Social Sciences

Cars, mobile phones, and smart home devices already provide automatic speech recognition (ASR) by default. However, human machine interfaces (HMI) in industrial settings, as opposed to consumer settings, operate under different conditions and thus, present different design challenges. Voice control, arguably the most natural form of communication, has the potential to shorten complex command sequences and menu structures in order to directly execute a final command.


This dataset (MegaGeoCOV Extended), which is an extended version of MegaGeoCOV, was introduced in this paper: A Twitter narrative of the COVID-19 pandemic in Australia (the paper will appear in proceedings of the 20th ISCRAM conference, Omaha, Nebraska, USA May 2023). Please refer to the paper for more details (e.g., keywords and hashtags used, descriptive statistics, etc.).



The purpose of this manuscript was to study the global structure of standards in the development of regularly published scientific articles. Writers and new researchers can improve their scientific writing skills by using measurable guidelines that are easy to learn by all circles, including non-scientific readers. Furthermore, this article was created using the second reading source adopted from the (department of psychology, 2023).


A viewer's existing beliefs can prevent accurate reasoning with data visualizations. In particular, confirmation bias can cause people to overweigh information that confirms their beliefs, and dismiss information that disconfirms them. We tested whether confirmation bias exists when people reason with visualized data and whether certain visualization designs can elicit less biased reasoning strategies. We asked crowdworkers to solve reasoning problems that had the potential to evoke both poor reasoning strategies and confirmation bias.


BillionCOV is a global billion-scale English-language COVID-19 tweets dataset with more than 1.4 billion tweets originating from 240 countries and territories between October 2019 and April 2022. This dataset has been curated by hydrating the 2 billion tweets present in COV19Tweets.


This dataport will be useful to those interested in visual design for complex physics phenomena. We have included two quantum physics data sample datasets and our empirical study results. 

(1) evaluation results from two experiments (20 participants in each and 40 in total) to empirically validate that separable bivariate pairs of large-magnitude-range vector 

magnitude representations are more efficient than integral pairs. 


Gestational diabetes is a type of high blood sugar that develops during pregnancy. It can occur at any stage of pregnancy and cause problems for both the mother and the baby, during and after birth. The risks can be reduced if they are early detected and managed, especially in areas where only periodic tests of pregnant women are available. Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. This study proposes a combined prediction model to diagnose gestational diabetes.


Sign languages are the most common mode of communication with and between hearing-impaired individuals. In the Arab world, Arabic sign language is used with different dialects supporting a distinct set of rules for the gestures used. With research on natural language processing advancing, models have been developed to translate sign language to spoken language and vice versa. However, Arabic sign language has rarely been studied due to the lack of availability of datasets dealing with Arabic sign language.


The data collection includes posts from social media networks popular among Russian-speaking people. The information was gathered using pre-defined keywords ("war," "special military operation," and so on) and is mainly relevant to Ukraine's continuing conflict with Russia. Following a thorough assessment and analysis of the data, propaganda and false news were detected. The information gathered has been anonymized. Feature engineering and text preparation can extract new insights and information from this data source.


Several fields of study can benefit from a large, structured, and accurate dataset of historical figures. Due to a lack of such a dataset, in this paper, we aim to use machine learning and text mining models to collect, predict, and cleanse online data with a focus on age and gender. We developed a five-step method and inferred birth and death years, binary gender, and occupation from community-submitted data to all language versions of the Wikipedia project.