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   

Ghost writer in the machine: the politics of determining the machinic/expressive functions of software     

Daniel M. Sutko 《传播与批判/文化研究》2018,15(1):18-34

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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   

Preservice teachers perspectives and experiences teaching personal and social responsibility     

K. Andrew R. Richards  Jennifer M. Jacobs  Victoria Nicole Ivy  Michael A. Lawson 《Physical Education & Sport Pedagogy》2020,25(2):188-200

ABSTRACT

Background: Pedagogical models have become an established component of physical education over the past several decades. One such model, the Teaching Personal and Social Responsibility model, has gained momentum in practice and research, though little is known regarding its use in preservice teacher training. The model follows a flexible format focused on teaching life skills (e.g. leadership) that can be applied in all lived ecologies. Occupational socialization theory provides insight into the pretraining and teacher education experiences of preservice teachers that shape their understanding and practice of physical education and associated pedagogical models.

Aims: The purpose of this study was to understand the influence of a sequence of methods courses and early field experiences on U.S. preservice teachers’ understanding and implementation of the teaching personal and social responsibility model with youth from a community affected by poverty.

Method: This study took on a phenomenological and social constructivist approach. Ten preservice teachers (9 males, 1 female) took part in the study. The participants were an average age of 22.10 years old (SD?=?4.38) and seven identified as White and three as Black. Each participant was enrolled in methods and early field experience coursework that provided scaffolded training in primary education in a community affected by poverty. Preservice teachers team-taught groups of 10–15 children twice a week along with one day committed to on-campus reflection. Data collection included autobiographical essays, critical incident reports, reflective journals, non-participatory observations and field notes, and semi-structured interviews. Data were analyzed deductively through the lens of occupational socialization theory, and inductively as theory divergent trends were sought. Open and axial coding was completed with member checking throughout, resulting in a final set of themes and subthemes.

Findings: The preservice teachers initially struggled to connect with their students due to conflicting backgrounds, but the teaching personal and social responsibility model guided the relationship-building process. As the model was continuously utilized, more empathy and care were shown towards the children. Preservice teachers felt there was a lack of progression in positive behaviors but were able to empower youth and felt that the model was culturally relevant. Overtime, the students began to appreciate the affective domain despite the challenge of working in a community affected by poverty through frustration towards the larger system limiting any potential progress was present.

Conclusions: Subjective theories transitioned to include relationship building and life skills learning, likely because of the extended field experience and faculty support. The preservice teachers desire to connect with and teach the students well displays the connection between models-based practice and positive relationships. Preservice teachers’ knowledge of their students was limited as it was based on secondhand knowledge of youth, teacher educators, and school staff. Evidence indicates some cultural responsiveness development though there were also elements of a deficit model due to white privilege and class differences. Further work explicitly integrating a culturally relevant approach and social justice in teacher education programming should occur.  相似文献   

沉浸互动模型:滑雪消费心流体验机制的质性研究     

翁银  李凌 《成都体育学院学报》2020,(2):78-84

刺激滑雪消费增长与促进滑雪产业发展是当今的热点议题。本研究引入心流体验与心流体验机制理论,探索滑雪消费心理特征与滑雪可持续消费心理模型。从微观消费心理视角切入,运用扎根理论质性研究手段,采取深度访谈与焦点团体访谈相结合的访谈技术,经"三级诠释"逻辑分析的过程,构建了滑雪消费心流体验机制的理论模型。结果显示:本研究共提取了滑雪消费心流体验机制的102个概念、36个范畴、4个主范畴,基于扎根理论故事线逻辑梳理并构建了"沉浸互动机制"质性模型。基于此,为提升本研究的理论可靠性,采用模型识别比较分析,检验本研究质性模型的创新性与科学性,为促进滑雪产业可持续发展提供相关理论参考。  相似文献   

基于ICWGT和灰色关联分析法的起重机安全评价     

肖汉斌  熊楚宸  祝锋  刘敏 《上海海事大学学报》2020,41(2):117-122

针对目前起重机安全评价过于依赖主观权重且忽视起重机安全状态变化趋势的问题，建立基于改进博弈论组合赋权法（improved combination weighting method of game theory，ICWGT）和灰色关联分析法的起重机安全评价体系。在该评价体系中，收集样本数据以确定评价值，构建评价等级空间来计算起重机行为特征序列与标准行为特征序列的关联系数。运用ICWGT在层次分析法和信息熵 未确知测度理论确定的主、客观权重中寻找平衡。充分考虑专家经验和样本数据，确定优化权重以改善关联系数的分配问题，从而获取起重机灰色关联度评价值和安全等级，旨在对起重机安全状态变化趋势进行更加准确的定量描述和定性分析。实例分析验证了该评价体系的有效性。  相似文献   

基于正态云可拓理论的锚泊安全评价     

鲁峰  李伟  宁君  于仁海 《上海海事大学学报》2020,41(4):30-35

为提高锚泊安全评价精度，保障船舶锚泊安全，针对传统锚泊安全评价方法未考虑评价等级界限的不确定性和模糊性等问题，将正态云可拓理论应用于锚泊安全评价中。采用层次分析法和基于可拓学的简单关联函数确定权重的方法分别确定锚泊安全评价指标的主观和客观权重，通过经验因子将主观与客观权重进行结合。构建基于正态云可拓理论的锚泊安全评价模型，计算待评物元与标准可拓物元的关联度，通过信息熵理论确定锚泊安全等级。根据信息熵理论定义可信因子，使得评价结果的可信度易于定量计算。实例计算验证了本模型的有效性和优越性。  相似文献