Debiaser for Multiple Variables to enhance fairness in classification tasks     

《Information processing & management》2023,60(2):103226

Nowadays assuring that search and recommendation systems are fair and do not apply discrimination among any kind of population has become of paramount importance. This is also highlighted by some of the sustainable development goals proposed by the United Nations. Those systems typically rely on machine learning algorithms that solve the classification task. Although the problem of fairness has been widely addressed in binary classification, unfortunately, the fairness of multi-class classification problem needs to be further investigated lacking well-established solutions. For the aforementioned reasons, in this paper, we present the Debiaser for Multiple Variables (DEMV), an approach able to mitigate unbalanced groups bias (i.e., bias caused by an unequal distribution of instances in the population) in both binary and multi-class classification problems with multiple sensitive variables. The proposed method is compared, under several conditions, with a set of well-established baselines using different categories of classifiers. At first we conduct a specific study to understand which is the best generation strategies and their impact on DEMV’s ability to improve fairness. Then, we evaluate our method on a heterogeneous set of datasets and we show how it overcomes the established algorithms of the literature in the multi-class classification setting and in the binary classification setting when more than two sensitive variables are involved. Finally, based on the conducted experiments, we discuss strengths and weaknesses of our method and of the other baselines.  相似文献   

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   

The zone of proximal creativity: What dynamic assessment of divergent thinking reveals about students’ latent class membership     

《Contemporary educational psychology》2021

Divergent Thinking is a domain-general mental attribute closely associated with creativity that can be quantified through the use of text-mining algorithms. Past research has shown that students’ Divergent Thinking is malleable in response to relatively simple contextual prompts. In addition, there is substantial variance in the degree to which individual students’ Divergent Thinking is malleable, suggesting the presence of a student-specific zone-of-proximal-development in relation to creativity. Here, we adopted a dynamic assessment paradigm that included multiple conditions under which student Divergent Thinking was measured and fit a latent profile analysis model to that dynamic assessment data. We found that, although on average the Originality of student responses can be augmented through a prompt to generate surprising or unusual ideas, three latent classes emerged that differed significantly on their patterns of augmentation. These three latent classes were termed: (a) Conventional Thinkers (7.80% of the sample), whose response to the Divergent Thinking task were highly constrained and unoriginal across all conditions (b) Prompted Shifters (66.56%), whose Originality significantly increased across conditions, and (c) Idea Generators (25.64%), whose responses were highly original across all conditions. These latent profiles were validated in regard to personality characteristics and domain-specific creative activities, with Idea Generators reporting significantly more Openness and Intellect, less Industriousness, and more creative activities across the domains of Literature, Music, Sports, Visual Art, Science, and Cooking than did the other latent classes.  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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Structural Analysis for Replicating Japanese Cultural Property Braids     

Masumi Tada  Kazuaki Yamashiro  Iwataro Oka  Akihiko Goto  Noriaki Kuwahara 《文物保护研究》2020,65(7):399-410

ABSTRACT

Historic Japanese textiles from over 1000 years ago generally show marked deterioration and only very rare examples show their original forms and much information about textile reproduction has been lost. The replication of textile braids lacks systematic methodology and is still being practiced by only few individual braiding experts. The recreation of original braids as close as possible to original braids is a part of Japan’s intangible cultural heritage. The aim of this study is to clarify the decision-making procedure through which the braiding experts can decipher the original braiding structures. As a preliminary study of this project, interviews of a braid researcher and a replicating expert, Makiko Tada were performed regarding her working practices. It is important to clarify the braiding parameters for structural analysis such as the number of transits and the balance of ridges, and it became clear that the orientation of multiple colored threads plays an important role. The expert’s replicate works were also analyzed using a text-mining statistical technique to clarify the relationship of braiding parameters. The relationship between each braiding parameter and production method such as loop manipulation and Taka-dai became clear. As a result, the process of deciphering the original braid structure has been compiled in simplified workflows, which could contribute to the standardization and improvement in efficiency of replication of cultural property braids.  相似文献   

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   

一种基于图卷积自编码模型的多维度学科知识网络融合方法     

李慧  胡吉霞 《图书情报工作》2020,64(18):114-125

[目的/意义] 针对包含单一类型知识单元的知识网络难以全面反映学科知识结构的问题，提出一种从多维度进行知识网络结构融合的方法，为学科领域知识结构挖掘提供借鉴。[方法/过程] 利用LDA及TF-IDF方法抽取学科知识单元，然后运用语义相似度和关键词共现分析方法构建3个学科知识子网络：主题网络、关键词网络和实体网络，并采用空间节点传递对齐方法对齐子网络节点，接着设计基于图卷积操作的自编码模型对知识节点进行表示，最后通过计算余弦相似度重构学科知识网络。[结果/结论] 实验部分以人工智能领域为例，构建融合主题、关键词和实体的学科知识网络并展开分析，实验结果表明，本文所提方法能有效地揭示学科领域研究内容和知识结构，为学科知识发现与组织研究提供有益参考。  相似文献   

数据管理与数据治理的比较分析及其对制定科学数据开放共享政策的启示     

盛小平  宋大成 《图书情报工作》2020,64(22):4-10

[目的/意义] 比较分析数据管理与数据治理差异与联系，为制定科学数据开放共享政策提供参考。[方法/过程] 运用比较分析法，解析数据管理与数据治理在定义与内涵、功能、目标、原则、焦点领域5个方面的异同，由此解析其对制定我国科学数据开放共享政策的启示。[结果/结论] 数据管理与数据治理在定义与内涵、功能、目标、原则、焦点领域上都有显著差异，但两者也有内在联系。数据治理是成功实施数据管理的关键。认清两者的关系有助于明晰目前我国科学数据管理政策的不足之处，为今后完善科学数据管理办法提供参考，从而规划与制定实用的科学数据开放共享细则。  相似文献   

武汉大学面向本科生的数据素养通识课程的建设与思考     

刘霞  方小利  郑怡萍 《图书情报工作》2020,64(22):69-76

[目的/意义] 旨在通过分享武汉大学数据素养通识课程的建设过程，为同行开展相关工作提供参考。[方法/过程] 通过案例分析、数据统计和数据挖掘可视化方法对"武大通识3.0"背景下的《数据素养与数据利用》课程的教学目标、教学内容、教学特色以及教学效果进行分析，探索课程体系的合理性。[结果/结论] 授课方式和课内外实践内容的设置能够很好地调动学生学习的积极性，提高学习效果；课程考核结果、学生学习前后的问卷调查及课后反馈表明，课程学习明显提升了学生的数据素养。  相似文献   

政务B站号信息传播效果影响因素与实证研究     

陈强  张杨一  马晓悦  曾润喜 《图书情报工作》2020,64(22):126-134

[目的/意义] 作为政府与青年群体沟通的重要平台，政务Bilibili账号的信息传播效果直接决定其沟通效率和效果。对政务Bilibili账号信息传播效果影响因素的研究有利于沟通效果的提升。[方法/过程] 本研究基于共青团中央Bilibili账号的471个样本，采用内容分析和回归分析等系统考察内容主题、视频类别、封面图类型、屏幕形式、字幕、组织形式、剪辑率和视频时长等因素对政务Bilibili账号信息传播效果的影响。[结果/结论] 结果表明，科技类、音乐类和时尚类主题的视频能够提升整体传播效果，科技类视频的贡献度最高；情景剧、实拍视频和监控视频都正向显著影响整体传播效果，实拍视频的贡献度最高。视频剪辑率、竖屏视频、字幕添加、合作创作等均能显著提高整体传播效果，竖屏在整体传播效果模型中的贡献度最大。视频时长和封面图的作用不显著。政务Bilibili账号运营应充分释放青年的爱国势能、创新视频形式、优化视频制作流程、创新组织形式。  相似文献