Artificial intelligence implication on energy sustainability in Internet of Things: A survey     

《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   

Directed collaboration patterns in funded teams: A perspective of knowledge flow     

《Information processing & management》2023,60(2):103237

Collaborations in funded teams are essential for understanding funded research and funding policies, although of high interest, are still not fully understood. This study aims to investigate directed collaboration patterns from the perspective of the knowledge flow, which is measured based on the academic age. To this end, we proposed a project-based team identification approach, which gives particular attention to funded teams. The method is applicable to other funding systems. Based on identified scientific teams, we detected recurring and significant subgraph patterns, known as network motifs, and under-represented patterns, known as anti-motifs. We found commonly occurred motifs and anti-motifs are remarkably characterized by different structures matching certain functions in knowledge exchanges. Collaboration patterns represented by motifs favor hierarchical structures, supporting intensive interactions across academic generations. Anti-motifs are more likely to show chain-like structures, hindering potentially various knowledge activities, and are thus seldom found in real collaboration networks. These findings provide new insights into the understanding of funded collaborations and also the funding system. Meanwhile, our findings are helpful for researchers, the public and policymakers to gain knowledge on research(ers) evolution, particularly in terms of primordial collaboration patterns.  相似文献   

Optimized hadoop map reduce system for strong analytics of cloud big product data on amazon web service     

《Information processing & management》2023,60(3):103271

Because of the rapid increase of data in the cloud of Amazon Web Service (AWS), the traditional methods for analyzing this data are not good and inappropriate, so unconventional methods of analysis have been proposed by many data scientists such as concurrent/ parallel techniques to meeting the requirements of performance and scalability entailed in such big data analyses. In this paper we are used Hadoop Map Reduce system that contains Hadoop Distributed File System (HDFS) and Hadoop cluster. We optimized it by combining it with five efficient Data Mining (DM) algorithms such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Correlative Naïve Bayes classifier (CNB), and Fuzzy CNB (FCNB) for strong analytics of cloud big data. The proposed system applied on product review data that taken form the cloud of AWS. The Evaluation of Hadoop Map Reduce done with important benchmarks as Mean Absolute Percentage Error (MPAE), Root Mean Square Error (RMSE), and runtime for word count, sort, inverted index. Also, the evaluation of DM models with Hadoop Map Reduce system done by using accuracy, sensitivity, specificity, memory, and running time. Experiments have shown that FCNB is effective in addressing the problem of big data.  相似文献   

Back to common sense: Oxford dictionary descriptive knowledge augmentation for aspect-based sentiment analysis     

《Information processing & management》2023,60(3):103260

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Business continuity of business models: Evaluating the resilience of business models for contingencies     

《International Journal of Information Management》2019

Company business models are vulnerable to various contingencies in the business environment that may unexpectedly render their business logic ineffective. In particular, technological advancements, such as the Internet of things, big data, sharing economy and crowdsourcing, have enabled new forms of business models that can effectively and abruptly make traditional business models obsolete. By disrupting or even diminishing companies’ revenue streams, environmental contingencies may present a significant threat to business continuity (BC). Evaluating the resilience of business models against these contingencies should therefore be a core area of BC. However, existing BC approaches tend to focus on the continuity of the resources and processes through which a particular business model is accomplished in practice but omit the business model itself. We argue that in order for BC approaches to become holistic and strategic, business models need to become a part of the BC considerations, entailing an expansion of the scope of BC from value preservation to value creation. We propose an approach of Strategic Business Continuity Management, which consists of two parts: (1) sustaining the continuity of the company business model (value preservation) and (2) evaluating and modifying the business model (value creation). We illustrate conceptually the value creation part with an example drawn from the sharing economy.  相似文献   

Franklin Institute Awards Call for Nominations     

《Journal of The Franklin Institute》2021,358(14):iv

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Team identification and sports fandom as predictors of fan aggression: The moderating role of ageing     

《Sport Management Review》2019,22(2):194-208

In the current study, the explored the moderating role of ageing in the relationship between team identification/fandom and fan aggression. The authors used an online panel-based survey that offered access to a realworld population of sports fans. Participants were 740 fans of Israeli professional basketball. Results from structural equation modelling demonstrated that older fans reported higher levels of mere sports fandom and lower levels of self-reported aggression and acceptance of aggression. Moreover, age moderated the relationships between team identification (or fandom) and self-reported aggression, such that team identification (or fandom) was more strongly associated with selfreported fan aggression among younger fans than among older fans. The moderating role of age in the relationships between team identification (or fandom) and perceptions of appropriateness of aggression was not supported. The findings contribute to our theoretical understanding of the role of ageing in the relationship between fan identification and fan aggression. Based on these findings, the authors assert that managers might particularly benefit from leveraging the potential, but often neglected, segment of senior fans, since older fans can play a key role in reducing the level of aggression during competitive sports events. Suggestions for future research are also discussed.  相似文献   

公共图书馆服务供给政社合作关系研究     

李杨 《图书情报工作》2019,63(7):30-37

[目的/意义]在当前政府职能转变的背景下，政府与社会组织合作提供公共图书馆服务是公共图书馆管理体制改革的有效措施，也是公共图书馆事业发展的重要趋势。研究政府与社会组织的合作关系，不仅能够丰富相关理论成果，而且能为公共图书馆服务供给政社合作的发展提供参考。[目的/意义]通过文献调研、网络调研与实地调研相结合，论述公共图书馆服务供给政社合作关系的概念及特征，对公共图书馆服务供给政社合作关系中各主体的角色、政社合作关系的成因、政社合作关系的类型及形成过程进行深入分析。在此基础之上，提出公共图书馆服务供给政社合作关系可持续发展的对策。[结果/结论]良好合作关系的建立需要端正合作动机，完善合作制度，规范合作行为。  相似文献   

网络招聘文本技能信息自动抽取研究   总被引：1，自引：1，他引：0  

俞琰  陈磊  姜金德  赵乃瑄 《图书情报工作》2019,63(13):105-113

[目的/意义]针对目前网络招聘文本手工抽取技能信息无法满足大数据量分析要求的问题，提出一种针对大量网络招聘文本的技能信息自动抽取方法。[方法/过程]根据网络招聘文本的特点，利用依存句法分析选取候选技能，然后提出领域相关性指标衡量候选技能，将其融入传统的术语抽取方法之中，形成一种网络招聘文本技能信息自动抽取方法。[结果/结论]实验表明，本文提出的方法能够从网络招聘文本中自动、快速、准确地抽取技能信息。  相似文献   

唐诗知识图谱的构建及其智能知识服务设计   总被引：2，自引：0，他引：2  

周莉娜  洪亮  高子阳 《图书情报工作》2019,63(2):24-33

[目的/意义]立足于当前大数据环境下的唐诗知识服务需求，以大规模唐诗数据为基础构建唐诗知识图谱并提供智能知识服务，推动人工智能环境下唐诗知识管理和知识服务方式的创新。[方法/过程]本文在对领域知识服务需求调研的基础上，设计领域知识服务驱动的唐诗本体模型，然后利用从Web上爬取的多源异构数据，采用知识抽取、知识融合、知识推理等技术自动构建唐诗知识图谱，统一表示和组织唐诗领域数据，实现对大规模唐诗数据的语义化处理。[结果/结论]本文设计基于唐诗知识图谱的智能知识服务平台KnowPoetry，提供唐诗领域的知识探索、时空轨迹、语义查询等智能化知识服务，推动人工智能环境下唐诗数字人文研究方法的创新转型。  相似文献