Visual Text Analysis in Digital Humanities

In 2005, Franco Moretti introduced Distant Reading to analyse entire literary text collections. This was a rather revolutionary idea compared to the traditional Close Reading, which focuses on the thorough interpretation of an individual work. Both reading techniques are the prior means of Visual Text Analysis. We present an overview of the research conducted since 2005 on supporting text analysis tasks with close and distant reading visualizations in the digital humanities. Therefore, we classify the observed papers according to a taxonomy of text analysis tasks, categorize applied close and distant reading techniques to support the investigation of these tasks and illustrate approaches that combine both reading techniques in order to provide a multi-faceted view of the textual data. In addition, we take a look at the used text sources and at the typical data transformation steps required for the proposed visualizations. Finally, we summarize collaboration experiences when developing visualizations for close and distant reading, and we give an outlook on future challenges in that research area

Instance-Based Lossless Summarization of Knowledge Graph With Optimized Triples and Corrections (IBA-OTC)

Knowledge graph (KG) summarization facilitates efficient information retrieval for exploring complex structural data. For fast information retrieval, it requires processing on redundant data. However, it necessitates the completion of information in a summary graph. It also saves computational time during data retrieval, storage space, in-memory visualization, and preserving structure after summarization. State-of-the-art approaches summarize a given KG by preserving its structure at the cost of information loss. Additionally, the approaches not preserving the underlying structure, compromise the summarization ratio by focusing only on the compression of specific regions. In this way, these approaches either miss preserving the original facts or the wrong prediction of inferred information. To solve these problems, we present a novel framework for generating a lossless summary by preserving the structure through super signatures and their corresponding corrections. The proposed approach summarizes only the naturally overlapped instances while maintaining its information and preserving the underlying Resource Description Framework RDF graph. The resultant summary is composed of triples with positive, negative, and star corrections that are optimized by the smart calling of two novel functions namely merge and disperse . To evaluate the effectiveness of our proposed approach, we perform experiments on nine publicly available real-world knowledge graphs and obtain a better summarization ratio than state-of-the-art approaches by a margin of 10% to 30% with achieving its completeness, correctness, and compactness. In this way, the retrieval of common events and groups by queries is accelerated in the resultant graph

Serverless Vehicular Edge Computing for the Internet of Vehicles

Rapid growth in the popularity of smart vehicles and increasing demand for vehicle autonomy brings new opportunities for vehicular edge computing (VEC). VEC aims at offloading the time-sensitive computational load of connected vehicles to edge devices, e.g., roadside units. However, VEC offloading raises complex resource management challenges and, thus, remains largely inaccessible to automotive companies. Recently, serverless computing emerged as a convenient approach to the execution of functions without the hassle of infrastructure management. In this work, we propose the idea of serverless VEC as the execution paradigm for Internet of Vehicles applications. Further, we analyze its benefits and drawbacks as well as identify technology gaps. We also propose emulation as a design, evaluation, and experimentation methodology for serverless VEC solutions. Using our emulation toolkit, we validate the feasibility of serverless VEC for real-world traffic scenarios.We would like to thank Asama Qureshi for his contribution to the traffic visualizer application. We would also like to acknowledge support through the Australian Research Council's funded projects DP230100081 and FT180100140. This work is also partially supported by the Spanish Ministry of Economic Affairs and Digital Transformation, the European Union-NextGenerationEU through the UNICO 5G IþD SORUS project and by the NWO OffSense, EU Horizon Graph-Massivizer and CLOUDSTARS projects

On Close and Distant Reading in Digital Humanities: A Survey and Future Challenges

We present an overview of the last ten years of research on visualizations that support close and distant reading of textual data in the digital humanities. We look at various works published within both the visualization and digital humanities communities. We provide a taxonomy of applied methods for close and distant reading, and illustrate approaches that combine both reading techniques to provide a multifaceted view of the data. Furthermore, we list toolkits and potentially beneficial visualization approaches for research in the digital humanities. Finally, we summarize collaboration experiences when developing visualizations for close and distant reading, and give an outlook on future challenges in that research area

Idiopathic scrotal calcinosis – a case report

Idiopathic scrotal calcinosis is formation of calcium deposits in the dermal layers of the scrotum. It results in the formation of single or multiple nodular calcifications that vary in size and number. First reported in 1883, this condition is common in the third decade of life. The presenting complaints range from disfigurement to itching, leading to decreased quality of life. The diagnosis is usually made on a clinical basis and can be confirmed by the histopathology of the excised nodules.  Surgical removal of the nodules is the generally recommended treatment. The surgery aims to eradicate the nodules leaving the scrotal skin enough for scrotoplasty. We present a case of idiopathic scrotal calcinosis in a 37 years old male who came for radiological examination. Keywords: Calcinosis, scrotum, calcium, pruritus

Lumped Parameter Model and Electromagnetic Performance Analysis of a Single-Sided Variable Flux Permanent Magnet Linear Machine

A new Single-sided Variable Flux Permanent Magnet Linear Machine with flux bridge in mover core is proposed in this paper. The flux bridge prevents the leakage flux from the mover and converts it into flux linkage, which greatly influences the performance of the machine. First, a lumped parameter model is used to find the suitable coil combination and no-load flux linkage of the proposed machine, which greatly reduces the computational time and drive storage. Secondly, the proposed machine replaces the expensive rare earth permanent magnets with ferrite magnets and provides improved flux controlling capability under variable excitation currents. Multivariable geometric optimization is utilized to optimize the leading design parameters like split ratio, stator pole width, width and height of permanent magnet, flux bridge width, the width of mover’s tooth, and stator slot depth at constant electric and magnetic loading. The optimized design increases the flux linkage by 44.11%, average thrust force by 35%, thrust force density by 35.02%, minimizes ripples in thrust force by 23%, and detent force by 87.5%. Furthermore, the results obtained by 2D analysis are verified by 3D analysis. Thermal analysis is done to set the operating limit of the proposed machine

Novel Fractional Swarming with Key Term Separation for Input Nonlinear Control Autoregressive Systems

In recent decades, fractional order calculus has become an important mathematical tool for effectively solving complex problems through better modeling with the introduction of fractional differential/integral operators; fractional order swarming heuristics are also introduced and applied for better performance in different optimization tasks. This study investigates the nonlinear system identification problem of the input nonlinear control autoregressive (IN-CAR) model through the novel implementation of fractional order particle swarm optimization (FO-PSO) heuristics; further, the key term separation technique (KTST) is introduced in the FO-PSO to solve the over-parameterization issue involved in the parameter estimation of the IN-CAR model. The proposed KTST-based FO-PSO, i.e., KTST-FOPSO accurately estimates the parameters of an unknown IN-CAR system with robust performance in cases of different noise scenarios. The performance of the KTST-FOPSO is investigated exhaustively for different fractional orders as well as in comparison with the standard counterpart. The results of statistical indices through Monte Carlo simulations endorse the reliability and stability of the KTST-FOPSO for IN-CAR identification

Adaptive Evolutionary Computation for Nonlinear Hammerstein Control Autoregressive Systems with Key Term Separation Principle

The knacks of evolutionary and swarm computing paradigms have been exploited to solve complex engineering and applied science problems, including parameter estimation for nonlinear systems. The population-based computational heuristics applied for parameter identification of nonlinear systems estimate the redundant parameters due to an overparameterization problem. The aim of this study was to exploit the key term separation (KTS) principle-based identification model with adaptive evolutionary computing to overcome the overparameterization issue. The parameter estimation of Hammerstein control autoregressive (HC-AR) systems was conducted through integration of the KTS idea with the global optimization efficacy of genetic algorithms (GAs). The proposed approach effectively estimated the actual parameters of the HC-AR system for noiseless as well as noisy scenarios. The simulation results verified the accuracy, convergence, and robustness of the proposed scheme. While consistent accuracy and reliability of the designed approach was validated through statistical assessments on multiple independent trials

Adaptive Evolutionary Computation for Nonlinear Hammerstein Control Autoregressive Systems with Key Term Separation Principle

The knacks of evolutionary and swarm computing paradigms have been exploited to solve complex engineering and applied science problems, including parameter estimation for nonlinear systems. The population-based computational heuristics applied for parameter identification of nonlinear systems estimate the redundant parameters due to an overparameterization problem. The aim of this study was to exploit the key term separation (KTS) principle-based identification model with adaptive evolutionary computing to overcome the overparameterization issue. The parameter estimation of Hammerstein control autoregressive (HC-AR) systems was conducted through integration of the KTS idea with the global optimization efficacy of genetic algorithms (GAs). The proposed approach effectively estimated the actual parameters of the HC-AR system for noiseless as well as noisy scenarios. The simulation results verified the accuracy, convergence, and robustness of the proposed scheme. While consistent accuracy and reliability of the designed approach was validated through statistical assessments on multiple independent trials