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《Information processing & management》2023,60(2):103212
The massive number of Internet of Things (IoT) devices connected to the Internet is continuously increasing. The operations of these devices rely on consuming huge amounts of energy. Power limitation is a major issue hindering the operation of IoT applications and services. To improve operational visibility, Low-power devices which constitute IoT networks, drive the need for sustainable sources of energy to carry out their tasks for a prolonged period of time. Moreover, the means to ensure energy sustainability and QoS must consider the stochastic nature of the energy supplies and dynamic IoT environments. Artificial Intelligence (AI) enhanced protocols and algorithms are capable of predicting and forecasting demand as well as providing leverage at different stages of energy use to supply. AI will improve the efficiency of energy infrastructure and decrease waste in distributed energy systems, ensuring their long-term viability. In this paper, we conduct a survey to explore enhanced AI-based solutions to achieve energy sustainability in IoT applications. AI is relevant through the integration of various Machine Learning (ML) and Swarm Intelligence (SI) techniques in the design of existing protocols. ML mechanisms used in the literature include variously supervised and unsupervised learning methods as well as reinforcement learning (RL) solutions. The survey constitutes a complete guideline for readers who wish to get acquainted with recent development and research advances in AI-based energy sustainability in IoT Networks. The survey also explores the different open issues and challenges. 相似文献
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《Information processing & management》2023,60(3):103247
Recently, models that based on Transformer (Vaswani et al., 2017) have yielded superior results in many sequence modeling tasks. The ability of Transformer to capture long-range dependencies and interactions makes it possible to apply it in the field of portfolio management (PM). However, the built-in quadratic complexity of the Transformer prevents its direct application to the PM task. To solve this problem, in this paper, we propose a deep reinforcement learning-based PM framework called LSRE-CAAN, with two important components: a long sequence representations extractor and a cross-asset attention network. Direct Policy Gradient is used to solve the sequential decision problem in the PM process. We conduct numerical experiments in three aspects using four different cryptocurrency datasets, and the empirical results show that our framework is more effective than both traditional and state-of-the-art (SOTA) online portfolio strategies, achieving a 6x return on the best dataset. In terms of risk metrics, our framework has an average volatility risk of 0.46 and an average maximum drawdown risk of 0.27 across the four datasets, both of which are lower than the vast majority of SOTA strategies. In addition, while the vast majority of SOTA strategies maintain a poor turnover rate of approximately greater than 50% on average, our framework enjoys a relatively low turnover rate on all datasets, efficiency analysis illustrates that our framework no longer has the quadratic dependency limitation. 相似文献
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《Information processing & management》2023,60(2):103233
Zero-shot object classification aims to recognize the object of unseen classes whose supervised data are unavailable in the training stage. Recent zero-shot learning (ZSL) methods usually propose to generate new supervised data for unseen classes by designing various deep generative networks. In this paper, we propose an end-to-end deep generative ZSL approach that trains the data generation module and object classification module jointly, rather than separately as in the majority of existing generation-based ZSL methods. Due to the ZSL assumption that unseen data are unavailable in the training stage, the distribution of generated unseen data will shift to the distribution of seen data, and subsequently causes the projection domain shift problem. Therefore, we further design a novel meta-learning optimization model to improve the proposed generation-based ZSL approach, where the parameters initialization and the parameters update algorithm are meta-learned to assist model convergence. We evaluate the proposed approach on five standard ZSL datasets. The average accuracy increased by the proposed jointly training strategy is 2.7% and 23.0% for the standard ZSL task and generalized ZSL task respectively, and the meta-learning optimization further improves the accuracy by 5.0% and 2.1% on two ZSL tasks respectively. Experimental results demonstrate that the proposed approach has significant superiority in various ZSL tasks. 相似文献
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The business process outsourcing industry has got disrupted, first by the significant shift in value creation activities from the clients to the service providers, and second by pervasive digital penetration, resulting in the emergence of Digital Transformational Outsourcing (DTO). Service providers now play a more significant role, making their capabilities important. In the new context, service providers require a uniquely different set of dynamic capabilities to handle end-to-end business functions on behalf of their clients while delivering digital value propositions.We study 26 of the largest global business process outsourcing providers to conceptualise and identify six dynamic capabilities of service providers salient in the new context, i.e., consultative, orchestration, insights, network management, knowledge access, and standardisation. Interviews conducted with industry experts provided evidence in support of the identified dynamic capabilities. A novel firm capability dataset was created using secondary data, and using fuzzy-set Qualitative Comparative Analysis (fsQCA), we identify configurations for high and low performance and find them to vary by the firm's broad/narrow scope. 相似文献
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[目的/意义]社交问答用户的社会资本受多种因素影响，本文旨在探究社交问答用户不同的健康信息行为对其社会资本积累的影响。[方法/过程]以知乎网上糖尿病话题下2 537个问题帖子，3 567个回答的1 650名用户为研究对象，依据L.Nan的社会资本理论和N.Uphoff对社会资本的分类，将社交问答用户的社会资本分为认知性和结构性两类，用多元线性回归的方法分析社会问答用户的健康信息行为与社会资本之间的关系。[结果/结论]用户的健康知识贡献行为和自我信息披露行为在不同程度上正向促进社会资本的累积，而不同的健康知识获取行为对认知性社会资本和结构性社会资本的影响有差异。这些结果有助于社交问答用户提高社会资本，平台完善用户服务和激励机制。 相似文献
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本文首先以国内中国知网文献数据为样本，对网络信息安全的文献数量分布、研究热点进行了深入的概述和总结。研究发现该领域研究主要呈现5个热点主题：网络环境与信息安全、网络技术与信息系统、网络信息类别、信息管理与安全策略。然后着重讨论了网络信息安全在国家、企业和个人层面的主要问题。最后针对该领域研究的主要问题提出了相关建议。 相似文献
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[目的/意义]对我国世界一流大学建设高校科研合作网络的结构特征、内部关系、演化规律及演化原因进行研究，可为我国世界一流大学建设提供参考。[方法/过程]基于42所高校历年（2013-2017）在WoS合著论文数据，构建历年科研合作频次矩阵、Salton指数矩阵。采用相关性分析法、社会网络分析法，借助可视化软件，以整体网络、个体网络和局部网络演化3个视角，分别从合作率、相关性、网络密度、点度中心性、中间中心性和网络聚类及网络演化原因等对高校科研合作关系网络结构特征、合作强度及演化规律进行揭示与分析。[结果/结论]高校在国际期刊发文总数、合作发文总数及合作率总体都呈上升趋势；高校科研合作程度、对科研资源占有或控制程度都与科研产出存在较强的正相关关系；科研合作更注重强强联合、优势互补，核心网络规模逐渐扩大，各高校向网络中心聚集的趋势不断增强，单一高校对于整体合作网络的控制度和影响力以及不可或缺性不断降低；高等教育的发展、基金支持力度、最小省力法则、马太效应原理、强学科性术业专攻是高校科研合作网络演化产生的主要原因；最后对高校科研合作提出建议。 相似文献
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在学科紧密相关和信息资源相融的当代,探究科研合作的影响因素成了诸多学者关注的话题。然而,现有研究将各种合作关系视同一律,即两人发表一篇合著文章,便形成合作关系;同时,大部分研究没有系统地研究多维邻近性的影响。为此本文提出了"科研主导力"的概念与测度,并从多维邻近性出发系统地分析科研合作的影响因素。本文基于Web of Science (WoS)上2013-2017年的生物医学(Life Sciences&Biomedicine)领域中国境内科研机构发表的论文数据,采用社会网络分析和引力模型,揭示了中国境内生物医学领域的科研主导网络的网络拓扑特征与多维邻近性机制,发现科研主导力两极分化严重,分散程度提高,互惠程度提高;主导机构(通信作者所在机构)和参与机构(非通信作者所在机构)的累积科研主导力显著促进科研主导力的扩散,且主导机构的促进作用大于参与机构;地理距离会阻碍科研主导力的扩散;认知背景相似度越高,社会关系越密切,越能形成科研主导关系;同样的制度环境、文化环境可以促进科研主导关系的形成。本文对全面理解多样化的科研合作模式、演化及其影响因素提供借鉴意义,研究科研主导力的相关方法可以推广到其他领域中。 相似文献
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我国政府信息公开的渠道、问题和展望 总被引：5，自引：0，他引：5
政府信息公开是当今社会的热点研究课题，对构建民主法制社会具有深远的意义。本文分析了我国政府信息公开的主要渠道，重点讨论了其存在的问题以及问题的解决方式，最后对我国政府信息公开的未来发展进行了综述。 相似文献