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   

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   

困境与应对:聚焦新型冠状病毒肺炎疫情对体育事业的影响     

钟秉枢  黄志剑  王凯  车冰清  宋昱  刘旭东  张晴  钞蕤 《体育学研究》2020,(2):9-33,40

2019-2020年之交,我国暴发新型冠状病毒肺炎疫情(下文简称“新冠肺炎疫情”)。新冠肺炎疫情给国家各项事业的正常运行带来了严峻挑战,为打赢这场新冠肺炎疫情攻坚战,全国科技工作者纷纷从各自不同的领域为疫情防控贡献着自己的力量。体育事业作为国民事业的重要组成部分,如何积极应对新冠肺炎疫情给各项体育事业带来的冲击,深刻思考新冠肺炎疫情背景下各项体育事业的发展路径,是体育事业健康发展的必然诉求,也是体育科技工作的应然使命。学者当以天下苍生为己任,为国家出谋,为社会担当,为人民谋福。体育学术期刊亦须做好学术担当,肩负起应有的使命与责任,传播体育科技工作者针对此次疫情所开展的社会服务和智慧贡献,探究体育事业逆疫发展之道。为此,《体育学研究》编辑部从开展征稿活动的50余篇来稿中,聚焦群众体育、竞技体育和体育产业等领域,对钟秉枢、黄志剑、王凯、车冰清、宋昱等体育专家及青年学者的观点和思考进行集成,形成了《困境与应对:聚焦新型冠状病毒肺炎疫情对体育事业的影响》一文。以期为疫情的科学防控和推动体育事业健康发展提供理论和实践参考。  相似文献   

基于网络教学平台的艺术类高职院校信息化教学实践的研究     

曹路舟  赵元鸣 《安徽职业技术学院学报》2020,(1):79-82,87

高速发展的互联网给我国高等职业教育的信息化教学模式改革带来了极大的发展契机。文章以网络教学平台为载体,在艺术类高职院校中应用信息化教学,打破了以往艺术类高职院校传统的教学模式,实现现代课堂教学的完全翻转,充分调动了艺术类高职学生学习的积极性和创造性,提高了艺术类高职教育教学质量,创新了艺术类高职教学模式,使艺术类高职课堂教学充满了活力和吸引力。  相似文献   

基于负样本多通道优化SSD网络的钢铁厂提取     

卢凯旋  李国清  陈正超  昝露洋  李柏鹏  高建威 《中国科学院大学学报》2020,37(3):352-359

准确提取钢铁厂对去产能监测和环境保护具有重要意义。传统的人工目视解译方法效率低、成本高，无法满足开展大区域钢铁厂监测的需求。以深度学习目标检测网络SSD为基础，构建面向遥感影像钢铁厂提取的深度学习目标检测网络，提出maxout模块，将负样本通路优化为多分支结构，突出难分负样本特征并提升网络对无用特征的抵制效果。利用国产GF-1数据对京津冀地区的钢铁厂进行快速自动提取实验。与人工解译的钢铁厂点位数据的对比表明，该目标检测方法的提取精度达到80%以上。  相似文献   

动态雾计算网络中基于在线学习的任务卸载算法     

谭友钰  陈蕾  周明拓  王昆仑  杨旸  张武雄 《中国科学院大学学报》2020,37(5):688-698

任务卸载是雾计算的主要技术之一，即计算能力不足的节点将任务卸载给具有富余资源的节点帮助计算。以优化任务平均卸载时延和提升卸载服务成功率为目标，利用多臂老虎机理论为动态雾计算网络提出一种基于在线学习的任务卸载算法，可实时做出最优卸载决策。将该算法扩展到非稳定网络状态，使之可以动态追踪网络中节点的资源与环境变化，实时调整卸载决策。详细分析所提出算法的性能、复杂度和存储占用情况。仿真结果表明，这两种算法可达到的长期平均任务卸载时延均十分接近理想算法下的最优时延，卸载服务成功率也得到显著提升。此外，所提算法在非稳定的网络状态下能够追踪到计算资源与环境的变化。  相似文献   

新型冠状病毒肺炎疫情期间在线健身服务用户持续使用意愿的影响因素研究     

刘东锋  傅钢强 《体育学研究》2020,(2):41-50

新冠肺炎疫情期间,在线健身服务作为“智慧抗疫”的重要组成部分受到了业界和学界的广泛关注。为了进一步探究在线健身服务持续使用意愿的影响因素,该文对技术接受模型(TAM)和任务—技术匹配模型(TTF)进行了整合,通过网络调查和数理统计等研究方法对用户持续使用在线健身服务的影响因素进行了实证分析。结果表明:(1)影响用户持续使用意愿的内外部因素主要包括感知有用性、感知易用性、感知趣味性、任务特征、技术特征和任务—技术匹配度6项;(2)感知有用性、感知易用性、感知趣味性以及任务—技术匹配度对用户持续使用意愿均有显著正向影响;(3)任务特征和技术特征显著正向影响任务—技术匹配度,感知易用性则显著正向影响感知有用性,而感知易用性则不显著正向影响感知趣味性。基于以上分析提出了增加用户持续使用意愿的相关建议。  相似文献