Existing touchscreen software keyboards prevent users from resting their hands, forcing slow and fatiguing index-finger tapping (“chicken typing”) instead of familiar hands-down ten-finger typing. We present KeySense, a purely software solution that preserves physical keyboard motor skills. KeySense isolates intentional taps from resting-finger noise with cognitive–motor timing patterns, and then uses a fine-tuned LLM decoder to turn the resulting noisy letter sequence into the intended word. In controlled component tests, this decoder substantially outperforms 2 statistical baselines (top-1 accuracy 84.8% vs 75.7% and 79.3%). A 12-participant study shows clear ergonomic and performance benefits: compared with the conventional hover-style keyboard, users rated KeySense as markedly less physically demanding (NASA-TLX median 1.5 vs 4.0), and after brief practice, typed significantly faster (WPM 28.3 vs 26.2, p <0.01). These results indicate that KeySense enables accurate, efficient and comfortable ten-finger text entry on commodity touchscreens, without any extra hardware.

The development of smart textile interfaces is hindered by the inclusion of rigid hardware components and batteries within the fabric, which pose challenges in terms of manufacturability, usability, and environmental concerns related to electronic waste. To mitigate these issues, we propose a smart textile interface and its wireless sensing system to eliminate the need for ICs, batteries, and connectors embedded into textiles. Our technique is established on the integration of multi-resonant circuits in smart textile interfaces, and utilizing near-field electromagnetic coupling between two coils to facilitate wireless power transfer and data acquisition from smart textile interface. A key aspect of our system is the development of a mathematical model that accurately represents the equivalent circuit of the sensing system. Using this model, we developed a novel algorithm to accurately estimate sensor signals based on changes in system impedance. Through simulation-based experiments and a user study, we demonstrate that our technique effectively supports multiple textile sensors of various types.

We summarize IEEE Big Data Cup: Predicting Escalations in Customer Support - a data mining competition organized jointly by companies Information Builders and QED Software at the KnowledgePit platform, in the frame of the 2020 IEEE International Conference on Big Data. We discuss the motivation for organizing this event and highlight the factors that make it such a challenging topic. We describe the data provided to participants and formulate the competition task. We also provide an overview of competition results with a detailed analysis of a few selected solutions. Finally, we present a novel functionality of the KnowledgePit platform - an analytic module that allows organizers to investigate selected solutions using a convenient GUI and provides in-depth insights about their quality.