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   

Multi-task learning based high-value patent and standard-essential patent identification model     

《Information processing & management》2023,60(3):103327

The high-value patent identification (HVPI) and the standard-essential patent identification (SEPI) are two important issues in the fields of intellectual property and the standardization, respectively. Almost all the HVPI and the SEPI are based on the single-task learning. In this paper, we unify the HVPI and the SEPI in a multi-task learning framework in consideration of the mutual reinforcement of the two tasks. In our model, we extract the patent structured features and embed the patent textual features using the pre-training model. Given these features, we explore a multi-task learning based identification model to identify the high-value patents and the standard-essential patents. We evaluate our model by comparing with two state-of-the-art models on the 5 balanced datasets and 2 imbalanced datasets. The results show our multi-task learning based model outperforms significantly these single-tasking learning based models in the measurements: precision, recall, F1 and accuracy. On the balanced datasets, the average increments of measurements are 1.3%, 1.29%, 1.28% and 1.28% respectively. On the imbalanced datasets, the average increments of measurements are 2.24%, 1.62%, 1.75% and 0.66% respectively.  相似文献   

Exploring an intervention’s impact on everyday school activities     

Marianne Björn  Idor Svensson 《Support for Learning》2021,36(1):5-19

The focus of this paper is on a group of pupils with reading and writing difficulties who have been participating in an intervention study using assistive technology. That intervention study contained supervised training sessions with reading and writing tasks using an iPad with special supportive applications. The current study is a qualitative investigation of whether there has been any transfer from the intervention, to the pupils’ everyday school activities. Interviews with pupils and their teachers and observations during classroom lectures have been used to collect data. The results show that the pupils were positive to the assistive technology (the applications on the iPads), they found the apps easy to learn how to use and they appreciated the benefits they could give. Even so, only a few of the pupils had found use for and continued to use the tools after the intervention period finished. Possible reasons are that when the novelty wore off, students reverted to their usual study habits and that older students with many teachers and different classrooms were less able to adapt to using the apps. To improve transfer, it is suggested to introduce assistive technology earlier to students, in the younger grades, before study habits have been formed and to inform teachers about the use of AT in the classroom, including what is available and how it can benefit students.  相似文献   

美国《图书馆杂志》“年度图书馆”奖获得者服务特色分析     

谈大军  王梦迪 《图书情报工作》2020,64(7):145-152

[目的/意义] 对美国《图书馆杂志》从1992年开始设立的"年度图书馆"奖的评选标准以及获奖图书馆的特色服务进行研究，旨在为我国图书馆开展特色化服务提供借鉴。[方法/过程] 通过网络调查法和案例分析法，对2000-2018年获得"年度图书馆"奖的19所图书馆开展的特色服务进行比较分析。[结果/结论] 获奖图书馆的服务特色：重视公民教育，强化教育职能；强调均等服务理念，体现人文关怀；提供多元社区信息服务，参与社区建设；树立品牌意识，打造品牌项目。对我国图书馆界的启示：科学合理地进行角色定位；积极参与社区贫困计划；强化特色服务品牌意识；拓宽合作领域。  相似文献   

我国政府大数据政策价值取向的分析研究     

陈慧  王晓晓  段尧清 《图书情报工作》2020,64(11):19-27

[目的/意义] 大数据政策是大数据应用和发展的推动力量，其价值取向分析可以为我国政府大数据政策的制定、执行和评估提供借鉴，为大数据政策未来的发展方向提供依据。[方法/过程] 收集国务院及其各部门门户网站发布的政务大数据政策文本共计58份，运用主题分析方法对政策文本中表达政务大数据价值取向的主题进行编码分析，编码过程以NVivo12软件为辅助工具。[结果/结论] 通过主题分析，构建大数据政策价值取向总体框架，框架总结政治、经济、社会、生态与科技5个维度的价值取向，并探讨各维度及其具体价值取向间的交互关系。  相似文献   

Assisting,Instructing, Assessing: 21st Century Student Centered Librarianship     

Lauren Newton  Daniel E. Feinberg 《The Reference Librarian》2020,61(1):25-41

ABSTRACT

For librarians at the University of North Florida, there was a need to move beyond information literacy instruction to one-on-one and small group research consultations to aid in student success. By staffing the research desk with staff and students, librarians were able to open their calendars to allow more time for in-person, phone, and online consultations to aid in meeting the research goals of students at the institution. After assessing the research consultation program for two years, there has been a positive correlation between research consultation usage, satisfaction in completing assignments, and student success measures throughout the university.  相似文献   

学术乃天下之公器——哈佛燕京图书馆馆长郑炯文访谈     

谢欢 《中国图书馆学报》2020,46(4):126-131

2019年7月22日,南京大学谢欢博士对哈佛燕京图书馆馆长郑炯文先生就北美东亚图书馆发展相关问题进行访谈。东亚图书馆肇始于欧洲,起源于传教士对文史哲印刷文献的专题收集,与汉学发展紧密相关；美国东亚图书馆在第二次世界大战后取得了快速的发展,如今美国已成为西方世界东亚研究的中心。华人图书馆员是北美东亚图书馆从业人员中非常重要的群体,三代华人图书馆员各具专长,为北美东亚图书馆发展做出了贡献。郑炯文先生自担任哈佛燕京图书馆馆长以来,主要致力于以下三方面工作:①整理裘开明、吴文津担任馆长期间收集的文献；②开放馆藏,将哈佛燕京图书馆的资源面向全世界学者开放；③推动中美图书馆员交流合作项目。  相似文献   

美国妇女选举权档案开发利用的特色分析及启示     

黄霄羽  位焕锦  黄敏 《北京档案》2020,(5):40-43

美国国家档案馆为纪念宪法《第十九条修正案》100周年,发起一项为期18个月的全国性倡议,带动各州庆祝妇女选举权。本文分析指出美国妇女选举权档案开发利用的内容特色——主题集中、关注个体和突出人物;方式特色——多媒介、数字化和关联式开发并行,最后总结出对我国的启示——档案开发利用宜主题集中、方式多样、把握时机的持续性。  相似文献   

美国大学图书馆馆藏资源组织进展     

蔡颖  蔡迎春 《图书馆论坛》2020,(6):166-173

文章对美国大学图书馆在信息资源组织与利用方面的创新与实践进行梳理总结,以期为国内图书馆的资源建设提供借鉴。选取美国顶尖的11所大学图书馆作为考察对象,重点介绍它们在馆藏特色文献数据化、资源多样化收集与元数据整合、数字资源全生命周期管理方面的特色和进展。图书馆在资源服务方面所扮演的角色在不断演变,美国大学图书馆正借助先进的信息生产、存储和传递技术,向着实现信息资源共建、共知和共享的目标迈进。国内图书馆需要关注新兴技术,加强特藏资源开发,促进纸质与数字资源融合,优化数字资源业务流程。  相似文献