Cross-disciplinary teams increasingly work with high-dimensional scientific datasets, yet fragmented toolchains and limited support for shared exploration hinder collaboration. Prior immersive visualization & analytics research has emphasized individual interaction, leaving open how multi-user collaboration can be supported at scale. To fill this critical gap, we conduct semi-structured interviews with 20 domain experts from diverse academic, government, and industry backgrounds. Using deductive–inductive hybrid thematic analysis, we identify four collaboration-focused themes: workflow challenges, adoption perceptions, prospective features, and anticipated usability and ethical risks. These findings show how current ecosystems disrupt coordination and shared understanding, while highlighting opportunities for effective multi-user engagement. Our study contributes empirical insights into collaboration practices for high-dimensional scientific data visualization & analysis, offering design implications to enhance coordination, mutual awareness, and equitable participation in next-generation collaborative immersive platforms. These contributions point toward future environments enabling distributed, cross-device teamwork on high-dimensional scientific data.

Deep learning techniques have emerged as valuable tools for video analysis and motion detection. Recent advancements in this field have shown promising results. Our objective is to leverage these video understanding techniques to aid teachers in evaluating their teaching quality and enhancing their effectiveness in the classroom. However, existing research on student behavior analysis primarily focuses on recognizing actions pertaining to classroom management, neglecting the identification of “learning behaviors” exhibited by students. To address this limitation, we introduce a novel video dataset specifically designed to capture the nuances of “learning behaviors” displayed by primary-grade students in the mathematics classroom, along with a dedicated student localization dataset focused on detecting the location of individuals. Our approach introduces a framework that utilizes deep learning-based object detection and action recognition techniques trained on our curated datasets to analyze and comprehend student learning behaviors in the classroom. To assess the performance of our approach, we conduct separate tests on our object detection and action recognition models. Sub-sequently, our framework is applied to a collection of recorded 360-degree classroom videos, enabling a thorough evaluation of its capabilities.

Recent years have witnessed an increment in the number of network components communicating through many network scenarios such as multi-layer and multi-domain, and it may result in a negative impact on resource utilization. An urgent requirement arises for routing the packets most efficiently and economically in large multi-domain networks. In tackling this complicated area, we consider the Inter-Domain Path Computation problem under Node-defined Domain Uniqueness Constraint (IDPC-NDU), which intends to find the minimum routing cost path between two nodes that traverses every domain at most once. Owing to the NP-Hard property of the IDPC-NDU, applying metaheuristic algorithms to solve this problem usually proves more efficient. In like manner, this paper proposes a Two-level Genetic Algorithm (PGA), where the first level determines the order of the visited domains, and the second level finds the shortest path between the two given nodes. Furthermore, to facilitate the finding process, a method to minimize the search space and a new chromosome encoding that would reduce the chromosome length to the number of domains are integrated into this proposed algorithm. To evaluate the efficiency of the proposal, experiments on various instances were conducted. The results demonstrated that PGA outperforms other algorithms and gives results no more than twice the optimal values.

Data structures and algorithms (DSA) are fundamental concepts introduced in the early stages of any undergraduate computing degree. Many students find DSA concepts challenging to learn, partly due to their abstract nature but also due to issues of low motivation. We examine these and other common difficulties faced by educators teaching DSA, and present a novel solution which combines a game-based learning approach with the use of engaging visualizations of algorithms in 2.5D. Game-based learning and algorithm visualizations are often employed in existing educational tools, but are rarely combined. Our game, DeCode, presents DSA concepts as in-game objects and animations within a 2.5D game-based world alongside traditional game-like mechanics such as progression. Feedback from an initial pilot study (n=51) suggests that students regard DeCode as an enjoyable and effective tool for learning DSA concepts. We also find evidence that DeCode's visualizations help students find efficient solutions to problems that have received prior attention in the literature, such as shifting all of the elements in an array.

Despite foreseeing tremendous speedups over conventional deep neural networks, the performance advantage of binarized neural networks (BNNs) has merely been showcased on general-purpose processors such as CPUs and GPUs. In fact, due to being unable to leverage bit-level-parallelism with a word-based architecture, GPUs have been criticized for extremely low utilization (1 percent) when executing BNNs. Consequently, the latest tensorcores in NVIDIA Turing GPUs start to experimentally support bit computation. In this article, we look into this brand new bit computation capability and characterize its unique features. We show that the stride of memory access can significantly affect performance delivery and a data-format co-design is highly desired to support the tensorcores for achieving superior performance than existing software solutions without tensorcores. We realize the tensorcore-accelerated BNN design, particularly the major functions for fully-connect and convolution layers - bit matrix multiplication and bit convolution. Evaluations on two NVIDIA Turing GPUs show that, with ResNet-18, our BTC-BNN design can process ImageNet at a rate of 5.6K images per second, 77 percent faster than state-of-the-art. Our BNN approach is released on https://github.com/pnnl/TCBNN.

Tactical wireless networks operate in complex environments with various resource constraints as well as constant link-state changes. These network topologies are predominantly based on some flavor of adhoc network design in order to accomplish rapid deployment goals. Utilizing the ns-3 simulator and a small tactical network topology with 50 nodes we evaluate the performance of both voice and video transmissions. A combination of nix-vector routing and link-state tables are used to create dynamic routing within the simulation. Then the performance of a SIP based real-time audio and video application in an adhoc network is analyzed. Additionally, we have implemented QoS in ns-3 to support traffic prioritization as needed by voice and video applications. Finally we compare the performance of the real-time voice and video applications using different traffic model.

There is no one display device or software package that is ideally suited for interactively visualizing related nonspatial, 1D, 2D, 3D, and 4D datasets. This is a major drawback, as the benefits of interactive visualization and advanced display technologies cannot be brought to bear. The Dynamic Collaborative Visualization Ecosystem (DynaCoVE) framework addresses this limitation by unifying SciVis, InfoVis, and display technology tools. Pre-existing packages are wrapped as DynaCoVE clients. This entails tracking user interactions with their GUI's and also automatically updating them in response to cues from the DynaCoVE server. The DynaCoVE server then acts as a message broker between many collaborating clients. A messaging protocol was defined based on ZeroMQ's majordomo protocol to enable efficient communication between clients and the server. Thus, visualization software packages that were never intended to be used together can be linked to perform cross display visual analytics. The system is intended to be data and user centric while remaining software, algorithm and display agnostic. This is accomplished in part by providing a common metavisualization graph interface to setup cross-display visualizations. To date DynaCoVE clients for Looker, VisIt, and VTK have been tested together with traditional monitors, tiled displays, and an immersive CAVE-type system.

We present multiple modes of visualizing a largescale Monte Carlo simulation. Due to disparity of the data, there is no one best visualization technique to understand the results of the simulation. By connecting the visualization methods directly to the simulation, we are able to visually analyze the simulation as it occurs in real time. This includes viewing the parameters of the model, the relationships among variables, and the 3D environment in which the simulation takes place. Furthermore, these environments may be coordinated for one seamless visual analytic experience.

We present a visualization architecture to support 2D and 3D visual analytics applications. The architecture is designed to be data-flow-oriented and reconfigurable such that several diverse visualization components can operate as one integrated system. Our prototype application allows users to visually analyze the results of a complex 3D network simulation data both on large high-resolution display and HTC Vive Head Mounted Display. The network simulation outputs variables describing various characteristics of network connectivity between the moving nodes on the ground and in the air interacting in a dynamically changing 3D environment. Our system uses 2D charting tools to visualize the statistical relationships between simulation variables. We developed a Unity application to animate the network simulation in a virtual environment showing the timevarying results in a 3D environment. The Unity application runs on a complete-immersive Head Mounted Display device. The 2D visualization framework running on our Large High-Resolution Display system supports multiple coordinated views across all the different 2D visualization components including a 2D map. Preliminary results show our data-centric design provides a usercentric visualization tool that can greatly enhance the analytical process and speed up the derivation of insights from data.

Convolutional Neural Networks(CNNs) are complex systems trained to recognize images, texts and more. However, once trained, they are regarded as black-boxes that are not easy to analyze and understand. Visualizing the dynamics within such deep artificial neural networks can provide a better understanding of how they are learning and making predictions. In the field of scientific simulations, visualization tools like Paraview have long been utilized to provide insights. We present in situ TensorView to visualize the training and functioning of CNNs as if they are systems of scientific simulations. In situ TensorView is a loosely coupled in situ visualization open framework that provides multiple viewers with the ability to visualize and understand their networks. It leverages the capability of co-processing from Paraview to provide real-time visualization during training and predicting phases, and avoids heavy I/O overhead. Tensorview is easily coupled with Tensorflow, as it only requires the insertion of a few lines of code into a TensorFlow framework. In this work, we showcase visualizing LeNet-5 and VGG16 using in situ TensorView. With the insight provided by Tensorview, users can adjust network architectures, or compress pre-trained networks guided by visualization results.

Tactical network environments are complex, resource constrained, highly mobile in nature and performance of the communication links between the nodes is significantly affected by the path loss caused by the buildings. In this paper, we have used the ns-3 discrete event simulator to simulate an urban tactical network environment using the Rosslyn, VA city model. The simulated model was visualized using unity/worldwind. We have calculated path loss data using VPL and injected the path loss data into link selection to visually study the effects of path loss on the link formation in urban environments.

We present a ParaViewWeb based visual analytics application running on large high-resolution display supporting standard mouse and keyboard interaction. The application relies on SAGE2 for user interaction and multi-display visualization. We also employ a scalable middleware system called "Cloudberry" that allows users to interactively query and analyze large amounts of temporal and spatial data stored on a back end Apache AsterixDB store to enable big data analytics and interactive visualization. Our Visual Analyzing Billion Tweets application shows interactive query and visualization of result from over a billion twitter feeds streamed in real-time to the back end Apache AsterixDB. In our setup, we ran the visual analytics application on a large high-resolution display with a 24-tiled display in a 6 x 4 configuration. We also run a comparative study of the application running on a single 24 inch display and the 24-tiled display with some very interesting findings supporting the benefit of using large high-resolution display for visual analytics.

We developed novel interactions to enable user interaction with the 3D virtual environment addressing the three tasks (Ladder Climbing, First-Person View Flying, and Tower Stacking) of the 3DUI contest 2018. Using Unity3D as our development environment, we implemented and deployed our application on HTC Vive HMD system.

We present a system called "Cloudberry" that allows users to interactively query, analyze, and visualize large amounts of data with temporal, spatial, and textual dimensions. As a general-purpose full-stack solution, it has a friendly UI, intelligent middleware, and a powerful big data management backend running Apache AsterixDB to enable big data analytics and visualization. We will demonstrate the system using Twitter data on a computer cluster.

The availability of low-cost virtual reality (VR) systems coupled with a growing population of researchers accustomed to newer interface styles makes this a ripe time to help domain science researchers cross the bridge to utilizing immersive interfaces. The logical next step is for scientists, engineers, doctors, etc. to incorporate immersive visualization into their exploration and analysis workflows. However, from past experience, we know having access to equipment is not sufficient. There are also several software hurdles to overcome. Obstacles must be lowered to provide scientists, engineers, and medical professionals low-risk means of exploring technologies beyond their desktops.

Wildfires are a frequent summer-time concern for land managers and communities neighboring wildlands throughout the world. Computational simulations have been developed to help analyze and predict wildfire behavior, but the primary visualization of these simulations has been limited to 2-dimensional graphics images. We are currently working with wildfire research groups and those responsible for managing the control of fire and mitigation of the wildfire hazard to develop an immersive visualization and simulation application. In our visualization application, the fire spread model will be graphically illustrated on a realistically rendered terrain created from actual DEM data and satellite photography. We are working to improve and benefit tactical and strategic planning, and provide training for firefighter and public safety with our application