain and evaluate IDSmodels with a focus on identifying potential intrusion traffics. The effectiveness of proposedscheme is demonstrated through testing, wherein it achieved an average accuracy of 99.90% on theN-BaIoT datasetas well as on the PreIoT dataset and 94.45% on UNSW-NB15 dataset. The outcomes of this research reveal thepotential of ResNeSt-biGRU to bolster security measures, diminish intrusion-related vulnerabilities, and preservethe overall security of IoT ecosystems.

Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic regularity.Despite these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle immobilization.This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly detection.IDS-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by cyber-attacks.These systems can autonomously create specific models based on network data to differentiate between regular traffic and cyber-attacks.Among these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational complexity.We evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and stability.This review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV systems.By examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

网络服务提供商适用“避风港规则”问题研究——以《谭谈交通》侵权案为视角

现代社会中,网络侵权已经占据侵权事件的高地,避风港原则作为网络侵权的免责事由也广受关注。其中,网络服务提供商在网络侵权事件中扮演着重要的中枢角色,一方面其为网络信息的交流传播提供了广阔的舞台,另一方面也潜在地增大了相关著作权作品面临侵权的风险。因此,对网络服务提供商的责任边界进行规制十分有必要,合理准确适用避风港规则厘清网络服务提供商的责任区间有助于构建优良有序的网络环境。本文将围绕“谭谈交通侵权案”展开,简要论述避风港规则在实践中面临的困境,并对此提出完善建议。