As immersive 360° video experiences through head-mounted displays (HMDs) gain widespread adoption, the need for real-time, fine-grained assessment of Quality of Experience (QoE) becomes increasingly critical for optimising user engagement and system performance. This paper introduces RCQoEA-360VR, a novel multi-modal dataset designed for continuous QoE evaluation in virtual reality (VR) environments. In a controlled study (N=32), participants watched five selected 360° video sequences across eight different video quality configurations (from the VQEG database) using a Vive Pro Eye while providing continuous QoE annotations via a touchpad-based input method, enhanced by the DotMorph peripheral visualisation technique. The dataset also includes synchronised physiological signals (electrocardiogram and galvanic skin response), behavioural data (eye and head movements) and post-viewing QoE ratings gathered through a within-VR interface. RCQoEA-360VR addresses a critical gap in existing public datasets by providing a fine-grained, synchronised multimodal data for immersive QoE analysis. It offers a unique and valuable resource for the research community, supporting a wide range of research applications, including QoE prediction, behavioural modelling, adaptive streaming, and implicit perceptual analysis.

Vehicle-to-everything (V2X) underlaid cellular networks in underlaid mode suffer catastrophic co-channel interference caused by spectrum sharing, results in a reduced system sum-rate. To cope with this, this work studies a social-mobility-aware nonorthogonal multiple access (NOMA)-enabled V2X underlaid cellular network to mitigate the co-channel interference and improve the sum rate. By jointly optimizing vehicle pairing and resources, a sum-rate maximization problem is formulated under the diverse quality of service requirements of both cellular and vehicular users. The formulated problem is proved to be a nondeterministic polynomial-time (NP)-hard problem and is difficult to solve. As an alternative, we propose a NOMA-based joint vehicle pairing, spectrum assignment, and power control algorithm (NOMA-JVP-SA-PCA), with which the original problem is decomposed into two disjoint subproblems, i.e., 1) joint vehicle pairing and spectrum assignment subproblem and 2) power control subproblem. Dealing the first subproblem, we propose a heuristic social-mobility-aware vehicle pairing algorithm (HSMA-VPA) and a revised Kuhn-Munkres-based spectrum assignment algorithm (KM-SAA) to acquire the vehicle pairing and spectrum assignment solutions. Then, solving the second subproblem, a closed-form power solution is obtained utilizing a 3-D geometric power control approach (3D-PCA). Finally, we solve the original problem through an iterative method. Simulation results show that the proposed NOMA-JVP-SA-PCA effectively enhances the sum rate and outperforms the baseline algorithms around 24%–53% within a specific range.

Watching 360$^\circ$videos using Virtual Reality (VR) head-mounted displays (HMDs) provides interactive and immersive experiences, where videos can evoke different emotions. Existing emotion self-report techniques within VR however are either retrospective or interrupt the immersive experience. To address this, we introduce theContinuous Physiological and Behavioral Emotion Annotation Dataset for 360$^\circ$Videos (CEAP-360VR). We conducted a controlled study (N=32) where participants used a Vive Pro Eye HMD to watch eight validated affective 360$^\circ$video clips, and annotated their valence and arousal (V-A) continuously. We collected (a) behavioral (head and eye movements; pupillometry) signals (b) physiological (heart rate, skin temperature, electrodermal activity) responses (c) momentary emotion self-reports (d) within-VR discrete emotion ratings (e) motion sickness, presence, and workload. We show the consistency of continuous annotation trajectories and verify their mean V-A annotations. We find high consistency between viewed 360$^\circ$video regions across subjects, with higher consistency for eye than head movements. We furthermore run baseline classification experiments, where Random Forest classifiers with 2s segments show good accuracies for subject-independent models: 66.80% (V) and 64.26% (A) for binary classification; 49.92% (V) and 52.20% (A) for 3-class classification. Our open dataset allows further experiments with continuous emotion self-reports collected in 360$^\circ$VR environments, which can enable automatic assessment of immersive Quality of Experience (QoE) andmomentary affective states.

Watching HMD-based 360° video has become in-creasing popular as a medium for immersive viewing of photo-realistic content. To evaluate subjective video quality, researchers typically prompt users to provide an overall Quality of Experience (QoE) score after viewing a stimulus. However, since users can adjust their viewport throughout a 360° video, a higher level of spatiotemporal granularity is needed for adaptive 360° video streaming. To address this, we design several real-time, continuous QoE annotation input and peripheral visualization techniques, with the goal of minimizing mental workload and distraction during score acquisition. Drawing on two parallel co-design sessions with seven experts, we find that touchpad and joystick are most suitable for continuous input, with DotMorph (circle with tick label that varies in filling) for peripheral state feedback. We contribute design findings for testing QoE score acquisition techniques during HMD-based 360° video watching, which enable more precise optimization of adaptive video streaming quality.

Succinct event description based on multiple documents is critical to news systems as well as search engines. Different from existing summarization or event tasks, Multi-document Event Summarization (MES) aims at the query-level event sequence generation, which has extra constraints on event expression and conciseness. Identifying and summarizing the key event from a set of related articles is a challenging task that has not been sufficiently studied, mainly because online articles exhibit characteristics of redundancy and sparsity, and a perfect event summarization needs high level information fusion among diverse sentences and articles. To address these challenges, we propose a two-phase framework for the MES task, that first performs event semantic graph construction and dominant event detection via graph-sequence matching, then summarizes the extracted key event by an event-aware pointer generator. For experiments in the new task, we construct two large-scale real-world datasets for training and assessment. Extensive evaluations show that the proposed framework significantly outperforms the related baseline methods, with the most dominant event of the articles effectively identified and correctly summarized.

Boxi Cao, Hongyu Lin, Xianpei Han, Le Sun, Lingyong Yan, Meng Liao, Tong Xue, Jin Xu. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Precise emotion ground truth labels for 360° virtual reality (VR) video watching are essential for fine-grained predictions under varying viewing behavior. However, current annotation techniques either rely on post-stimulus discrete self-reports, or real-time, continuous emotion annotations (RCEA) but only for desktop/mobile settings. We present RCEA for 360° VR videos (RCEA-360VR), where we evaluate in a controlled study (N=32) the usability of two peripheral visualization techniques: HaloLight and DotSize. We furthermore develop a method that considers head movements when fusing labels. Using physiological, behavioral, and subjective measures, we show that (1) both techniques do not increase users’ workload, sickness, nor break presence (2) our continuous valence and arousal annotations are consistent with discrete within-VR and original stimuli ratings (3) users exhibit high similarity in viewing behavior, where fused ratings perfectly align with intended labels. Our work contributes usable and effective techniques for collecting fine-grained viewport-dependent emotion labels in 360° VR.

The increasing demand for the internet of vehicle (IoV) services has prompted the prosperity of cellular-based vehicle-to-everything (C-V2X). This paper investigates the resource allocation in an uplink SCMA-enabled C-V2X network. At the aim to maximize the sum rate of cellular links in condition of guaranteeing the reliability of V2X links, we propose a Two-Stage space code multiple access (SCMA) codebook allocation algorithm. The complicated resource matching problem is transformed into a 2-D matching problem by using V2X grouping algorithm at first. Then a Matching-Auction-based codebook allocation algorithm is adopted to address the issue of matching conflict. Finally, numerical simulation results indicate that the proposed scheme outperforms in terms of the sum data transmission rate of the cellular links and the reliability of V2X links.

Duplicating the audience experience of an art performance with VR technology is a promising VR application, which is considered to provide better viewer experience than the conventional video. As various forms of art performances are recorded by the panoramic camera and broadcasted on the Internet, the impact of this new VR-based media to the viewers needs to be systematically studied. In this work, a two-level usability framework is proposed, which combines the traditional concepts of presence and the quality evaluation of art performances, aiming to systematically study the usability of such VR application. Both the conventional video and the panoramic video of a theatre performance were captured simultaneously, and were replayed to two groups of viewers in a cinematic setup and through an HMD respectively. The psychological measurement methods, including the questionnaire and the interview, as well as the psychophysical measurement methods, including the EEG and the motion capture techniques were both used in the study. The results show that the such VR application duplicates the live performance better by providing a higher sense of presence, higher engagement levels, and stronger desire to see live performance. For visual intensive performance contents, the new VR-based media can provide a better user experience. The future development of the new media forms based on the panoramic video technique could benefit from this work.

Changes in operating environment may result in the performance degradation and cost overruns to an Internetware application. An efficient way to solve such problems is to optimize its deployment architecture according to the changes. However, there may be many different deployment architectures and the optimal ones should exhibit right trade-offs among conflicting objectives. Finding optimal deployment architectures for an Internetware application is hard and time consuming. This paper propose to employ a multi-objective ant colony algorithm MACO-DO to explore the search space automatically, aiming at finding a set of pareto optimal deployment architectures for an Internetware application. This algorithm is an improved version of traditional algorithms. It introduces a discarding elitist strategy to prevent algorithm from premature convergence. A series of experiments are implemented on three simulated instances of different sizes to compare the proposed MACO-DO with recently proposed P-ACO and NSGA-II. The results show that MACO-DO has better performance than others on the considered problem.

This paper considers the application of compressed sensing (CS) to a wireless sensor network for data measurement communication and reconstruction, where N sensor nodes compete for medium access to a single receiver. Sparsity of the sensor data in three domains due to time correlation, space correlation and multiple access are being utilized. We first provide an in-depth analysis on the CS-based medium access control schemes from a physical layer perspective and reveal the impact of communication signal-to-noise ratio on the reconstruction performance. We show the process of the sensor data converted to the modulated symbols for physical layer transmission and how the modulated symbols being recovered via compressed sensing. This paper further identifies the decision problem of distinguishing between active and inactive transmitters after symbol recovery and shows a comprehensive performance comparison between carrier sense multiple access and the proposed CS-based scheme. Second, a network data recovery scheme that exploits both spatial and temporal correlations is proposed. Simulation results validate the effectiveness of the proposed method in terms of communication throughput and show that enhanced performance can be obtained by utilizing the sensed signal's temporal and spatial correlations.

Compressed sensing can be utilized in multiple access to save channel resource and power consumption to realize green communications. This paper considers the application of compressed sensing to a wireless sensor network for data measurement reconstruction collected in a natural environment. We consider a network with N sensor nodes, sharing a wireless channel with M degrees of freedom, and compete for medium access to a single receiver. A multiple access scheme based on multi-dimensional compressed sensing is proposed, which exploits spatial and time correlations that exist in natural signals. The proposed scheme further removes the necessity of conveying identity information, which improves system power efficiency and enhances system security. Simulation results validate the effectiveness of the proposed method in utilizing the sensed signal's time and spatial correlations to obtain enhanced performances.

This paper investigates signal predictive quantization subject to channel loss. It uses the analysis and design tools of H∞optimal filtering of discrete-time Markov jump linear system to obtain an optimal predictor for a signal predictive quantization system with channel loss. A linear matrix inequality (LMI) based design method is derived to design the predictor that minimizes the variance of the reconstruction error and removes the unrealistic assumption on quantizer model in the present literature. Simulation results are presented to show the advantages of the obtained design method.