Integrating artificial intelligence (AI) with immersive analytics (IA) represents a promising means of leveraging advanced computational techniques to enhance data visualization and analysis. This study examines the state-of-the-art of AI-IA integration by addressing three key research issues: the significant application domains, the AI techniques used and their combinations, and current challenges and future directions. Results of reviewing 43 relevant studies reveal that AI-IA integration is still in its early stages, as existing research has mainly focused on a limited range of data types and application scenarios. By analyzing the application domains, this systematic literature review supports previous findings of important applications in the fields of education, manufacturing, and healthcare. At the same time, it identifies emerging applications that have progressed from XR and AI domains to AI-IA integration, such as sports events, assistive systems, urban planning, and disaster management. We contribute to extending established visual analytics (VA) pipelines into XR environments with integrated AI techniques. AI techniques are identified as contributing in five ways to this IA pipeline. Our contribution also includes identifying four key challenges and seven opportunities for future exploration. The review concludes that combining AI and IA holds the potential to create innovative applications using advanced AI and immersive visualization techniques. We present an overview of these applications and address key issues for future development.

Abstract In this paper, we investigate the current state and development of personalized smart immersive extended reality environments (PSI-XR). PSI-XR has gained increasing traction across various fields such as education, entertainment, and healthcare, offering customized immersive experiences that address users’ personalized needs. This study performs a systematic literature review by collecting and analyzing related journal and conference papers in the domain. Following a comprehensive search across three databases, which yielded 1276 papers, a refined selection of 94 publications was made to conduct an in-depth analysis of cutting-edge research in the field of PSI-XR. This review focused on examining application domains, relevant technologies, and smart techniques, including artificial intelligence, with particular emphasis on advancements in personalization. The study provides insights into prospective advancements while also identifying the opportunities and challenges in this evolving field. This review is beneficial for both researchers and developers interested in exploring the state-of-the-art personalized perspective in a smart immersive extended reality environment.

Abstract Snow and ice animation methods are becoming increasingly popular in the field of computer graphics (CG). The applications of snow and ice in CG are varied, ranging from generating realistic background landscapes to avalanches and physical interaction with objects in movies, games, etc. Over the past two decades, several methods have been proposed to capture the time‐evolving physical appearance or simulation of snow and ice using different models at different scales. This state‐of‐the‐art report aims to identify existing animation methods in the field, provide an up‐to‐date summary of the research in CG, and identify gaps for promising future work. Furthermore, we also attempt to identify the primarily related work done on snow and ice in some other disciplines, such as civil or mechanical engineering, and draw a parallel with the similarities and differences in CG.

Abstract Clouds play an important role in enhancing the realism of outdoor scenes in computer graphics (CG). Realistic cloud generation is a challenging task, which entails processes such as modeling, photorealistic rendering and simulation of the clouds. To these ends, several techniques have been proposed within the CG community in the last 4 decades with one or more of the above stated focuses. The growth of modern hardware has also enabled development of techniques that can achieve cloud display and animation at interactive frame rates. In this survey, we review the prominent work in the domain and also summarize the evolution of the research over the time.

Reliable and accurate short-term traffic prediction plays a key role in modern intelligent transportation systems (ITS) for achieving efficient traffic management and accident detection. Previous work has investigated this topic but lacks study on automated anomaly detection and conditional information fusion for ensemble methods. This works aims to improve prediction accuracy by fusing information considering different traffic conditions in ensemble methods. In addition to conditional information fusion, a day-week decomposition (DWD) method is introduced for preprocessing before anomaly detection. A k-nearest neighbours (kNN) based ensemble method is used as an example. Real-world data are used to test the proposed method with stratified ten-fold cross-validation. The results show that the proposed method with incident labels improves predictions up to 15.3% and the DWD enhanced anomaly-detection improves predictions up to 8.96 %. Conditional information fusion improves ensemble prediction methods, especially for incident traffic. The proposed method works well with enhanced detections and the procedure is fully automated. The accurate predictions lead to more robust traffic control and routing systems.

The vast amount of Web services brings the problem of discovering desired services for composition and orchestration. The syntactic service matching methods based on the classical set theory have a difficulty to capture the imprecise information. Therefore, an approximate service matching approach based on fuzzy control is explored in this paper. A service description matching model to the OpenAPI specification, which is the most widely used standard for describing the defacto REST Web services, is proposed to realize the fuzzy service matching with the fuzzy inference method developed by Mamdani and Assilian. An evaluation shows that the fuzzy service matching approach performed slightly better than the weighted approach in the setting context.

Robust and accurate traffic prediction is critical in modern intelligent transportation systems (ITS). One widely used method for short-term traffic prediction is k-nearest neighbours (kNN). However, choosing the right parameter values for kNN is problematic. Although many studies have investigated this problem, they did not consider all parameters of kNN at the same time. This paper aims to improve kNN prediction accuracy by tuning all parameters simultaneously concerning dynamic traffic characteristics. We propose weighted parameter tuples (WPT) to calculate weighted average dynamically according to flow rate. Comprehensive experiments are conducted on one-year real-world data. The results show that flow-aware WPT kNN performs better than manually tuned kNN as well as benchmark methods such as extreme gradient boosting (XGB) and seasonal autoregressive integrated moving average (SARIMA). Thus, it is recommended to use dynamic parameters regarding traffic flow and to consider all parameters at the same time.

Cumulus clouds are ubiquitous in outdoor naturalistic scenes. However, realistic and scalable cloud simulation continues to be a challenge in computer graphics. Computational fluid dynamics models (CFD) are computationally intensive and hence impractical for real time methods. Eulerian models [Harris et al. 2003] on the other hand require modeling even the transparent volume, placing serious restrictions on the domain size. More importantly, both these methods do not offer convenient handles on initial, border and atmospheric conditions which play a key role in the natural evolution of the cloud phenomena. We introduce Lagrangian framework for the cloud physics and rendering at landscape size. The use of Lagrangian formalism, aerology physics, and separation of environment from the dynamics, brings more robustness, offers meaningful high-level handles for user control - environment profile and evolution, cloud base and top.

Accurate soil moisture data, critical for many applications such as agriculture and estimation of ground water, is limited worldwide, and particularly over India, by the absence of sustained multisite observations. A long-term sustained soil moisture observation at four vertical levels (5, 15, 50, and 100 cm) is now available at several locations over India under a multiinstitutional program Climate Observations and Modeling Network (COMoN) led by CSIR, India. At the same time, a high resolution (0.1° × 0.1°) daily (moving 5-day mean) surface relative soil moisture data set has now become available from the Advanced Scatterometer (ASCAT). However, there is a need to compare remotely sensed data and in situ observations to ensure consistency and quantify uncertainties. This is particularly true for India characterized by diverse climatic zones. We present a comparative analysis of gridded ASCAT soil moisture data and in situ COMoN station data over six locations in India during the period 2010-2013. A multiscale analysis is carried out involving daily, weekly, monthly, and seasonal timescales at different geographical locations. Analyses show that the two data sets are generally consistent, although there are seasonalities in the agreement; the correlation coefficient is higher for the wet season (summer, autumn), and moderate for dry season (winter, spring). The correlation coefficients are ranged from 0.73 to 0.91 and above 99% significance level. The results quantify the reliability and robustness of ASCAT soil moisture over different climatic regions in India; the results also identify certain differences between the two data sets.

A reciprocal frame (RF) is a self-supported three-dimensional structure made up of three or more sloping rods, which form a closed circuit, namely an RF-unit. Large RF-structures built as complex grillages of one or a few similar RF-units have an intrinsic beauty derived from their inherent self-similar and highly symmetric patterns. Designing RF-structures that span over large domains is an intricate and complex task. In this paper, we present an interactive computational tool for designing RF-structures over a 3D guiding surface, focusing on the aesthetic aspect of the design. There are three key contributions in this work. First, we draw an analogy between RF-structures and plane tiling with regular polygons, and develop a computational scheme to generate coherent RF-tessellations from simple grammar rules. Second, we employ a conformal mapping to lift the 2D tessellation over a 3D guiding surface, allowing a real-time preview and efficient exploration of wide ranges of RF design parameters. Third, we devise an optimization method to guarantee the collinearity of contact joints along each rod, while preserving the geometric properties of the RF-structure. Our tool not only supports the design of wide variety of RF pattern classes and their variations, but also allows preview and refinement through interactive controls.