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   

A multimodal time-series method for gifting prediction in live streaming platforms     

《Information processing & management》2023,60(3):103254

Viewer gifting is an important business mode in live streaming industry, which closely relates to the income of the platforms and streamers. Previous studies on gifting prediction are often limited to cross-section data and consider the problem from the macro perspective of the whole live streaming. However, the multimodal information and the time accumulation effect of live streaming content on viewer gifting behavior are ignored. In this paper, we put forward a multimodal time-series method (MTM) for predicting real-time gifting. The core module of the method is the multimodal time-series analysis (MTA), which targets at effectively fusing multimodal information. Specifically, the proposed orthogonal projection (OP) model can promote cross-modal information interaction without introducing additional parameters. To achieve the interaction of multi-modal information at the same level, we also design a stackable joint representation layer, which makes each target modality's representation (visual, acoustic and textual modality) can benefit from all the other modalities. The residual connections are introduced as well to ensure the integration of low-level and high-level information. On our dataset, our model shows improved performance compared to other advanced models by at least 8% on F1. Meanwhile, the MTA is able to meet the real-time requirements of the live streaming setting, and has demonstrated its robustness and transferability in other tasks. Our research may offer some insights about how to efficiently fuse multimodal information, and contribute to the research on viewer gifting behavior prediction in the live streaming context.  相似文献   

Analysis of Common Grammatical“Errors”in Learner English of China's Mainland     

王子倩 《海外英语》2021,(2)

By examining twenty TOEFL mock writings from Shenzhen high school students,this paper attempts to have a further analysis on learner English in China with regard to its common grammatical"errors"and their possible underlying causes.Some common grammatical errors include misuse of past tense,word class,existential structure,and also topic comment sentence,most of which are due to transfer of Chinese.  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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Auditing fairness under unawareness through counterfactual reasoning     

《Information processing & management》2023,60(2):103224

Artificial intelligence (AI) is rapidly becoming the pivotal solution to support critical judgments in many life-changing decisions. In fact, a biased AI tool can be particularly harmful since these systems can contribute to or demote people’s well-being. Consequently, government regulations are introducing specific rules to prohibit the use of sensitive features (e.g., gender, race, religion) in the algorithm’s decision-making process to avoid unfair outcomes. Unfortunately, such restrictions may not be sufficient to protect people from unfair decisions as algorithms can still behave in a discriminatory manner. Indeed, even when sensitive features are omitted (fairness through unawareness), they could be somehow related to other features, named proxy features. This study shows how to unveil whether a black-box model, complying with the regulations, is still biased or not. We propose an end-to-end bias detection approach exploiting a counterfactual reasoning module and an external classifier for sensitive features. In detail, the counterfactual analysis finds the minimum cost variations that grant a positive outcome, while the classifier detects non-linear patterns of non-sensitive features that proxy sensitive characteristics. The experimental evaluation reveals the proposed method’s efficacy in detecting classifiers that learn from proxy features. We also scrutinize the impact of state-of-the-art debiasing algorithms in alleviating the proxy feature problem.  相似文献   

Being my own gatekeeper,how I tell the fake and the real – Fake news perception between typologies and sources     

《Information processing & management》2023,60(2):103228

The phenomenal spread of fake news online necessitates further research into fake news perception. We stress human factors in misinformation management. This study extends prior research on fake news and media consumption to examine how people perceive fake news. The objective is to understand how news categories and sources influence individuals' perceptions of fake news. Participants (N = 1008) were randomly allocated to six groups in which they evaluated the believability of news from three categories (misinformation, conspiracy, and correction news) coupled with six online news sources whose background (official media, commercial media, and social media) and expertise level varied (the presence or absence of a professional editorial team). Our findings indicated people could distinguish media sources, which have a significant effect on fake news perception. People believed most in conspiracy news and then misinformation included in correction news, demonstrating the backfire of correction news. The significant interaction effects indicate people are more sensitive to misinformation news and show more skepticism toward misinformation on social media. The findings support news literacy that users are capable to leverage credible sources in navigating online news. Meanwhile, challenges of processing correction news require design measures to promote truth-telling news.  相似文献   

Alone with others: Understanding physical environmental needs of students within an academic library setting     

《The Journal of Academic Librarianship》2020,46(2):102098

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基于知识元的学术论文内容创新性智能化评价研究   总被引：1，自引：0，他引：1  

李贺  杜杏叶 《图书情报工作》2020,64(1):93-104

[目的/意义] 创新性是对学术论文质量最基本的要求，是学术论文的灵魂，是学术论文评价的核心。知识元是学术论文基本组成单元。基于知识元理论和机器学习相关理论与算法，从学术论文内容层面研究计算机如何智能化地进行创新性评价及其实现过程与方法。[方法/过程] 首先，构建学术论文的研究问题、理论、方法、结论4个知识元本体，接着提出基于知识元的学术论文创新性判断模型。其次，根据学术论文研究特点，构建理论与方法机器分类模型及知识元的抽取规则与抽取方法，建立规则库和知识语料库。最后，基于语义相似度计算方法，根据判断规则和相关权重对学术论文4个维度的创新性进行评分。[结果/结论] 基于知识元抽取的学术论文创新性评分系统的实证结果表明，该智能化评价方法具有一定的可行性，可为学术论文内容创新性智能化评价系统的最终实现提供方法借鉴。  相似文献   

高校图书馆中华传统文化立体阅读推广探究——以北京师范大学图书馆“二十四节气”阅读推广为例     

孙媛媛  张玲  于静  张珅 《图书情报工作》2020,64(9):57-64

[目的/意义] 以北京师范大学图书馆"二十四节气"传统文化立体阅读推广为例，探讨基于中华传统文化的高校图书馆立体阅读推广模式。[方法/过程] 论述高校图书馆开展中华传统文化阅读推广的意义及采用立体阅读模式的必要性，从活动准备、活动内容和特点、活动效果评价三方面阐述北京师范大学图书馆"二十四节气"传统文化阅读推广工作。[结果/结论] 提出高校图书馆以立体阅读模式开展中华传统文化阅读推广的策略。  相似文献