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   

Reliably Assessing Growth with Longitudinal Diagnostic Classification Models     

Matthew J. Madison 《Educational Measurement》2019,38(2):68-78

Recent advances have enabled diagnostic classification models (DCMs) to accommodate longitudinal data. These longitudinal DCMs were developed to study how examinees change, or transition, between different attribute mastery statuses over time. This study examines using longitudinal DCMs as an approach to assessing growth and serves three purposes: (1) to define and evaluate two reliability measures to be used in the application of longitudinal DCMs; (2) through simulation, demonstrate that longitudinal DCM growth estimates have increased reliability compared to longitudinal item response theory models; and (3) through an empirical analysis, illustrate the practical and interpretive benefits of longitudinal DCMs. A discussion describes how longitudinal DCMs can be used as practical and reliable psychometric models when categorical and criterion‐referenced interpretations of growth are desired.  相似文献   

澳大利亚数字人文项目透视——兼论对我国数字人文项目的启示     

徐彤阳  杨明睿 《图书情报工作》2020,64(22):145-152

[目的/意义] 分析澳大利亚数字人文项目特征，总结澳大利亚数字人文项目概况，获取对我国数字人文的建设性意见。[方法/过程] 运用网络调查法和访谈法对澳大利亚数字人文项目开展情况进行分析，总结项目概况并梳理其发展阶段和各阶段代表性项目，按照研究方法和方向的不同进行分类，同时根据项目成果和影响力建立分析评价体系来获取有价值的特征，并据此讨论为丰富我国数字人文建设的理论和实践提供借鉴的诸多意见。[结果/结论] 澳大利亚数字人文项目整体阶段特征明显、意义显著，本文总结出其项目的社会服务、图书馆深度参与、创新合作共享等特征，针对我国数字人文提出加大投入、释放图书馆潜力和形成特色的主要意见。  相似文献   

反兴奋剂基础知识考试情况分析--以反兴奋剂教育准入试题为例     

王清  徐起麟  刘阳  高晓 《当代体育科技》2020,(3):252-254

近年来,兴奋剂问题逐渐成为我国体育行业的关键问题,兴奋剂问题的影响逐步扩大。为保证中国体育健儿干干净净参加比赛,需要建立长效教育机制,将反兴奋剂教育融入到训练、比赛及日常生活中去。本文通过对河北省2018年反兴奋剂基础知识轮训考试成绩进行汇总分析,发现反兴奋剂教育工作中的难点以及突出问题,并对今后教学工作提出探索性意见。  相似文献   

我国冬季项目跨界跨项选材研究与启示     

郝月蓉 《河北体育学院学报》2020,34(3):42-46

运用文献资料法和逻辑分析法，对冬季项目跨界跨项选材问题进行辩证分析。认为，2022年北京冬奥会、政府专项政策导向及国内外成功的跨界经验，为我国开展冬季项目跨界跨项选材工作提供了重要的机遇和支持，但跨界跨项选材任务艰巨、冬季项目人才缺口大、运动员培养时间不足、跨界跨项风险大等问题也不容忽视。为进一步做好跨界跨项选材工作，为我国冬季项目持续发展提供充足的人力支持，应积极落实国家政策，做到科学跨选；依托同项群项目选材，跨用传统优势项目人才；抓住冬奥发展机遇，尽快建立完善的人才培养体系。  相似文献   

分级视角下残疾人冬季两项运动的关键竞技特征分析     

王润极  徐亮  阎守扶  吴昊 《首都体育学院学报》2020,(2):178-185

残疾人冬季两项项目竞技特征的理论研究薄弱和对残疾分级体系的认知模糊是影响我国残疾人冬季两项项目发展的关键问题。基于残疾分级视角,对有关残疾人冬季两项项目竞技特征的文献进行梳理与分析,认为:1)可通过加强我国冬季两项残疾人运动员运动技术的时效性和经济性练习,提升冬季两项残疾人运动员滑雪技术的动态平衡能力和滑行过程中的有氧代谢供能水平。2)在进行射击项目训练时,通过增加脱敏训练和电子激光靶系统训练等专项训练加强我国冬季两项残疾人运动员的射击技术稳定性和动作控制准确性。3)残疾人冬季两项竞技运动要求运动员具备良好的中枢神经系统调节能力,以最短时间激活副交感神经降低心率来提高动静转换效率,通过体能分配与技术运用等专项训练来全面提高冬季两项残疾人运动员的快速应变能力。4)冬季两项专项训练还需通过兼顾与整合2个小项相结合的训练方法来提升训练的整体效果。5)同一残疾分级的坐式运动员的不同坐姿对身体关键发力部位的肌肉募集有一定影响,在规则允许范围内,通过外部辅助和内部增强技术优化运动员竞技能力结构是冬季两项残疾人运动员达到国际水平的关键。  相似文献   

“流空间”视角下粤港澳大湾区空间布局分级特征研究   总被引：1，自引：0，他引：1  

杨思蓓  胡志华 《上海海事大学学报》2020,41(1):93-99

为加快粤港澳大湾区区域一体化进程,基于“流空间”视角,以粤港澳大湾区11个核心城市为研究对象,从客运流、经济流、信息流三个维度建立数理统计模型,分析粤港澳大湾区的交通、物流和经济信息化水平,并综合运用Jenks自然断裂点法和Moran散点图对粤港澳大湾区进行空间布局分级特征研究。研究结果表明:粤港澳大湾区有两个增长极——广州和深圳;粤港澳大湾区以珠江为界,珠江东西两岸分级明显;港珠澳大桥的连通有助于加强流要素空间互动,发挥更强的区域辐射和带动作用,扩大H-H连绵区的分布范围。  相似文献   

Ancient Mural Classification Method Based on Improved AlexNet Network     

Jianfang Cao  Hongyan Cui  Qi Zhang  Zibang Zhang 《文物保护研究》2020,65(7):411-423

ABSTRACT

As an important part of art and culture, ancient murals depict a variety of different artistic images, and these individual images have important research value. For research purposes, it is often important to first determine the type of objects represented in a painting. However, the mural painting environment makes datasets difficult to collect, and long-term exposure leads to underlying features that are not distinct, which makes this task challenging. This study proposes a convolutional neural network model based on the classic AlexNet network model and combines it with feature fusion to automatically classify ancient mural images. Due to the lack of large-scale mural datasets, the model first expands the dataset by applying image enhancement algorithms such as scaling, brightness conversion, noise addition, and flipping; then, it extracts the underlying features (such as fresco edges) shared by the first stage of a dual channel structure. Subsequently, a second-stage deep abstraction is conducted on the features extracted by the first stage using a two-channel network, each of which has a different structure. The obtained characteristics from both channels are merged, and a loss function is constructed to obtain the classification result. This approach improves the model's robustness and feature expression ability. The model achieves an accuracy of 84.24%, a recall rate of 84.15%, and an F1-measure of 84.13% when applied to a constructed mural image dataset. Compared with the AlexNet model and other improved convolutional neural network models, the proposed model improves each evaluation index by approximately 5%, verifying the rationality and effectiveness of the model for automatic mural image classification. The mural classification model proposed in this paper comprehensively considers the influences of network width and depth and can extract rich details from mural images from multiple local channels. An effective classification method could help researchers manage and protect mural images in an orderly fashion and quickly and effectively search for target images in a digital mural library based on a specified image category, aiding mural condition monitoring and restoration efforts as well as archaeological and art historical research.  相似文献   

Validating the Sedentary Sphere method in children: Does wrist or accelerometer brand matter?     

Liezel Hurter  Alex V. Rowlands  Stuart J. Fairclough  Karl C. Gibbon  Zoe R. Knowles  Lorna A. Porcellato 《Journal of sports sciences》2019,37(16):1910-1918

This study aimed to validate the Sedentary Sphere posture classification method from wrist-worn accelerometers in children. Twenty-seven 9–10-year-old children wore ActiGraph GT9X (AG) and GENEActiv (GA) accelerometers on both wrists, and activPAL on the thigh while completing prescribed activities: five sedentary activities, standing with a phone, walking (criterion for all 7: observation) and 10-min free-living play (criterion: activPAL). In an independent sample, 21 children wore AG and GA accelerometers on the non-dominant wrist and activPAL for two days of free-living. Per cent accuracy, pairwise 95% equivalence tests (±10% equivalence zone) and intra-class correlation coefficients (ICC) analyses were completed. Accuracy was similar, for prescribed activities irrespective of brand (non-dominant wrist: 77–78%; dominant wrist: 79%). Posture estimates were equivalent between wrists within brand (±6%, ICC > 0.81, lower 95% CI ≥ 0.75), between brands worn on the same wrist (±5%, ICC ≥ 0.84, lower 95% CI ≥ 0.80) and between brands worn on opposing wrists (±6%, ICC ≥ 0.78, lower 95% CI ≥ 0.72). Agreement with activPAL during free-living was 77%, but sedentary time was underestimated by 7% (GA) and 10% (AG). The Sedentary Sphere can be used to classify posture from wrist-worn AG and GA accelerometers for group-level estimates in children, but future work is needed to improve the algorithm for better individual-level results.  相似文献   

Digital Module 05: Diagnostic Measurement—The G‐DINA Framework https://ncme.elevate.commpartners.com     

Wenchao Ma  Jimmy de la Torre 《Educational Measurement》2019,38(2):114-115

In this ITEMS module, we introduce the generalized deterministic inputs, noisy “and” gate (G‐DINA) model, which is a general framework for specifying, estimating, and evaluating a wide variety of cognitive diagnosis models. The module contains a nontechnical introduction to diagnostic measurement, an introductory overview of the G‐DINA model, as well as common special cases, and a review of model‐data fit evaluation practices within this framework. We use the flexible GDINA R package, which is available for free within the R environment and provides a user‐friendly graphical interface in addition to the code‐driven layer. The digital module also contains videos of worked examples, solutions to data activity questions, curated resources, a glossary, and quizzes with diagnostic feedback.  相似文献