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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.
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   
4.
《中华人民共和国食品安全法》的修订和国务院机构改革,进一步理顺了食品安全监管关系,明确了食品安全面临的监管问题,从多方面、深层次强化了我国的食品安全监管制度,但层出不穷的食品安全事件表明,食品安全制度和监管层面的改革在广度和深度上仍有欠缺,体制机制依旧存在障碍并且亟待破除。食品安全法律规制不仅应通过立法技术填补法律漏洞、协调监管冲突,还应运用食品安全规制技术进行风险评估模式的革新,推动构建食品安全规制主体的多元化协同治理体制。  相似文献   
5.
近年来,人们越来越多地推动自闭症儿童家长有意义地参与科学研究。这种参与式研究能够更好地满足自闭症谱系障碍儿童及其家庭的评估和干预需求。从自闭症儿童自身特征、家庭因素、研究课题本身等方面分析影响家长参与课题研究的因素,基于对影响因素的综合分析,分别从家长及研究者层面提出相关对策建议,以促进家长更积极、更深入地参与课题研究,从而为自闭症儿童的干预提供更多机会。  相似文献   
6.
唐宋词真迹今天已很少见,古籍文献特别是唐宋文献对唐宋词真迹的记载,可视为是唐宋词真迹保存的一种方式。文献记载的唐宋词真迹,在校勘、还原、鉴赏方面有一定的价值,如李煜?临江仙?词的创作场景,黄庭坚?念奴娇?词“爱听临风笛”之“笛”、?西江月?词“杯行到手莫留残”之“莫留残”,秦观?踏莎行?词“杜鹃声里斜阳暮”之“斜阳暮”等。词人词作真迹记载,不仅可以据之考察词作初创之面貌,还可以考察词人笔迹,了解其书法水平,并进一步考察词人人品、情趣。  相似文献   
7.
采用文献资料法、专家访谈、逻辑分析等方法,探讨竞技体育跨项选材现实动因和一般规律,并针对目前竞技体育领域跨项选材存在问题进行理性反思。我国竞技体育领域跨项选材呈现以下规律:借助身体素质优势,由运动基础大项向其他项目流转;抓住关键技术环节,在奥运各大项之间高频互跨;依据项目特征和制胜规律,在同一项群互跨;依托综合竞技能力迁移,从早期专项化项目向晚期专项化项目流转。针对跨项选材中存在的理论与实践方面的问题,提出针对性建议:未来竞技体育跨项选材应精准把握项目特征,科学构建指标体系;深化跨项规律认识,准确捕捉跨项时机;科学划定选材范围,引导人才合理流动;加强跨项统筹管理,建立健全保障机制。  相似文献   
8.
刘艳东 《天津教育》2021,(6):137-138
初中道德与法治课程要求结合理论知识和实践教学,不仅能对学生的知识结构体系进行构建,还能对学生正确的世界观、人生观和价值观等进行培养。对此,在初中的道德与法治课中,教师应该利用生活化教学对学生开展实际的教学和探索,使学生能够在实际生活中通过对所学知识的运用,高效解决生活中的问题。  相似文献   
9.
在当今教育背景下,学生的素质教育已经成为教育重点方向。而小学是学生初步教育阶段,也是培养学生良好学习习惯的关键时期。因此,在开展小学语文高年级生活化作业教学过程中,教师应革新教学观念,将生活化内容合理引入,丰富写作教学课堂,拓展学生写作思路,培养学生全面发展能力,提升学生学习效果。  相似文献   
10.
为迎合时代发展,促进教育质量提升,解决教学弊病,就需要重视教育改革。新课改对其提出很多新颖要求,尤其是学生的学以致用能力和可持续发展素养培养。阅读作为语文课堂必不可少的内容,更要将其作为重要话题。合作学习有助于提升学生的综合学习能力、知识应用能力。本文以小学语文阅读为讨论对象,畅谈语文阅读对合作模式的应用,夯实基础,提高质量。  相似文献   
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