The singing voice is a multi-dimensional structure, produced by variation of vocal techniques and expressions in phonation, resonance, vibrato patterns and more. The singing voice is being demanded to produce diversity due to differences in style and language, which are difficult to capture accurately using traditional static methods. While existing acoustic analysis provides valuable insights through parameters such as fundamental frequency (F0), intensity, formant, and vibrato rate and extent, much of this complexity is lost without an effective cross-comparison means of visual representation. Building on recent studies of singing across styles and languages, this work reviews current approaches to singing voice data visual analysis, with a focus on how they support or limit the interpretation of expressive strategies in comparative vocal contexts. It identifies the need for visualisations that present acoustic data in a temporally aligned, multi-dimensional, and musically meaningful way, and also highlights key limitations in current practice. The proposed research direction centres on the development of real-time visualisation methods capable of mapping the interaction of multiple acoustic features over time. The conceptual roadmap aims to support future analytical research by providing clearer insights into stylistic and linguistic variations in the singing voice, with potential benefits for vocal pedagogy and practical applications.

This paper proposes a hybrid Convolutional Neural Network – Gated Recurrent Unit (CNN-GRU) architecture for producing algorithmic trading signals for the Binance Coin (BNB) cryptocurrency dataset. It uses the standalone models, CNN and GRU, as benchmarks for comparing both classification and trading performance. Results show that classification performance of CNN-GRU is quite subpar comparing to that of the standalone models; however, this model achieves the highest mean trading performance. Although the outcomes are financially promising, the paper has not explored algorithmic trading in its full glory, so results are open to further improvements. Possible future works include employing other methods for improving imbalanced classification, more feature engineering and testing with different timeframes, and a more involved approach with feature explainability.

The current research on acoustic properties in singing voice analysis has mainly focused on individual song segments, and analysed them using simple data tables and basic charts. However, there has been limited exploration of comparing data from multiple sources, and visual analysis in crossover singing has been either too simplistic or too complex to provide a comprehensive view. This study aims to address this gap by incorporating song segments from different musical styles and utilising an innovative graph drawing method to generate interactive graphs for comprehensive musical data analysis. The findings provide additional support for existing statements in the field of musical data analysis, and demonstrate the effectiveness of the proposed graph method for analysing multiple song segments. At this stage, the study's findings confirm that the formant frequency$F_{1}$of singing across styles is less modified, but$F_{2}-F_{5}$varies in styles singing in English. Additionally, the formant frequency in Mandarin and Cantonese singing may be associated with pitch. The study also identifies that visualised graphs can produce similar results as current vocal research and are convenient for reading multiple data simultaneously. The methodology has the potential to be extended to other areas of musical visualisation to uncover insights from complex music datasets.

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, combining COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from local government area-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.

Virus outbreaks are threats to humanity, and coronaviruses are the latest of many epidemics in the last few decades. In this work, we conduct a non-medical/clinical approach, generating graphs from five features concluded from the COVID-19 outbreak data and local community data in NSW (New South Wales), Australia, and offering insights from a visual analysis perspective. The results show that household income, population density and ethnicity affect the infection in different areas. Features such as human behaviours need to be imported for further COVID-19 research in the data science sector. This work is an initial step into this area and allows more insights to be brought into future COVID-19 research through a visual analysis perfective.

This paper introduces the use of pictogram design concept in ancient China for the development of a set of today's "graphic icons" or "infographics" in modern social network visualization systems. These graphic icons should be close to sensory symbols that derive their expressive power from their ability to use the perceptual processing power of the brain without learning. Therefore, with the use of such a set of "sensory symbols" we aim to achieve the identification of corresponding physical objects (or their attributes) to be performed close to the pre-attentive time.

This paper discusses the mathematical model and implements the physical verification when Carrier synchronization (carrier loop) and Timing synchronization (Gardner algorithm) working simultaneously. Firstly, a brief analysis of the above mentioned systems is completed, and the functional simulation is accomplished by Verilog HDL code on ModelSim platform. In addition, a systematic analysis is done for the workflow and interrelation of Carrier synchronization and Timing synchronization when both of them are active. Finally, we implement the hardware verification on the Stratix series field programmable gate array (FPGA) device EP3SL150F1152C2N of Altera Company, and give the engineering results through the RF part and space transmission.

Clustered graph drawing is widely considered as a good method to overcome the scalability problem when visualizing large (or huge) graphs. Force-directed algorithm is a popular approach for laying graphs yet small to medium size datasets due to its slow convergence time. This paper proposes a new method which combines clustering and a force-directed algorithm, to reduce the computational complexity and time. It works by dividing a Long Convergence: LC into two Short Convergences: SC1, SC2, where SC1+SC2 <; LC. We also apply our work on weighted graphs. Our experiments show that the new method improves the aesthetics in graph visualization by providing clearer views for connectivity and edge weights.

Modern visual analytics tools provide mechanism for users to gain unknown knowledge through effective visual interactions for user to quickly understand the progress of algorithms and adjust the input parameters on intermediate visualizations that towards the production of most satisfied outcome. This requires the quick production of a sequence of graph visualizations. However, the traditional force-directed graph drawing algorithms are very slow to reach an equilibrium configuration of forces. They usually spend tens of seconds producing the layout of a graph converge. Thus, they do not satisfy the requirement of rapid drawing of graphs. This paper proposes a fast convergence method for drawing force-directed graphs. We essentially pre-calculate the geometrical position of all vertices before applying a force-directed layout algorithm to reach the energy minimization of the graph layout. The experimental results have shown that this approach could not only reduce the convergence time but also the number of edge crossings that approves the quality of layout significantly.