Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability

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.     

Cyberbullying detection for low-resource languages and dialects: Review of the state of the art

The struggle of social media platforms to moderate content in a timely manner, encourages users to abuse such platforms to spread vulgar or abusive language, which, when performed repeatedly becomes cyberbullying — a social problem taking place in virtual environments, yet with real-world consequences, such as depression, withdrawal, or even suicide attempts of its victims. Systems for the automatic detection and mitigation of cyberbullying have been developed but, unfortunately, the vast majority of them are for the English language, with only a handful available for low-resource languages. To estimate the present state of research and recognize the needs for further development, in this paper we present a comprehensive systematic survey of studies done so far for automatic cyberbullying detection in low-resource languages. We analyzed all studies on this topic that were available.We investigated more than seventy published studies on automatic detection of cyberbullying or related language in low-resource languages and dialects that were published between around 2017 and January 2023. There are 23 low-resource languages and dialects covered by this paper, including Bangla, Hindi, Dravidian languages and others. In the survey, we identify some of the research gaps of previous studies, which include the lack of reliable definitions of cyberbullying and its relevant subcategories, biases in the acquisition, and annotation of data. Based on recognizing those research gaps, we provide some suggestions for improving the general research conduct in cyberbullying detection, with a primary focus on low-resource languages. Based on those proposed suggestions, we collect and release a cyberbullying dataset in the Chittagonian dialect of Bangla and propose a number of initial ML solutions trained on that dataset. In addition, pre-trained transformer-based the BanglaBERT model was also attempted. We conclude with additional discussions on ethical issues regarding such studies, highlight how our survey improves on similar surveys done in the past, and discuss the usefulness of recently popular AI-enhanced tools for streamlining such scientific surveys.     

Learning through informal local and global linkages: The case of Taiwan's machine tool industry

Most existing studies of successful late industrialization, which draw on findings from high-technology industries, emphasize the need to invest in formal channels of technology acquisition to allow latecomers to catch up. This line of reasoning neglects the fact that in some industries, including low- and medium-technology (LMT) sectors, much knowledge can be acquired by informal means. Through the study of Taiwan's machine tool (MT) industry, this article demonstrates the significance of informal learning activities in LMT industries and the possibility for latecomer clusters to climb the technological ladder through exploiting various local and global informal knowledge linkages.     

基于集成学习的科研计划项目绩效预测研究

提出基于集成学习的项目绩效预测方法,利用多分类集成监督学习算法,对网络爬虫得到的已结题项目数据中隐含的关于项目绩效的信息进行有效挖掘,形成项目绩效预测模型.基于国家自然科学基金项目数据,利用多种指标对模型的性能进行评估,将模型对项目的绩效预测结果与专家的评估结果进行比较,结果显示模型的有效性.     

Beyond effective use: Integrating wise reasoning in machine learning development

The introduction of machine learning (ML), as the engine of many artificial intelligence (AI)-enabled systems in organizations, comes with the claim that ML models provide automated decisions or help domain experts improve their decision-making. Such a claim gives rise to the need to keep domain experts in the loop. Hence, data scientists, as those who develop ML models and infuse them with human intelligence during ML development, interact with various ML stakeholders and reflect their views within ML models. This interaction comes with (often conflicting) demands from various ML stakeholders and potential tensions. Building on the theories of effective use and wise reasoning, this mixed method study proposes a model to better understand how data scientists can use wisdom for managing these tensions when they develop ML models. In Study 1, through interviewing 41 analytics and ML experts, we investigate the dimensions of wise reasoning in the context of ML development. In Study 2, we test the overall model using a sample of 249 data scientists. Our results confirm that to develop effective ML models, data scientists need to not only use ML systems effectively, but also practice wise reasoning in their interactions with domain experts. We discuss the implications of these findings for research and practice.     

基于机器视觉的工业机器人分拣技术应用研究与系统开发

随着科学技术的不断进步,与机器人有关的技术也得到飞速发展,专家和学者关注的焦点也转移到机器人视觉技术方面,从而让机器人也拥有和人眼相似的使用功能.文章研究了基于机器视觉的工业机器人分拣系统,该系统主要包括机器人本体及工件平台、机器视觉和运动控制三大模块,对它们的结构分别进行了设计;最后重点分析了分拣系统中应用的关键技术.     

Feature selection based on mutual information and redundancy-synergy coefficient

Mutual information is an important information measure for feature subset. In this paper,a hashing mechanism is proposed to calculate the mutual information on the feature subset. Redundancy-synergy coefficient,a novel redundancy and synergy measure of features to express the class feature,is defined by mutual information. The information maximization rule was applied to derive the heuristic feature subset selection method based on mutual information and redundancy-synergy coefficient. Our experiment results showed the good performance of the new feature selection method.     

基于MA-SVM的建筑企业精益建设技术采纳决策研究

在元分析方法 (Meta-Analysis)得到的建筑企业采纳精益建设技术影响因素基础上,构建建筑企业精益建设技术采纳决策指标体系,建立精益建设技术采纳决策模型,并应用支持向量机算法(SVM)对样本数据进行训练、仿真模拟。研究结果表明,元分析方法能够科学、全面地筛选出采纳精益建设技术影响因素,而基于支持向量机算法的企业精益建设技术采纳决策模型,预测精度较高,可为潜在采纳企业提供有价值的理论参考,对组织提高建设项目管理水平,提高生产效果具有重要的现实意义。     

Automated estimation of item difficulty for multiple-choice tests: An application of word embedding techniques

Pretesting is the most commonly used method for estimating test item difficulty because it provides highly accurate results that can be applied to assessment development activities. However, pretesting is inefficient, and it can lead to item exposure. Hence, an increasing number of studies have invested considerable effort in researching the automated estimation of item difficulty. Language proficiency tests constitute the majority of researched test topics, while comparatively less research has focused on content subjects. This paper introduces a novel method for the automated estimation of item difficulty for social studies tests. In this study, we explore the difficulty of multiple-choice items, which consist of the following item elements: a question and alternative options. We use learning materials to construct a semantic space using word embedding techniques and project an item's texts into the semantic space to obtain corresponding vectors. Semantic features are obtained by calculating the cosine similarity between the vectors of item elements. Subsequently, these semantic features are sent to a classifier for training and testing. Based on the output of the classifier, an estimation model is created and item difficulty is estimated. Our findings suggest that the semantic similarity between a stem and the options has the strongest impact on item difficulty. Furthermore, the results indicate that the proposed estimation method outperforms pretesting, and therefore, we expect that the proposed approach will complement and partially replace pretesting in future.     

高职高专商务英语学生职业生涯规划浅析

随着高校“扩招”,毕业生就业问题凸现,作为商务英语毕业生同样面临着就业的矛盾和问题。大学生职业生涯指导有着十分重要的意义。高职高专院校应从正确自我认知、进行职业分析、确立职业目标和自我能力培养等方面对商务英语专业学生进行系统的职业生涯规划指导。