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1.
As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development. 相似文献
2.
《Information processing & management》2023,60(1):103150
Nowadays, researchers are investing their time and devoting their efforts in developing and motivating the 6G vision and resources that are not available in 5G. Edge computing and autonomous vehicular driving applications are more enhanced under the 6G services that are provided to successfully operate tasks. The huge volume of data resulting from such applications can be a plus in the AI and Machine Learning (ML) world. Traditional ML models are used to train their models on centralized data sets. Lately, data privacy becomes a real aspect to take into consideration while collecting data. For that, Federated Learning (FL) plays nowadays a great role in addressing privacy and technology together by maintaining the ability to learn over decentralized data sets. The training is limited to the user devices only while sharing the locally computed parameter with the server that aggregates those updated weights to optimize a global model. This scenario is repeated multiple rounds for better results and convergence. Most of the literature proposed client selection methods to converge faster and increase accuracy. However, none of them has targeted the ability to deploy and select clients in real-time wherever and whenever needed. In fact, some mobile and vehicular devices are not available to serve as clients in the FL due to the highly dynamic environments and/or do not have the capabilities to accomplish this task. In this paper, we address the aforementioned limitations by introducing an on-demand client deployment in FL offering more volume and heterogeneity of data in the learning process. We make use of containerization technology such as Docker to build efficient environments using any type of client devices serving as volunteering devices, and Kubernetes utility called Kubeadm to monitor the devices. The performed experiments illustrate the relevance of the proposed approach and the efficiency of the deployment of clients whenever and wherever needed. 相似文献
3.
Guo Tian Wenda Yang Deyang Chen Zhen Fan Zhipeng Hou Marin Alexe Xingsen Gao 《国家科学评论(英文版)》2019,6(4):684
Multiferroic nanostructures have been attracting tremendous attention over the past decade, due to their rich cross-coupling effects and prospective electronic applications. In particular, the emergence of some exotic phenomena in size-confined multiferroic systems, including topological domain states such as vortices, center domains, and skyrmion bubble domains, has opened a new avenue to a number of intriguing physical properties and functionalities, and thus underpins a wide range of applications in future nanoelectronic devices. It is also highly appreciated that nano-domain engineering provides a pathway to control the magnetoelectric properties, which is promising for future energy-efficient spintronic devices. In recent years, this field, still in its infancy, has witnessed a rapid development and a number of challenges too. In this article, we shall review the recent advances in the emergent domain-related exotic phenomena in multiferroic nanostructures. Specific attention is paid to the topological domain structures and related novel physical behaviors as well as the electric-field-driven magnetic switching via domain engineering. This review will end with a discussion of future challenges and potential directions. 相似文献
4.
Web2.0、Library2.0与E-Learning2.0 总被引：1，自引：0，他引：1
本文讨论了Web2.0、Library2.0与E-Learning2.0的概念、本质特征及相互关系,以及Web2.0技术在Library2.0和E-Learning2.0中的应用,并对Web2.0、Library2.0与E-Learning2.0发展提出了展望。 相似文献
5.
自动驾驶汽车技术轨道具有高度的复杂性和不确定性,对企业创新战略规划和产业政策制定带来了巨大挑战。本文基于1995—2018年自动驾驶汽车专利数据,综合运用社群分析、主路径分析等方法对自动驾驶汽车的技术热点、发展脉络和主导路径进行了系统分析,在此基础上结合主导企业战略和产业发展实践揭示了自动驾驶汽车技术轨道演进的路线、方向和驱动因素。研究发现：（1）1995年以来自动驾驶汽车领域先后形成了十个主导的技术社群,技术发展具有明显的阶段性和群组化特征。（2）自动驾驶汽车技术主导路径经历了融合-分离-再融合-再分离的过程,技术焦点从辅助驾驶向部分自动驾驶继而向复杂场景的自动驾驶技术逐渐演进。（3）当前自动驾驶汽车技术存在两种不同的演化路径和发展方向,未来的技术轨道演进将由技术、市场与政策多重因素共同决定。 相似文献
6.
《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
7.
Artificial Intelligence tools have attracted attention from the literature and business organizations in the last decade, especially by the advances in machine learning techniques. However, despite the great potential of AI technologies for solving problems, there are still issues involved in practical use and lack of knowledge as regards using AI in a strategic way, in order to create business value. In this context, the present study aims to fill this gap by: providing a critical literature review related to the integration of AI to organizational strategy; synthetizing the existing approaches and frameworks, highlighting the potential benefits, challenges and opportunities; presenting a discussion about future research directions. Through a systematic literature review, research articles were analyzed. Besides gaps for future studies, a conceptual framework is presented, discussed according to four sources of value creation: (i) decision support; (ii) customer and employee engagement; (iii) automation; and (iv) new products and services. These findings contribute to both theoretical and managerial perspectives, with extensive opportunities for generating novel theory and new forms of management practices. 相似文献
8.
数据库管理系统是基于某种前台的开发工具和后台数据库,并在软件工程相关理论的指导下所形成的应用系统,在企业,教育,医疗,航空,生物等领域有着广泛的应用。本文分析了数据库管理系统的发展历史及研究进展,最后展望了数据库管理系统的未来发展趋势及方向。 相似文献
9.
《Information processing & management》2023,60(3):103279
The wide spread of false information has detrimental effects on society, and false information detection has received wide attention. When new domains appear, the relevant labeled data is scarce, which brings severe challenges to the detection. Previous work mainly leverages additional data or domain adaptation technology to assist detection. The former would lead to a severe data burden; the latter underutilizes the pre-trained language model because there is a gap between the downstream task and the pre-training task, which is also inefficient for model storage because it needs to store a set of parameters for each domain. To this end, we propose a meta-prompt based learning (MAP) framework for low-resource false information detection. We excavate the potential of pre-trained language models by transforming the detection tasks into pre-training tasks by constructing template. To solve the problem of the randomly initialized template hindering excavation performance, we learn optimal initialized parameters by borrowing the benefit of meta learning in fast parameter training. The combination of meta learning and prompt learning for the detection is non-trivial: Constructing meta tasks to get initialized parameters suitable for different domains and setting up the prompt model’s verbalizer for classification in the noisy low-resource scenario are challenging. For the former, we propose a multi-domain meta task construction method to learn domain-invariant meta knowledge. For the latter, we propose a prototype verbalizer to summarize category information and design a noise-resistant prototyping strategy to reduce the influence of noise data. Extensive experiments on real-world data demonstrate the superiority of the MAP in new domains of false information detection. 相似文献
10.
随着互联网及计算机等智能设备技术的发展,在线学习由于其学习内容的自主性、学习空间的分布性及个性化,越来越被人们所接受。学习空间是学习行为发生的场所,对学习行为具有一定的影响,学习空间的选择设置直接影响着学习者的学习成效。可个性化设置的在线学习空间有助于学习者思维活动的展开,促进学习者融入学习过程,进行创新式学习。 相似文献
11.
本文采用学习动机策略调查问卷对某本科院校旅游专业二年级高职生自主学习能力进行调查研究,结果发现：1）本科院校高职生的自主学习能力总体上属于中等偏上水平,同一专业同一年级本科生和高职生的自主学习能力仅在内在动机因子上存在显著性差异,其余各因子差异都不显著;2）高职生的学习成绩与自我效能和内部动机因子呈低度正相关关系,与考试焦虑和学习策略各因子不相关。 相似文献
12.
为探讨大数据的概念及全球的研究现状,以Web of Science TM核心集合作为数据源,对时间为2010—2016年期间有关大数据的经济管理类核心期刊进行文献梳理研究,利用知识图谱法、共词分析法和引文分析法对大数据研究领域的基础知识、知识演进以及研究热点及趋势展开分析和评述。基于文献计量软件Citespace绘制出时区视图、聚类图等,得出对大数据概念的多视角理解以及研究热点和研究趋势。 相似文献
13.
The identification of a favorable location for investment is a key aspect influencing the real estate market of a smart city. The number of factors that influence the identification easily runs into a few hundreds (including floor space area, crime in the locality and so on). Existing literature predominantly focuses on the analysis of price trends in a given location. This paper aims to develop a set of tools to compute an optimal location for investment, a problem which has received little attention in the literature (analysis of house price trends has received more attention). In previous work the authors proposed a machine learning approach for computing optimal locations. There are two main issues with the previous work. All real estate factors were assumed to be independent and identically distributed random variables. To address this, in the current paper we propose a network structure to derive the relational inferences between the factors. However, solving the location identification problem using only a network incurs computational burden. Hence, the machine learning layers from the previous work is combined with a network layer for computing an optimal location with proven lower computational cost. A second issue is that the computations are performed on an online database which has inherent privacy risks. The online data, user information and the algorithms can be tampered through privacy breaches. We present a privacy preservation technique to protect the algorithms, and use blockchains to secure the identity of the user. This paper presents solutions to two interesting problems in the analysis of real estate networks: a) to design tools that can identify an optimal location for investment and b) to preserve the privacy of the entire process using privacy preserving techniques and block chains. 相似文献
14.
“学进去,讲出来”是以学生自主学习作为主要学习方式,以合作学习作为主要教学组织形式,以“学进去”、“讲出来”作为学生学习方式的导向和学习目标达成的基本要求的课堂教学方式。 相似文献
15.
目的：通过对某高校口腔医学生的自主学习现状以及“口腔医学导论”课开展前后的状况比较,分析研究“口腔医学导论”课对口腔医学本科生专业自主学习能力及主动性的作用影响,最终获得引导学生加强专业自主学习的有效途径。方法：在某医学院校,采用问卷调查的形式,对在校全体口腔医学生进行自主学习状况调查,增设“口腔医学导论”课程,对课程开展前后的自主学习状况进行问卷调查研究,并对调查结果进行统计分析。结果：调查统计结果显示,学生的专业自主学习平均时间随着年级的增加而递增;“口腔医学导论”开设后,学生的专业自主学习时间平均每天增加18．4分钟,课外进行专业自主学习的主动性显著增加,主要的学习途径除了阅读教科书、期刊外以网络学习为主,包括百度、谷歌搜索,校数字化图书馆等。结论：“口腔医学导论”课的开设有助于提高口腔医学生专业自主学习能力及主动性,有助于提升学生对口腔医学专业的理解和热爱。 相似文献
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17.
《International Journal of Information Management》2016,36(3):403-413
Big data has captured the interests of scholars across many disciplines over the last half a decade. Business scholars have increasingly turned their attention to the impact of this emerging phenomenon. Despite the rise in attention, our understanding of what big data is and what it means for organizations and institutional actors remains uncertain. In this study, we conduct a systematic review on “big data” across business scholarship over the past six years (2009–2014). We analyzed 219 peer-reviewed academic papers from 152 journals from the most comprehensive business literature database. We conducted the systematic review both quantitatively and qualitatively using the data analysis software NVivo10. Our results reveal several key insights about the scholarly investigation of big data, including its top benefits and challenges. Overall, we found that big data remains a fragmented, early-stage domain of research in terms of theoretical grounding, methodological diversity and empirically oriented work. These challenges serve to improve our understanding of the state of big data in contemporary research, and to further prompt scholars and decision-makers to advance future research in the most productive manner. 相似文献
18.
百度国学和CNKI是国内两个重要的中文学术搜索引擎,Google Scholar和Scirus是国际公认的专业学术搜索引擎,介绍了它们各自的特点及其功能,分别从数据库、检索功能、检索结果与用户界面3个方面来比较分析了它们各自的异同点,并对学术搜索引擎的发展趋势做了展望。 相似文献
19.
专利联盟中的组织学习与技术能力提升——以NOKIA为例 总被引：2，自引：0，他引：2
专利联盟的技术本质更加明显，通过专利联盟进行学习是企业进行技术学习的一种重要途径。诺基亚（NOKIA）公司通过在专利联盟中积极学习技术领先者的知识，取得了在移动通信技术领域综合能力的快速提升和在全球市场的成功。 相似文献
20.
Materials of nanoscale size exhibit properties that macroscopic materials often do not have. The same holds for bubbles on the nanoscale: nanoscale gaseous domains on a solid-liquid interface have surprising properties. These include the shape, the long life time, and even superstability. Such so-called surface nanobubbles may have wide applications. This prospective article covers the basic properties of surface nanobubbles and gives several examples of potential nanobubble applications in nanomaterials and nanodevices. For example, nanobubbles can be used as templates or nanostructures in surface functionalization. The nanobubbles produced in situ in a microfluidic system can even induce an autonomous motion of the nanoparticles on which they form. Their formation also has implications for the fluid transport in narrow channels in which they form. 相似文献