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1.
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   
2.
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   
3.
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   
4.
《左传》是我国首部以编年为次序、以鲁国十二公为传主,系统记述春秋战国时期历史,对中国文化产生了巨大影响的文史作品。文章在概述《左传》文化价值的基础上,梳理了两千多年来关于《左传》文本与作者的主要学术争论,甄别《左传》流变过程中的22个关键文本。  相似文献   
5.
全民都获取全面的健康是能否成为强国的重要标准之一,是"以人为本"的基本体现。在健康中国行动的背景下,国家越来越重视以体育健身来促进人民的健康发展,也相继出台了一系列关于全民健康相关的文件和纲要。本文通过对国内国外研究现状进行了分析,通过对体育健康的梳理,了解体育健康目前发展现状,分析不足之处,为体育健康在以后的界定打下基础。  相似文献   
6.
与其他的体育运动项目相比,田径运动的历史要悠久许多。在田径运动项目中,成绩高低与田径训练中使用的训练方式有着很大的关联,同时田径训练方式的使用得当与否对于运动员的心理素质、训练效率、运动负荷等方面的提升有着十分显著的影响。本文就从当前的田径训练方式的特征分析出发,并以此为基础展望了未来田径训练方式的发展新趋势,以期对今后的田径训练方式发展提供相应的帮助。  相似文献   
7.
CBA2016-2017赛季常规赛已经结束,通过数据分析法、逻辑分析法等对20支队伍在常规赛38轮的比赛数据(如攻防效率、主场优势、与季后赛队伍和非季后赛队伍比赛的差异、每节净胜分、逆转能力)进行统计分析。针对中国男篮在攻防效率,球队队员的心理承受能力及外援的引进等方面提出发展对策,旨在为CBA及中国男篮的发展提供理论参考。  相似文献   
8.
社会快速发展、生活日趋便捷背景下,医学生面临着生理和心理上的问题,且医学生由于专业特殊性,认知能力中记忆力的重要性被凸显出来。本文运用查阅文献资料、专家访谈、问卷调查、逻辑分析等研究方法,探究从运动处方角度出发的实验--"高强度间歇运动对医学生不同时长记忆力的影响"具有多方面重要性。同时建议医学院校应当跟据研究结果和实际情况,制定科学、合理的运动训练计划,为医学生生理和心理健康提供保障。  相似文献   
9.
体育与健康学科核心素养不断成为体育教育领域关注的重点,养成良好的核心素养,对于培养学生成为一个高素质人员,树立完善人格有着深远的意义。高校体育课程在大学生人格完善过程具有重要意义,但从现实情况来看,目前的体育教学存在或多或少的问题,故本文主要通过文献资料法研究我国高校体育教学在体育与健康学科核心素养视域下的一些改进措施。  相似文献   
10.
随着我国复合型人才培养目标的不断深化,人才培养应根据新世纪人才结构及高等体育功能特征,不断满足新时期社会开展的需求。长期以来,体育课程忽视考核中学生身心素质及学生在体育学习中进步的幅度与努力的内容。高校体育中适当穿插一些与体育知识点有关的太极"阴阳观",这样可以起到调动学生学习积极性的作用。本文说明了太极"阴阳观"对高校体育教学的影响,阐述了太极"阴阳观"融入到体育教学中的有效渠道。  相似文献   
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