E-commerce has emerged as a significant endeavour in which technological advancements influence the shopping experience. Simultaneously, the metaverse is the next breakthrough to transform multimedia engagement. However, under such situations, deceiving designs aimed at deceiving users into making desired choices might be more successful. This paper proposes the design space of manipulative techniques in e-commerce applications for the metaverse. We construct our arguments by evaluating user interaction with manipulative design in metaverse shopping experiences, followed by a survey among users to understand the effect of counteracting manipulative e-commerce scenarios. Our findings can understanding of design guidelines according to metaverse e-commerce experiences and the possibility of opportunities to improve user awareness of manipulative experiences. © 2024 ACM.

Augmented Reality (AR) applications are becoming more mainstream, with successful examples in the mobile environment like Pokemon GO. Current malicious techniques can exploit these environments’ immersive and mixed nature (physical-virtual) to trick users into providing more personal information, i.e., dark patterns. Dark patterns are deceiving techniques (e.g., interface tricks) designed to influence individuals’ behavioural decisions. However, there are few studies regarding dark patterns’ potential issues in AR environments. In this work, using scenario construction to build our prototypes, we investigate the potential future approaches that dark patterns can have. We use VR mockups in our user study to analyze the effects of dark patterns in AR. Our study indicates that dark patterns are effective in immersive scenarios, and the use of novel techniques, such as “haptic grabbing” to draw participants’ attention, can influence their movements. Finally, we discuss the impact of such malicious techniques and what techniques can mitigate them.

Human habitation across multiple planets requires communication and social connection between planets. When the infrastructure of a deep space network becomes mature, immersive cyberspace, known as the Metaverse, can exchange diversified user data and host multitudinous virtual worlds. Nevertheless, such immersive cyberspace unavoidably encounters latency in minutes, and thus operates in a turn-taking manner. This Blue Sky paper illustrates a vision of an interplanetary Metaverse that connects Earthian and Martian users in a turn-based Metaverse. Accordingly, we briefly discuss several grand challenges to catalyze research initiatives for the ‘Digital Big Bang’ on Mars.

The ubiquity of smart devices, combined with a lack of information about data garnered by them, make privacy a significant challenge for adopting smart devices. Ensuring users can safeguard their privacy without compromising the devices’ functionality requires effective yet intuitive ways to manage personal privacy preferences. Current solutions for privacy management are severely lacking as they are ineffective in making users aware of potential privacy risks or how to mitigate them and as they offer limited support for interaction. As our first contribution, we develop a novel AR privacy management interface (PARA) that uses AR visualization to contextualize data disclosure and improve user’s perceptions of privacy threats. Besides offering support for enhancing user’s privacy perceptions, our interface supports privacy control on compatible devices through privacy-enhancing technologies. As our second contribution, we systematically study factors affecting privacy perceptions and privacy control for two device classes (smart camera and smart speaker) through a user study with N = 32 participants. Our results show that PARA’s contextualization and visualization of privacy disclosure strongly affect the participants’ privacy perceptions. For privacy control, we demonstrate that our prototype improves the participant’s capability to identify risks and provides an effective and easy-to-use mechanism for controlling privacy disclosure, in contrast to existing state-of-the-art privacy management interfaces.

Research on smart device privacy has consistently highlighted how privacy is an important concern for users, but they fail to act on their concerns. While this discrepancy between user perceptions and actions has been consistently reported, currently there is a limited understanding of why this is the case or how the situation can be ameliorated. This paper systematically studies how visualizations in privacy assistants can improve the situation, reporting on two studies that explore the users' privacy perceptions in smart device ecosystems. The first study shows that displaying device location and data type reduces the users' privacy perceptions. Participants also weigh the use of media such as online news as a source to inform users about the possible inferences. The second study analyzes participants' preferences to visualize smart device information and privacy policies using augmented reality. Through these two studies, we derive insights and guidelines on how to design effective privacy assistants and to improve user's knowledge of risks associated with data disclosure in smart home scenarios.

The continued advancement in user interfaces comes to the era of virtual reality that requires a better understanding of how users will interact with 3D buttons in mid-air. Although virtual reality owns high levels of expressiveness and demonstrates the ability to simulate the daily objects in the physical environment, the most fundamental issue of designing virtual buttons is surprisingly ignored. To this end, this paper presents four variants of virtual buttons, considering two design dimensions of key representations and multi-modal cues (audio, visual, haptic). We conduct two multi-metric assessments to evaluate the four virtual variants and the baselines of physical variants. Our results indicate that the 3D-lookalike buttons help users with more refined and subtle mid-air interactions (i.e. lesser press depth) when haptic cues are available; while the users with 2D-lookalike buttons unintuitively achieve better keystroke performance than the 3D counterparts. We summarize the findings, and accordingly, suggest the design choices of virtual reality buttons among the two proposed design dimensions.

Data protection regulatory policies, such as the European Union's General Data Protection Regulation (GDPR), force website operators to request users' consent before collecting any personal information revealed through their web browsing. Website operators, motivated by the potential value of the collected personal data, employ various methods when designing consent notices (e.g., dark patterns) in order to convince users to allow the collection of as much of their personal data as possible. In this paper, we design and conduct a user study where 1100 MTurk workers interact with eight different designs of cookie consent notices. We show that the nudging designs used in the different cookie consent notices have a large effect on the choices user make. Our results show that color-based nudging bars can significantly impact the participants' decisions to change the default cookie settings, despite using dark patterns. Also, in contrast to previous works, we report that users who do not use ad-blocking software are less likely to modify default cookie settings. Our findings demonstrate the importance of nudged interfaces and the effects orthogonal nudging techniques can have on users' choices.

The advancement of artificial intelligence and wearable computing triggers the radical innovation of cognitive applications. In this work, we propose VIMES, an augmented reality-based memory assistance system that helps recall declarative memory, such as whom the user meets and what they chat. Through a collaborative method with 20 participants, we design VIMES, a system that runs on smartglasses, takes the first-person audio and video as input, and extracts personal profiles and event information to display on the embedded display or a smartphone. We perform an extensive evaluation with 50 participants to show the effectiveness of VIMES for memory recall. VIMES outperforms (90% memory accuracy) other traditional methods such as self-recall (34%) while offering the best memory experience (Vividness, Coherence, and Visual Perspective all score over 4/5). The user study results show that most participants find VIMES useful (3.75/5) and easy to use (3.46/5).

Recent advances in machine and deep learning allow for enhanced retail analytics by applying object detection techniques. However, existing approaches either require laborious installation processes to function or lack precision when the customers turn their back in the installed cameras. In this paper, we present EyeShopper, an innovative system that tracks the gaze of shoppers when facing away from the camera and provides insights about their behavior in physical stores. EyeShopper is readily deployable in existing surveillance systems and robust against low-resolution video inputs. At the same time, its accuracy is comparable to state-of-the-art gaze estimation frameworks that require high-resolution and continuous video inputs to function. Furthermore, EyeShopper is more robust than state-of-the-art gaze tracking techniques for back head images. Extensive evaluation with different real video datasets and a synthetic dataset we produced shows that EyeShopper estimates with high accuracy the gaze of customers.

The increasing complexity of smartphone applications and services necessitate high battery consumption, but the growth of smartphones' battery capacity is not keeping pace with these increasing power demands. To overcome this problem, researchers gave birth to the Mobile Cloud Computing (MCC) research area. In this paper, we advance on previous ideas, proposing and implementing a Near Field Communication (NFC)-based computation offloading framework. This research is motivated by the advantages of NFC's short distance communication, its better security, and its low battery consumption characteristics. We design a new NFC communication protocol that overcomes the limitations of the default NFC protocol; removing the need for constant user interaction, the one-way communication restraint, and the limit on low data size transfer. Via the implemented framework, parts of mobile applications can be offloaded to other mobile devices or cloudlets equipped with an NFC reader. We present experimental results of the energy consumption and the time duration of computationally and data intensive representative applications: (i) RSA key generation and encryption, (ii) gaming/puzzles, (iii) face detection, (iv) media download from the Internet, and (v) data transferring between the mobile and the cloudlet. We show that when the helper device is more powerful than the device offloading the computations, the execution time of the tasks is reduced. Finally, we show that devices that offload application parts considerably reduce their energy consumption due to the low-power NFC interface and the benefits of offloading.

We develop Hyperion a Wearable Augmented Reality (WAR) system based on Google Glass to access text information in the ambient environment. Hyperion is able to retrieve text content from users' current view and deliver the content to them in different ways according to their context. We design four work modalities for different situations that mobile users encounter in their daily activities. In addition, user interaction interfaces are provided to adapt to different application scenarios. Although Google Glass may be constrained by its poor computational capabilities and its limited battery capacity, we utilize code-level offloading to companion mobile devices to improve the runtime performance and the sustainability of WAR applications. System experiments show that Hyperion improves users ability to be aware of text information around them. Our prototype indicates promising potential of converging WAR technology and wearable devices such as Google Glass to improve people's daily activities.

The wide spread of smart mobile devices such as tablets and phones makes mobile crowdsensing a viable approach for collecting data and monitoring phenomena of common interest. Smart devices can sense and compute their surroundings and contribute to mechanisms that examine social and collective behaviours. Crowdsensing offers a feasible alternative to exchange and compute sensing tasks and data between devices. Due to the limited resources (i.e., battery, processing power, memory) of smart mobile devices, the cooperation and hence, the performance of the mobile crowdsensing applications may be affected. We empirically show that collective incentives, such as trust (social ties) among participants, and resources availability can boost the performance of mobile crowdsensing applications. This collective incentive together with the existing cooperation enforcing mechanisms, can enhance the cooperation of the participants and incentify them to cooperate in social based mobile crowdsensing applications.