Yuza chushin sumato obujekuto no tame no dokyumento besu no furemu waku

制度:新 ; 報告番号:甲2792号 ; 学位の種類:博士(工学) ; 授与年月日:2009/3/15 ; 早大学位記番号:新501

A miniaturized display network for situated glyphs

We demonstrate a novel approach for building situated information systems using wirelessly connected miniaturized displays. These displays are spatially distributed in a physical work environment and present situated glyphs - human-readable abstract graphical signs - to provide activity centric notification and feedback. The demo will showcase how such miniaturized display networks can be used in dynamic workplaces, e.g., a hospital to support complex activities

Libri-Adapt: A New Speech Dataset for Unsupervised Domain Adaptation

This paper introduces a new dataset, Libri-Adapt, to support unsupervised domain adaptation research on speech recognition models. Built on top of the LibriSpeech corpus, Libri-Adapt contains English speech recorded on mobile and embedded-scale microphones, and spans 72 different domains that are representative of the challenging practical scenarios encountered by ASR models. More specifically, Libri-Adapt facilitates the study of domain shifts in ASR models caused by a) different acoustic environments, b) variations in speaker accents, c) heterogeneity in the hardware and platform software of the microphones, and d) a combination of the aforementioned three shifts. We also provide a number of baseline results quantifying the impact of these domain shifts on the Mozilla DeepSpeech2 ASR model.Comment: 5 pages, Published at IEEE ICASSP 202

Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems

Mobile and embedded devices are increasingly using microphones and audio-based computational models to infer user context. A major challenge in building systems that combine audio models with commodity microphones is to guarantee their accuracy and robustness in the real-world. Besides many environmental dynamics, a primary factor that impacts the robustness of audio models is microphone variability. In this work, we propose Mic2Mic -- a machine-learned system component -- which resides in the inference pipeline of audio models and at real-time reduces the variability in audio data caused by microphone-specific factors. Two key considerations for the design of Mic2Mic were: a) to decouple the problem of microphone variability from the audio task, and b) put a minimal burden on end-users to provide training data. With these in mind, we apply the principles of cycle-consistent generative adversarial networks (CycleGANs) to learn Mic2Mic using unlabeled and unpaired data collected from different microphones. Our experiments show that Mic2Mic can recover between 66% to 89% of the accuracy lost due to microphone variability for two common audio tasks.Comment: Published at ACM IPSN 201

Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing

The field of mobile, wearable, and ubiquitous computing (UbiComp) is undergoing a revolutionary integration of machine learning. Devices can now diagnose diseases, predict heart irregularities, and unlock the full potential of human cognition. However, the underlying algorithms are not immune to biases with respect to sensitive attributes (e.g., gender, race), leading to discriminatory outcomes. The research communities of HCI and AI-Ethics have recently started to explore ways of reporting information about datasets to surface and, eventually, counter those biases. The goal of this work is to explore the extent to which the UbiComp community has adopted such ways of reporting and highlight potential shortcomings. Through a systematic review of papers published in the Proceedings of the ACM Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) journal over the past 5 years (2018-2022), we found that progress on algorithmic fairness within the UbiComp community lags behind. Our findings show that only a small portion (5%) of published papers adheres to modern fairness reporting, while the overwhelming majority thereof focuses on accuracy or error metrics. In light of these findings, our work provides practical guidelines for the design and development of ubiquitous technologies that not only strive for accuracy but also for fairness

The State of Algorithmic Fairness in Mobile Human-Computer Interaction

This paper explores the intersection of Artificial Intelligence and Machine Learning (AI/ML) fairness and mobile human-computer interaction (MobileHCI). Through a comprehensive analysis of MobileHCI proceedings published between 2017 and 2022, we first aim to understand the current state of algorithmic fairness in the community. By manually analyzing 90 papers, we found that only a small portion (5%) thereof adheres to modern fairness reporting, such as analyses conditioned on demographic breakdowns. At the same time, the overwhelming majority draws its findings from highly-educated, employed, and Western populations. We situate these findings within recent efforts to capture the current state of algorithmic fairness in mobile and wearable computing, and envision that our results will serve as an open invitation to the design and development of fairer ubiquitous technologies.Comment: arXiv admin note: text overlap with arXiv:2303.1558

Energy Efficient Scheduling for Mobile Push Notifications

Push notifications are small and succinct messages used by mobile applications to inform users of new events and updates. These notifications are pushed to the user devices by a set of dedicated notification servers (e.g., Apple Push Notification Server, Google Cloud Messaging Server, etc.) as they arrive from the content providers of the mobile applications. However, due to their intrinsic small size and sporadic nature, the transfer of these messages is not power efficient, especially on cellular networks. To address this, we propose a network centric scheduling mechanism that delays the delivery of these messages as appropriate by sensing and predicting users' cellular network activities. A trace based evaluation with 60 users' cellular network logs of 30 days shows that we can reduce the energy consumption of mobile devices by 10% for an average delay of 150 seconds in notification delivery. As a network based system that does not require any modifications to user devices, scheduling push notifications opens up interesting opportunities for mobile operators to provide value added and differentiating services, especially considering the sharp rise of non-critical push notification messages