Machine learning & state detection

Paper session

会議の名前
CHI 2020
Charge for a whole day: Extending Battery Life for BCI Wearables using a Lightweight Wake-Up Command
要旨

Commercially available EEG-based Brain-Computer Interface (BCI) wearable headsets are always-on and are thus power hungry, requiring users to charge the headsets multiple times a day. In this paper, we tackle the problem of wake-up command design and detection for BCI headsets, and explore how battery life can be made to last for approximately a whole day. The key challenge that we address is enabling the headset to operate in a near-sleep mode but still reliably detect and interpret an EEG-based wake-up command from the user. Towards addressing the challenge, we present a solution that is built upon eye-blinks. Our core contribution is Trance, a user-friendly and robust wake-up command for BCI headsets that is computationally lightweight. We show using experimental results coupled with multiple data sets collected through user-studies that Trance can extend battery life by approximately 2.7x or to approximately 10 hours for a typical wearable battery, while remaining user-friendly.

キーワード
Brain-Computer Interfaces (BCIs)
Wearable systems
Input Techniques
Eye Tracking
Interaction Design
著者
Mohit Agarwal
Georgia Institute of Technology, Atlanta, GA, USA
Raghupathy Sivakumar
Georgia Institute of Technology, Atlanta, GA, USA
DOI

10.1145/3313831.3376738

論文URL

https://doi.org/10.1145/3313831.3376738

動画
DFSeer: A Visual Analytics Approach to Facilitate Model Selection for Demand Forecasting
要旨

Selecting an appropriate model to forecast product demand is critical to the manufacturing industry. However, due to the data complexity, market uncertainty and users' demanding requirements for the model, it is challenging for demand analysts to select a proper model. Although existing model selection methods can reduce the manual burden to some extent, they often fail to present model performance details on individual products and reveal the potential risk of the selected model. This paper presents DFSeer, an interactive visualization system to conduct reliable model selection for demand forecasting based on the products with similar historical demand. It supports model comparison and selection with different levels of details. Besides, it shows the difference in model performance on similar products to reveal the risk of model selection and increase users' confidence in choosing a forecasting model. Two case studies and interviews with domain experts demonstrate the effectiveness and usability of DFSeer.

キーワード
Interactive Visualization
Model Selection
Product Demand Forecasting
Time Series
著者
Dong Sun
Hong Kong University of Science and Technology, Hong Kong, China
Zezheng Feng
Hong Kong University of Science and Technology, Hong Kong, China
Yuanzhe Chen
Huawei Technologies Investment Co. Ltd, Shenzhen, China
Yong Wang
Hong Kong University of Science and Technology, Hong Kong, China
Jia Zeng
Huawei Technologies Investment Co. Ltd, Shenzhen, China
Mingxuan Yuan
Huawei Technologies Investment Co. Ltd, Shenzhen, China
Ting-Chuen Pong
The Kong Kong University of Science and Technology, Hong Kong, China
Huamin Qu
Hong Kong University of Science and Technology, Hong Kong, China
DOI

10.1145/3313831.3376866

論文URL

https://doi.org/10.1145/3313831.3376866

動画
Integrated Development Environment with Interactive Scatter Plot for Examining Statistical Modeling
要旨

The development of a statistical modeling program requires example data to observe and verify the behavior of the program. Such example data are either taken from an existing dataset or synthesized using commands. Programmers may want to directly design an arbitrary dataset or modify it interactively, but it is difficult to do so in current development environments. We therefore propose combining a code editor with an interactive scatter plot editor to efficiently understand the behavior of statistical modeling algorithms. The user interactively creates and modifies the dataset on the scatter plot editor, while the system continuously executes the code in the editor, taking the data as input, and shows the result in the editor. This paper presents the design rationale behind the system and introduces several usage scenarios.

キーワード
Interactive data design and editing
Statistical modeling
Live programming
著者
Kentaro Asai
The University of Tokyo, Tokyo, Japan
Tsukasa Fukusato
The University of Tokyo, Tokyo, Japan
Takeo Igarashi
The University of Tokyo, Tokyo, Japan
DOI

10.1145/3313831.3376455

論文URL

https://doi.org/10.1145/3313831.3376455

動画
FDHelper: Assist Unsupervised Fraud Detection Experts with Interactive Feature Selection and Evaluation
要旨

Online fraud is the well-known dark side of the modern Internet. Unsupervised fraud detection algorithms are widely used to address this problem. However, selecting features, adjusting hyperparameters, evaluating the algorithms, and eliminating false positives all require human expert involvement. In this work, we design and implement an end-to-end interactive visualization system, FDHelper, based on the deep understanding of the mechanism of the black market and fraud detection algorithms. We identify a workflow based on experience from both fraud detection algorithm experts and domain experts. Using a multi-granularity three-layer visualization map embedding an entropy-based distance metric ColDis, analysts can interactively select different feature sets, refine fraud detection algorithms, tune parameters and evaluate the detection result in near real-time. We demonstrate the effectiveness and significance of FDHelper through two case studies with state-of-the-art fraud detection algorithms, interviews with domain experts and algorithm experts, and a user study with eight first-time end users.

キーワード
Human Computer Interaction
Fraud Detection
Visualization
著者
Jiao Sun
Tsinghua University, Beijing, China
Yin Li
Tsinghua University , Beijing, China
Charley Chen
Tsinghua University, Beijing, China
Jihae Lee
Tsinghua University, Beijing, China
Xin Liu
Tsinghua University, Beijing, China
Zhongping Zhang
Boston University, Boston, MA, USA
Ling Huang
Tsinghua University & AHI Fin-tech Inc., Beijing, China
Lei Shi
Beihang University, Beijing, China
Wei Xu
Tsinghua University, Beijing, China
DOI

10.1145/3313831.3376140

論文URL

https://doi.org/10.1145/3313831.3376140

動画
Understanding and Visualizing Data Iteration in Machine Learning
要旨

Successful machine learning (ML) applications require iterations on both modeling and the underlying data. While prior visualization tools for ML primarily focus on modeling, our interviews with 23 ML practitioners reveal that they improve model performance frequently by iterating on their data (e.g., collecting new data, adding labels) rather than their models. We also identify common types of data iterations and associated analysis tasks and challenges. To help attribute data iterations to model performance, we design a collection of interactive visualizations and integrate them into a prototype, Chameleon, that lets users compare data features, training/testing splits, and performance across data versions. We present two case studies where developers apply \system to their own evolving datasets on production ML projects. Our interface helps them verify data collection efforts, find failure cases stretching across data versions, capture data processing changes that impacted performance, and identify opportunities for future data iterations.

キーワード
Data iteration
evolving datasets
machine learning iteration
visual analytics
interactive interfaces
著者
Fred Hohman
Georgia Institute of Technology & Apple Inc., Atlanta, GA, USA
Kanit Wongsuphasawat
Apple Inc., Seattle, WA, USA
Mary Beth Kery
Carnegie Mellon University, Pittsburgh, PA, USA
Kayur Patel
Apple Inc, Seattle, WA, USA
DOI

10.1145/3313831.3376177

論文URL

https://doi.org/10.1145/3313831.3376177

動画