A Meta-analysis of the Uncanny Valley's Independent and Dependent Variables

The uncanny valley (UV) effect is a negative affective reaction to human-looking artificial entities. It hinders comfortable, trust-based interactions with android robots and virtual characters. Despite extensive research, a consensus has not formed on its theoretical basis or methodologies. We conducted a meta-analysis to assess operationalizations of human likeness (independent variable) and the UV effect (dependent variable). Of 468 studies, 72 met the inclusion criteria. These studies employed 10 different stimulus creation techniques, 39 affect measures, and 14 indirect measures. Based on 247 effect sizes, a three-level meta-analysis model revealed the UV effect had a large effect size, Hedges’ g = 1.01 [0.80, 1.22]. A mixed-effects meta-regression model with creation technique as the moderator variable revealed face distortion produced the largest effect size, g = 1.46 [0.69, 2.24], followed by distinct entities, g = 1.20 [1.02, 1.38], realism render, g = 0.99 [0.62, 1.36], and morphing, g = 0.94 [0.64, 1.24]. Affective indices producing the largest effects were threatening, likable, aesthetics, familiarity , and eeriness , and indirect measures were dislike frequency, categorization reaction time, like frequency, avoidance , and viewing duration . This meta-analysis—the first on the UV effect—provides a methodological foundation and design principles for future research.

How Do Home Computer Users Browse the Web?

With the ubiquity of web tracking, information on how people navigate the internet is abundantly collected yet, due to its proprietary nature, rarely distributed. As a result, our understanding of user browsing primarily derives from small-scale studies conducted more than a decade ago. To provide an broader updated perspective, we analyze data from 257 participants who consented to have their home computer and browsing behavior monitored through the Security Behavior Observatory. Compared to previous work, we find a substantial increase in tabbed browsing and demonstrate the need to include tab information for accurate web measurements. Our results confirm that user browsing is highly centralized, with 50% of internet use spent on 1% of visited websites. However, we also find that users spend a disproportionate amount of time on low-visited websites, areas with a greater likelihood of containing risky content. We then identify the primary gateways to these sites and discuss implications for future research.

A Language-independent Network to Analyze the Impact of COVID-19 on the World via Sentiment Analysis

Towards the end of 2019, Wuhan experienced an outbreak of novel coronavirus, which soon spread worldwide, resulting in a deadly pandemic that infected millions of people around the globe. The public health agencies followed many strategies to counter the fatal virus. However, the virus severely affected the lives of the people. In this paper, we study the sentiments of people from the top five worst affected countries by the virus, namely the USA, Brazil, India, Russia, and South Africa. We propose a deep language-independent Multilevel Attention-based Conv-BiGRU network (MACBiG-Net) , which includes embedding layer, word-level encoded attention, and sentence-level encoded attention mechanisms to extract the positive, negative, and neutral sentiments. The network captures the subtle cues in a document by focusing on the local characteristics of text along with the past and future context information for the sentiment classification. We further develop a COVID-19 Sentiment Dataset by crawling the tweets from Twitter and applying topic modeling to extract the hidden thematic structure of the document. The classification results demonstrate that the proposed model achieves an accuracy of 85%, which is higher than other well-known algorithms for sentiment classification. The findings show that the topics which evoked positive sentiments were related to frontline workers, entertainment, motivation, and spending quality time with family. The negative sentiments were related to socio-economic factors like racial injustice, unemployment rates, fake news, and deaths. Finally, this study provides feedback to the government and health professionals to handle future outbreaks and highlight future research directions for scientists and researchers.

Study Behavior in Computing Education—A Systematic Literature Review

As the field of computing education grows and matures, it has become essential to unite computing education and higher education research. Educational research has highlighted that how students study is crucial to their learning progress, and study behaviors have been found to play an important role in students’ academic success. This article presents the main results of a systematic literature review intended to determine what we know about the study behaviors of computing students and the role of educational design in shaping them. A taxonomy of study behaviors was developed and used to clarify and classify the definitions of study behavior, process, strategies, habits, and tactics as well as to identify their relationship to the educational context. The literature search resulted in 107 included papers, which were analyzed according to defined criteria and variables. The review of study behavior terminology found that the same terms are used to describe substantially different study behaviors, and the lack of standard terminology makes it difficult to compare findings from different papers. Furthermore, it was more common for papers to use study behaviors to explain other aspects of students rather than exploring and understanding them. Additionally, the results revealed a tendency to focus on specific educational contexts, predominantly introductory programming courses. Although computing education as a field is well equipped to expand the knowledge about both study behaviors and their connection to the educational context, the lack of common terminology and theories limits the impact. The taxonomy of study behaviors in computing education proposed in this article can contribute to contextualizing the research in such a way that researchers and educators across institutional borders can compare and utilize results. Last, the article outlines some areas for future research and recommendations for practice.

Diversity of culturable endophytic fungi vary through time in Manihot esculenta Crantz

Endophytic fungi are a ubiquituos group that colonize all plant species on earth. Studies comparing the location of endophytic fungi within the leaves and the sampling time in Manihot esculenta Crantz (cassava) are limited. In this study, mature leaves of M. esculenta from Panama were collected in order to compare the cultivable diversity of endophytic fungi and to determine their distribution within the leaves. A total of one hundred sixty endophytes belonging to 97 species representing 13 genera and 8 morphospecies determined as mycelia sterilia that containing 63 isolates were isolated. Cladosporium, Nigrospora, Periconia, and mycelia sterilia 1 and 3 were the most predominant isolated endophytes. We detected that endophytes varied across the sampling time, but not amongst locations within leaves. The endophytes composition across sampling and the location of endophytes within leaf was similar, except for Periconia and mycelia sterilia 3 and 7. The data generated in this study contribute to the knowledge on the biodiversity of endophytic fungi in Panama, and establish the bases for future research focused on understanding the function of endophytes in M. esculenta crops.

Semantic Pattern Detection in COVID-19 Using Contextual Clustering and Intelligent Topic Modeling

The Covid-19 pandemic is the deadliest outbreak in our living memory. So, it is need of hour, to prepare the world with strategies to prevent and control the impact of the epidemics. In this paper, a novel semantic pattern detection approach in the Covid-19 literature using contextual clustering and intelligent topic modeling is presented. For contextual clustering, three level weights at term level, document level, and corpus level are used with latent semantic analysis. For intelligent topic modeling, semantic collocations using pointwise mutual information(PMI) and log frequency biased mutual dependency(LBMD) are selected and latent dirichlet allocation is applied. Contextual clustering with latent semantic analysis presents semantic spaces with high correlation in terms at corpus level. Through intelligent topic modeling, topics are improved in the form of lower perplexity and highly coherent. This research helps in finding the knowledge gap in the area of Covid-19 research and offered direction for future research.

A Review of Cross-Border E-commerce and Directions for Future Research

The emergence of Cross-Border E-commerce (CBeC) has brought substantial changes to both businesses and consumers. Although CBeC businesses have existed for less than a decade, many academic researchers addressed important issues in this context. It is essential to evaluate what has been studied through a structured review of the literature and derive meaningful insights given that research on this topic is new and largely fragmented. Therefore, this study conducts a review of CBeC literature to find the current gaps and fragmentation to provide guidelines for future research. The review shows that research in this domain needs more attention and enforcement to address the current research gaps. Addressing the current gaps helps academia build a rigorous body of knowledge and enables practitioners to solve challenging business problems.