Visual Informatics

It has long been known that museum education has the ability to motivate and excite visitors whilst providing them with new insights and experiences. Nevertheless, activities that learning goal, for example, visiting a museum is found to disinterest, not appealing and give insignificant impact to children as compared to visiting the amusement park, playground, or even zoo. Thus, museums are increasingly being equipped with digital and mobile technologies. The main goal of using technologies is to improve the museum-going experience for visitors. In this research, we present a study of a museum interactive quest based on the proposed interaction design model. The study involves children in the age of 9 to 11 to visit a museum located in Malaysia. The findings from the study have highlighted the potential of the proposed interaction model that has affected the children enjoyment and engagement during the museum visit.

Scaliness of psoriasis lesions is one of the parameters to be determined during Psoriasis Area and Severity Index (PASI) scoring. Dermatologists typically use their visual and tactile senses to assess PASI scaliness. However, it is known that the scores are subjective resulting in inter-and intra-rater variability. In this paper, an objective 3D imaging method is proposed to assess PASI scaliness parameter of psoriasis lesions. As scales on the lesion invariably causes roughness, a surface-roughness measurement method is proposed for 3D curved surfaces. The method applies a polynomial surface fitting to the lesion surface to extract the estimated waviness from the actual lesion surface. Surface roughness is measured from the vertical deviations of the lesion surface from the estimated waviness surface. The surface roughness algorithm has been validated against 328 lesion models of known roughness on a medical mannequin. The proposed algorithm is found to have an error 0.0013 ± 0.0022 mm giving an accuracy of 89.30%. The algorithm is invariant to rotation of the measured surface. Accuracy of the rotated lesion models is found to be greater than 95%. System repeatability has been evaluated to successive measurements of 456 psoriasis lesions. The system repeatability can be accepted since 95.27% of the measurement differences are less than two standard deviation of measurement difference.

The global fatality caused by the deadly COVID-19 already took 4.5 million lives and is still rapidly increasing. At the same time, Malaysian citizens have been inundated with the overabundance of news and information about COVID-19 since it hit the world in December 2019. Recent study by ISIS Malaysia discovered that WhatsApp and Facebook are the most used social media for misinformation at 39% and 34%, respectively. This phenomenon is termed as an infodemic which occurs when there is an excessive amount of information with undetermined level of accuracy. Hence, this situation makes it difficult for people to find reliable and truthful sources of information when they require it. Infodemiology is the scientific term used to describe the massive spread of information in a digital format particularly on the Internet which aims to guide the stakeholders such as the government on public health policy. Artificial Intelligence (AI) techniques hold potential solutions to address infodemic issue. This paper conceptualizes an Infodemiology Framework for COVID-19 and future pandemics towards addressing the proliferation of misinformation and disinformation on the Internet. Leveraging on AI techniques such as classification via clustering and decision tree algorithms, the research works will be conducted in five phases beginning with dataset collection phase, model building and algorithm selection phase, model refinement phase, model verification phase, and the model deployment phase. The proposed infodemiology framework has the potential to be integrated into the nation’s healthcare data warehousing system, the Malaysian Health Data Warehouse (MyHDW). © 2021, Springer Nature Switzerland AG.

h i g h l i g h t s • Variants of stacked bar charts have different levels of effectiveness for supporting overall-attribute comparisons. • Appropriate visual representations of data can reduce the complexity of the task at hand. • Spatial separation of information can help users manage the perceived difficulty of the task at hand.

Narrative Visualisation (NarVis) is the pairing of data visualisation with narrative techniques. Due to its interdisciplinary applications and scholarship, NarVis presentations often feature vastly different interpretations of “narrative” and “visualisation”, which is echoed in NarVis authoring tools. To map the morphology of how the narratives of NarVis manifest, we identify three different trajectories for the field. These trajectories are identified through an analysis of selected NarVis presentations and tools, with an emphasis on identifying how traditional narrative techniques are adopted, transposed or indeed challenged by NarVis examples. We then populate our categories with additional examples and tools, providing a foundational point of reference for NarVis scholars, authors and tool developers.

In the application domain of digital humanities network visualization is increasingly being used to conduct research as the main interests of the domain experts lie in exploring and analyzing relationships between entities and their changes over time. Visualizing the dynamics and different perspectives of such data is a non-trivial task but it enables researchers to explore connections between disparate entities and investigate historical narratives that emerge. In this paper we present Circular, an interactive exploration environment to visualize event-based networks and support research in digital humanities through visualization of historical subjects in space and time. Our radial design is the result of iterative collaboration with domain experts, and we discuss the process of collaborative development and exploration of public music festivities in Vienna as an example of immersive development methodology. We validate our approach by means of both domain and visualization expert interviews and show the potential of this approach in supporting the visual exploration of historical subjects. We discuss our design rationales, visual encodings, and interactions as to allow the reproducibility of this approach within a framework of transdisciplinary collaboration with digital humanities.

Self-regulated learning (SRL) is an academically effective form of learning, which learners must set their goals and make plans before starting to learn. As an ongoing process, learners need to monitor and regulate their cognition, motivation, and behavior as well as reflect on their learning process. These processes will be repeated as a cyclic process. The emerging technologies have changed the learning environments. Technology delivers teaching to learners via online. In online learning, information of education and learners do not share the same physical setting. Online learning should provide opportunities for learners to master necessary tasks. Online learners may use SRL strategies. In this research, we have collected, synthesized, and analyzed 130 articles on various topics related to SRL that published from 1986 to 2017, focusing on online learning and mathematics. We noted several models, phases, and few other topics discussed under SRL.

In the bio-inspired area of membrane computing, P system is a versatile model providing a rich framework for many computational problems. Array P system and its variant with parallel rewriting facilitate the study of picture languages within this area of membrane computing. Here another variant of array P system, called tabled parallel array P system (TPAP), is introduced, by endowing it with the features of parallel rewriting and tables of array-rewriting rules. The generative power of TPAP as well as the ability of this system in describing picture patterns are investigated.

... Muhammad Abdulkarim, Afza Shafie, Radzuan Razali, Wan Fatimah Wan Ahmad, and AgusArif Universiti Teknologi PETRONAS, Seri Iskandar, 31750 Tronoh, Perak, Malaysia {mmmhammmad,agusarif}@gmail.com, {afza,radzuan_razali,fatimhd}@petronas.com.my Abstract. ...

Human's daily movements exhibit high regularity in a space-time context that typically forms circadian rhythms. Understanding the rhythms for human daily movements is of high interest to a variety of parties from urban planners, transportation analysts, to business strategists. In this paper, we present an interactive visual analytics design for understanding and utilizing data collected from tracking human's movements. The resulting system identifies and visually presents frequent human movement rhythms to support interactive exploration and analysis of the data over space and time. Case studies using real-world human movement data, including massive urban public transportation data in Singapore and the MIT reality mining dataset, and interviews with transportation researches were conducted to demonstrate the effectiveness and usefulness of our system.

This paper outlines the research conducted to test a visual measurement model of the Human Capital and ICT dimensions to develop a framework of a Knowledge-based Society for Malaysia towards an Innovative Digital Economy. Based on an initial analysis on Knowledge-based Society (KS),it was found that Human capital and ICT represent important enabling dimensions in a KS framework. The content domain to measure both these dimensions were developed based on empirical data and literature available. It was then tested using a sample of 450 respondents from both government and private sectors as well as NGOs. Four factors were extracted from the exploratory factor analysis based on 44 items proposed in the survey questionnaire. For the Human Capital dimension, 42 items were loaded into the four factors, and 2 items were deleted due to loadings less than 0.3; whilst for the ICT dimension, 39 items were loaded into the four factors and 2 items were also deleted due to loadings less than 0.3. The visual measurement model was validated using the Structural Equation Modelling (SEM).The confirmatory factor analysis results indicated that generally, Human Capital and ICT dimensions provided a reasonably good fit measurement model, thus necessary to be used as a guide in the development of a KS framework in Malaysia.

Entrepreneurship education plays a crucial role in higher education to increase student’s interests and capability of having an entrepreneurial mindset. This study aims to evaluate student’s perception of their institution, instructors and themselves towards entrepreneurship education to inform gamification design of entrepreneurship education in the 4IR era. Through knowledge transfer programs, a self-reported questionnaire was distributed to 3 participating universities in 3 ASEAN countries namely Malaysia, Indonesia and Vietnam measuring their perception of the current entrepreneurship education at their respective institutions. In total, 209 responses were received, comprising students from Malaysia (27.3%), Indonesia (42.6%) and Vietnam (30.1%). The results suggest that the combination of student’s perception of the institutions that provide necessary supports, the instructors’ abilities to deliver teaching materials in an engaging way and students’ openness to learn entreprene...

Many studies have been conducted to determine how data mining can be used in predicting climate change. Previous studies showed many data mining methods have been used in related to climate prediction, however classification and clustering methods are widely used to generate the climate prediction model. In this study, Association Rule Mining (ARM) is used to discover hidden rules in time series climate data from previous years and to analyze the relationship between the discovered rules. The dataset used in this study is a set of weather data from the Petaling Jaya observation station in Selangor for the year 2013 to 2015. This paper aims to utilize ARM for extracting behavioural patterns within the climate data that can be used to develop the prediction model for climate variability. The proposed framework is developed to provide a better approach in understanding how ARM can be used to find meaningful patterns in the climate data and generate rules that can be used to build a pre...

Academic writing is a benchmark in most academic institution towards maintaining academic excellence among graduate students. However, to avoid plagiarism was found to be one of the most challenging tasks in academic writing among graduates in the digital era. A deeper understanding of plagiarism would be an important addition to students’ competencies in academic writing. This study aimed to develop a one-stop e-learning platform that is able to assist graduate students in addressing their knowledge and understanding towards plagiarism. ICCEE instructional design method was used in developing the proposed platform. Need analysis was the first step approached in the development process. A survey was conducted among 440 post-graduate students using self-designed questionnaire. The outcome of the survey was quantitatively analyzed using descriptive technique. Results show that majority of the respondents have general knowledge about plagiarism. Even though most respondents agreed plagiarism is a serious matter, their attitude shows the opposite. This may be because they have never taken any specific course related to plagiarism nor used any plagiarism detection tool. The respondents also stated the need for a specific online course on plagiarism in the form of Massive Open Online Course (MOOC). This information about students’ need can be a guide in developing the one-stop e-learning platform to understand about plagiarism.

Given that mental stress issue is becoming a big concern in today’s society, it is important to highlight available alternatives that may help in reducing stress. One of the options available is virtual reality-based therapy (VR-based therapy). Previous studies conducted show that VR-based therapy has promising results in reducing stress and inducing relaxation. However, most of the studies were only focusing on the effectiveness of the system without taking into consideration the elements that should be included while designing its virtual environment. Therefore, this study aims to identify the possible design elements that should be taken into consideration when designing a virtual environment for a VR-based stress therapy system. An existing system review consisted of system demonstration and interview session was conducted with 12 random participants. During the review, the users were required to use an existing VR-based system and provide their feedback and comments during a short interview session that follows. Based on the review, eight design elements were identified, which were then grouped into five themes; visual attraction, environment setting, user comfort, interaction and navigation.

This paper propose a robust image watermarking scheme based on the singular value decomposition (SVD) and genetic algorithm (GA). SVD based watermarking techniques suffer with an issue of false positive problem. This leads to even authentication the wrong owner. Prevention of false positive errors is a major challenge for ownership identification and proof of ownership application using digital watermarking. We employed GA algorithm to optimize the watermarked image quality (robustness) of the extracted watermarks. The former can be overcome by embedding the owner’s components of the watermark into the host image, the latter is dependent on how much the quantity for the scaling factor of the principle components is embedded. To improve the quality of watermarking (robustness), GA is used for optimize the suitable scaling factor. Experimental result of the proposed technique proves the watermark image ownership and can be reliably identified even after severe attacks. The comparison of the proposed technique with the state of the art show the superiority of our proposed technique where it is outperforming the methods in comparison.

Human's daily movements exhibit high regularity in a space-time context that typically forms circadian rhythms. Understanding the rhythms for human daily movements is of high interest to a variety of parties from urban planners, transportation analysts, to business strategists. In this paper, we present an interactive visual analytics design for understanding and utilizing data collected from tracking human's movements. The resulting system identifies and visually presents frequent human movement rhythms to support interactive exploration and analysis of the data over space and time. Case studies using real-world human movement data, including massive urban public transportation data in Singapore and the MIT reality mining dataset, and interviews with transportation researches were conducted to demonstrate the effectiveness and usefulness of our system.

For decades, researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks. Experiments involving human participants have also explored the readability of different styles of layout and representations for such networks. In both bodies of literature, networks are frequently referred to as being 'large' or 'complex', yet these terms are relative. From a human-centred, experiment point-of-view, what constitutes 'large' (for example) depends on several factors, such as data complexity, visual complexity, and the technology used. In this paper, we survey the literature on human-centred experiments to understand how, in practice, different features and characteristics of node-link diagrams affect visual complexity.