Accessibility and Usability Evaluation of Digital Library Article Pages

This study aims to present an exploratory study about the accessibility and usability evaluation of digital library article pages. For this purpose, four widely known digital libraries (DLs), namely Science Direct, Institute of Electric and Electronic Engineering Xplore, Association for Computing Machinery, and SpringerLink, were examined. In the first stage, article web interfaces of these selected DLs were analyzed based on standard web guidelines using automatic evaluation tools to assess their accessibility. In the second stage, to evaluate the usability of these web interfaces, eye-tracking experiments with 30 participants were conducted. Obtained results of the analysis show that article pages of digital libraries are not of free of accessibility and usability problems. Overall, this study highlights accessibility and usability problems of digital library article interfaces, and these findings can provide the feedback to web developers in making their article pages more accessible and usable for their users.

Reproducibility in Evolutionary Computation

Experimental studies are prevalent in Evolutionary Computation ( EC ), and concerns about the reproducibility and replicability of such studies have increased in recent times, reflecting similar concerns in other scientific fields. In this article, we discuss, within the context of EC, the different types of reproducibility and suggest a classification that refines the badge system of the Association of Computing Machinery ( ACM ) adopted by ACM Transactions on Evolutionary Learning and Optimization ( TELO ). We identify cultural and technical obstacles to reproducibility in the EC field. Finally, we provide guidelines and suggest tools that may help to overcome some of these reproducibility obstacles.

The Benefits of Learning Analytics to Higher Education Institutions: A Scoping Review

Learning analytics is a form of data analysis that allows teachers, lecturers, educational experts, and administrators of online learnings to look for students’ online traces and information associated with the learning processes. The fundamental goal of learning analytics in online classrooms and com-puter-supported instruction is to enhance the learning experience and the entire learning process. This review aims at reviewing some of the benefits available through using learning analytics in higher education institutions (HEI) for the students, teaching staff and the management. The search for relevant literature was conducted by searching online databases which in-clude Web of Science, SCOPUS, Science Direct, IEEE, Emerald, Springer, ERIC and Association for Computing Machinery (ACM). The analysis of the literatures obtained from the online databases revealed that learning analytics provide series of benefits to students, teaching staffs and the management of higher education institutions. The benefits include prediction and identification of target courses, curriculum development and improvement, improved students’ learning outcomes, improved instructors’ performance and monitoring of students’ dropout and retention. It is recommended that higher education institutions adopt the use of learning analytics in their online teaching and learning.

Opinion mining for national security: techniques, domain applications, challenges and research opportunities

Background Opinion mining, or sentiment analysis, is a field in Natural Language Processing (NLP). It extracts people’s thoughts, including assessments, attitudes, and emotions toward individuals, topics, and events. The task is technically challenging but incredibly useful. With the explosive growth of the digital platform in cyberspace, such as blogs and social networks, individuals and organisations are increasingly utilising public opinion for their decision-making. In recent years, significant research concerning mining people’s sentiments based on text in cyberspace using opinion mining has been explored. Researchers have applied numerous opinions mining techniques, including machine learning and lexicon-based approach to analyse and classify people’s sentiments based on a text and discuss the existing gap. Thus, it creates a research opportunity for other researchers to investigate and propose improved methods and new domain applications to fill the gap. Methods In this paper, a structured literature review has been done by considering 122 articles to examine all relevant research accomplished in the field of opinion mining application and the suggested Kansei approach to solve the challenges that occur in mining sentiments based on text in cyberspace. Five different platforms database were systematically searched between 2015 and 2021: ACM (Association for Computing Machinery), IEEE (Advancing Technology for Humanity), SCIENCE DIRECT, SpringerLink, and SCOPUS. Results This study analyses various techniques of opinion mining as well as the Kansei approach that will help to enhance techniques in mining people’s sentiment and emotion in cyberspace. Most of the study addressed methods including machine learning, lexicon-based approach, hybrid approach, and Kansei approach in mining the sentiment and emotion based on text. The possible societal impacts of the current opinion mining technique, including machine learning and the Kansei approach, along with major trends and challenges, are highlighted. Conclusion Various applications of opinion mining techniques in mining people’s sentiment and emotion according to the objective of the research, used method, dataset, summarized in this study. This study serves as a theoretical analysis of the opinion mining method complemented by the Kansei approach in classifying people’s sentiments based on text in cyberspace. Kansei approach can measure people’s impressions using artefacts based on senses including sight, feeling and cognition reported precise results for the assessment of human emotion. Therefore, this research suggests that the Kansei approach should be a complementary factor including in the development of a dictionary focusing on emotion in the national security domain. Also, this theoretical analysis will act as a reference to researchers regarding the Kansei approach as one of the techniques to improve hybrid approaches in opinion mining.

Infrastructures of Play: Labor, Materiality, and Videogame Education

Preparing students for the job market is not the limit of our responsibilities as videogame educators. We must also prepare them to be ethical actors within the industries they may join. This paper argues for augmenting player-centric videogame design education and game studies pedagogies with approaches that situate videogames in context as operational components of extractivist business models and the political and financial economies that support them. This approach entails teaching videogames as technical systems with complex and expansive upstream and downstream supports and impacts. These supports and impacts have real and frequently detrimental effects on the environment, communities, and individual human lives, and yet are relatively rarely discussed in the literature, especially in comparison to discussions that focus on representation and rhetoric. By looking beyond the frame of the individual videogame as an expressive artifact, educators can help learners to apprehend issues such as the growing material and environmental costs of computer-based entertainment and the many tiers of labor exploitation involved in producing videogames and the computing machinery that makes them possible, among other concerns. The paper concludes by suggesting that students equipped with these kinds of understandings will be able to make more informed ethical assessments, and thus wiser choices, as they percolate into the videogames industries and, in some cases, into positions of leadership.

Science vs. Sophistry—A historical debate on bipolar fuzzy sets and equilibrium-based mathematics for AI&QI

The road from bipolar fuzzy sets to equilibrium-based mathematical abstraction is surveyed. A continuing historical debate on bipolarity and isomorphism is outlined. Related literatures are critically reviewed to counter plagiarism, distortion, renaming, and sophistry. Based on the debate, the term “isomorphistry” is coined. It is concluded that if isomorphism is used correctly it can be helpful in mathematics. If abused it may become isomorphistry—a kind of historical, socially constructed, entrenched, and “noble” hypocrisy hindering major scientific advances. It is shown that isomorphistry can be motivated by “denying” the originality of bipolar fuzzy sets and aimed at “justifying” plagiarism and distortion. Thus, isomorphistry is sophistry on isomorphism . Some (-,+)-bipolar isomorphistry behaviors are critiqued. YinYang vs. YangYin are distinguished. The geometrical and logical basis of equilibrium-based AI&QI computing machinery is introduced as a new computing paradigm with logically definable causality for mind-body unity. A philosophical joke on sophistry is appended.

De las facultades de ingeniería… a las facultades de los ingenieros…

“Va a poder reconocer, también, los grandes dilemas presentes en las diferentes épocas y las decisiones que se han tomado al respecto. Uno de ellos, quizás el más importante, es sobre el enfoque que debe tener el programa académico. Tiendo a pensar que la situación sería más clara si reconociéramos que gran parte del problema se debe a que estamos proponiendo varios programas de formación en uno solo, por lo cual las tensiones son inevitables. En la visión de la Asociación for Computing Machinery (ACM), la Ingeniería de Sistemas, tal y como la entendemos en Colombia, está relacionada con cinco programas distintos, lo cual facilita las cosas. Quizás es hora de que pensemos en hacer diferentes programas, como ha empezado a ocurrir en algunos lugares. Relacionado con el anterior hay otro dilema que ha estado presente en toda la historia del Departamento: si se debe tener una visión puramente técnica o una que tome en consideración las necesidades de las organizaciones y de las personas.” (Prieto Ñañez, 2015).

A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise

Human Pose Estimation (HPE) has received considerable attention during the past years, improving its performance thanks to the use of Deep Learning, and introducing new interesting uses, such as its application in Sport and Physical Exercise (SPE). The aim of this systematic review is to analyze the literature related to the application of HPE in SPE, the available data, methods, performance, opportunities, and challenges. One reviewer applied different inclusion and exclusion criteria, as well as quality metrics, to perform the paper filtering through the paper databases. The Association for Computing Machinery Digital Library, Web of Science, and dblp included more than 500 related papers after the initial filtering, finally resulting in 20. In addition, research was carried out regarding the publicly available data related to this topic. It can be concluded that even if related public data can be found, much more data is needed to be able to obtain good performance in different contexts. In relation with the methods of the authors, the use of general purpose systems as base, such as Openpose, combined with other methods and adaptations to the specific use case can be found. Finally, the limitations, opportunities, and challenges are presented.

Systematic Review of Enhancement of Artificial Bee Colony Algorithm Using Ant Colony Pheromone

The artificial bee colony (ABC) is a well-studied algorithm developed to solve continuous function optimization problems by Karboga and Akay in 2009. ABC has been proven to be more effective than other biological-inspired algorithms with good exploration. However, ABC suffers from low exploitation and slow convergence in some cases. The ABC algorithm study has risen significantly over the past decade, with many researchers trying to improve ABC performance and apply it to solve problems. One method to enhance ABC is to borrow exploration technique from other algorithms. Researchers use pheromone, which is a technique used by Ant Colony optimization algorithm, to enhance ABC and addressed several aspects of using a pheromone to enhance the ABC. This systematic review aims to review and analysis articles about using pheromone to enhance ABC. Articles on related topics were systematically searched in four major databases namely Scopus, Web of Science, Association for Computing Machinery ACM and Google Scholar. To ensure that all research articles were considered the start date is not restrictions the search carry out till February 2021.Five articles were selected based on our inclusion and exclusion criteria for the systematic review. The results show that the use Pheromone to enhance ABC can increase the ABC exploitation ability and overcoming the late convergence. This paper also illustrates several potential pheromone using for future work.

Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review

Objective Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. Materials and Methods We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus. Results There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values). Discussion Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research.