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   

A rule induction framework for the determination of representative learning design in skilled performance     

Sam Robertson  Bart Spencer  Nicole Back  Damian Farrow 《Journal of sports sciences》2019,37(11):1280-1285

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   

特质情绪智力对大学生射箭后即刻心率变异性的影响     

闫笑  王霆 《体育科技文献通报》2020,(4):34-35,61

本研究旨在探讨特质情绪智力与大学生射箭后即刻心率变异性(HRV)恢复反应的关联性。样本包括34名年龄在18至22岁之间的射箭初学者。第一,这些受试者要完成特质情绪智力量表。第二,他们在四分钟内从18米处向直径为80cm的靶子射出10支箭。第三,在4分钟的恢复期内测量受试者的心率变异性恢复的反应。在本研究中,心率变异性指标采用低频(LF)、高频(HF)和LF/HF比值。结果表明,情绪智力和心率变异性恢复反应之间有显著关系。包含情绪智力子维度的回归模型能够解释HRV频域参数的显著差异。此外,高情绪智力大学生的低频和高频功率高于低情绪智力的大学生,但LF/HF数值低于低情绪智力射箭运动员。综上所述,本研究观察到的结果表明,在严格的射箭训练之后,情绪智力可能会产生更多适应性的HRV恢复反应。  相似文献   

Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities     

《International Journal of Information Management》2021

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   

云平台驱动的应急决策情报工程架构研究     

储节旺  汪敏  郭春侠 《图书情报工作》2019,63(16):5-13

[目的/意义]将情报工程思维融入云平台驱动的应急决策，旨在使应急决策更具效率性、科学性。[方法/过程]分析应急决策需求特性，在工程价值模型和情报系统基础上引入情报工程思维，即"事实数据"+"方法工具"+"专家智慧"3个要素，进而构建云平台驱动的应急决策情报工程架构。[结果/结论]云平台驱动的应急决策情报工程架构根据函数效应，从需求层（聚合需求，甄选信息）、工作层（挖掘情报，提升价值）、运行层（整合知识，集成智慧）以及服务层（快速响应，精准服务）4方面构建，使应急决策情报工程化及平行化，为应急决策在实际应用中提供一定思路。  相似文献   

The effect of athletic expertise and trait emotional intelligence on decision-making     

Robert Vaughan  Sylvain Laborde  Christopher McConville 《European Journal of Sport Science》2019,19(2):225-233

The quality of decision and assessment of risk are key determinants of successful sport performance. Athletes differ fundamentally in their decision-making ability according to their athletic expertise level. Moreover, given the influence of emotions on decision-making, it is likely that a trait reflecting emotional functioning, trait emotional intelligence, may also influence decision-making. Therefore, the aim of this research was to investigate the respective contribution of athletic expertise and trait emotional intelligence to non-athletic decision-making. In total, 269 participants aged between 18 and 26 years with a range of athletic experience i.e. none (n?=?71), novice (n?=?54), amateur (n?=?55), elite (n?=?45) and super-elite (n?=?44), completed the Emotional Intelligence Scale and the Cambridge Gambling Task. Regression modelling indicated a significant positive relationship of athletic expertise and trait emotional intelligence with the quality of decision-making, and a negative relationship with deliberation time and risk-taking. Cognitive skills transfer may explain the higher decision-making scores associated with higher athletic expertise, while individuals with higher trait emotional intelligence may anticipate better the emotional consequences linked with a gambling task, which may help individuals make better decisions and take less risks.  相似文献   

Applying artificial intelligence technique to predict knowledge hiding behavior     

《International Journal of Information Management》2019

Drawing on psychological ownership and social exchange theories, this study suggests theoretical arguments and empirical evidence for understanding employee reactions to distributive, procedural, and interactional (in)justice — three crucial bases of employees’ feelings of social self-worth. Utilizing field data and artificial intelligence technique, this paper reveals that distributive, procedural, and interactional (in)justice contribute to higher levels of knowledge hiding behavior among employees and that this impact is non-linear (asymmetric). By reuniting the discourses of organizational justice and knowledge management, this study indicates that feelings of psychological ownership of knowledge and the degree of social interaction are mechanisms that work with organizational (in)justice to influence knowledge hiding behavior. The current research may inform contemporary theories of business research and provide normative guidance for managers.  相似文献   

安全情报概念的由来、演进趋势及涵义——来自安全科学学理角度的思辨     

王秉  吴超 《图书情报工作》2019,63(3):45-53

[目的/意义]安全问题是当前国家、政府、企业、社会、大众及学界广泛关注的一个重要现实问题，而安全情报是保障安全的必用之宝，故安全情报研究具有十分重要的理论与现实意义。[方法/过程]运用文献分析法和思辨法，从安全科学学理角度出发，论证基于安全科学视角解读与界定安全情报概念的必要性、重要性与紧迫性，探讨安全情报概念的由来与演进趋势，并分析安全情报的涵义。[结果/结论]从安全科学学理角度看，从安全信息到安全情报的安全管理新认识是提出安全情报概念的根本动力，安全情报概念的总体演进趋势是从分散到统一。同时，从系统安全学角度看，安全情报是指所有影响系统安全行为的安全信息。  相似文献   

Setting the future of digital and social media marketing research: Perspectives and research propositions     

《International Journal of Information Management》2021

The use of the internet and social media have changed consumer behavior and the ways in which companies conduct their business. Social and digital marketing offers significant opportunities to organizations through lower costs, improved brand awareness and increased sales. However, significant challenges exist from negative electronic word-of-mouth as well as intrusive and irritating online brand presence. This article brings together the collective insight from several leading experts on issues relating to digital and social media marketing. The experts’ perspectives offer a detailed narrative on key aspects of this important topic as well as perspectives on more specific issues including artificial intelligence, augmented reality marketing, digital content management, mobile marketing and advertising, B2B marketing, electronic word of mouth and ethical issues therein. This research offers a significant and timely contribution to both researchers and practitioners in the form of challenges and opportunities where we highlight the limitations within the current research, outline the research gaps and develop the questions and propositions that can help advance knowledge within the domain of digital and social marketing.  相似文献