Interactive Digital Testimonies (IDTs) allow users to learn virtually about the life stories of contemporary witnesses as recounted by the witnesses themselves. Although several IDTs have been created in recent years, there is little empirical research on their effects on users. We investigated how different levels of visual modality (audio-only, audio-visual 2D, audio-visual stereoscopic 3D) affect user perception by conducting two separate mixed-methods studies: A 2x2 between-subjects study comparing audio-only with audio-visual 2D in in-person and online settings (n = 82) and a within-subjects study comparing audio-visual 2D with audio-visual stereoscopic 3D (n = 51). We found that audio-visual 2D improves user experience, immersion, and perceived authenticity over audio-only versions. Audio-visual 3D IDTs are more authentic and immersive than audio-visual 2D IDTs, however, this is diminished by a less comfortable interaction. Our findings broaden empirical research on user perception of realistic Embodied Conversational Agents and help guide future thanatosensitive designs.

Adaptive virtual reality (VR) training adjusts applications in VR specifically to the user. This paper examines the current state of the art in the field of adaptive VR training. We conduct a Systematic Literature Review (SLR) using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. Building on previous results, addressing identified weaknesses and using 16 different evaluation categories, we present the most comprehensive SLR currently available, including studies until July 2024. We identified several research gaps for potential future work. First, an increasing number of studies over time. Second, a strong focus on the rehabilitation domain. Third, small sample sizes per group in the conducted user studies. Fourth, a strong increase in the use of head-mounted displays (HMDs). Fifth, a strong focus on using scenario difficulty as adaptive content variable, and sixth, only little research on learning transfer.

Adaptive Virtual Reality Training (VRT) enables personalised training of simulated tasks in 3D interactive virtual environments. The transfer of knowledge from a training context into a real-world setting, also called learning transfer, is an essential part of the learning process. This paper examines the effectiveness of adaptive VRT on learning transfer. We conduct a between subject user study with three groups (control group (n=26), non-adaptive VRT group (n=26), adaptive VRT group (n=28)). The effectiveness on learning transfer is measured with three objective (round time, memorisation of tasks, number of errors) and two subjective metrics (mental workload, physical effort). We slightly modify an existing adaptive VRT building on physiological measurement data (PMD) and create a cor-responding real-world implementation. Results show significant differences among the three groups for round time, number of errors, memorisation of tasks and perceived physical effort.

Quantum computing (QC) introduces a novel mode of computation with the possibility of greater computational power that remains to be exploitedpresenting exciting opportunities for high-performance computing (HPC) applications. However, recent advancements in the field have made clear that QC does not supplant conventional HPC, but can rather be incorporated into current heterogeneous HPC infrastructures as an additional accelerator, thereby enabling the optimal utilization of both paradigms. The desire for such integration significantly affects the development of software for quantum computers, which in turn influences the necessary software infrastructure. To date, previous review articles have investigated various quantum programming tools (QPTs) (such as languages, libraries, frameworks) in their ability to program, compile, and execute quantum circuits. However, the integration effort with classical HPC frameworks or systems has not been addressed. This study aims to characterize existing QPTs from an HPC perspective, investigating if existing QPTs have the potential to be efficiently integrated with classical computing models and determining where work is still required. This work structures a set of criteria into an analysis blueprint that enables HPC scientists to assess whether a QPT is suitable for a high-performance computing quantum computing (HPCQC) environment.

Cloud computing provisions computer resources at a cost-effective way based on demand. Therefore it has become a viable solution for big data analytics and artificial intelligence which have been widely adopted in various domain science. Data security in certain fields such as biomedical research remains a major concern when moving their workflows to cloud, because cloud environments are generally outsourced which are more exposed to risks. We present a secure cloud architecture and describes how it enables workflow packaging and scheduling while keeping its data, logic and computation secure in transit, in use and at rest.

Deep Learning (DL) is one of many successful methodologies to extract informative patterns and insights from ever increasing noisy large-scale datasets (in our case, satellite images). However, DL models consist of a few thousand to millions of training parameters, and these training parameters require tremendous amount of electrical power for extracting informative patterns from noisy large-scale datasets (e.g., computationally expensive). Hence, we employ a quantum-inspired tensor network for compressing trainable parameters of physics-informed neural networks (PINNs) in Earth science. PINNs are DL models penalized by enforcing the law of physics; in particular, the law of physics is embedded in DL models. In addition, we apply tensor decomposition to HyperSpectral Images (HSIs) to improve their spectral resolution. A quantum-inspired tensor network is also the native formulation to efficiently represent and train quantum machine learning models on big datasets on GPU tensor cores. Furthermore, the key contribution of this paper is twofold: (I) we reduced a number of trainable parameters of PINNs by using a quantum-inspired tensor network, and (II) we improved the spectral resolution of remotely-sensed images by employing tensor decomposition. As a benchmark PDE, we solved Burger’s equation. As practical satellite data, we employed HSIs of Indian Pine, USA and of Pavia University, Italy.

Summary Load balancing is often a challenge in task‐parallel applications. The balancing problems are divided into static and dynamic. “Static” means that we have some prior knowledge about load information and perform balancing before execution, while “dynamic” must rely on partial information of the execution status to balance the load at runtime. Conventionally, work stealing is a practical approach used in almost all shared memory systems. In distributed memory systems, the communication overhead can make stealing tasks too late. To improve, people have proposed a reactive approach to relax communication in balancing load. The approach leaves one dedicated thread per process to monitor the queue status and offload tasks reactively from a slow to a fast process. However, reactive decisions might be mistaken in high imbalance cases. First, this article proposes a performance model to analyze reactive balancing behaviors and understand the bound leading to incorrect decisions. Second, we introduce a proactive approach to improve further balancing tasks at runtime. The approach exploits task‐based programming models with a dedicated thread as well, namely . Nevertheless, the main idea is to force not only to monitor load; it will characterize tasks and train load prediction models by online learning. “Proactive” indicates offloading tasks before each execution phase proactively with an appropriate number of tasks at once to a potential victim (denoted by an underloaded/fast process). The experimental results confirm speedup improvements from to in important use cases compared to the previous solutions. Furthermore, this approach can support co‐scheduling tasks across multiple applications.

The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2–3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silico methodologies need to be improved both to select better lead compounds, so as to improve the efficiency of later stages in the drug discovery protocol, and to identify those lead compounds more quickly. No known methodological approach can deliver this combination of higher quality and speed. Here, we describe an Integrated Modeling PipEline for COVID Cure by Assessing Better LEads (IMPECCABLE) that employs multiple methodological innovations to overcome this fundamental limitation. We also describe the computational framework that we have developed to support these innovations at scale, and characterize the performance of this framework in terms of throughput, peak performance, and scientific results. We show that individual workflow components deliver 100 × to 1000 × improvement over traditional methods, and that the integration of methods, supported by scalable infrastructure, speeds up drug discovery by orders of magnitudes. IMPECCABLE has screened ∼ 1011 ligands and has been used to discover a promising drug candidate. These capabilities have been used by the US DOE National Virtual Biotechnology Laboratory and the EU Centre of Excellence in Computational Biomedicine.

In this paper we describe the process of designing an educational Virtual Reality (VR) application for university medical students to learn how to set up a work place ergonomically correct. We focus on commercially available hardware and a commercial game engine. This was a collaboration between medical experts and VR experts to achieve the ideal results. This use case builds on research of 3D user interfaces (UI) to create an UI system that novice VR-users can use intuitively. Next steps include a user study to compare immersive and non-immersive versions, as well as learn effects in VR.

Many providers of research data services officially embrace the FAIR guiding principles for scientific data management and stewardship. To assess the compliance of their services to these principles and to indicate possible improvements, use-case-centric metrics are needed as an addendum to existing approaches. The retrieval of spatially and temporally annotated images can exemplify such a use case. A prototypical benchmark based on that use case indicates that currently no research data repository achieves the full score according to the proposed metric. Suggestions on how to increase the score include automatic annotation based on the metadata inside the image file and support for content negotiation to retrieve the research data. This can lead to an improvement of data integration workflows, resulting in a better and more FAIR approach to manage research data.

Summary In Autumn 2013, we—an international team of climate scientists, computer scientists, eScience researchers, and e‐Infrastructure specialists—participated in the enlighten your research global competition, organized to showcase advanced lightpath technologies in support of state‐of‐the‐art research questions. As one of the winning entries, our enlighten your research global team embarked on a very ambitious project to run an extremely high resolution climate model on a collection of supercomputers distributed over two continents and connected using an advanced 10 G lightpath networking infrastructure. Although good progress was made, we were not able to perform all desired experiments due to a varying combination of technical problems, configuration issues, policy limitations and lack of (budget for) human resources to solve these issues. In this paper, we describe our goals, the technical and non‐technical barriers, we encountered and provide recommendations on how these barriers can be removed so future project of this kind may succeed. Copyright © 2016 John Wiley & Sons, Ltd.

Environmental computing is becoming a recognized specialty focusing on producing actionable knowledge by advanced environmental modelling. At the moment the environmental computing community shares a tacit understanding of what are the initiatives, tools and approaches that belong to the core scope of this emerging discipline. The environmental computing website[1] has been used as an interim community resource for collecting links to resources that are of relevance to this discipline. The information on the site has been collected using the presentations in environmental computing events (listed at the end of this summary) as starting points, complemented with some targeted desk research efforts. This presentation outlines the first attempts to analyse this initial dataset as a way to identify common topics, initiatives and approaches that could serve as basis for clustering of these initiatives. There are several reasons to attempt to identify commonalities beyond the simple, intuitive categorisation of something being “within the scope of environmental computing”. From the pragmatic perspective, commonalities represent opportunities for identifying synergies, either through the reuse of existing tools or through joining forces to solve common problems. The clustering may also identify areas that could serve as links to other disciplines, potentially indicating a need for a special interest group or another sub-specialty. An example of such a case is data management (especially consistent metadata) that is mentioned as an issue on most of the presentations in the environmental computing events. While the data management issues are very closely aligned with the broader “big data” and “open data” research and policy activities, environmental computing data management has probably a distinct scope involving issues that may be specific to environmental computing (e.g. requirements stemming from the environmental computing applications aiming to provide decision support tools in urgent situations). In the longer term, repeating this analysis using a more consistent and comprehensive search approach should help consolidation of the shared body of knowledge of the community. The goal of the presentation is to stimulate discussion on the community approaches and tools that could help the practitioners reach this goal. The challenge in building a shared, common “community hub” is that until the system and the active community around it reaches the critical mass, the efforts needed to add information to the repository and curation of the already stored data will not bring immediate benefits in day-to-day work. Thus a shared vision and roadmap are crucial success factors. The presentation also highlights some of the semantic challenges, e.g. environmental computing organisations that have departments with distinct profiles necessitating a conceptual model that is capable to deal with “nested” entries. Limitations of the commodity tools in keeping track and cross-linking publications, datasets and tools will also be discussed as a topic for further study.

Textures are widely used in modern computer graphics. Their size, however, is often a limiting factor. Considering the widespread adaptation of mobile virtual and augmented reality applications, efficient storage of textures has become an important factor.

Cloud services promise benefits for customers and providers such as scalability, elasticity and reduced investment costs. Unfortunately, many of the promised benefits are not fulfilled by today’s cloud offerings and not every service can be cloudified, e.g. if the service’s intrinsic structure contains unavoidable time-consuming or manual tasks. A new cloud definition, based on a survey and comparison of existing cloud definitions, but derived from beneficial cloud characteristics, leads to a service-oriented understanding of clouds and provides an extension to the usual cloud service types. The characteristics of the given cloud definition uncover the so-called ”MOUSETRAPS” of cloud services. The term ”semi-cloudification” for the transformation of services towards a cloudified state presents a solid foundation for further discussions on the topic and enables the improvement of non-cloudifiable services by semi-cloudification. Even services which partly consist of unavoidable time-consuming or manual tasks qualify for semi-cloudification.

In this work we describe the steps required to render scientific data for real-time immersive displays. First, the input data needs to be taken into account for further processing. As a second step, the data needs to be reduced without losing significant information in order to enable real-time interaction. Therefore, we describe different reduction techniques and discuss advantages and disadvantages of those. As a third step, the data is integrated in a framework that is able to render the information on different display technologies. We elaborate the general workflow on a geoscientific dataset that illustrates the temperature distribution of the Earth's mantle.

Presentations and discussions between architects and clients during the early stages of design usually involve sketches, paper and models, with digital information in the form of simulations and analyses used to assess variants and underpin arguments. Laypeople, however, are not used to reading plans or models and find it difficult to relate digital representations to the real world. Immersive environments represent an alternative approach but are laborious and costly to produce, particularly in the early design phases where information and ideas are still vague. Our project shows, how linking analogue design tools and digital VR representation has given rise to a new interactive presentation platform that bridges the gap between analogue design methods and digital architectural presentation. The prototypical platform creates a direct connection between a physical volumetric model and interactive digital content using a large-format multi-touch table as a work surface combined with real-time 3D scanning. Coupling the 3D data from the scanned model with the 3D digital environment model makes it possible to compute design relevant simulations and analyses. These are displayed in real-time on the working model to help architects assess and substantiate their design decisions. Combining this with a 5-sided projection installation based on the concepts Carolina Cruz Neiras CAVE Automatic Virtual Environment (CAVE)1offers an entirely new means of presentation and interaction. The design (physical working model), the surroundings (GIS data) and the simulations and analyses are presented stereoscopically in real-time in the virtual environment. While the architect can work as usual, the observer is presented with an entirely new mode of viewing. Different ideas and scenarios can be tried out spontaneously and new ideas can be developed and viewed directly in three dimensions. The client is involved more directly in the process and can contribute own ideas and changes, and then see these in user-centred stereoscopic 3D. By varying system parameters, the model can be walked through at life size.

The distributed research infrastructure for hydrometeorology (DRIHM) project focuses on the development of an e-Science infrastructure to provide end-to-end hydro meteorological research (HMR) services (models, data, and post processing tools) by exploiting HPC, Grid and Cloud facilities. In particular, the DRIHM infrastructure supports the execution and analysis of high-resolution simulations through the definition of workflows composed by heterogeneous HMR models in a scalable and interoperable way, while hiding all the low level complexities. This contribution gives insights into best practices adopted to satisfy the requirements of an emerging multidisciplinary scientific community composed of earth and atmospheric scientists. To this end, DRIHM supplies innovative services leveraging high performance and distributed computing resources. Hydro meteorological requirements shape this IT infrastructure through an iterative "learning-by-doing" approach that permits tight interactions between the application community and computer scientists, leading to the development of a flexible, extensible, and interoperable framework.

We present the conceptual design and implementation of the Collaborative Design Platform Protocol (CDPP), a communication protocol that offers the synchronisation of virtual worlds between two mixed reality peers. The CDPP is applied to connect the Collaborative Design Platform (CDP), a design tool which supports the architectural design process in an early stage, with the immersive Cave Automatic Virtual Environment (CAVE) display, where the design is visualised in life-size. This creates a prototype which enables a cost-efficient and easily comprehensible presentation of the early-staged design, and thus significantly simplifies the incorporation of laypeople in the early design process. By this means, the creative capabilities of laymen are exploited to a greater extent.

ShoeSoleSense is a proof of concept, novel body worn interface - an insole that enables location independent hands-free interaction through the feet. Forgoing hand or finger interaction is especially beneficial when the user is engaged in real world tasks. In virtual environments as moving through safety training applications is often conducted via finger input, which is not very suitable. To enable a more intuitive interaction, alternative control concepts utilize gesture control, which is usually tracked by statically installed cameras in CAVE-like-installations. Since tracking coverage is limited, problems may also occur. The introduced prototype provides a novel control concept for virtual reality as well as real life applications. Demonstrated functions include movement control in a virtual reality installation such as moving straight, turning and jumping. Furthermore the prototype provides additional feedback by heating up the feet and vibrating in dedicated areas on the surface of the insole.

Determining qualitative availability aspects of Grid resources is a complex and cumbersome task because of the inherent characteristics of a Grid like Virtual Organizations. To cope with these challenges we argue for a more qualitative specification of availability which respects inter-organizational aspects besides pure technical values and percentages. Although there are several existing approaches and tools to determine the availability of Grid resources, none of them supports our perspective of availability adequate enough. We propose with DAGA (Determining the Availability of Grid-Resources using Active Probing) a new approach which is both service oriented and active probing based. DAGA respects the specifics of Grid architectures, such as VO-specific authorization and authentication.

E-health makes use of information and communication methods and the latest e-research tools to support the understanding of body functions. E-scientists in this field take already advantage of one single infrastructure to perform computationally-intensive investigations of the human body that tend to consider each of the constituent parts separately without taking into account the multiple important interactions between them. But these important interactions imply an increasing complexity of applications that embrace multiple physical models (i.e. multi-physics) and consider a larger range of scales (i.e. multi-scale) thus creating a steadily growing demand for interoperable infrastructures that allow for new innovative application types of jointly using different infrastructures for one application. But interoperable infrastructures are still not seamlessly provided and we argue that this is due to the absence of a realistically implementable infrastructure interoperability reference model that is based on lessons learned from e-science usage. Therefore, the goal of this paper is to explore the potential of using multiple infrastructures for one scientific goal with a particular focus on e-health. Since e-scientists gain more interest in using multiple infrastructures there is a clear demand for interoperability between them to enable a use with one e-research tool. The paper highlights work in the context of an e-Health blood flow application while the reference model is applicable to other e-science applications as well.

Collective operations can have a large impact on the performance of parallel applications. However, the ideal implementation of a particular collective communication often depends on both the application and the targeted machine structure. Our approach combines dynamic and static analysis techniques to identify common collective communication patterns expressed as point-to-point calls and transforms them into equivalent MPI collectives. We first detect potential collective communication patterns in runtime traces and associate them with the corresponding source code regions. If our static analysis verifies that the introduction of collectives is safe for any program flow, we then replace the original communication primitives with their collective counterpart. In this paper we introduce the necessary algorithms to determine the safety of these transformations and we demonstrate several use cases, including automatic use of new extensions to the MPI standard such as nonblocking collective operations. The use of dynamic analysis significantly reduces compile times, resulting in a speed-up of about 50 for source transformations of HPL due to more directed analysis capabilities and also dramatically decreases complexity of the underlying static analysis.

Simulation and thus scientific computing is the third pillar alongside theory and experiment in todays science and engineering. The term e-science evolved as a new research field that focuses on collaboration in key areas of science using next generation infrastructures to extend the powers of scientific computing. This paper contributes to the field of e-science as a study of how scientists actually work within currently existing Grid and e-science infrastructures. Alongside numerous different scientific applications, we identified several common approaches with similar characteristics in different domains. These approaches are described together with a classification on how to perform e-science in next generation infrastructures. The paper is thus a survey paper which provides an overview of the e-science research domain.

Since processor counts in supercomputers are increasing dramatically, efficient interprocessor communication is becoming even more important for the applications that run on them. A high level, abstract understanding of an application's communication behavior would not only simplify debugging of that communication but would also support more directed performance optimization. We explore automated identification of communication patterns to provide that high level abstraction. We introduce an algorithm to extract communication patterns from MPI traces automatically. Our algorithm first finds locally repeating sequences and then iteratively grows them into global patterns. We demonstrate our technique on three realistic codes using traces from up to 128 processors. Our results show that our approach detects the underlying communication pattern within reasonable time andmemory constraints, even for large trace sizes.

Scalability and complexity of HPC software requires corresponding support from program analysis tools capable of dealing with possibly huge amounts of data. E.g., MPI traces capture low-level communication behavior essential to understand program behavior, but are typically too detailed and large and hence need to be reduced, pre-processed, and mapped to a higher level of abstraction before being comprehensible by human users. One possible approach is to utilize the occurrence of patterns in the execution of a program for the benefits of the user's understanding. Such patterns can be either user-defined or automatically extracted from program traces, requiring minimal interaction from users. This poster describes such an enhanced approach to pattern detection in program traces, which is available within a program analysis framework. In addition, the poster reports about patterns in existing, realistic applications and the pattern detection mechanism to extract useful information about a program's behavior.

Today's HPC environments are increasingly complex in order to achieve highest performance. Hardware platforms introduce features like out-of-order execution, multi-level caches, multi-cores, non-uniform memory access etc. Application software combines OpenMP, MPI, optimized libraries and various types of compiler optimization to exploit potential performance.To reach a reasonable percentage of the theoretical peak performance, three fundamental steps need to be accomplished. First, correctness must be guaranteed especially during the course of optimization. Second, the actual performance achieved needs to be determined. In particular the contributions/limitations of all sub-systems involved (CPU, memory, network, I/O) have to be identified. Third, actual optimization can only be successful with the previously obtained knowledge.Those steps are by no means trivial. There are sophisticated tools beyond simple profiling to support the HPC user. The tutorial introduces a variety of such tools: it shows how they play together and how they scale with long-running massively parallel cases.

The grid has received a substantial amount of interest (and funding) over the past few years, even though the majority of grid applications are still batch-oriented. For this reason, extensions to grid middleware are a basic necessity to advance the idea of the grid into new application domains. The glogin tool and its integration into the Grid Visualization Kernel (GVK) yield both, interactivity and visualization, into the grid. While glogin enables the tunneling of arbitrary interactive traffic in and out of the grid, GVK offers transparent visualization services for grid applications while exploiting the grid’s performance for time-consuming visualization tasks. In this paper, the utilization of glogin and GVK is demonstrated for Virtual Reality (VR) environments. Two distinct approaches for VR, using either abstract visualization objects or video streams, are explained, highlighting advantages and disadvantages of each.

Future teleconferencing may enhance communication between remote people by supporting non-verbal communication within an unconstrained space where people can move around and share the manipulation of artefacts. By linking walk-in displays with a Collaborative Virtual Environment (CVE) platform we are able to physically situate a distributed team in a spatially organised social and information context. We have found this to demonstrate unprecedented naturalness in the use of space and body during non-verbal communication and interaction with objects.However, relatively little is known about how people interact through this technology, especially while sharing the manipulation of objects. We observed people engaged in such a task while geographically separated across national boundaries. Our analysis is organised into collaborative scenarios, that each requires a distinct balance of social human communication with consistent shared manipulation of objects.Observational results suggest that walk-in displays do not suffer from some of the important drawbacks of other displays. Previous trials have shown that supporting natural non-verbal communication, along with responsive and consistent shared object manipulation, is hard to achieve. To better understand this problem, we take a close look at how the scenario impacts on the characteristics of event traffic. We conclude by suggesting how various strategies might reduce the consistency problem for particular scenarios.

Examining a program's state during execution is of major importance for software developers when analyzing an application for performance tuning or error detection. This demand for suitable analysis tools is further pushed by parallel and distributed programs, which process huge amounts of complex data. A solution to this problem is offered by visualization, such that even a large amount of data can be presented efficiently for the user's investigations. This paper presents an immersive approach, where monitoring and steering activities combined with virtual reality visualization components help the user to understand the behavior of running parallel and distributed programs. On the one hand, program visualization tries to induce a "feeling" for the program. On the other hand, data visualization provides a facility to display preliminary results of any kind of scientific calculations.