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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 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   
4.
高等数学是一门注重理论研究的学科,对高专学生来说具有一定的学习困难。从多个角度分析产生学习困难的原因,提出相应的对策。  相似文献   
5.
随着融合教育的不断推进,越来越多的特殊学生能够进入普通学校学习,与正常学生有更多交流与互动学习的机会。但是特殊学生在交往过程中难免面临层层挑战,校园欺凌则是其中最为严重的问题之一。通过梳理分析国内外特殊儿童欺凌的相关研究,为改善我国随班就读特殊儿童的欺凌现状提出建议。  相似文献   
6.
针对古漆器漆膜数据类间不平衡、样本规模小,以及传统机器学习算法分类效果较差的问题,提出一种改进SMOTE的过采样方法改变漆器漆膜数据样本分布,使其达到平衡。该方法通过比较各类样本间的欧式距离,删除了人工样本中的噪声数据,然后运用集成学习中的随机森林算法进行分类,提高了少数类的分类准确率。UCI数据集上的实验结果表明,改进的过采样方法性能更优,评价指标F1-score与AUC值分别得到2%、5%以上的提升。结合改进的过采样方法与机器学习算法进行对比实验,结果证明,随机森林算法精度更高,在对古漆器年代的判别中,随机森林算法的F1-score与AUC值高达87.76%、89.34%。  相似文献   
7.
以英语专业大一学生为研究对象,对其语音焦虑和语音学习策略使用现状以及语音焦虑、语音学习策略和语音成绩三者之间的关系进行研究。结果表明:(1)受试的语音焦虑、语音学习策略使用都处于中等程度;(2)语音焦虑与语音成绩、语音学习策略呈显著负相关,语音学习策略与语音成绩呈显著正相关;(3)语音学习策略对语音成绩产生显著正面影响,语音焦虑对语音成绩有一定负面影响,语音学习策略可以通过降低语音焦虑对语音成绩产生间接影响。  相似文献   
8.
罗玉红 《天津教育》2021,(11):91-92,95
小学高年级语文单元知识,所包含的内容比较广泛。教学的时候,既要照顾到学困生在拼音、识字等方面的基础知识积累,又要兼顾优等生在语文基础知识方面的拓展延伸,满足不同知识层次的学生的学习需求,因材施教,从而全面提高学生的语文学习能力。小学生在学习方法上由于年龄的限制,积累的学习经验不多,因此在学习方法上对他们进行辅导,有助于提高学生的自我学习能力,所以注重语文基础的积累和培养,也是教学的一个重点。  相似文献   
9.
高中物理教学要注重实验教学和课堂教学相结合,提高学生的探究能力。科学合理的实验教学有利于学生在丰富的物理知识海洋中感知物理知识的魅力,也能够对学生的动手能力和科学的思维能力产生积极的影响,能够激励学生独立思考问题、积极主动地探究问题,在探究问题的过程中,逐渐形成独立思考的能力、自主探究的能力。  相似文献   
10.
赵莉丹 《天津教育》2021,(11):179-180
在阅读教学中教师应帮助学生掌握正确的阅读策略,提升学生阅读能力。预测策略是最常用的阅读策略。预测策略作为第一个出现在统编教材的阅读策略,具有划时代的意义。教师应培养学生的预测能力,使学生自觉、灵活地运用阅读策略进行阅读。  相似文献   
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