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相关反馈技术在知识检索中的应用 总被引：4，自引：1，他引：4
本文从相关反馈技术的基本原理出发，综合应用信息管理与机器学习，探讨了相关反馈技术在知识检索中的应用模式、相关反馈检索算法和相关反馈学习算法，最后提出了对该领域研究的建议。 相似文献
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[目的/意义]通过构建移动学习用户隐私信息披露行为影响因素的理论模型,探究移动学习用户隐私行为的影响关系,以提高用户隐私信息披露意愿和对隐私信息的控制能力。[方法/过程]本文通过对隐私信息披露文献的查阅,运用问卷调查和实证研究相结合的方法,从行为态度、主观规范和行为控制3个方面分析了移动学习用户隐私信息披露行为,利用结构方程对提出的假设进行分析验证。[结果/结论]数据研究结果表明,隐私信息披露意愿对隐私信息披露行为有正向影响,感知移动学习收益性、移动学习用户社会影响、隐私控制自我效能和感知移动学习便利性对隐私信息披露意愿呈正向影响,而感知移动学习风险性则对隐私信息披露意愿呈负向影响。本研究能够帮助移动学习平台开发商更好地收集信息,为用户定制个性化的服务。[局限]研究的调查对象覆盖面较窄,对年龄、性别等因素之间的关系缺乏深入地分析。 相似文献
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将手机的移动学习功能有效引入高校思政课堂辅助教学,把传统教学方式的优势和网络教学的优势结合起来,构建课前、课中及课后的移动学习模式,积极探索和推进思政课堂教学方式方法的改革,激发学生对思政课的兴趣、乐趣和情趣,真正发挥思想政治理论课的育人使命。 相似文献
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Financial decisions are often based on classification models which are used to assign a set of observations into predefined groups. Different data classification models were developed to foresee the financial crisis of an organization using their historical data. One important step towards the development of accurate financial crisis prediction (FCP) model involves the selection of appropriate variables (features) which are relevant for the problems at hand. This is termed as feature selection problem which helps to improve the classification performance. This paper proposes an Ant Colony Optimization (ACO) based financial crisis prediction (FCP) model which incorporates two phases: ACO based feature selection (ACO-FS) algorithm and ACO based data classification (ACO-DC) algorithm. The proposed ACO-FCP model is validated using a set of five benchmark dataset includes both qualitative and quantitative. For feature selection design, the developed ACO-FS method is compared with three existing feature selection algorithms namely genetic algorithm (GA), Particle Swarm Optimization (PSO) algorithm and Grey Wolf Optimization (GWO) algorithm. In addition, a comparison of classification results is also made between ACO-DC and state of art methods. Experimental analysis shows that the ACO-FCP ensemble model is superior and more robust than its counterparts. In consequence, this study strongly recommends that the proposed ACO-FCP model is highly competitive than traditional and other artificial intelligence techniques. 相似文献
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《Information processing & management》2022,59(6):103064
Occupational stress has a significant adverse effect on workers’ well-being, productivity, and performance and is becoming a major concern for both individual companies and the overall economy. To reduce negative consequences, early detection of stress is a key factor. In response several stress prediction methods have been proposed, whose primary aim is to analyse physiological and behavioural data. However, evidence suggests that solutions based on physiological and behavioural data alone might be challenging when implemented in real-world settings. These solutions are sensitive to data problems arising from losses in signal quality or alterations in body responses, which are common in everyday activities. The contagious nature of stress and its sensitivity to the surroundings can be used to improve these methods. In this study, we sought to investigate automatic stress prediction using both surrounding stress data, which we define as close colleagues’ stress levels and the stress level history of the individuals. We introduce a real-life, unconstrained study conducted with 30 workers monitored over 8 weeks. Furthermore, we propose a method to investigate the effect of stress levels of close colleagues on the prediction of an individual’s stress levels. Our method is also validated on an external, independent dataset. Our results show that surrounding stress can be used to improve stress prediction in the workplace, where we achieve 80% of F-score in predicting individuals’ stress levels from the surrounding stress data in a multiclass stress classification. 相似文献
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电子白板和移动设备的使用时常发生交汇,引发了人们对新型教学应用技巧的思考。以电子白板为教学服务核心,使用移动设备作为学生端交互工具的新型课堂架构（MD-EW架构）应运而生。MD-EW架构的课堂实现了从传统课堂到微型移动课堂的扩展,体现了泛在学习和移动学习的理念,是电子白板与智能移动设备联合应用的一次有益尝试。 相似文献
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On the web, a huge variety of text collections contain knowledge in different expertise domains, such as technology or medicine. The texts are written for different uses and thus for people having different levels of expertise on the domain. Texts intended for professionals may not be understandable at all by a lay person, and texts for lay people may not contain all the detailed information needed by a professional. Many information retrieval applications, such as search engines, would offer better user experience if they were able to select the text sources that best fit the expertise level of the user. In this article, we propose a novel approach for assessing the difficulty level of a document: our method assesses difficulty for each user separately. The method enables, for instance, offering information in a personalised manner based on the user’s knowledge of different domains. The method is based on the comparison of terms appearing in a document and terms known by the user. We present two ways to collect information about the terminology the user knows: by directly asking the users the difficulty of terms or, as a novel automatic approach, indirectly by analysing texts written by the users. We examine the applicability of the methodology with text documents in the medical domain. The results show that the method is able to distinguish between documents written for lay people and documents written for experts. 相似文献
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Metrics derived from user visits or sessions provide a means of evaluating Websites and an important insight into online information seeking behaviour, the most important of them being the duration of sessions and the number of pages viewed in a session, a possible busyness indicator. However, the identification of session (termed often ‘sessionization’) is fraught with difficulty in that there is no way of determining from a transactional log file that a user has ended their session. No one logs out. Instead a session delimiter has to be applied and this is typically done on the basis of a standard period of inactivity. To date researchers have discussed the issue of a time out delimiter in terms of a single value and if a page view time exceeds the cut-off value the session is deemed to have ended. This approach assumes that page view time is a single distribution and that the cut-off value is one point on that distribution. The authors however argue that page time distribution is composed of a number of quite separate view time distributions because of the marked differences in view times between pages (abstract, contents page, full text). This implies that a number of timeout delimiters should be applied. Employing data from a study of the OhioLINK digital journal library, the authors demonstrate how the setting of a time out delimiter impacts on the estimate of page view time and the number of estimated session. Furthermore, they also show how a number of timeout delimiters might apply and they argue that this gives a better and more robust estimate of the number of sessions, session time and page view time compared to an application of a single timeout delimiter. 相似文献
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本研究采用大学生学习动机现状调查问卷和访谈相结合的方式,以509名普通职教类本科院校大学生为被试进行测试,分析大学生学习动机的状况,为有效培养大学生提供参考依据. 相似文献
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职业教育是我国教育事业的重要组成部分,是我国教育教学改革的一支不可缺少的力量。本文探讨将移动学习包裹进有鲜明需求和特点的职业教育中,以提高课程教学质量为目的,建构基于移动技术的交互教学模式,并以计算机应用基础课程为例进行实证研究。 相似文献
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大学生移动学习现状分析 总被引：2，自引：0，他引：2
移动学习（M-learning）是近年兴起的一种全新的学习模式。本文通过对延边大学不同专业和年级的320名大学生进行问卷调查,在借鉴相关专家、学者研究的基础上,探讨了移动学习中存在的问题,并提出改善方法及对策。 相似文献
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当前科技前沿识别研究方法难以得到更细粒度的分析结果,同时传统计量方法已不能够满足对当前来自网络的开源信息的情报挖掘需求,而机器学习方法可以实现数据细粒度的知识挖掘,因此成为解决科技前沿识别问题的重要手段。对2013—2021年中国知网和Web of Science(WoS)数据库收录的机器学习相关文献,在运用文献计量统计方法进行时间分布、研究主题及热点分析基础上,构建包含数据感知与处理层、情报计算和感知层、情报产品刻画层的开源情报环境下的科技前沿识别体系延伸架构,解读机器学习方法在各层次上的应用问题及关联关系,并提出不同层次需求发展的意见和建议;进而以7 944篇从WoS核心期刊库采集到的“深度学习”主题相关文献作为实验对象,主要针对数据处理中的知识单元构建进行论证。实证结果显示：从应用场景来看,多媒体信息处理的主题热度变化不大,智能机器人的主题热度逐年增高;从机器学习任务来看,目标检测和追踪的主题热度逐年降低,特征工程和数据分类则呈增长趋势。案例分析证明了所提出理论框架的科学性。 相似文献
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《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
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Acquiring information properly through machine learning requires familiarity with the available algorithms and understanding how they work and how to address the given problem in the best possible way. However, even for machine-learning experts in specific industrial fields, in order to predict and acquire information properly in different industrial fields, it is necessary to attempt several instances of trial and error to succeed with the application of machine learning. For non-experts, it is much more difficult to make accurate predictions through machine learning.In this paper, we propose an autonomic machine learning platform which provides the decision factors to be made during the developing of machine learning applications. In the proposed autonomic machine learning platform, machine learning processes are automated based on the specification of autonomic levels. This autonomic machine learning platform can be used to derive a high-quality learning result by minimizing experts’ interventions and reducing the number of design selections that require expert knowledge and intuition. We also demonstrate that the proposed autonomic machine learning platform is suitable for smart cities which typically require considerable amounts of security sensitive information. 相似文献
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在新冠肺炎疫情防控期间,江西科技学院按照教育部的要求积极开展各种在线教学活动。广大教师充分利用超星(学习通)等各类教学平台及腾讯会议、QQ、微信、钉钉、腾讯课堂等多种教学工具与学生进行交流,开展辅导,学校线上教学运行整体情况平稳有序。学校对授课教师和学生的问卷调查结果显示,不同性别、所在地、年级和学科的学生,均会对在线学习的平台、工具和交互方式有着不一样的需求度。而教师、课程和学科的差异性也会对在线教学的效果产生不同的影响。研究结果表明,要想取得更好的教学效果,在线教学的具体方法和措施需要根据学生、教师、课程和学科的不同而进行精细化的调整。 相似文献
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We study several machine learning algorithms for cross-language patent retrieval and classification. In comparison with most of other studies involving machine learning for cross-language information retrieval, which basically used learning techniques for monolingual sub-tasks, our learning algorithms exploit the bilingual training documents and learn a semantic representation from them. We study Japanese–English cross-language patent retrieval using Kernel Canonical Correlation Analysis (KCCA), a method of correlating linear relationships between two variables in kernel defined feature spaces. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. We also investigate learning algorithms for cross-language document classification. The learning algorithm are based on KCCA and Support Vector Machines (SVM). In particular, we study two ways of combining the KCCA and SVM and found that one particular combination called SVM_2k achieved better results than other learning algorithms for either bilingual or monolingual test documents. 相似文献
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Machine learning algorithms enable advanced decision making in contemporary intelligent systems. Research indicates that there is a tradeoff between their model performance and explainability. Machine learning models with higher performance are often based on more complex algorithms and therefore lack explainability and vice versa. However, there is little to no empirical evidence of this tradeoff from an end user perspective. We aim to provide empirical evidence by conducting two user experiments. Using two distinct datasets, we first measure the tradeoff for five common classes of machine learning algorithms. Second, we address the problem of end user perceptions of explainable artificial intelligence augmentations aimed at increasing the understanding of the decision logic of high-performing complex models. Our results diverge from the widespread assumption of a tradeoff curve and indicate that the tradeoff between model performance and explainability is much less gradual in the end user’s perception. This is a stark contrast to assumed inherent model interpretability. Further, we found the tradeoff to be situational for example due to data complexity. Results of our second experiment show that while explainable artificial intelligence augmentations can be used to increase explainability, the type of explanation plays an essential role in end user perception. 相似文献