With the widespread application of artificial intelligence (AI) in Internet of Vehicles (IoV), particularly in autonomous driving and intelligent traffic management. IoV is facing tremendous pressure for large amounts of data transmission and real-time perception. As a key technology of 6G, integrated sensing and communication (ISAC) is expected to alleviate this pressure by improving spectrum utilization and reducing transmission latency. In addition, when obstacles exist in IoV, communication performance is seriously affected, which is detrimental to driving safety. To address this issue, Reconfigurable Intelligent Surfaces (RIS) as a relay is a feasible solution. Therefore, we construct a RIS-assisted ISAC IoV scenario, and further consider adding dynamic obstacles. Our optimization problem aims to enhance overall performance by maximizing a weighted combination of communication rate and sensing accuracy through the optimization of beamforming and RIS phase shifts. The problem has temporal characteristics and is a Markov Decision Process (MDP). To this end, we use the Long Short-Term Memory-Deep Deterministic Policy Gradient (LSTM-DDPG) algorithm to solve the problem. The results from the simulation illustrate the efficacy of the proposed algorithm in handling dynamic and complex blockage scenarios. Additionally, introducing RIS in blockage scenarios significantly increases the communication rate by up to 58.33%.

As users transition from passive subjects to active partners in the co-design process, they bring unique insights based on their experiences, collaboratively envisioning a better future with designers. However, unlike designers who are adept at various forms of representation, most users lack advanced modeling or sketching skills to concretely present the three-dimensional (3D) forms or dynamic features of a design proposal. This hinders user expression and increases the cognitive load on designers, thereby reducing communication efficiency in the co-design process. Mixed Reality (MR) technology enables users to depict 3D information in real physical space using natural gestures. This means that MR can provide a low-learning-cost concrete expression method without compromising traditional communication methods. This study explores the role of MR in enhancing communication between designers and users during the early stages of design. A formative study was conducted to identify four key requirements, which informed the development of the DuoMR system. DuoMR supports designers and users in expressing design ideas through gesture modeling in a collaborative MR space. Results from the user study and practical case study show that DuoMR effectively reduces cognitive load and enhances mutual understanding during the co-design process.

In Internet of Vehicles (IoV), vehicles communicate with Roadside Units (RSU) to ensure driving safety. The variability in complex road trajectories intensifies the angle changes between vehicles and RSU, impacting the stability of IoV Millimeter Wave (mmWave) communications. The paper proposes an Integrated Sensing and Communication (ISAC) RCG-Net beam tracking solution for IoV on complex road trajectories, integrating Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). The RCG-Net beam tracking solution leverages the powerful feature learning capability of deep neural networks, predicting and tracking angles using spatial features of echo signals and historical information features. This enhances beam tracking precision and resolves communication instability issues in IoV on complex road trajectories. Simulation results demonstrate that the proposed solution achieves high angle tracking accuracy and communication performance on intricate road trajectories, outperforming beam tracking solutions based on communication feedback and state evolution model.

Future 6G wireless networks will integrate communication and sensing functions, sharing resources across time, frequency, and space domains to achieve Integrated Sensing and Communication (ISAC). The millimeter-wave (mmWave) spectrum is poised to offer ISAC systems high communication rates and precise sensing capabilities, while the integration of RIS can enhance the stability of communication links. This advancement is significant for the Internet of Things (IoT), where device interconnectivity and accurate environmental sensing are crucial for intelligent operations and decision-making. This paper introduces a passive relay RIS-assisted mmWave ISAC system for Multiple Input Single Output (MU-MISO) communication and multi-target sensing in IoT, aiming for efficient information exchange and high-precision sensing. Simulation results show that the proposed algorithms achieve high spectral efficiency (SE) and low bit error rates (BER), further propelling IoT technology towards more advanced levels of intelligence.

Abstract Beam tracking is commonly employed in millimetre wave (mmWave) based vehicle‐to‐vehicle (V2V) networks to align the beams towards the intended targets and compensate for the path loss of mmWave signals. To mitigate the high latency issue arising from the tracking processes, integrated sensing and communication (ISAC) technology leverages the echo signal to sense the motion parameters of the target, achieving low‐latency beam tracking without requiring pilot and uplink feedback. Existing studies mainly focus on utilizing ISAC for beam alignment to track the target, without integrating beam tracking with resource allocation. In this paper, we propose the ISAC beam tracking based on deep reinforcement learning (IBTD) algorithm to address this problem. Specifically, we introduce the concept of packet age to measure communication performance. To achieve accurate beam tracking and optimize the transmit power, we integrate the sensing results, such as the position and velocity of the target vehicle, along with the buffer pool status information, with deep reinforcement learning (DRL) to select an appropriate policy. Furthermore, we consider the effect of inter‐vehicle distance and incorporate the changing of tracking targets into the DRL‐based policy. Simulation results demonstrate that the proposed IBTD algorithm achieves lower packet age and transmit power consumption compared to the baseline algorithms.

Product presentation video (PPV) is a genre of short-form video in online retailing that presents product features and facilitates online shopping. To improve PPVs, quality and provide a better experience for viewers, PPV producers, most of whom are online retailers and non-professionals in video production, have made efforts to enrich these short-form videos. Despite the great demand for PPV enrichment, these methods have not been systematically explored, and their impacts on user experience remain unclear, impeding the improvement of PPVs. This study combined qualitative and quantitative methods to explore the impacts of PPV enrichment methods on user experience. As an exploratory study, we focused on PPVs of female fashion products on Chinese e-commerce platforms. We collected 240 PPVs and summarized ten enrichment methods from them accordingly. A questionnaire-based experiment, including 48 participants, was then conducted to explore the impacts of these methods on the experience of PPVs. Results indicated that eight out of ten methods effectively improved PPV experience from multiple dimensions. This study brings insights for exploring PPV enrichment from the perspective of user experience and provides support for PPV production process.

Short video is an increasingly prevalent medium in online shopping environments to present products. To cope with the great demand for short videos rising from the enormous number and the rapid update of online products, computer-supported video production is becoming a trend. The optimization of short videos considering user experience is essential. Currently, using text and voice to integrate extra information into short videos is a potential and promising approach for optimizing computer-supported video production, while the effects of these elements on user experience remain unclear. In this study, we conducted a questionnaire-based experiment including 580 participants to explore the impacts of presenting extra information in short videos via text and voice on multi-dimensional user experience. Results indicated that these two elements positively impacted user experience from different dimensions. Gender differences were also found in this study. Based on experimental results, we provided suggestions to support the use of text and voice elements in short video production considering user experience.

Video-meetings essentially determine remote work life. However, video-meetings experience challenges originating from human emotions. Therefore, emotional competence, the ability to perceive, understand, and regulate emotions, is of the highest relevance. With limited transfer capacity of emotional information and various communication challenges, developing emotional competence, however, is complex. To overcome this complexity, we present TeamSpiritous, an individual, retrospective emotional competence development system for video-meetings. TeamSpiritous allows to upload and analyze recorded video-meetings on emotional processes and provides support for individual development of emotional competence. We evaluated TeamSpiritous quantitatively and qualitatively in a six-week, longitudinal field study with 47 participants from China and Germany. Results of our study show that intra- and interpersonal emotional competence significantly increased over time for the whole sample. In particular, intrapersonal emotion regulation and interpersonal emotion perception and understanding improved. Since remote work video-meetings are often multicultural, we also investigated cultural differences and observed in our results that the effects of TeamSpiritous exist beyond cultural backgrounds (China, Germany). With our work, we contribute with the design of TeamSpiritous and understanding of its effects on emotional competence development.

Digital services are a key driver of contemporary businesses. In order to scale the implementation of design-centric development processes, companies increasingly assign design work to design novices. As design novices have limited design knowledge and experience, they are challenged to select adequate design techniques throughout the entire lifecycle of digital services. Thus, providing decision aids to design novices is becoming increasingly important. In this research, we investigate taxonomy-based and tags-based decision aids. We draw on cognitive fit theory to construct a research model explaining the relationship between different decision aids and selection accuracy while considering the cognitive effort and the decision styles of novice designers. To test our hypotheses, we conducted a between-subject laboratory experiment with 195 subjects. Our experimental results provide extensive support to our hypotheses. Taxonomy-based decision aids outperform tags-based decision aids concerning selection accuracy mediated by cognitive effort. Furthermore, the results suggest rational decision style as a moderator in the relationship between taxonomy-based decision aids and selection accuracy. Our results have practical implications: First, taxonomy-based decision aids should be primarily leveraged on decision support platforms supporting design processes. Second, design novices' decision style and cognitive effort are influential factors when developing decision aids to support digital service design processes.

As a particular type of artificial intelligence technology, machine learning (ML) is widely used to empower user experience (UX). However, designers, especially the novice designers, struggle to integrate ML into familiar design activities because of its ever-changing and growable nature. This paper proposes a design method called Material Lifecycle Thinking (MLT) that considers ML as a design material with its own lifecycle. MLT encourages designers to regard ML, users, and scenarios as three co-creators who cooperate in creating ML-empowered UX. We have developed ML Lifecycle Canvas (Canvas), a conceptual design tool that incorporates visual representations of the co-creators and ML lifecycle. Canvas guides designers to organize essential information for the application of MLT. By involving design students in the “research through design” process, the development of Canvas was iterated through its application to design projects. MLT and Canvas have been evaluated in design workshops, with completed proposals and evaluation results demonstrating that our work is a solid step forward in bridging the gap between UX and ML.