Dynamic virtual environments pose growing challenges for users who must manage attention across competing visual elements, where distractors can divert focus from relevant objects in these scenarios. Because human attention functions as a filter, it is shaped by competing influences from bottom-up salience and top-down relevance. We explore the salience and relevance of objects and introduce suppression-based visual filtering mechanisms, implemented through Dim and Blur visual filters at Weak and Strong intensity levels. A controlled abstract virtual environment with colorful moving objects was used to evaluate these against Baseline (no filtering) across nine varied salience-relevance situations, involving 38 participants in visual search and sustained monitoring tasks. Results showed that visual suppression enhanced participants’ attention over Baseline, with Dim outperforming Blur, Strong exceeding Weak, and Dim-Strong achieving superior performance overall. These findings imply the principle of attention redistribution and offer insights for domains involving objects with varying salience and relevance.

There are many problem domains where users have to pay attention to a series of targets. In this article, we evaluated visual cues to support attention in dynamic situations in virtual environments. Three immersive visualizations (Emphasis, Deemphasis, Emphasis+Deemphasis) have been examined across four different attention types (sustained, alternating, divided, and selective attention) to support the user's attention on targets. Emphasis highlights targets of interest using outlines; Deemphasis removes details with masks, and Emphasis+Deemphasis combines both techniques. An experiment with 34 participants examined the cues through four scenarios that required different attention types in dynamic situations. Results indicated that while Emphasis supported significantly faster reactions in most attention types, the other two visual cues showed superiority across other measurements. Although evaluated in a virtual environment, the findings offer guidance for designing visual cues in other dynamic situations that involve different attention types.

To enhance the efficiency of crowd evacuation and inform collaborative design strategies, it is essential to investigate the effects of exit features on human exit choices. This study explores how exit distance, crowd density near exits, and exit location settings influence individual exit selection. We conducted a virtual reality experiment, revealing that all targeted features significantly impact exit choices, with density exerting the most substantial influence, followed by distance and exit location being the least impactful. Additionally, the interaction between distance and location significantly affected exit decisions. By integrating our findings into machine learning models, we demonstrate the potential of these exit features for informing collaborative evacuation strategies and designing systems that support effective decision-making in crowd dynamics. This research contributes to understanding human behavior in evacuation scenarios, emphasizing the importance of collaborative approaches in optimizing crowd management.

Smart contracts, the cornerstone of decentralized applications, have become increasingly prominent in revolutionizing the digital landscape. However, vulnerabilities in smart contracts pose great risks to user assets and undermine overall trust in decentralized systems. Fuzzing, a prominent security testing technique, is extensively explored to detect vulnerabilities. But current smart contract fuzzers fall short of expectations in testing efficiency for two primary reasons. Firstly, smart contracts are stateful programs, and existing approaches, primarily coverage-guided, lack effective feedback from the contract state. Consequently, they struggle to effectively explore the contract state space. Secondly, coverage-guided fuzzers, aiming for comprehensive program coverage, may lead to a wastage of testing resources on benign code areas. This wastage worsens in smart contract testing, as the mix of code and state spaces further complicates comprehensive testing. To address these challenges, we propose Vulseye, a stateful directed graybox fuzzer for smart contracts guided by vulnerabilities. Different from prior works, Vulseyeachieves stateful directed fuzzing by prioritizing testing resources to code areas and contract states that are more prone to vulnerabilities. We introduceCode TargetsandState Targetsinto fuzzing loops as the testing targets of Vulseye. We use static analysis and pattern matching to pinpointCode Targets, and propose a scalable backward analysis algorithm to specifyState Targets. We design a novel fitness metric that leverages feedback from both the contract code space and state space, directing fuzzing toward these targets. With the guidance of code and state targets, Vulseyealleviates the wastage of testing resources on benign code areas and achieves effective stateful fuzzing. In comparison with state-of-the-art fuzzers, Vulseyedemonstrated superior effectiveness and efficiency. Notably, it uncovered 4,845 vulnerabilities in 42,738 real-world smart contracts, outperforming existing approaches by up to$9.7\times $, and identified 11 previously unknown vulnerabilities within the top 50 Ethereum DApps, involving approximately 2,500,000 USD.

Immersive embodiment holds great promise for motor skill acquisition, however the design and effect of real-time visual guidance across perspectives and body representations remain underexplored. This study introduces a puppet-inspired visual feedback framework that uses continuous visual linkages - line, color, and thickness cues - to externalize spatial deviation and scaffold embodied learning. To evaluate its effectiveness, we conducted a controlled virtual reality experiment (N = 40) involving gesture imitation tasks with fine (sign language) and gross (aviation marshalling) motor components, under first- and third-person viewpoints. Results showed that color-based guidance significantly improved imitation accuracy, short-term learning, and perceived embodiment, especially in finger-based and first-person settings. Subjective assessments (NASA-TLX, Motivation, IPQ, Embodiment) confirmed improvements in presence, agency, and task engagement.

To decode Motor Imagery EEG signals (MI-EEG), most studies have increasingly complicated network models and parameters without fully considering EEG characteristics, thereby limiting advancements in Brain-Computer Interface (BCI) systems and classification performance. To address these issues, we propose a lightweight end-to-end tri-domain feature fusion network named LTDFNet. Firstly, we introduce an Attention-based Spatio-temporal Convolution module (ABST) to extract low-dimensional spatio-temporal features from EEG. This module employs a lightweight Squeeze-and-Excitation (SE) attention mechanism to enhance the model's perception of crucial information. Secondly, Temporal Domain Convolutional (TDC) and Frequency Domain Convolution (SDC) modules utilize Temporal Convolutional Networks (TCN) and Fast Fourier Transform (FFT) to respectively learn high-dimensional temporal and frequency domain information. Finally, the Feature Fusion (FF) module integrates low-dimensional spatio-temporal features and high-dimensional temporal-frequency features effectively through learnable parameters. LTDFNet is trained with joint constraints of Softmax loss and Center loss functions to achieve optimal inter-class separation and intra-class compactness, thereby enhancing overall model performance. This study conducts extensive experimental validation on BCI Competition IV datasets 2a (BCI 2a) and 2b (BCI 2b). LTDFNet achieves classification accuracies of 76.89% (kappa score: 0.692) and 85.22% (kappa score: 0.704) on the BCI 2a and BCI 2b datasets, respectively. Compared to other high-performance decoding methods, LTDFNet utilizes only 20,576 parameters, balancing network scale and decoding performance requirements.

As technology grants us superhuman powers, looking into what the future may hold is no longer science fiction. Artificial Intelligence and Mixed Reality technologies can allow users to see what the future may hold. In this paper, we present our work evaluating visualizations of future predictions in the Football domain. We explore the problem space, examining what a future may be. Three visualizations—2 Arrow Lines, 5 Arrow Lines, and Heatmap—are introduced as representations that show both individual predictions of movement (2 Arrow Lines and 5 Arrow Lines) and more generalized predictions (Heatmap). Whilst football is used as an example domain in this work, the visualizations and findings aim to generalize to other scenarios that contain trajectory information. Two VR studies $(2 \times \mathrm{n}=24)$ examined the visualizations in both simple/complex, timed/non-timed, and short/long-range viewing situations. Results show Heatmap as the most effective and preferred by the vast majority of participants. Findings offer insights into future visualization, serving as visual heuristics beyond the realm of sports and into the real world.

This paper investigates the use of immersive virtual reconstructions as an aid for jurors during a courtroom trial. The findings of a between-participant user study on memory and decision-making are presented in the context of viewing a simulated hit-run-death scenario. Participants listened to the opening statement of a prosecutor and a defence attorney before viewing the crime scene in Virtual Reality (VR) or as still images. We compare the effects on cognition and usability of using VR over images presented on a screen. We found several significant improvements, including that VR led to more consistent decision-making among participants. This shows that VR could provide a promising solution for the court to present crime scenes when site visitations are not possible.

In this work, we explore if the immersion afforded by Virtual Reality can improve the cognitive integration of information in Cinematic Virtual Reality (CVR). We conducted a user study examining participants' cognitive activities (recall performance and cortical response) when consuming visual information of emotional and emotionally neutral scenes in a non-CVR environment (i.e. a monitor) versus a CVR environment (i.e. a head-mounted display). Cortical response was recorded using electroencephalography. The results showed that participants had greater early visual attention with neutral emotions in a CVR environment, and showed higher overall alpha power in a CVR environment. The use of CVR did not significantly affect participants' recall performance.

Data videos are a highly impactful method of communication and are becoming a prevalent medium for communicating information. While the majority of current research focuses on the cinematic aspects of data videos, very little is known about the narrative methodologies involved. This paper presents our insights derived from an initial exploration of this area. We present a taxonomy based on the analysis of 70 existing data videos examining their narrative and visual approaches. We propose that our taxonomy can be used to explain the characteristics or design of data videos. Applying this taxonomy, we present our observations, including the trend of popular technologies applied in current data videos, the under-utilization of promising methods, and highlight research opportunities in the field.