The NFT ecosystem represents an interconnected, decentralized environment that encompasses the creation, distribution, and trading of Non-Fungible Tokens (NFTs), where key actors, such as marketplaces, sellers, and buyers, utilize smart contracts to facilitate secure, transparent, and trustless transactions. Scam tokens are deliberately created to mislead users and facilitate financial exploitation, posing significant risks in the NFT ecosystem. Prior work has explored the NFT ecosystem from various perspectives, including security challenges, actor behaviors, and risks from scams and wash trading, leaving a gap in understanding the semantics and interactions of smart contracts during transactions, and how the risks associated with scam tokens manifest in relation to the semantics and interactions of contracts. To bridge this gap, we conducted a large-scale empirical study on smart contract semantics and interactions in the NFT ecosystem, using a curated dataset of nearly 100 million transactions across 20 million blocks on Ethereum. We observe a limited semantic diversity among smart contracts in the NFT ecosystem, dominated by proxy, token, and DeFi contracts. Marketplace and proxy registry contracts are the most frequently involved in smart contract interactions during transactions, engaging with a broad spectrum of contracts in the ecosystem. Token contracts exhibit bytecode-level diversity, whereas scam tokens exhibit bytecode convergence. Certain interaction patterns between smart contracts are common to both risky and non-risky transactions, while others are predominantly associated with risky transactions. Based on our findings, we provide recommendations to mitigate risks in the blockchain ecosystem, and outline future research directions.

Throat microphones show potential as wearable IoT sensors for voice and larynx movement recognition. By picking up vocal fold vibrations directly from the human throat, these can detect speech in noisy or windy environments where traditional microphones fail. Recent studies have investigated soft throat microphones due to their conformable fit with human skin. However, previous work has focused primarily on speaker recognition rather than the speech recognition capabilities of these sensors. This paper presents a flexible sponge-structured throat microphone that can accurately detect the fundamental frequency (F0) and F0 contour of human speech. Comparison with commercial contact microphones and air microphones demonstrates the proposed IoT throat microphone’s ability to capture vocal fold vibrations. While high throat vibration frequencies are damped by biological tissue filtering, the sensor can still achieve 89.80% accuracy in classifying 15 English words and 97.84% for 15 Chinese Mandarin words using signals lowpass filtered at 500Hz. Beyond voice recognition, a non-verbal “speaking bandage” system was also built to map throat movements like swallowing, coughing and mouth opening to words in real-time. This novel soft sensor demonstrates promise as an effective wearable for advanced larynx movement and voice recognition via IoT technologies. Potential applications include augmentative communication, rehabilitation, and human-computer interaction – opening new directions for assistive technologies powered by the subtleties of human speech production.

Designing ergonomic interfaces for man-machine interaction is a major task for today's computer design engineers. A new type of tangible man-machine communication interface which is based on acoustics is presented in this paper. The proposed new approach is the use of pattern recognition to match the pattern of a received signal's feature with a template, acquired during a learning stage, associated with a predefined location. Theoretically this method, referred as location patten matching (LPM) can work on heterogeneous medium of any shape or material using one or two sensors. Therefore they overcome the limitations of the more widely used approaches based on time delay of arrival (TDOA).