Brief Announcement: Performance Anomalies in Concurrent Data Structure Microbenchmarks

Recent decades have witnessed a surge in the development of concurrent data structures with an increasing interest in data structures implementing concurrent sets (CSets). Microbenchmarking tools are frequently utilized to evaluate and compare performance differences across concurrent data structures. The underlying structure and design of the microbenchmarks themselves can play a hidden but influential role in performance results. However, the impact of microbenchmark design has not been well investigated. In this work, we illustrate instances where concurrent data structure performance results reported by a microbenchmark can vary 10-100x depending on the microbenchmark implementation details. We investigate factors leading to performance variance across three popular microbenchmarks and outline cases in which flawed microbenchmark design can lead to an inversion of performance results between two concurrent data structure implementations. We further derive a prescriptive approach for best practices in the design and utilization of concurrent data structure microbenchmarks

Performance Anomalies in Concurrent Data Structure Microbenchmarks

Recent decades have witnessed a surge in the development of concurrent data structures with an increasing interest in data structures implementing concurrent sets (CSets). Microbenchmarking tools are frequently utilized to evaluate and compare the performance differences across concurrent data structures. The underlying structure and design of the microbenchmarks themselves can play a hidden but influential role in performance results. However, the impact of microbenchmark design has not been well investigated. In this work, we illustrate instances where concurrent data structure performance results reported by a microbenchmark can vary 10-100x depending on the microbenchmark implementation details. We investigate factors leading to performance variance across three popular microbenchmarks and outline cases in which flawed microbenchmark design can lead to an inversion of performance results between two concurrent data structure implementations. We further derive a set of recommendations for best practices in the design and usage of concurrent data structure microbenchmarks and explore advanced features in the Setbench microbenchmark

STUDENTS’ ATTITUDE TOWARDS ORAL PRESENTATION IN VIRTUAL LEARNING AT ENGLISH EDUCATION STUDY PROGRAM OF PATTIMURA UNIVERSITY

Studies about an attitude toward learning English have been conducted almost everywhere because of its important influence on the English learning process. The attitude can be categorized into three components namely cognitive, affective/emotional, and behavioral. The study is focused on the emotional component which covers the person’s emotions and feeling towards an object. It can directly affect one’s preferences in order to stand for or against or to like or dislike something. Due to the pandemic situation where all the subjects must be done virtual, the researcher attempts to explore what are students’ emotional attitudes toward the virtual oral presentation. The researcher finds it interesting since virtual learning is an uncommon method for students, especially in doing an oral presentation as well as the novelty of the previous related study. The research objective is to find out the students’ emotional attitudes towards oral presentation in virtual learning, especially in the fifth semester of the English education study program at Pattimura University. This study applied a survey as a research design by using a Questionnaire and Interview. The subject of this study consists of fifteen students who enrolled in SLA class. The result of the questionnaire showed that most students in the second language acquisition (SLA) class held a positive attitude toward the virtual oral presentation. The majority or 12 students (80%) strongly agreed that the oral presentation in virtual learning is more fun and preferable. On the other hand, 11 students (73.3%) strongly agreed that it reduces their anxiety level and also improves their self-confidence. In conclusion, it benefits so much to improve their motivation in English virtual learning compared to face-to-face oral presentations. In a face-to-face class, they had high speaking anxiety by having a great fear of getting involved in a conversation, great fear of the audience, and a great fear of failure. In addition, further researchers can focus on the technology application in virtual learning and its impact on students’ language ability as the novelty element of this study

A Pragmatic Analysis of Deixis and Reference on Taylor Swift Songs

This research examines the pragmatic aspects of deixis and reference in song lyrics. Pragmatics is concerned with how speakers or writers convey meaning, and how listeners or readers interpret it. Deixis refers to linguistic elements that directly refer to the personal, temporal, or locational characteristics of the situation in which an utterance occurs. The study explores the different types of deixis, including person deixis, spatial deixis, temporal deixis, discourse deixis, and social deixis, and their role in establishing meaning and creating a connection between the songwriter and the audience. Reference, on the other hand, focuses on the identification of things in the world, with deixis serving as the mechanism for achieving reference. The analysis highlights the importance of context in determining the referents of deictic expressions and the impact of deixis and reference in conveying the songwriter's intended message. By understanding the pragmatic use of deixis and reference in song lyrics, researchers and readers can gain insights into the communicative strategies employed by songwriters and the ways in which meaning is constructed within the lyrics

A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules

Our goal in this paper is to automatically extract a set of decision rules (rule set) that best explains a classification data set. First, a large set of decision rules is extracted from a set of decision trees trained on the data set. The rule set should be concise, accurate, have a maximum coverage and minimum number of inconsistencies. This problem can be formalized as a modified version of the weighted budgeted maximum coverage problem, known to be NP-hard. To solve the combinatorial optimization problem efficiently, we introduce a nested genetic algorithm which we then use to derive explanations for ten public data sets