Tian MIN | 閔 天

Soil-based Microbial Fuel Cells (SMFCs) offer a sustainable method for powering low-energy computing devices by harnessing electricity from microbial activity in soil. In this paper, we introduce SoilSense, a novel approach that repurposes SMFCs as tangible interfaces, transforming soil into an interactive, computationally responsive medium, instead of energy sources. We explore the voltage variations that occur when pressure is applied to the cathode and systematically characterize this mechanism across different electrode configurations and soil moisture levels. To demonstrate the feasibility of SMFC-based interfaces, we present a series of modular and proof-of-concept prototypes that support diverse interaction modalities. We further illustrate how SoilSense enables interactions through example applications and provide implications and envision for future studies to employ soil as an ecologically compatible material in interactive system design.

We present SoilTile, a novel ground sensing approach that explores soil as an interactive medium. Built upon the principles of Soil-based Microbial Fuel Cells (SMFCs), SoilTile draws on the natural electrochemical interactions between soil and embedded electrodes—not as a source of energy, but as a way to sense physical contact through electrical response. When pressure applied to the cathode, SMFC produce measurable variations in output voltage. Building on this phenomenon, we construct modular ground sensing units that detect localized pressure through a matrix of electrodes, structured to respond under human activities or object placement. With its minimalist structure, DIY-friendly fabrication, and potential for seamless integration into natural environments, SoilTile offers a simple, material-driven platform for designing interactive spaces.

The emergence of virtual sensors in recent years has opened up new possibilities for the development of human activity recognition (HAR) systems. For instance, we can synthesized virtual sensor data for those scarce datasets, such as accelerometer data, from the widely available multimedia resources online through cross-modal approaches. However, existing solutions on virtual sensors primarily focus on batch pipelines, relying heavily on lengthy processing workflows and sophisticated computer vision techniques, which often lack interactivity and flexibility for the usage in customized and small-scale scenarios. In this work, we present the Vsens Toolkit, an AR-based open-ended system for virtual sensors, which serves as an preliminary exploration of the user interface for virtual sensors. It integrates functionalities such as scene construction, data collection, data augmentation, and visualization. In this interactivity demonstration, we showcase exemplar scenarios including wearable accelerometers, capacitive sensing, wrist-worn sensor tracking, and sandbox for free exploration (Figure 1).

Guiding users with limb exercise can assist in muscle training or physical recovery. However, traditional vision-based methods often require multiple camera angles to help users understand the motions and require them to be within the range of the screen. Therefore, we propose a non-visual system that can guide users with multiple-directional limb motions utilizing spatial audio, AudioMove, with commercial-off-the-shelf (COTS) devices (i.e., smartphones and earphones). The proposed system addresses the challenge of conveying directional information encompassing multiple planes in real-time. We conduct a mixed-method user study to evaluate the effectiveness of the system with three methods combining motion data with spatial audio perception. Additionally, a user interface is built to collect users’ comments. The results conclude that spatial audio guidance could create a natural, pervasive, and non-visual exercise training solution in daily life.

The rise of the Internet of Things (IoT) has given birth to transformative and massively deployed computing applications that raise the significant issue of energy sources. It is impractical and irresponsible to rely on wires and batteries to power trillion-level devices. One promising prediction is that energy harvesting technologies will serve as alternative power sources for IoT devices. However, we might be losing this prophecy for lack of understanding of how novice developers comprehend energy in developing IoT. In response, we conducted a mentored physical prototyping study with a two-day workshop involving eight novice developers. The study consisted of qualitative and quantitative analyses, the artifacts, interviews with both novice developers and an expert, and implications of designs for future tools. The findings reveal informational gaps that demand educational efforts and assistive features to facilitate novice developers. We present major findings from the study and implications for the design of future tools.

Human activities can introduce variations in various environmental cues, such as light and sound, which can serve as inputs for interfaces. However, one often overlooked aspect is the airflow variation caused by these activities, which presents challenges in detection and utilization due to its intangible nature. In this paper, we have unveiled an approach using mist to capture invisible airflow variations, rendering them detectable by Time-of-Flight (ToF) sensors. We investigate the capability of this sensing technique under different types of mist or smoke, as well as the impact of airflow speed. To illustrate the feasibility of this concept, we created a prototype using a humidifier and demonstrated its capability to recognize motions. On this basis, we introduce potential applications, discuss inherent limitations, and provide design lessons grounded in mist-based airflow sensing.

Guiding users with limb motion exercises can assist them in their physical recovery and muscle training. However, traditional visual tracking methods often require multiple camera angles to help the user understand their movements and require the user to be within the range of the screen. This is not effective for more ubiquitous situations. Therefore, we propose a method that uses spatial audio to guide the user with a multiple-directional limb motion exercise. Depending on the user’s perception of spatial audio’s direction, we designed feedback to help the user adjust the movement height and complete the move to the designated position. We attached a smartphone to the body limb to capture the user’s motion data and generate feedback. The experiment showed that with our designed methods, the user could conduct the multiple-directional limb exercise to the designated position. The spatial audio-based limb’s motion exercise system could create a natural, pervasive, and non-visual exercise training system in daily life.

Regardless of which community, incentivizing users is a necessity for well-sustainable operations. In the blockchain-backed Web3 communities, known for their transparency and security, airdrop serves as a widespread incentive mechanism for allocating capital and power. However, it remains a controversy on how to justify airdrop to incentive and empower the decentralized governance. In this paper, we use ParaSwap as an example to propose a role taxonomy methodology through a data-driven study to understand the characteristic of community members and the effectiveness of airdrop. We find that users receive more rewards tend to take positive actions towards the community. We summarize several arbitrage patterns and confirm the current detection is not sufficient in screening out airdrop hunters. In conjunction with the results, we discuss from the aspects of interaction, financialization, and system design to conclude the challenges and possible research directions for decentralized communities.

The boom of liquidity mining has attracted enormous attention, which has brought tens of times increment in total value locked (TVL) to decentralized finance (DeFi) community. Meanwhile, governance tokens, as part of the liquidity mining reward, have been adopted by most decentralized applications (DApps) to attract users. However, the effectiveness of this method has not been proven in detail. In this paper, we choose one of the most representative cases where SushiSwap absorbed a significant amount of Uniswap liquidity in no time by forking Uniswap’s code and issuing the governance token ahead to understand the governance tokens in liquidity mining. Specifically, we collect transaction records of Uniswap and SushiSwap for over a year and perform a detailed analysis of liquidity providers’ (LPs) activities. Moreover, we design a scalable unsupervised clustering method, which uses metrics between transaction flows to build a similarity graph that can capture patterns between LPs with similar behaviour. These LPs range from inactive and cautious LPs, providing tiny liquidity to risk-seeking LPs, focusing on short time-intervals. On this basis, we discuss how the governance token affects liquidity mining, and use its impact on behaviours and decision-making to explain its attractiveness to users.

Decentralized application (DApp) is an emerging technology designed to address distrust, privacy, and security issues. However, we note that the research community in human factors has not conducted in-depth research on user behavior in this unique distributed environment yet. In this paper, unlike a small sample of user interviews, we attempt to profile DApp users through publicly available data. Using Ethereum as an example, we build a series of datasets containing more than 73.8 million transactions generated by 230,000 addresses. By transforming hexadecimal addresses into readable application names, we analyze the behavioral characteristics of the user based on the categories of DApp. Furthermore, we apply an unsupervised clustering method on the 230,000 addresses to distinguish investors and players and analyze their behavioral patterns and sensitivity to blockchain markets, such as ETH prices. In addition, we implement heuristics to demonstrate how blockchain data mining can facilitate practical systems, including anomaly detection and recommend systems. Finally, we discuss future directions for studying human factors in a decentralized context and hope that this work will attract more research attention and support the development of DApp and further Metaverse ecosystems.

With the support of the blockchain systems, the cryptocurrency has changed the world of virtual assets. Digital games, especially those with massive multi-player scenarios, will be significantly impacted by this novel technology. However, there are insufficient academic studies on this topic. In this work, we filled the blank by surveying the state-of-the-art blockchain games. We discuss the blockchain integration for games and then categorize existing blockchain games from the aspects of their genres and technical platforms. Moreover, by analyzing the industrial trend with a statistical approach, we envision the future of blockchain games from technological and commercial perspectives.