Informatics, Volume 7, Issue 1 (March 2020) – 9 articles

Universities are becoming aware of the importance of social networking sites for the reinforcement of their institutional brands. This study was conducted to analyze the extent to which best-ranked universities use social networking sites. A quantitative methodology and digital methods were used to measure the social media presence of the first 400 universities listed in the QS (Quacquarelli Symonds) World University Ranking 2018. The results reveal a high degree of involvement of those universities on social media; some factors that are associated with significant differences in the adoption of social networking sites include the public versus private management model, the level of academic prestige, and the region of origin. Full article

Web accessibility is becoming a relevant topic with an increased number of people with disabilities and the elderly using the web. Numerous legislations are being passed that require the web to be universally accessible to all people, regardless of their abilities and age. Despite this trend, university curricula still teach traditional web development without addressing accessibility as a topic. To investigate this matter closely, we studied the syllabi of web development courses at one university to evaluate whether the topic of accessibility was taught there. Additionally, we conducted a survey with nineteen students who were enrolled in a web development course, and we interviewed three lecturers from the same university. Our findings suggest that the topic of accessibility is not covered in web development courses, although both students and lecturers think that it should. This generates lack of competence in accessibility. The findings also confirm the finding of previous studies that, among web developers, there is a low familiarity with accessibility guidelines and policies. An interesting finding we uncovered was that gender affects the motivation to learn about accessibility. Females were driven by personal reasons, which we attribute to females having an increased sense of empathy. Finally, our participants were divided in their opinions whether accessibility contributes to usability. Full article

Increasing complexity of societal questions requires participatory processes that engage with capable participants. We adopted Horellis’ stance on participation as not an isolated event but a constant communication between different groups that can be assured by using enabling tools. We applied the Capability Approach to frame a capacity-building process and understand how this framework can support a collective of entrepreneurs to become aware of their capabilities (and the impact of an ongoing urban renewal process on these capabilities). The Capability Approach emphasizes the personal and structural conditions that impact a person’s capability to choose—the conditions that affect the process of determining what a person values. The paper builds on a two year capacity-building process conducted in Genk, Belgium, and proposes a conceptual framework for building capacities, in which the process and outputs collide with ideas of choice, ability, and opportunity, notions central to the Capability Approach. The case study looks at one of the main commercial streets of the city (Vennestraat) and reflects on a set of enabling artefacts used to engage proprietors in the capacity-building process. This capacity-building process, characterized by the idea of space and capabilities, advances a critical viewpoint on issues related to participatory processes and gives practitioners a set of enabling tools to start a conversation over complex urban transformations, such as the one in Vennestraat. Full article

Facial emotion recognition is a crucial task for human-computer interaction, autonomous vehicles, and a multitude of multimedia applications. In this paper, we propose a modular framework for human facial emotions’ recognition. The framework consists of two machine learning algorithms (for detection and classification) that could be trained offline for real-time applications. Initially, we detect faces in the images by exploring the AdaBoost cascade classifiers. We then extract neighborhood difference features (NDF), which represent the features of a face based on localized appearance information. The NDF models different patterns based on the relationships between neighboring regions themselves instead of considering only intensity information. The study is focused on the seven most important facial expressions that are extensively used in day-to-day life. However, due to the modular design of the framework, it can be extended to classify N number of facial expressions. For facial expression classification, we train a random forest classifier with a latent emotional state that takes care of the mis-/false detection. Additionally, the proposed method is independent of gender and facial skin color for emotion recognition. Moreover, due to the intrinsic design of NDF, the proposed method is illumination and orientation invariant. We evaluate our method on different benchmark datasets and compare it with five reference methods. In terms of accuracy, the proposed method gives 13% and 24% better results than the reference methods on the static facial expressions in the wild (SFEW) and real-world affective faces (RAF) datasets, respectively. Full article

As one of the most interactive economic activities, tourism has improved significantly since the Internet allowed customers (travelers) to look for and create their trips without the need to go to a travel agency. Through the development of Internet marketing, tourists are able to receive information in real-time and view them on the screens of their devices (computers, laptops, mobile phones or tablets), and consequently they can create their own content and share it with others. Due to this development, we are witnessing a new phenomenon, so-called app capitalism, in which companies like Uber and Airbnb make money on services and goods they do not own and finding new business models in the tourism sector which enable customer-to-customer models. To be able to respond to these changes, the tourism industry needs to bring together knowledge, money, and technology for the purpose of creating new business models. The development of technology in all social spheres, including tourism, has provided a strong tool for consumers in terms of both acquiring and disclosing information to others. One of the rapidly growing technologies which is also one of the cutting-edge technologies entering tourism is blockchain. Blockchain technology captured worldwide attention in 2017 and its implementation has been revolutionizing various industries (e.g., retail, healthcare, tourism). The aim of this paper is to analyze the potential of the tourism industry in terms of blockchain implementation. Blockchain technology provides significant benefits to the tourism industry since its implementation can help increase competitive advantage, improve customer satisfaction and enhance performance. In this paper, the authors present the current situation in the Republic of Croatia and the Republic of Macedonia regarding the use of blockchain technology in the tourism industry. The main findings of this paper comprise the detection of key areas regarding why blockchain technology is not implemented in the tourist industry, and what processes should be handled. By presenting a case study of the implementation of blockchain technology in tourism, the authors analyze the potential of using blockchain technology in the tourism industry and discuss topics for further research. Full article

Due to the constantly evolving social media and different types of sources of information, we are facing different fake news and different types of misinformation. Currently, we are working on a project to identify applicable methods for identifying fake news for floating language types. We explored different approaches to detect fake news in the presented research, which are based on morphological analysis. This is one of the basic components of natural language processing. The aim of the article is to find out whether it is possible to improve the methods of dataset preparation based on morphological analysis. We collected our own and unique dataset, which consisted of articles from verified publishers and articles from news portals that are known as the publishers of fake and misleading news. Articles were in the Slovak language, which belongs to the floating types of languages. We explored different approaches in this article to the dataset preparation based on morphological analysis. The prepared datasets were the input data for creating the classifier of fake and real news. We selected decision trees for classification. The evaluation of the success of two different methods of preparation was carried out because of the success of the created classifier. We found a suitable dataset pre-processing technique by morphological group analysis. This technique could be used for improving fake news classification. Full article

Online debates, specifically the ones about public health issues (e.g., vaccines, medications, and nutrition), occur frequently and intensely, and are having an impact on our world. Many public health topics are debated online, one of which is the efficacy and morality of vaccines. When people examine such online debates, they encounter numerous and conflicting sources of information. This information forms the basis upon which people take a position on such debates. This has profound implications for public health. It necessitates a need for public health stakeholders to be able to examine online debates quickly and effectively. They should be able to easily perform sense-making tasks on the vast amount of online information, such as sentiments, online presence, focus, or geographic locations. In this paper, we report the results of a user study of a visual analytic system (VAS), and whether and how this VAS can help with such sense-making tasks. Specifically, we report a usability evaluation of VINCENT (VIsual aNalytiCs systEm for investigating the online vacciNe debaTe), a VAS previously described. To help the reader, we briefly discuss VINCENT’s design in this paper as well. VINCENT integrates webometrics, natural language processing, data visualization, and human-data interaction. In the reported study, we gave users tasks requiring them to make sense of the online vaccine debate. Thirty-four participants were asked to perform these tasks by investigating data from 37 vaccine-focused websites. Half the participants were given access to the system, while the other half were not. Selected study participants from both groups were subsequently asked to be interviewed by the study administrator. Examples of questions and issues discussed with interviewees were: how they went about completing specific tasks, what they meant by some of the feedback they provided, and how they would have performed on the tasks if they had been placed in the other group. Overall, we found that VINCENT was a highly valuable resource for users, helping them make sense of the online vaccine debate much more effectively and faster than those without the system (e.g., users were able to compare websites similarities, identify emotional tone of websites, and locate websites with a specific focus). In this paper, we also identify a few issues that should be taken into consideration when developing VASes for online public health debates. Full article

The integration of high-throughput data to build predictive computational models of cellular metabolism is a major challenge of systems biology. These models are needed to predict cellular responses to genetic and environmental perturbations. Typically, this response involves both metabolic regulations related to the kinetic properties of enzymes and a genetic regulation affecting their concentrations. Thus, the integration of the transcriptional regulatory information is required to improve the accuracy and predictive ability of metabolic models. Integrative modeling is of primary importance to guide the search for various applications such as discovering novel potential drug targets to develop efficient therapeutic strategies for various diseases. In this paper, we propose an integrative predictive model based on techniques combining semantic web, probabilistic modeling, and constraint-based modeling methods. We applied our approach to human cancer metabolism to predict in silico the growth response of specific cancer cells under approved drug effects. Our method has proven successful in predicting the biomass rates of human liver cancer cells under drug-induced transcriptional perturbations. Full article