Open Access. Powered by Scholars. Published by Universities.®
Artificial Intelligence and Robotics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Activity Recognition (1)
- Cross-validation (1)
- Daily routines (1)
- Effective location (1)
- Energy-effciency (1)
-
- Event Detection (1)
- In contexts (1)
- Indoor Navigation (1)
- Information Fusion (1)
- Maximum likelihood estimation (1)
- Mobile Computing (1)
- Mobile crowdsourcing (1)
- Mobility traces (1)
- Mother logit (1)
- Multi-Modal Sensing (1)
- Perturbation techniques (1)
- Privacy invasions (1)
- Push-based (1)
- Random regret minimization (1)
- Recursive logit (1)
- Route choice modeling (1)
- Smartglasses (1)
- Urban Analytics (1)
- User location (1)
- Video (1)
- Wearable Computing (1)
- Wearable Device (1)
Articles 1 - 7 of 7
Full-Text Articles in Artificial Intelligence and Robotics
Capsense: Capacitor-Based Activity Sensing For Kinetic Energy Harvesting Powered Wearable Devices, Guohao Lan, Dong Ma, Weitao Xu, Mahbub Hassan, Wen Hu
Capsense: Capacitor-Based Activity Sensing For Kinetic Energy Harvesting Powered Wearable Devices, Guohao Lan, Dong Ma, Weitao Xu, Mahbub Hassan, Wen Hu
Research Collection School Of Computing and Information Systems
We propose a new activity sensing method, CapSense, which detects activities of daily living (ADL) by sampling the voltage of the kinetic energy harvesting (KEH) capacitor at an ultra low sampling rate. Unlike conventional sensors that generate only instantaneous motion information of the subject, KEH capacitors accumulate and store human generated energy over time. Given that humans produce kinetic energy at distinct rates for different ADL, the KEH capacitor can be sampled only once in a while to observe the energy generation rate and identify the current activity. Thus, with CapSense, it is possible to avoid collecting time series motion …
Adviser+: Toward A Usable Web-Based Algorithm Portfolio Deviser, Hoong Chuin Lau, Mustafa Misir, Xiang Li Li, Lingxiao Jiang
Adviser+: Toward A Usable Web-Based Algorithm Portfolio Deviser, Hoong Chuin Lau, Mustafa Misir, Xiang Li Li, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
The present study offers a more user-friendly and parallelized version of a web-based algorithm portfolio generator, called ADVISER. ADVISER is a portfolio generation tool to deliver a group of configurations for a given set of algorithms targeting a particular problem. The resulting configurations are expected to be diverse such that each can perform well on a certain type of problem instances. One issue with ADVISER is that it performs portfolio generation on a single-core which results in long waiting times for the users. Besides that, it lacks of a reporting system with visualizations to tell more about the generated portfolios. …
On The Similarities Between Random Regret Minimization And Mother Logit: The Case Of Recursive Route Choice Models, Tien Mai, Fabian Bastin, Emma Frejinger
On The Similarities Between Random Regret Minimization And Mother Logit: The Case Of Recursive Route Choice Models, Tien Mai, Fabian Bastin, Emma Frejinger
Research Collection School Of Computing and Information Systems
This paper focuses on the comparison of the random regret minimization (RRM) and mother logit models for analyzing the choice between alternatives having deterministic attributes. The mother logit model allows utilities of a given alternative to depend on attributes of other alternatives. It was designed to relax the independence from irrelevant alternatives (IIA) property while keeping the random terms independently and identically distributed extreme value distributed (McFadden et al., 1978).We adapt and extend the RRM model proposed by Chorus (2014) to the case of recursive logit (RL) route choice models (Fosgerau et al., 2013). We argue that these RRM models …
Towards Distributed Machine Learning In Shared Clusters: A Dynamically-Partitioned Approach, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Shengen Yan
Towards Distributed Machine Learning In Shared Clusters: A Dynamically-Partitioned Approach, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Shengen Yan
Research Collection School Of Computing and Information Systems
Many cluster management systems (CMSs) have been proposed to share a single cluster with multiple distributed computing systems. However, none of the existing approaches can handle distributed machine learning (ML) workloads given the following criteria: high resource utilization, fair resource allocation and low sharing overhead. To solve this problem, we propose a new CMS named Dorm, incorporating a dynamicallypartitioned cluster management mechanism and an utilizationfairness optimizer. Specifically, Dorm uses the container-based virtualization technique to partition a cluster, runs one application per partition, and can dynamically resize each partition at application runtime for resource efficiency and fairness. Each application directly launches …
Follow-My-Lead: Intuitive Indoor Path Creation And Navigation Using See-Through Interactive Videos, Quentin Roy, Simon T. Perrault, Shengdong Zhao, Richard Davis, Anuroop Pattena Vaniyar, Velko Vechev, Youngki Lee, Archan Misra
Follow-My-Lead: Intuitive Indoor Path Creation And Navigation Using See-Through Interactive Videos, Quentin Roy, Simon T. Perrault, Shengdong Zhao, Richard Davis, Anuroop Pattena Vaniyar, Velko Vechev, Youngki Lee, Archan Misra
Research Collection School Of Computing and Information Systems
We present Follow-My-Lead, an alternative indoor navigation technique that uses visual information recorded on an actual navigation path as a navigational guide. Its design revealed a trade-off between the fidelity of information provided to users and their effort to acquire it. Our first experiment revealed that scrolling through a continuous image stream of the navigation path is highly informative, but it becomes tedious with constant use. Discrete image checkpoints require less effort, but can be confusing. A balance may be struck by adding fast video transitions between image checkpoints, but precise control is required to handle difficult situations. Authoring still …
Discovering Anomalous Events From Urban Informatics Data, Kasthuri Jayarajah, Vigneshwaran Subbaraju, Dulanga Kaveesha Weerakoon Mudiyanselage, Archan Misra, La Thanh Tam, Noel Athaide
Discovering Anomalous Events From Urban Informatics Data, Kasthuri Jayarajah, Vigneshwaran Subbaraju, Dulanga Kaveesha Weerakoon Mudiyanselage, Archan Misra, La Thanh Tam, Noel Athaide
Research Collection School Of Computing and Information Systems
Singapore's "smart city" agenda is driving the government to provide public access to a broader variety of urban informatics sources, such as images from traffic cameras and information about buses servicing different bus stops. Such informatics data serves as probes of evolving conditions at different spatiotemporal scales. This paper explores how such multi-modal informatics data can be used to establish the normal operating conditions at different city locations, and then apply appropriate outlier-based analysis techniques to identify anomalous events at these selected locations. We will introduce the overall architecture of sociophysical analytics, where such infrastructural data sources can be combined …
Privacy In Context-Aware Mobile Crowdsourcing Systems, Thivya Kandappu, Archan Misra, Shih-Fen Cheng, Hoong Chuin Lau
Privacy In Context-Aware Mobile Crowdsourcing Systems, Thivya Kandappu, Archan Misra, Shih-Fen Cheng, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Mobile crowd-sourcing can become as a strategy to perform time-sensitive urban tasks (such as municipal monitoring and last mile logistics) by effectively coordinating smartphone users. The success of the mobile crowd-sourcing platform depends mainly on its effectiveness in engaging crowd-workers, and recent studies have shown that compared to the pull-based approach, which relies on crowd-workers to browse and commit to tasks they would want to perform, the push-based approach can take into consideration of worker’s daily routine, and generate highly effective recommendations. As a result, workers waste less time on detours, plan more in advance, and require much less planning …