Class (philosophy)

About: Class (philosophy) is a research topic. Over the lifetime, 821 publications have been published within this topic receiving 28000 citations.

Techniques for binding resources for use by a consumer tier

Abstract: Described are techniques for configuring resources of a data storage system. A definition for each of one or more tiers is provided. Each of the tiers corresponds to a different class of consumer of data storage system resources and has a different corresponding definition including a set of one or more clauses and a priority of each clause in said set relative to other clauses in said set. Each of the clauses in the set is one of a plurality of predefined types of clauses. One or more data storage consumers are associated with each tier. A first set of data storage system resources is associated with a first of said one or more tiers in accordance with a corresponding first definition for said first tier. The first set is used when processing storage provisioning requests and I/O requests for data storage consumers associated with the first tier.

OBGAN: Minority oversampling near borderline with generative adversarial networks

Abstract: Class imbalance is a major issue that degrades the performance of machine learning classifiers in real-world problems. Oversampling methods have been widely used to overcome this issue by generating synthetic data from minority classes. However, conventional oversampling methods often focus only on the minority class and ignore relationships between the minority and majority classes. In this study, we propose an oversampling method called minority oversampling near the borderline with a generative adversarial network (OBGAN). To consider the minority and majority classes, OBGAN employs one independent discriminator for each class. Each discriminator competitively affects the generator to be trained to capture each region of the minority and majority classes. However, the sensitivity of the generator to the discriminator of the minority class is greater than that of the majority class. Hence, the generator learns the minority class with a focus near the borderline. In addition, the architecture and loss function of OBGAN are designed to avoid the mode collapse problem, which commonly occurs in GANs trained on relatively small datasets. Experimental results, involving 21 datasets and 6 benchmark methods, reveal that OBGAN exhibits excellent performance and stability.

Evaluating the impact of malleable factors on percent time lecturing in gateway chemistry, mathematics, and physics courses

Abstract: Abstract Background Active learning used in science, technology, engineering, and mathematics (STEM) courses has been shown to improve student outcomes. Nevertheless, traditional lecture-orientated approaches endure in these courses. The implementation of teaching practices is a result of many interrelated factors including disciplinary norms, classroom context, and beliefs about learning. Although factors influencing uptake of active learning are known, no study to date has had the statistical power to empirically test the relative association of these factors with active learning when considered collectively. Prior studies have been limited to a single or small number of evaluated factors; in addition, such studies did not capture the nested nature of institutional contexts. We present the results of a multi-institution, large-scale ( N = 2382 instructors; N = 1405 departments; N = 749 institutions) survey-based study in the United States to evaluate 17 malleable factors (i.e., influenceable and changeable) that are associated with the amount of time an instructor spends lecturing, a proxy for implementation of active learning strategies, in introductory postsecondary chemistry, mathematics, and physics courses. Results Regression analyses, using multilevel modeling to account for the nested nature of the data, indicate several evaluated contextual factors, personal factors, and teacher thinking factors were significantly associated with percent of class time lecturing when controlling for other factors used in this study. Quantitative results corroborate prior research in indicating that large class sizes are associated with increased percent time lecturing. Other contextual factors (e.g., classroom setup for small group work) and personal contexts (e.g., participation in scholarship of teaching and learning activities) are associated with a decrease in percent time lecturing. Conclusions Given the malleable nature of the factors, we offer tangible implications for instructors and administrators to influence the adoption of more active learning strategies in introductory STEM courses.

Adapting the Graphic Novel to Improve Speaking Fluency for EFL Learners

Abstract: This study examines the viability of the literature in terms of escalating English speaking fluency for EFL learners. The focus of this study applied graphic novels as a particular research object. As part of literature, the graphic novel has become one of the resources allowing learners to access attractive, inspired, conceptual, and artistic encounters for learners. This study used a quantitative method focused on online class observation and survey analysis. The purpose of this study is to show the impact of literature on EFL learners to speak, to outline the potency of graphic novels to escalate their speaking skills, and to boost their fluency.

Customer sentiment analysis and prediction of halal restaurants using machine learning approaches

Abstract: Purpose There is a strong prerequisite for organizations to analyze customer review behavior to evaluate the competitive business environment. The purpose of this study is to analyze and predict customer reviews of halal restaurants using machine learning (ML) approaches. Design/methodology/approach The authors collected customer review data from the Yelp website. The authors filtered the reviews of only halal restaurants from the original data set. Following cleaning, the filtered review texts were classified as positive, neutral or negative sentiments, and those sentiments were scored using the AFINN and VADER sentiment algorithms. Also, the current study applies four machine learning methods to classify each review toward halal restaurants into its sentiment class. Findings The experiment showed that most of the customer reviews toward halal restaurants were positive. The authors also discovered that all of the methods (decision tree, linear support vector machine, logistic regression and random forest classifier) can correctly classify the review text into sentiment class, but logistic regression outperforms the others in terms of accuracy. Practical implications The results facilitate halal restaurateurs in identifying customer review behavior. Social implications Sentiment and emotions, according to appraisal theory, form the basis for all interactions, facilitating cognitive functions and supporting prospective customers in making sense of experiences. Emotion theory also describes human affective states that determine motives and actions. The study looks at how potential customers might react to a halal restaurant’s consensus on social media based on reviewers’ opinions of halal restaurants because emotions can be conveyed through reviews. Originality/value This study applies machine learning approaches to analyze and predict customer sentiment based on the review texts toward halal restaurants.