The document summarizes Radhika Dharurkar's Masters thesis defense on context-aware middleware for activity recognition. It provides an overview of her motivation, approach, implementation, experiments and results. Her work involved developing a prototype system that can predict 10 activities using data from smartphone sensors and other sources with better than average precision. Experiments were conducted collecting data from 2 users over 2 weeks to evaluate different classification algorithms on recognizing activities like working, studying, sleeping, etc. The most confused activities in classification were working/studying with others like coffee/snacks and sleeping.
This document provides an overview and schedule for a course on developing gesture-based natural user interfaces using the Kinect sensor. The course aims to teach students how to use programming environments like Processing, Pure Data, and Unity to build interactive applications involving full-body interaction. Students will first work on small individual projects, then collaborate in groups on a larger final project to be exhibited at the end of the semester. The document also covers topics like the components of a user interface, the evolution of interface styles, and how the Kinect sensor works using infrared and skeletal tracking.
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The document describes a smart healthcare monitoring system for independent living. Some key points:
- The system collects data from sensors monitoring daily living activities, physiological signals, and the environment to determine a person's wellness and ability to live independently.
- Sensors are deployed throughout the home to monitor activities like using appliances, mobility, and vital signs. The data is analyzed to recognize patterns and forecast wellness.
- Wellness is determined based on indices measuring inactive time and excess usage of appliances. The indices are improved over time using dynamic thresholds.
- Patterns in sensor activity are analyzed to detect irregular behaviors that could indicate issues. Forecasting is used to predict daily activities and identify deviations.
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1. The document discusses the challenges of widespread adoption of e-research technologies by everyday researchers. While early adopters found success, most researchers are not using the infrastructure services that have been created.
2. It argues that repositories and other e-research tools need to focus on the needs and perspectives of researchers. Researchers work with data, so tools should emphasize data sharing and metadata. They should also support collaboration and open participation in the scientific process.
3. For technologies to truly enable new forms of research, their use needs to become integrated into the everyday work of all researchers, not just a specialized few. Systems must be easy to use, empower researchers' autonomy, and intersect seamlessly with digital and physical
1. The document discusses the challenges of adopting e-research technologies by everyday researchers and moving from specialized scientists doing specialized science to widespread adoption.
2. It proposes a more data-centric and collaborative approach focused on the social process of science and empowering researchers.
3. Key lessons for repositories include understanding user needs, being open-minded about problems and solutions, embracing the web instead of creating barriers, and thinking of repositories as a cloud service instead of an institutional system.
Presentation Title: Grand Challenges and Big Data: Implications for Public Participation in Scientific Research
Presenter: William Michener, Professor and PI/Director of DataONE, University Libraries, University of New Mexico
This document provides an overview and schedule for a course on developing gesture-based natural user interfaces using the Kinect sensor. The course aims to teach students how to use programming environments like Processing, Pure Data, and Unity to build interactive applications involving full-body interaction. Students will first work on small individual projects, then collaborate in groups on a larger final project to be exhibited at the end of the semester. The document also covers topics like the components of a user interface, the evolution of interface styles, and how the Kinect sensor works using infrared and skeletal tracking.
Koios - Collective Intelligence and Crowd sourcing for public goodRoy Lachica
The document proposes a crowdsourcing platform called Koios to harness the cognitive surplus of internet users to collectively solve difficult social problems through structured collaboration and by leveraging theories of systems thinking, collective intelligence, and other problem solving approaches; while challenges remain in design, participation, and quality control, the platform aims to make problem solving more efficient by coordinating efforts online through shared tools, data, and workflows.
The document describes a smart healthcare monitoring system for independent living. Some key points:
- The system collects data from sensors monitoring daily living activities, physiological signals, and the environment to determine a person's wellness and ability to live independently.
- Sensors are deployed throughout the home to monitor activities like using appliances, mobility, and vital signs. The data is analyzed to recognize patterns and forecast wellness.
- Wellness is determined based on indices measuring inactive time and excess usage of appliances. The indices are improved over time using dynamic thresholds.
- Patterns in sensor activity are analyzed to detect irregular behaviors that could indicate issues. Forecasting is used to predict daily activities and identify deviations.
Presentation Smart Home With Home AutomationArifur Rahman
This document provides an overview of a presentation on smart home automation. It discusses how home automation can automate lighting, HVAC, appliances and other systems for improved convenience, comfort, energy efficiency and security. It describes how smart homes can be remotely controlled and monitored, including security, entertainment and information functions. It outlines the various wired and wireless devices used in home automation and popular software options like Linux, Mister House and Heyu. The presentation also includes diagrams of sample home automation architectures and a remote web interface.
SenSec: Mobile Application Security through Passive SensingJiang Zhu
The document proposes a smartphone-based behavioral authentication system called SenSec. It collects sensor data to build user behavior models. Features are extracted from the sensor data and used to build risk analysis trees to detect anomalies. When anomalies are detected, a certainty score is broadcast and can trigger authentication for sensitive applications. The system was tested on a dataset of 25 users, achieving over 98% accuracy in user identification. Extensions and integrations with other systems are discussed to enhance security, privacy, and energy efficiency.
1. The document discusses the challenges of widespread adoption of e-research technologies by everyday researchers. While early adopters found success, most researchers are not using the infrastructure services that have been created.
2. It argues that repositories and other e-research tools need to focus on the needs and perspectives of researchers. Researchers work with data, so tools should emphasize data sharing and metadata. They should also support collaboration and open participation in the scientific process.
3. For technologies to truly enable new forms of research, their use needs to become integrated into the everyday work of all researchers, not just a specialized few. Systems must be easy to use, empower researchers' autonomy, and intersect seamlessly with digital and physical
1. The document discusses the challenges of adopting e-research technologies by everyday researchers and moving from specialized scientists doing specialized science to widespread adoption.
2. It proposes a more data-centric and collaborative approach focused on the social process of science and empowering researchers.
3. Key lessons for repositories include understanding user needs, being open-minded about problems and solutions, embracing the web instead of creating barriers, and thinking of repositories as a cloud service instead of an institutional system.
Presentation Title: Grand Challenges and Big Data: Implications for Public Participation in Scientific Research
Presenter: William Michener, Professor and PI/Director of DataONE, University Libraries, University of New Mexico
TraitCapture: NextGen Monitoring and Visualization from seed to ecosystemTimeScience
This document discusses next generation software and hardware for plant phenotyping and ecosystem monitoring. It outlines challenges such as processing and managing large datasets, and optimizing open data sharing. Emerging tools discussed for high resolution field phenotyping include gigapixel imaging, drones, LiDAR, virtual/augmented reality, and sensor networks. A case study is presented of a sensor array installed at the Australian National Arboretum to monitor the environment, tree growth phenotypes, and genotypes over time at high precision across the landscape. The goal is to address fundamental ecological questions by capturing data at finer spatial and temporal resolutions than previously possible.
HiPEAC 2019 Workshop - Real-Time Modelling Visual Scenes with Biological Insp...Tulipp. Eu
- Computer vision has improved with more data and processing power, but global scene understanding remains challenging.
- The document proposes a multidisciplinary approach combining CNNs and human visual cognition to better model scene understanding, with the goal of applications like autonomous vehicles.
- It describes experiments observing how humans and primates recognize scenes to inform modeling, incorporating global and local descriptors with relationships. This approach aims to advance scene understanding capabilities.
This document provides an introduction to data mining. It defines data mining as the process of exploring and analyzing large amounts of data to discover meaningful patterns. It discusses some common data mining techniques such as classification, regression, clustering, and association rule mining. It also introduces some popular data mining tools like R, SAS Enterprise Miner, and XLMiner. Finally, it mentions some notable researchers in the field of data mining.
Activity Monitoring Using Wearable Sensors and Smart PhoneDrAhmedZoha
The document discusses two problems related to real-time activity recognition using data from wearable sensors and mobile phones. For problem 1 of developing an algorithm to recognize exercises from a raw sensor data stream, the solution involves a two-phase learning and recognition process using techniques like filtering, time-windowing, feature extraction and selection, and classification models. For problem 2 of enabling real-time recognition on mobile phones, the document recommends using Android and Java APIs to receive Bluetooth sensor data, train models on servers, and locally recognize activities on phones for efficiency. Key challenges discussed include energy usage, response time, and developing flexible models for different users.
This document provides an introduction and overview of data mining. It discusses why organizations mine data from both commercial and scientific viewpoints. Large amounts of data are now collected that traditional techniques cannot analyze. Data mining can help discover useful patterns in large data sets. The document outlines common data mining tasks like prediction, description, and classification. It also introduces popular data mining techniques such as decision trees, clustering, association rules, neural networks, and support vector machines. An example of data mining sky survey images to classify astronomical objects is provided. Further readings on data mining are recommended.
The document discusses a Faculty Development Program (FDP) on database management systems that was held on December 6, 2018 at the University College of Engineering Tindivanam in Tindivanam, India. The FDP covered recent research perspectives in different database management systems and the importance of database management systems in Digital India. It was conducted by Dr. A. Karthirvel, Professor and Head of the Computer Science and Engineering Department at MNM Jain Engineering College in Chennai.
The document discusses the development of a mobile application called SETapp that uses near field communication (NFC) to provide awareness support for scientific events. It aims to address common information needs like seeing who will attend an event, accessing schedules and profiles. The application was tested against QR codes and found to be much faster. User evaluations indicated clear preference for NFC over manual activities and high satisfaction with the interface. Future work may involve integrating it with event management systems and exploring uses in workplace and academic settings.
This 3-sentence summary provides the high-level information about the ICWSM'11 tutorial document:
The tutorial document announces a workshop on exploratory network analysis using Gephi, an open-source graph visualization and manipulation software, to be held on July 17, 2011 from 1-4 PM with instructors Sébastien Heymann and Julian Bilcke. The tutorial will provide an introduction to Gephi and guide participants through importing data, network visualization and manipulation, analysis, and aesthetics refinements using real datasets. Participants will work in teams and present preliminary results with the goal of learning practical skills for using Gephi on their own projects.
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Scientific discovery and innovation in an era of data-intensive science
William (Bill) Michener, Professor and Director of e-Science Initiatives for University Libraries, University of New Mexico; DataONE Principal Investigator
The scope and nature of biological, environmental and earth sciences research are evolving rapidly in response to environmental challenges such as global climate change, invasive species and emergent diseases. Scientific studies are increasingly focusing on long-term, broad-scale, and complex questions that require massive amounts of diverse data collected by remote sensing platforms and embedded environmental sensor networks; collaborative, interdisciplinary science teams; and new tools that promote scientific data preservation, discovery, and innovation. This talk describes the challenges facing scientists as they transition into this new era of data intensive science, presents current solutions, and lays out a roadmap to the future where new information technologies significantly increase the pace of scientific discovery and innovation.
If Big Data is data that exceeds the processing capacity of conventional systems, thereby necessitating alternative processing measures, we are looking at an essentially technological challenge that IT managers are best equipped to address.
The DCC is currently working with 18 HEIs to support and develop their capabilities in the management of research data and, whilst the aforementioned challenge is not usually core to their expressed concerns, are there particular issues of curation inherent to Big Data that might force a different perspective?
We have some understanding of Big Data from our contacts in the Astronomy and High Energy Physics domains, and the scale and speed of development in Genomics data generation is well known, but the inability to provide sufficient processing capacity is not one of their more frequent complaints.
That’s not to say that Big Science and its Big Data are free of challenges in data curation; only that they are shared with their lesser cousins, where one might say that the real challenge is less one of size than diversity and complexity.
This brief presentation explores those aspects of data curation that go beyond the challenges of processing power but which may lend a broader perspective to the technology selection process.
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Workshop session given at the Institutional Web Management Workshop 2012 (IWMW 2012) event held at the University of Edinburgh on 18th - 20th June 2012.
SP1: Exploratory Network Analysis with GephiJohn Breslin
ICWSM 2011 Tutorial
Sebastien Heymann and Julian Bilcke
Gephi is an interactive visualization and exploration software for all kinds of networks and relational data: online social networks, emails, communication and financial networks, but also semantic networks, inter-organizational networks and more. Designed to make data navigation and manipulation easy, it aims to fulfill the complete chain from data importing to aesthetics refinements and interaction. Users interact with the visualization and manipulate structures, shapes and colors to reveal hidden properties. The goal is to help data analysts to make hypotheses, intuitively discover patterns or errors in large data collections.
In this tutorial we will provide a hands-on demonstration of the essential functionalities of Gephi, based on a real case scenario: the exploration of student networks from the "Facebook100" dataset (Social Structure of Facebook Networks, Amanda L. Traud et al, 2011). The participants will be guided step by step through the complete chain of representation, manipulation, layout, analysis and aesthetics refinements. Particular focus will be put on filters and metrics for the creation of their first visualizations. They will be incited to compare the hypotheses suggested by their own exploration to the results actually published in the academic paper afterwards. They finally will walk away with the practical knowledge enabling them to use Gephi for their own projects. The tutorial is intended for professionals, researchers and graduates who wish to learn how playing during a network exploration can speed up their studies.
Sébastien Heymann is a Ph.D. Candidate in Computer Science at Université Pierre et Marie Curie, France. His research at the ComplexNetworks team focuses on the dynamics of realworld networks. He leads the Gephi project since 2008, and is the administrator of the Gephi Consortium.
Julian Bilcke is a Software Engineer at ISC-PIF (Complex Systems Institute of Paris, France). He is a founder and a developer for the Gephi project since 2008.
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TraitCapture: NextGen Monitoring and Visualization from seed to ecosystemTimeScience
This document discusses next generation software and hardware for plant phenotyping and ecosystem monitoring. It outlines challenges such as processing and managing large datasets, and optimizing open data sharing. Emerging tools discussed for high resolution field phenotyping include gigapixel imaging, drones, LiDAR, virtual/augmented reality, and sensor networks. A case study is presented of a sensor array installed at the Australian National Arboretum to monitor the environment, tree growth phenotypes, and genotypes over time at high precision across the landscape. The goal is to address fundamental ecological questions by capturing data at finer spatial and temporal resolutions than previously possible.
HiPEAC 2019 Workshop - Real-Time Modelling Visual Scenes with Biological Insp...Tulipp. Eu
- Computer vision has improved with more data and processing power, but global scene understanding remains challenging.
- The document proposes a multidisciplinary approach combining CNNs and human visual cognition to better model scene understanding, with the goal of applications like autonomous vehicles.
- It describes experiments observing how humans and primates recognize scenes to inform modeling, incorporating global and local descriptors with relationships. This approach aims to advance scene understanding capabilities.
This document provides an introduction to data mining. It defines data mining as the process of exploring and analyzing large amounts of data to discover meaningful patterns. It discusses some common data mining techniques such as classification, regression, clustering, and association rule mining. It also introduces some popular data mining tools like R, SAS Enterprise Miner, and XLMiner. Finally, it mentions some notable researchers in the field of data mining.
Activity Monitoring Using Wearable Sensors and Smart PhoneDrAhmedZoha
The document discusses two problems related to real-time activity recognition using data from wearable sensors and mobile phones. For problem 1 of developing an algorithm to recognize exercises from a raw sensor data stream, the solution involves a two-phase learning and recognition process using techniques like filtering, time-windowing, feature extraction and selection, and classification models. For problem 2 of enabling real-time recognition on mobile phones, the document recommends using Android and Java APIs to receive Bluetooth sensor data, train models on servers, and locally recognize activities on phones for efficiency. Key challenges discussed include energy usage, response time, and developing flexible models for different users.
This document provides an introduction and overview of data mining. It discusses why organizations mine data from both commercial and scientific viewpoints. Large amounts of data are now collected that traditional techniques cannot analyze. Data mining can help discover useful patterns in large data sets. The document outlines common data mining tasks like prediction, description, and classification. It also introduces popular data mining techniques such as decision trees, clustering, association rules, neural networks, and support vector machines. An example of data mining sky survey images to classify astronomical objects is provided. Further readings on data mining are recommended.
The document discusses a Faculty Development Program (FDP) on database management systems that was held on December 6, 2018 at the University College of Engineering Tindivanam in Tindivanam, India. The FDP covered recent research perspectives in different database management systems and the importance of database management systems in Digital India. It was conducted by Dr. A. Karthirvel, Professor and Head of the Computer Science and Engineering Department at MNM Jain Engineering College in Chennai.
The document discusses the development of a mobile application called SETapp that uses near field communication (NFC) to provide awareness support for scientific events. It aims to address common information needs like seeing who will attend an event, accessing schedules and profiles. The application was tested against QR codes and found to be much faster. User evaluations indicated clear preference for NFC over manual activities and high satisfaction with the interface. Future work may involve integrating it with event management systems and exploring uses in workplace and academic settings.
This 3-sentence summary provides the high-level information about the ICWSM'11 tutorial document:
The tutorial document announces a workshop on exploratory network analysis using Gephi, an open-source graph visualization and manipulation software, to be held on July 17, 2011 from 1-4 PM with instructors Sébastien Heymann and Julian Bilcke. The tutorial will provide an introduction to Gephi and guide participants through importing data, network visualization and manipulation, analysis, and aesthetics refinements using real datasets. Participants will work in teams and present preliminary results with the goal of learning practical skills for using Gephi on their own projects.
The DemaWare Service-Oriented AAL Platform for People with DementiaYiannis Kompatsiaris
This work presents DemaWare, an Ambient Intelligence platform that targets Ambient Assisted Living for people with Dementia. DemaWare seamlessly integrates diverse hardware (wearable and ambient sensors), as well as soft- ware components (semantic interpretation, reasoning), involved in such context. It also enables both online and offline processes, including sensor analysis and storage of context semantics in a Knowledge Base. Consequently, it orchestrates semantic interpretation which incorporated defeasible logics for uncertainty handling. Overall, the underlying functionality aids clinicians and carers to timely assess and diagnose patients in the context of lab trials, homes or nursing homes.
Scientific discovery and innovation in an era of data-intensive science
William (Bill) Michener, Professor and Director of e-Science Initiatives for University Libraries, University of New Mexico; DataONE Principal Investigator
The scope and nature of biological, environmental and earth sciences research are evolving rapidly in response to environmental challenges such as global climate change, invasive species and emergent diseases. Scientific studies are increasingly focusing on long-term, broad-scale, and complex questions that require massive amounts of diverse data collected by remote sensing platforms and embedded environmental sensor networks; collaborative, interdisciplinary science teams; and new tools that promote scientific data preservation, discovery, and innovation. This talk describes the challenges facing scientists as they transition into this new era of data intensive science, presents current solutions, and lays out a roadmap to the future where new information technologies significantly increase the pace of scientific discovery and innovation.
If Big Data is data that exceeds the processing capacity of conventional systems, thereby necessitating alternative processing measures, we are looking at an essentially technological challenge that IT managers are best equipped to address.
The DCC is currently working with 18 HEIs to support and develop their capabilities in the management of research data and, whilst the aforementioned challenge is not usually core to their expressed concerns, are there particular issues of curation inherent to Big Data that might force a different perspective?
We have some understanding of Big Data from our contacts in the Astronomy and High Energy Physics domains, and the scale and speed of development in Genomics data generation is well known, but the inability to provide sufficient processing capacity is not one of their more frequent complaints.
That’s not to say that Big Science and its Big Data are free of challenges in data curation; only that they are shared with their lesser cousins, where one might say that the real challenge is less one of size than diversity and complexity.
This brief presentation explores those aspects of data curation that go beyond the challenges of processing power but which may lend a broader perspective to the technology selection process.
Object extraction from satellite imagery using deep learningAly Abdelkareem
This document presents an overview of using deep learning for object extraction from satellite imagery. It discusses the needed data, training process, evaluation methods, appropriate tools, and literature review on the subject. Code samples applying techniques like VGGNet, Faster R-CNN, YOLO, and fully convolutional networks to datasets like SpaceNet and DSTL achieve preliminary results, with the YOLO model obtaining a maximum F1 score of 0.21 on test data.
Workshop session given at the Institutional Web Management Workshop 2012 (IWMW 2012) event held at the University of Edinburgh on 18th - 20th June 2012.
SP1: Exploratory Network Analysis with GephiJohn Breslin
ICWSM 2011 Tutorial
Sebastien Heymann and Julian Bilcke
Gephi is an interactive visualization and exploration software for all kinds of networks and relational data: online social networks, emails, communication and financial networks, but also semantic networks, inter-organizational networks and more. Designed to make data navigation and manipulation easy, it aims to fulfill the complete chain from data importing to aesthetics refinements and interaction. Users interact with the visualization and manipulate structures, shapes and colors to reveal hidden properties. The goal is to help data analysts to make hypotheses, intuitively discover patterns or errors in large data collections.
In this tutorial we will provide a hands-on demonstration of the essential functionalities of Gephi, based on a real case scenario: the exploration of student networks from the "Facebook100" dataset (Social Structure of Facebook Networks, Amanda L. Traud et al, 2011). The participants will be guided step by step through the complete chain of representation, manipulation, layout, analysis and aesthetics refinements. Particular focus will be put on filters and metrics for the creation of their first visualizations. They will be incited to compare the hypotheses suggested by their own exploration to the results actually published in the academic paper afterwards. They finally will walk away with the practical knowledge enabling them to use Gephi for their own projects. The tutorial is intended for professionals, researchers and graduates who wish to learn how playing during a network exploration can speed up their studies.
Sébastien Heymann is a Ph.D. Candidate in Computer Science at Université Pierre et Marie Curie, France. His research at the ComplexNetworks team focuses on the dynamics of realworld networks. He leads the Gephi project since 2008, and is the administrator of the Gephi Consortium.
Julian Bilcke is a Software Engineer at ISC-PIF (Complex Systems Institute of Paris, France). He is a founder and a developer for the Gephi project since 2008.
This document summarizes a presentation on applying nanotechnology principles to cognitive radio. It outlines the project members, problem statement, approach, and current status. The problem is applying nanotechnology to address spectrum sensing requirements for cognitive radio. The approach includes a literature review on integrating nanotechnology into wireless networks and relating nanotechnology, GPUs, and cognitive radio networks. Students studied these topics and neural networks concepts. Results show GPUs can speed calculations for real-time cognitive radio applications. Current research includes papers and book chapters on related topics. The scheduled is outlined through March 2014 to develop a nanocomputing solution for cognitive radio networks.
This talk describes our experiences from hosting scientific research application in the Microsoft Cloud. Covers an overview of Microsoft Azure capabilities, examples of big data analysis for science, data collections, science gateways and science virtual machine libraries.
Keynote IEEE International Workshop on Cloud Analytics. Dennis Gannon
Radhika Thesis
1. Context-Aware Middleware for
Activity Recognition
Masters Thesis Defense
Radhika Dharurkar
Advisor: Dr. Tim Finin
Committee: Dr. Anupam Joshi
Dr. Yelena Yesha
Dr. Laura Zavala
1
2. Overview
• Motivation
• Problem Statement
• Related work
• Approach
• Implementation
• Experiments and Results
• Contribution
• Limitations
• Future Work
• Conclusion
2
3. Mobile Market
• 5.3 Billion mobile subscribers
(77% of world’s population)
• Smart Phone Market -
Predicted 30% growth/year
• 85% mobile handsets access
mobile web
Pictures Courtesy: Mobile Youth
3
4. Motivation
• Enhance User Experience
o Richer notion of context that includes functional and social aspects
• Co-located social organizations
• Nearby devices and people
• Typical and inferred activities
• Roles of the people
• Device understanding “Geo-Social Location” and
perhaps Activity
• System by Service Providers and Administrators
o Collaboration
o Privacy
o Trust
4
5. Motivation
• Platys Project
Conceptual Place
• Tasks
• Semantic Context Modeling
• Mobility Tracking
• Collaborative Localization
• Privacy and Information Sharing
• Context Representation, reasoning, and inference
• Activity Recognition
5
6. Problem
• Predict Activity of the user with the use of “Smart
Phone”
• Capture data from different sensors present in smart
phone (atmospheric, transitional, temporal, etc.)
• Capture information of surrounding devices
• Capture statistics about usage of phone (e.g.
battery usage, call list)
• Capture information from other sources of
information (e.g. calendar)
• Developed prototype system which can predict
almost 10 activities with better precision.
6
9. Related Work
• Roy Want , Veronica Falcao , Jon Gibbons. “The
Active Badge Location System” (1992)
• Guanling Chen, David Kotz. “A survey of context-
aware mobile computing research” (2000)
• Gregory D. Abowd, Anind K. Dey, Peter J. Brown,
Nigel Davies, Mark Smith, and Pete Steggles.
“Towards a better understanding of context and
context-awareness” (1999)
• Stefano Mizzaro, Elena Nazzi, and Luca Vassena.
“Retrieval of context-aware applications on mobile
devices: how to evaluate?”(2008)
9
10. Related Work
• Nicholas D. Lane, Emiliano Miluzzo, Hong Lu, Daniel Peebles,
Tanzeem Choudhury, and Andrew T. Campbell. “A Survey
of Mobile Phone Sensing”, (2010)
• Hong Lu, Jun Yang, Zhigang Liu, Nicholas D. Lane, Tanzeem
Choudhury, Andrew T. Campbell.
“The Jigsaw Continuous Sensing Engine for Mobile
Phone Applications”, (2010)
• Nathan Eagle, Alex (Sandy) Pentland, and David Lazer.
“Inferring friendship network structure by using mobile
phone data”, (2009)
• Locale
• “ActiveCampus”. William G. Griswold, Patricia Shanahan
Steven W. Brown, Robert T. Boyer, UCSD (2003)
• Context COBRA. Harry Chen, Tim Finin, Anupam Joshi (2002)
10
12. Approach
o Automatically extract data from various data sources with
the help of smart phone
o Provide context modeling
• Representation of context as ontologies
• Represent the contextual information in a database
o Learning and Reasoning
• Supervised learning approach
• Identify feature set
• Prediction of the Activity of the user
12
19. Toy Experiment
• Data collected though framework developed by
eBiquity member which stored it in MySQL DB.
• We added data from Google Calendar data
• Data collected for one Student and one staff
member
• Automated understanding of Calendar data
• Manual cleaning up of data
• Labeled instances to find “Conceptual Place”
o Student : 422 -Home, Lab, Class, Else where
o Staff Member : 280 – Home Vs. Office
19
21. Toy Experiment
• Data collected though framework developed by
senior members (Tejas) which stored in MySQL DB.
• Captured Google Calendar data
• Data collected for one Student and one staff
member
• Automated understanding of Calendar data
• Manual cleaning up of data
• Labeled instances
o Student : 422 -Home, Lab, Class, Else where
o Staff Member : 280 – Home, Office
21
22. Toy Experiment
Sr. No Captured Data
1 Device Id
2 Timestamp
3 Latitude
4 Longitude
5 Wi-Fi Status
6 Wi-Fi Count
7 Wi-Fi ID
8 Battery Status
9 Light
10 Proximity
11 Power Connected
12 User Present
13 Handset Plugged
14 Calendar Data
15 Temperature 22
23. Toy Experiment
100
90
80
A 70
c
c 60
u
% 50
r
a 40
Student
c
30 Post Doc
y
20
10
0
Naïve Bayes J48 trees Random Trees Bayes Net Random Forest
Classifier
23
24. Analysis
• Only few activities –> therefore good accuracy
• Data Sparse -> cannot do proper training
• Presence of Noise
• Artificially high decision-value to the information
• Overfitting
24
25. Experiment 1- Statistics
• Data collected though Application built for Android
phone by Dr. Laura Zavala
• Added Bluetooth devices capture functionality
• Data collected every 12 min
for duration of 1 min (Notification)
• Last activity saved, if user ignores.
• Collects data from different
o Sensors
o Nearby Wi-Fi devices
o Nearby Bluetooth devices (Paired, not paired)
o GPS coordinates, Geo-location
o Call history
o User tagging for place and activity
25
26. Experiment 1- Statistics
• Collected data for 2 users for 2 weeks continuously.
• Captured Fine detailed activities
o 19 for Student
o 14 or staff member
• Parsing for raw text data
• Cleaning up the data
• Transformation of data into feature vector
• Use of Discretization techniques for continuous
attributes
26
27. Experiment 1- Accuracy
100
90
80
A
70
c
c 60
u
% 50
r
a 40
Student
c
30 Post Doc
y
20
10
0
Naïve Bayes J48 trees Random Bayes Net Random
Trees Forest
Classifier
27
28. Experiment 1- Analysis
• Comparing with TOY experiment accuracy
o Similar accuracy for Naïve Bayes and Decision Trees in Toy Exp.
o Big drop in accuracy for decision trees here
• In Toy Experiment
o Overfitting
o Noise
o Missing Data
• In This Experiment
o We tried to work on cleanup
o Discretization for sensor values
o Still have timestamp, Wi-Fi ids, such attributes as 1 feature.
28
31. Experiment 2- Statistics
• Collected data for users for a month continuously.
• Finer detailed activities captured
o 19 for Student
• Some activities were hard to distinguish -> reduced to
small set of 9 activities for prediction
• Parsing for raw text data
• Cleaned up the data
• Use of Discretization techniques for continuous attributes
• Used “Bag of Words” approach
o Wi-Fi
o Geo-location
o Bluetooth
o Timestamp
31
32. Experiment 2- Accuracy
90
80
70
A
60
c
c
50
u
%
r
40
a
c 30
y
20
10
0
Naïve Bayes J48 trees Bagging + J48 LibSVM LibLinear
trees
Percentage split 66%
Classifier
Cross Validation 10 Folds
32
33. Experiment 2- Confusion
Matrix
a b c d e f g h i j k <-- classified as
677 1 0 0 0 0 4 0 0 0 2 | a = [Sleeping]
0 186 0 0 20 0 3 0 5 0 0 | b = [Walking]
0 0 27 0 0 0 0 0 0 0 0 | c = [In Meeting]
0 2 0 65 0 4 0 0 0 0 0 | d = [Playing]
0 37 0 0 37 0 0 0 4 0 0 | e = [Driving/Transporting]
0 0 0 2 0 146 1 0 0 2 0 | f = [Class-Listening]
8 0 0 0 0 2 52 2 0 0 8 | g = [Lunch]
9 0 0 0 0 0 8 11 0 0 0 | h = [Cooking]
0 11 0 0 6 0 0 0 13 0 0 | i = [Shopping]
0 2 0 0 0 5 0 0 0 7 0 | j = [Talk-Listening]
5 0 0 0 0 0 1 0 0 0 34 | k = [Watching Movie]
33
34. Experiment 2- Analysis
• Small Set of Activities analyzed
• Individual basis
• Naïve Bayes performance reduced
o More features included
o Less functional independence
• Decision Trees Accuracy Improved
o Bag of words approach
o Concept Hierarchy
o Conjunctions
Inline with Research
1) “Physical Activity monitoring” by Aminian, Robert
2) “Activity Recognition from user annotated accelerometer data”
by Bao, intille
• Recognition accuracy is highest for Decision tree
classifier => Proved Best for our Model
34
35. Accuracy for Models
100
98
96
94
92
90
% Accuracy
88
86
84
82
11 Activities Stationary Vs. Moving 10 Activities In Meeting Vs. In Class Home Vs. School Vs. Home Vs. School
Else Where
Classification for Activities
35
36. Small subset of Activities
• These activities do not have simple characteristics
and are easily confused with other activities.
o Phone kept on table while working, lunch, coffee
o Driving and Walking in school
• Not more sensor data to capture some activities
• Model mostly relies on features like
o Wi-Fi IDs
o Geographic location
o Bluetooth Ids
o Time of day
• Therefore, Hard to predict activities across users
o E.g In Class, cooking (Does not predict relying on sound levels)
36
38. Classifiers Evaluating
Our Data
Machine Learning Algorithm Evaluation Problems
Naive Bayes classifier Independence Assumption
Support vector machines Noise and Missing values
Decision trees Robust to errors, missing values,
conjunctions
Random Trees No Pruning
Ensembles of classifiers Reduces Variance
38
39. Discretization
• Filters – unsupervised attribute
• Binning
• Concept Hierarchy
• Division in intervals
• Smoothening the data
39
40. Bagging with J48
• Ensemble Learning Algorithms
• Averaging over bootstrap samples reduces error
from variance, esp. when small differences in
training set can produce big difference between
hypotheses.
40
41. Example J48+Bagging
Afternoon = False
Place = Home: Sleeping (9.0/2.0) | Evening = False
Place = ITE346: In Meeting (1.0) | | Place = Outdoors: Walking (1.0)
Place = Outdoors | | Place = Elsewhere: Sleeping (0.0)
| G1 = False | Evening = True: Walking (4.0)
| | Morning = True: Walking (5.0/2.0) Afternoon = True
| | Morning = False: Driving/Transporting (17.0/2.0) | Wifi Id8 = True: In Meeting (3.0)
| G1 = True: Walking (2.0) | Wifi Id8 = False
Place = Home | | Place = Home: Lunch (0.0)
| Evening = False: Sleeping (20.0) | | Place = Restaurant: Lunch (4.0)
| Evening = True | | Place = Movie Theater: Watching Movie (2.0)
| | noise = '(-inf-28.19588]': Cooking (0.0) | | Place = Work/School: Working (1.0)
| | noise = '(28.19588-32.71862]': Cooking (2.0) | | Place = ITE346: Lunch (0.0)
| | noise = '(32.71862-inf)': Watching Movie (1.0) | | Place = Outdoors: Walking (1.0)
Place = Restaurant: Lunch (5.0) | | Place = ITE3338/ITE377: Lunch (0.0)
Place = Movie Theater: Watching Movie (2.0)
Place = Elsewhere: Walking (1.0)
Place = ITE325: Talk-Listening (4.0) Wifi Id8 = True: In Meeting (6.0/1.0)
Place = ITE3338/ITE377: In Meeting (2.0) Wifi Id8 = False
Place = Groceries store: Shopping (1.0) | Afternoon = False
| | Evening = False: Sleeping (24.0/1.0)
| | Evening = True: Walking (5.0)
loc2 = '(-inf-39.17259]': Watching Movie (2.0) | Afternoon = True
loc2 = '(39.17259-39.18528]': Sleeping (0.0) | | Place = Work/School: Working (1.0)
loc2 = '(39.18528-39.19797]': Lunch (4.0) | | Place = ITE346: Lunch (0.0)
loc2 = '(39.24873-39.26142]': Walking (9.0/2.0) | | Place = Outdoors: Walking (1.0)
| | Place = Home: Lunch (0.0)
| | Place = ITE3338/ITE377: Lunch (0.0)
41
42. Contribution
• Smart phone usage for Mid-level Activity
recognition (Supervised Learning Approach)
• High level notion of context
• Accuracy of 88% for 9 Activities for a user
• Accuracy Inline with other researches
o Home Vs Work 100% compared to 95% accuracy- MIT project using HMM
o Mid-level detailed activity recognition – Bao and Intille (MIT).
o Highest Recognition Accuracy for Decision Tree classifier - Bao and intille
(MIT)
• General Model
42
43. Applications
Activity Distribution over a Week
Walking 1
Working 2
Sun
In Meeting 3
Sat Driving 4
Other/Idle 5
Fri Watching TV 6
D
a Sleeping 7
Thu
y Cooking 8
Talk-Listening 9
Wed Lunch 10
Tue Watching
Movie 11
Mon Reading 12
Shopping 13
Coffee/Snacks 14
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Activity
43
44. Applications
Weekday Activity Distribution
11
Sleeping 1
10 Studying 2
9 Coffee/Snacks 3
Reading 4
A 8
Driving/Transp
c 7 orting 5
t Walking 6
6
i In Meeting 7
v 5 Lunch 8
i Class-Listening 9
4
t
Class-Taking
y 3 Notes 10
2 Chatting 11
1
0
0:00 1:12 2:24 3:36 4:48 6:00 7:12 8:24 9:36 10:48 12:00 13:12 14:24 15:36 16:48 18:00 19:12 20:24 21:36 22:48
Timeline
44
45. Applications
Weekend Activity Distribution
10
9
8 Walking 5
Studying 2
A 7 Transporting 6
c Chatting 8
6
t Playing 9
i 5 Sleeping 1
v
Other 10
i 4
Reading 4
t
3 Shopping 7
y
Coffee/Snacks 3
2
1
0
0:00 1:12 2:24 3:36 4:48 6:00 7:12 8:24 9:36 10:48 12:00 13:12 14:24 15:36 16:48 18:00 19:12 20:24 21:36 22:48
Timeline
45
46. Applications
• Understand Pattern of Activities for users
• Keep a check on time spent
o Planner
o Study Schedules
o Program Meetings
• Update Phone settings according to context
• Recommendation Systems
• Locate specific service nearby
• Adjust presence of user
• Update Calendar of a user
46
47. Limitations
• Set of Experiments
o Duration of Data capture
o Number of users for capturing data
• Information captured through Phone
• Audio, sound processing
• Training on data from different individuals for
general model
47
48. Future
• Robust General Model
• Multiple feature sets for different kind of predictions
• Roles management
• Rules for some ground truths or profiles
• Collaborative activity inference
• Models to incorporate sequence of activities
48
50. ES – Decision Trees
• Each node = attribute
• End leaf gives classification results
• Root node = Most information gain(Claude
Shannon) If there are equal numbers of yeses and
no's, then there is a great deal of entropy in that
value. In this situation, information reaches a
maximum Info = -SUMi=1tom p1logp1
• attr 2 yes, 3 no=I([2,3])= -2/5 x log 2/5 - 3/5 x log 3/5
• Average them n subtract frm I(whole)
50
51. Classification via
Decision Trees
• Effective with Nominal data
• Pruning – correct potential overfitting
• Confidence Factor = 0.25
• Minimum number of Objects = 2
• Error Estimation = (e+1)/(N+m)
• Reduced Error Pruning - False
• Sub tree Raising - True
“Decision Tree Analysis using Weka”- Sam Drazin, Matt Montag
51
Editor's Notes
Last couple of yrshv seen STRONGEST GROWTH in Smart phones...0.5 billion use smart phonesSmartphones and other mobile devices have a simple notion of context largely restricted to temporal and spatial coordinates.Service providers and enterprise administrators can deploy systems incorporating activity and relations context to enhancethe user experience, but this raises considerable collaboration, trust and privacy issues between different service providers.
Our work is an initial step toward enabling devices themselves to represent, acquire and use a richer notion of context thatincludes functional and social aspects such as co-located social organizations, nearby devices and people, typical and inferredactivities, and the roles people fill in them. Geo-social locations
Motivation of platys prj was to represent location with such conceptual place notione.g. instead of saying I m at 1000 hilltop circle phone can actually understand that u r in school and with the ur context it can predict that u are giving a talk..
1. predicting location of the user with the use of infrared technology to forward calls to nearby phones2. context-aware systems that support collecting and disseminating context and applications that adapt to the changing context. It gives summary of different applications like Teleporting, Shopping Assistant, Cyber guide, etc. which uses context information. But these applications use small pieces of context information and were specifically developed to suit a particular model.3. Dey provides survey of context-aware apps, defs and categories of context..4. framework (MoBe) to dynamically n automatically download, configure, exe unload applications acc to user’s current context. 5. Audio Tourist Guide in museums
1. Use of different sensors in mobiles 2. sensing applications on mobile phones - sound samples from microphone, accelerometer data, GPS reading and random photos3. MIT infer the friendship network structure of an individual by collecting information from mobile phones over an extended period. 4. Locale manages settings based on conditions, like Location and Time – static rules set up by user5. Uses a person's context, like location, to help engage them in campus life.PROBLEMS:New situations don’t fit examplesLack generalityHow to use in practice?Traditional information Generalized Context-Aware Application
Except 1st and last letter, all other letters are been rearranged but since our brain is powerful it can find d context and hence the data makes sense..Zimmerman explains5 categories of context info – Individual-natural, human, artificial and group entities
Slide shows the approach we hv taken to solve our problem of Activity recognition..First we built an application which can capture data from various possible sources ..Then we model the context by representing it as ontologies..We use supervised learning approach to classify the data.Why supervised is good…why we need learning in our Problem
Timestamp Day of week Weekend (True/False) Place Activity User Added (True/False) Orientation (Azimuth, Pitch, Roll) Magnetic Field Accelerometer (Gx, Gy, Gz) Light Proximity Connected Wi-Fi ID Wi-Fi devices List 631 Wi-Fi IDs (True/False) Undefined Wi-Fi ID (True/False)Latitude Longitude AltitudeLocation Bearing Location SpeedGeocode Calendar data Paired Bluetooth devices Unpaired Bluetooth devices
We need to work on the input raw data ..The data is been captured every 12 mins..We need to parse the input text data..Also, we capture data for sensors for a duration assuming that there can be noise and average over them..We need to accumulate values for some multi – valued attributes like wifi ids, bluetooth ids
Transformer works on selection of attributes contributing to activity recognition and working on some of the attributeslike wifi ids, bluetooth ids , geocodes which we change from a list to range of different features..
We classify the feature vector with the help of different machine learning algorithms..like naïve bayes, svmlib, decision trees, etcWe try to use some ensemble methods to obtain better predictive performance..(an ensemble is a technique for combining many weak learners in an attempt to produce a strong learner.)The model takes reference of the earlier model built and updates it with the new model..
Student: Home, Lab , Class , ElsewherePost Doc: Home , OfficeSparse DATA…Lot of NOISE..Not proper feature extraction..data not processed like timestamp used likdat..n wifietclatitude, longitude, battery percentage, light (some nulls observed), proximity,Wi-Fi count, Wi-Fi ids, and user present (some nulls observed), Google calendar dataCross validation 10 fold
Student: Home, Lab , Class , Elsewhere Post Doc: Home , OfficeData Sparse since application was not stableartificially high decision-value to the information (e.g. timestamp, wifi id, geolocation, etc)Strong independence assumptions played a significant role in here for other algos like naïve bayes.
Class Taking Notes, Class ListeningCleanup - Removing attributes like timestamp, averaging sensor values, checking if user did not just forgot to select-Discretization used: divide the number of values for a continuous attribute into intervals which reduces and simplifies the data. Use of such techniques helped us to have a concise, easy-to-use knowledge-level representation of mining results-All the machine learning algorithms cannot handle this situation of “bag of words”. Wifi, timestamp – morning, afternoon, etc..
If you compare with accuracy which we had for toy exp, we had almost similar accu 4 Naïve n Deci TreesBt here we can identify a big drop in accufr decision trees..In toy=overfitting..here naïve is still doing overfitting..We tried to work on cont do cleanup, discretization..Since we had timestamp, wifi ids, such attributes as 1 feature.
Decision trees cannot understand the model since we had data like timestamp which is just 1 value..wifi which is a set of wifi devices..bt this set can differ fr the same place..
We hv a model which evaluates on attributes likwifideviices found in vicinity, gps location, geocode. -> we get conflictsTalk abt accuracies fr activities
9 Activities :Working/Studying,Sleeping,Walking,InClass,Outdoors,InMeeting,Talk-Listening,Other/Idle,Shopping“bag of words”. Wifi, timestamp – morning, afternoon, etc..DiscretizationBaggingNaïve bayes reduced a lot –since there was overfitting before which got removed.
Movingvs stationary isnt that good bcz for Moving we hv data of school shuttle which moves very slow
In class / talk listening : geolocation +wifi id ..our model doesn’t consider noise or light values fr predictionsLaura dataset- cooking activity –geolocation +wifi id cannot be mapped to cooking activity of othersStudying /Working activity depends on - time of day +wifi id+ geolocation => XTHIS IS BCZ WE MODEL ON INDIVI USER AND TRAINING DATA FR SUCH ACTIVITIES SHWS THAT ITS EVIDENT FRM FEATURES MAIN FEATURES..IF WE WUD HV TRAINED DATA ON DIFFERENT USERS WHERE CLASSIFIER CANNOT SAY PARTI ACTIVITY ON ONLY THOSE FEATURES DEN IT WL CONSIDER OTHERSTherefore we come down to only activities which can be generalized..
Walking Sleeping Lunch In Meeting Watching Movie 1 in meeting, watching movie -Walking walking in school(GPS loc)2 Watching Movie conflict with sleeping3 Watching movie – walking since some instances of walking in movie theatre- focus on LOCATION4 in meeting Walking, watching movie- lunch(at Arundel mills)I M NOT TRYING TO OVERFIT data
Naïve –INDE ASSUMPTION..good- Completely indep features n Funcdepe features, USED dataset is small and there are many attribute..WE HV MIXTURE..cannot use conjunctions on attributes..J48- robost2 errs n missing attrivals..disjunct+conjunct..Most algocnt do conjunctions..J48 is gud-real valued opsSVM radial basis- lot of noise and missing attributes..decision trees prune it wellRandom trees: randomly chosen attributes at each node. Performs no pruning. Ensembles of classifiers-Bootstrap aggregating-Averaging over bootstrap samples can reduce error from variance, especially when small differences in training sets can produce big differences between hypotheses.Bayesian nw- represent probability distributions that generalize the naive Bayesian classifier and explicitly represent statements about independence..
Sensors: orientation, Accelerometer, proximity, light, latitude, longitude, noiseConcept hierarchy- replace low level concept by higher – timestamp –morning,etcDivides cont values data in equal freq intervalsConcise easy to use knw level repre.
learns a hypothesis by training a number of base hypotheses and combining their predictions
Planner Recommendation systemsCalendar data – updations to and from
e’: misclassified examples at the given node, ‘N’: examples that reach the given node,‘m’: all training examples.LESS confidence = Nodes reached by very few instances from the training data are penalized…Reduce size of tree – filter moreAt each junction, the algorithm compares (1) the weighted error of each child node versus (2) the misclassification error if the child nodes were deleted and the decision node were assigned the class label of the majority classReduced Error Pruning- we do not want most accurate tree since we do nthvdat good data…It tries to split data in train, test..greedily remove most helping attribute n check accuracy..gets very accurate small tree.reduces training data,overfittingSubtree raising- node may be moved upwards towards the root of the tree, replacing other nodes along the way.