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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.
This study investigated the effect of including explicit nature of science (NOS) content in read-alouds of elementary science trade books on the teaching and learning of NOS. We focused on three aspects of NOS: the creative, the empirical, and the inferential NOS. The trade books were read aloud by teachers in three hierarchical levels: Level I served as a control and consisted of a trade book that remained unmodified, Level II consisted of a trade book that had been modified to include explicit references to NOS, and Level III consisted of a modified trade book accompanied by educative curriculum materials that were aimed at improving the teachers' views of NOS as well as supporting teaching about NOS. We used the Views of Nature of Science Questionnaire-form CE (VNOS-CE) preintervention and postintervention to determine changes in teachers' views of NOS and interviews preintervention and postintervention to determine changes in students' views. Audio recordings of read-alouds were used to determine changes in teaching practice, including the frequency and the quality (i.e., naïve or informed) of the NOS references in the discussions. Interviews were used to determine teachers' perceptions of the modified trade books and educative curriculum materials. We found that both teachers and students developed more informed views of the targeted NOS aspects after the intervention and that teachers addressed NOS more often, and in a more informed manner, when they had trade books that explicitly supported NOS instruction and educative curriculum materials that supported their learning about NOS. Furthermore, they perceived the intervention materials favorably. Teachers' views and practices were able to change in tandem because of the intervention materials that supported explicit NOS instruction. We highlight the need for more widespread development of similar educative curriculum materials.  相似文献   
3.
在浙江私家藏书史上,有关女性藏书的记载不多。但在男性藏书家一统天下的背后,却有不少优秀的女性。本文旨在对这些优秀的女性进行收集和梳理,并对她们在这段辉煌历史中所发挥的作用,进行较深入地分析与探讨。  相似文献   
4.
ABSTRACT

This paper aims to analyse the initiatives undertaken by some immigrant communities residing in Latin America to record their mobilisation around the First World War. After the armistice, European communities in Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, Uruguay and other countries gathered detailed information about their activities during the conflict, published as profusely illustrated books of remembrance, offered to their governments as proof of their loyalty. The article intends to establish the peculiar nature of these publications as records of the war effort, and analyse the agents and processes of their elaboration, and their role as commemorative but also as archival documents.  相似文献   
5.
Review helpfulness is attracting increasing attention of practitioners and academics. It helps in reducing risks and uncertainty faced by users in online shopping. This study examines uninvestigated variables by looking at not only the review characteristics but also important indicators of reviewers. Several significant review content and two reviewer variables are proposed and an effective review helpfulness prediction model is built using stochastic gradient boosting learning method. This study derived a mechanism to extract novel review content variables from review text. Six popular machine learning models and three real-life Amazon review data sets are used for analysis. Our results are robust to several product categories and along three Amazon review data sets. The results show that review content variables deliver the best performance as compared to the reviewer and state-of-the-art baseline as a standalone model. This study finds that reviewer helpfulness per day and syllables in review text strongly relates to review helpfulness. Moreover, the number of space, aux verb, drives words in review text and productivity score of a reviewer are also effective predictors of review helpfulness. The findings will help customers to write better reviews, help retailers to manage their websites intelligently and aid customers in their product purchasing decisions.  相似文献   
6.
Representative learning design provides a framework for the extent to which practice simulates key elements of a performance setting. Improving both the measurement and analysis of representative learning design would allow for the refinement of sports training environments that seek to replicate competition conditions and provide additional context to the evaluation of athlete performance. Using rule induction, this study aimed to develop working models for the determination of high frequency, representative events in Australian Rules football kicking. A sample of 9005 kicks from the 2015 Australian Football League season were categorised and analysed according to the following constraints: type of pressure, kick distance, possession source, time in possession, velocity and kick target. The Apriori algorithm was used to develop two models. The first consisted of 10 rules containing the most commonly occurring constraint sets occurring during the kick in AF, with support values ranging from 0.15 to 0.22. None of the rules contained more than three constraints and confidence values ranged from 0.63 to 0.84. The second model considered ineffective and effective kick outcomes and displayed 70% classification accuracy. This research provides a measurement approach to determine the degree of representativeness of sports practice and is directly applicable to various team sports.  相似文献   
7.
This article describes the conceptualisation and development of a pedagogical framework to support the design of e‐books for children to enhance literacy development. It emerged from research undertaken within the Q‐Tales international consortium project of the EU's Horizon 2020 Programme for Research and Innovation, where the aim was to facilitate key stakeholders to collaborate and participate in the online production and publication of high‐quality, educational e‐books for children. The pedagogical framework described here sought to answer the question “What concepts and principles undergird the effective design of pedagogically impactful e‐books for children?” It is grounded by the theoretical underpinnings of socio‐constructivism, constructionism and skill theory, and how they relate to children's literacy development. A framework describing different narrative forms and component features, key pedagogical activities appropriate for different stages of reading development and design recommendations regarding the integration of multimedia into e‐books are also central to the pedagogical framework. As well as informing the design of the Q‐Tales infrastructure for children's e‐book design and publication, we hope the guidelines and pedagogical activities enumerated here will be widely useful for those designing and developing digital, interactive narratives, particularly e‐books to enhance children's emerging literacy.  相似文献   
8.
Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. These events are represented by three main dimensions: time, location and entity-related information. The focus of this paper is location, which is an essential dimension for geo-spatial applications, either when helping rescue operations during a disaster or when used for contextual recommendations. While the first type of application needs high recall, the second is more precision-oriented. This paper studies the recall/precision trade-off, combining different methods to extract locations. In the context of short posts, applying tools that have been developed for natural language is not sufficient given the nature of tweets which are generally too short to be linguistically correct. Also bearing in mind the high number of posts that need to be handled, we hypothesize that predicting whether a post contains a location or not could make the location extractors more focused and thus more effective. We introduce a model to predict whether a tweet contains a location or not and show that location prediction is a useful pre-processing step for location extraction. We define a number of new tweet features and we conduct an intensive evaluation. Our findings are that (1) combining existing location extraction tools is effective for precision-oriented or recall-oriented results, (2) enriching tweet representation is effective for predicting whether a tweet contains a location or not, (3) words appearing in a geography gazetteer and the occurrence of a preposition just before a proper noun are the two most important features for predicting the occurrence of a location in tweets, and (4) the accuracy of location extraction improves when it is possible to predict that there is a location in a tweet.  相似文献   
9.
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   
10.
Energy efficiency of public sector is an important issue in the context of smart cities due to the fact that buildings are the largest energy consumers, especially public buildings such as educational, health, government and other public institutions that have a large usage frequency. However, recent developments of machine learning within Big Data environment have not been exploited enough in this domain. This paper aims to answer the question of how to incorporate Big Data platform and machine learning into an intelligent system for managing energy efficiency of public sector as a substantial part of the smart city concept. Deep neural networks, Rpart regression tree and Random forest with variable reduction procedures were used to create prediction models of specific energy consumption of Croatian public sector buildings. The most accurate model was produced by Random forest method, and a comparison of important predictors extracted by all three methods has been conducted. The models could be implemented in the suggested intelligent system named MERIDA which integrates Big Data collection and predictive models of energy consumption for each energy source in public buildings, and enables their synergy into a managing platform for improving energy efficiency of the public sector within Big Data environment. The paper also discusses technological requirements for developing such a platform that could be used by public administration to plan reconstruction measures of public buildings, to reduce energy consumption and cost, as well as to connect such smart public buildings as part of smart cities. Such digital transformation of energy management can increase energy efficiency of public administration, its higher quality of service and healthier environment.  相似文献   
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