The use of wearable technology allows to quickly collect behavioral patterns from users, analyze them and intervene in the environment. In this talk we explore the potential of this technology with the comoditization of sensors.
British American Tobacco Theatre Pack Product Launch ConceptGetLyndon
Introducing a new product is difficult enough. Doing it in a way that draws attention to the product in a novel way, slightly more difficult. Here are the concepts I developed for BAT.
iConnect 2015 – the Coca Cola Enterprises intranetIntranätverk
Presented by Jonathan Phillips, Coca-Cola Enterprises at Intranätverk 2015: Gothenburg, 21 May.
The presentation will focus on the following topics:
- From research to delivery – a journey through the new Coca-Cola Enterprises’ intranet
- Designing for the mobile worker
- Cross-functional governance; cross-functional site
Learning and Behavioral Analytics From concept to realityAbelardo Pardo
How can learning analytics be taken from its design to its deployment in an educational institution? What are the issues, limitations, strategies? This presentation includes a descirption of Learning Analytics, examples, how to tackle systemic deployment and suggestions on how to build institutional capacity.
British American Tobacco Theatre Pack Product Launch ConceptGetLyndon
Introducing a new product is difficult enough. Doing it in a way that draws attention to the product in a novel way, slightly more difficult. Here are the concepts I developed for BAT.
iConnect 2015 – the Coca Cola Enterprises intranetIntranätverk
Presented by Jonathan Phillips, Coca-Cola Enterprises at Intranätverk 2015: Gothenburg, 21 May.
The presentation will focus on the following topics:
- From research to delivery – a journey through the new Coca-Cola Enterprises’ intranet
- Designing for the mobile worker
- Cross-functional governance; cross-functional site
Learning and Behavioral Analytics From concept to realityAbelardo Pardo
How can learning analytics be taken from its design to its deployment in an educational institution? What are the issues, limitations, strategies? This presentation includes a descirption of Learning Analytics, examples, how to tackle systemic deployment and suggestions on how to build institutional capacity.
Behavioral Analytics for Preventing Fraud Today and TomorrowGuardian Analytics
This presentation introduces Guardian Analytics Omni-Channel Fraud Prevention solution as the only solution to meet the new requirements of fraud prevention.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
Suggestions:
1) For best quality, download the PDF before viewing.
2) Open at least two windows: One for the Youtube video, one for the screencast (link below), and optionally one for the slides themselves.
3) The Youtube video is shown on the first page of the slide deck, for slides, just skip to page 2.
Screencast: http://youtu.be/VoL7JKJmr2I
Video recording: http://youtu.be/CJRvb8zxRdE (Thanks to Al Friedrich!)
In this talk, we take Deep Learning to task with real world data puzzles to solve.
Data:
- Higgs binary classification dataset (10M rows, 29 cols)
- MNIST 10-class dataset
- Weather categorical dataset
- eBay text classification dataset (8500 cols, 500k rows, 467 classes)
- ECG heartbeat anomaly detection
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
OntoSoft: A Distributed Semantic Registry for Scientific Softwaredgarijo
Credit to Yolanda Gil.
OntoSoft is a distributed semantic registry for scientific software. This paper describes three major novel contributions of OntoSoft: 1) a software metadata registry designed for scientists, 2) a distributed approach to software registries that targets communities of interest, and 3) metadata crowdsourcing through access control. Software metadata is organized using the OntoSoft ontology along six dimensions that matter to scientists: identify software, understand and assess software, execute software, get support for the software, do research with the software, and update the software. OntoSoft is a distributed registry where each site is owned and maintained by a community of interest, with a distributed semantic query capability that allows users to search across all sites. The registry has metadata crowdsourcing capabilities, supported through access control so that software authors can allow others to expand on specific metadata properties.
Analytics to understand learning environmentsAbelardo Pardo
Seminar for the CHAI Group at The University of Sydney. A summary of the initiatives I have worked on in the past years plus a brief account of my current work.
Software Metadata: Describing "dark software" in GeoSciencesdgarijo
Credit to Yolanda Gil.
In this talk I provide an overview of the current state of the art for software description in geosciences, along with our approach to facilitate this task in OntoSoft, a distributed semantic registry for scientific software. Three key aspects of OntoSoft are: a software metadata ontology designed for scientists, a distributed approach to software registries that targets communities of interest, and metadata crowdsourcing through access control. Software metadata is organized using the OntoSoft ontology, designed to support scientists to share, document, and reuse software, and organized along six dimensions: identify software, understand and assess software, execute software, get support for the software, do research with the software, and update the software.
Feedback at scale with a little help of my algorithmsAbelardo Pardo
Talk exploring how to use data to provide scalable feedback in learning experiences. The solutions explored propose the use of algorithms to enhance how humans instructors provide feedback to students more effectively
presents the foundational aspects of web analytics and some specifics such as the hotel problem. Discusses trace data, behaviorism, and other cool web analytics stuff
Implementation of Ear Biometrics as Emerging Technology in Human Identification System ...............1
B. Srinivasan and V. K. Narendira Kumar
Determining the Security Enhancement of Biometrics in Internet Passport Scheme using Cryptographic
Algorithms ........................................................................................................................................1
B. Srinivasan and V. K. Narendira Kumar
Novel Image Fusion Techniques using DCT .........................................................................................1
V. P. S. Naidu
High Performance Data mining by Genetic Neural Network ................................................................1
Dadmehr Rahbari
Parallel Ensemble Techniques for Data Mining Application .................................................................1
M. Govindarajan
A Hybrid Cryptosystem for Image using Chaotic Mapping ...................................................................1
Nidhi Sethi and Sandip Vijay
Investigating Factors Affecting Adoption and Implementation of m-Government in the South African
Department of Home Affairs: An on-going Research ..........................................................................1
Maleshoane Sepeame and Emmanuel Babatunde Ajala
Prospects of Thermal Management Techniques in Microprocessor Architecture .................................1
Ajaegbu Chigozirim, Shodiya A.S and Kuyoro Shade O.
Capacity Based Clustering Model for Dense Wireless Sensor Networks ...............................................1
S. R. Boselin Prabhu and S. Sophia
How Big Data Generates New Insights into What’s Happening in Tropical Ecosyst...Dana Gardner
Transcript of a sponsored discussion on how large-scale monitoring of rainforest, biodiversity and climate has been enabled and accelerated by cutting-edge, big-data capture, retrieval and analysis.
Behavioral Analytics for Preventing Fraud Today and TomorrowGuardian Analytics
This presentation introduces Guardian Analytics Omni-Channel Fraud Prevention solution as the only solution to meet the new requirements of fraud prevention.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
Suggestions:
1) For best quality, download the PDF before viewing.
2) Open at least two windows: One for the Youtube video, one for the screencast (link below), and optionally one for the slides themselves.
3) The Youtube video is shown on the first page of the slide deck, for slides, just skip to page 2.
Screencast: http://youtu.be/VoL7JKJmr2I
Video recording: http://youtu.be/CJRvb8zxRdE (Thanks to Al Friedrich!)
In this talk, we take Deep Learning to task with real world data puzzles to solve.
Data:
- Higgs binary classification dataset (10M rows, 29 cols)
- MNIST 10-class dataset
- Weather categorical dataset
- eBay text classification dataset (8500 cols, 500k rows, 467 classes)
- ECG heartbeat anomaly detection
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
OntoSoft: A Distributed Semantic Registry for Scientific Softwaredgarijo
Credit to Yolanda Gil.
OntoSoft is a distributed semantic registry for scientific software. This paper describes three major novel contributions of OntoSoft: 1) a software metadata registry designed for scientists, 2) a distributed approach to software registries that targets communities of interest, and 3) metadata crowdsourcing through access control. Software metadata is organized using the OntoSoft ontology along six dimensions that matter to scientists: identify software, understand and assess software, execute software, get support for the software, do research with the software, and update the software. OntoSoft is a distributed registry where each site is owned and maintained by a community of interest, with a distributed semantic query capability that allows users to search across all sites. The registry has metadata crowdsourcing capabilities, supported through access control so that software authors can allow others to expand on specific metadata properties.
Analytics to understand learning environmentsAbelardo Pardo
Seminar for the CHAI Group at The University of Sydney. A summary of the initiatives I have worked on in the past years plus a brief account of my current work.
Software Metadata: Describing "dark software" in GeoSciencesdgarijo
Credit to Yolanda Gil.
In this talk I provide an overview of the current state of the art for software description in geosciences, along with our approach to facilitate this task in OntoSoft, a distributed semantic registry for scientific software. Three key aspects of OntoSoft are: a software metadata ontology designed for scientists, a distributed approach to software registries that targets communities of interest, and metadata crowdsourcing through access control. Software metadata is organized using the OntoSoft ontology, designed to support scientists to share, document, and reuse software, and organized along six dimensions: identify software, understand and assess software, execute software, get support for the software, do research with the software, and update the software.
Feedback at scale with a little help of my algorithmsAbelardo Pardo
Talk exploring how to use data to provide scalable feedback in learning experiences. The solutions explored propose the use of algorithms to enhance how humans instructors provide feedback to students more effectively
presents the foundational aspects of web analytics and some specifics such as the hotel problem. Discusses trace data, behaviorism, and other cool web analytics stuff
Implementation of Ear Biometrics as Emerging Technology in Human Identification System ...............1
B. Srinivasan and V. K. Narendira Kumar
Determining the Security Enhancement of Biometrics in Internet Passport Scheme using Cryptographic
Algorithms ........................................................................................................................................1
B. Srinivasan and V. K. Narendira Kumar
Novel Image Fusion Techniques using DCT .........................................................................................1
V. P. S. Naidu
High Performance Data mining by Genetic Neural Network ................................................................1
Dadmehr Rahbari
Parallel Ensemble Techniques for Data Mining Application .................................................................1
M. Govindarajan
A Hybrid Cryptosystem for Image using Chaotic Mapping ...................................................................1
Nidhi Sethi and Sandip Vijay
Investigating Factors Affecting Adoption and Implementation of m-Government in the South African
Department of Home Affairs: An on-going Research ..........................................................................1
Maleshoane Sepeame and Emmanuel Babatunde Ajala
Prospects of Thermal Management Techniques in Microprocessor Architecture .................................1
Ajaegbu Chigozirim, Shodiya A.S and Kuyoro Shade O.
Capacity Based Clustering Model for Dense Wireless Sensor Networks ...............................................1
S. R. Boselin Prabhu and S. Sophia
How Big Data Generates New Insights into What’s Happening in Tropical Ecosyst...Dana Gardner
Transcript of a sponsored discussion on how large-scale monitoring of rainforest, biodiversity and climate has been enabled and accelerated by cutting-edge, big-data capture, retrieval and analysis.
Using OnTask for Student Coaching in Large Student CohortsAbelardo Pardo
The provision of student feedback is a challenging and resource intensive
task for any instructor but at the same time it has the potential of
significantly improve the overall quality of a learning experience.
This challenge is magnified even further in the context of large student
cohorts. Current initiatives such as the one captured by the OnTask project
have explored how to use data about student engagement to support instructors
of large student cohorts in this process. But despite the use of technology
there are still important aspects to consider. What is the ideal tone of the
message? Should they focus on the material? Assessments? Strategies? How
often is idea to send these messages? In this talk we will cover some
principles and examples of how instructors are addressing the problem.
Using data to provide personalised feedback at scaleAbelardo Pardo
The current state of higher education has increasing pressure over academics to offer high quality experience at scale. But what could be the actions that can be deployed to achieve this increase? What would be a good guiding principle to decide these actions? In this talk we explore first the possibility of using feedback and a coach mentality to provide student support, and then how data can help us scale that technique. There are examples of potential scenarios to deploy this at the level of a course, program or overall student experience.
Facilitating feedback processes at scale through personalised support actionsAbelardo Pardo
As education keeps advancing into the era of ubiquitous data availability there are certain challenges that are also increasing. The connection between data and direct improvements or benefit for students in terms of the overall quality of the learning experience is still an area under significant evolution. Learning analytics promises the use of data to improve learning experiences, but bridging the distance between widespread data availability and meaningful, effective and relevant actions informed by this data is still important. The current focus when considering the use of data tends to gravitate towards institutional interventions that target only a subset of the students (e.g. those at risk of dropping a course or abandoning the institution). But the student experience is much more complex and varied.
In this talk we will describe OnTask, a platform and approach to facilitate the connection between data and actions in the context of a learning experience. The framework used by the tool contains a generic architecture to simplify the combination of multiple data sources under a single data structure with an intuitive design of rule-based personalized support actions that can be scaled to large student cohorts. OnTask approaches the problem from the benefits of feedback processes that rely on a conversation between students and instructors at the level of a course.
Providing personalised student support in blended learning at scaleAbelardo Pardo
Blended learning environments can be used to deploy strategies to increase student engagement in learning experiences. However, for these strategies to be effective, this increase in engagement requires an increase in student support which can pose serious challenges for large cohorts. The increase in technology mediation offers unprecedented opportunities to collect information
about how students interact in a learning environment. Can this data be used to provide student support at scale? Is it feasible to blend data management techniques as part of a learning design to provide personalised suggestions to students? This talk will offer various practical examples of personalised
student support actions in the context of a large flipped classroom.
Designing Engaging Learning Experiences in Digital EnvironmentsAbelardo Pardo
Talk about how to address the design of learning experiences in the current digital environments and how to take into account the student perspective, motivation, feedback, and other various aspects.
Provision of personalized feedback at scale using learning analyticsAbelardo Pardo
The increasing presence of technology mediation offers an unprecedented opportunity to use detailed data sets about the interactions that occur while a learning experience is being enacted. Areas such as Learning Analytics or Educational Data Mining have explored numerous algorithms and techniques to process these data sets. Additionally, technology now offers the opportunity to increase the immediacy of interventions. However, not much emphasis has been placed on how to extract truly actionable knowledge and how to bring it effectively as part of a learning experience. In this talk, we will use the concept of feedback as the focus to establish a specific connection between the knowledge derived from data-analysis procedures and the actions that can be immediately deployed in a learning environment. We will discuss how there is a trade-off between low-level automatic feedback and high-level complex feedback and how technology can provide efficient solutions for the case of large or highly diverse cohorts.
Articulating the connection between Learning Design and Learning AnalyticsAbelardo Pardo
Learning analytics is a discipline that uses data captured by technology during a learning experience to increase our level of understanding, increase its quality, and improve the environment in which it occurs. But these experiences need to be designed first. In this talk we start from the statement that there is no such thing as a neutral design. In the era of increasing technology mediation Learning experiences need to be designed considering the capacity to capture data, the possibility of making sense and derive knowledge from the data, and the need to act on that knowledge. In this talk we will explore some initiatives to make these connections explicit in a learning design. Using a flipped learning experience, we will explore how to embed data and data analysis as part of the design tasks.
The role of data in the provision of feedback at scaleAbelardo Pardo
Technology mediation allows to capture comprehensive data sets about interactions occurring in learning experiences. Although these data sets have the potential of increasing the insight on how learning occurs, their use strongly depends on two aspects: the data has to be properly situated in the learning design, and the insights derived need to be translated into actions. In this talk we will explore how to establish this connection for the case of the provision of feedback. We will approach the problem from the point of view of intelligence amplification, that is, how data can support instructors to provide better support to learners through feedback. The talk will discuss some preliminary results from the Ontasklearning.org project.
Increasing student engagement has been one of the main focus to improve the quality of a learning experience. In this talk we cover two aspects that can contribute to this increase: flipped learning, and feedback.
The role of data in the provision of feedback at scaleAbelardo Pardo
The abundance of data in learning environments poses both a potential and a challenge. Improvements in the student experience need a strong connection between data, learning design and the delivery platform. In this talk we explore some ideas on how to establish this connection with respect to feedback.
Exploring hands-on multidisciplinary STEM with Arduino EsploraAbelardo Pardo
In this presentation we describe the Madmaker project. The use of Arduino Esplora to promote STEM activities in High Schools. It contains a description of our approach and data derived from the evaluation.
One of the objectives of the recently created Faculty of Engineering and IT Education Innovation unit is to promote "sustained innovation" in engineering educaiton. Innovation is a word that gets thrown around quite frequently and it is assumed we all know what it means. In recent times the term appears in more complex expressions such as "sustained innovation" or
"culture of innovation". Organisations in general are facing challenges to go from stating the intent of adopting a culture of innovation and actually achieving it. Engineering and IT education is no exception. In fact, there are recent studies that point to the disparity of perception among academics about
what exactly means innovation in the context of learning and teaching engineering and IT disciplines. In this session we will discuss several elements that need to be present for innovation to occur and collaboratively distil some conditions that would provide the right climate so that learning and teaching innovation flourishes in the faculty.
The role of institutional data in Learning AnalyticsAbelardo Pardo
Learning analytics has the potential of improving how higher education institutions operate. A significant portion of this potential derives from the use of institutional data. In this talk we review the role of these units in achieving institutional capacity and show some examples of the type of solutions possible at the level of instructors.
Generating Actionable Predictive Models of Academic PerformanceAbelardo Pardo
Exploring predictive models that are closer to action by instructors. The talk proposes the use of hierarchical partitioning algorithms to produce decision trees that can be used to divide students into groups and simplify how feedback is provided.
Exploring the relation between Self-regulation Online Activities, and Academi...Abelardo Pardo
Can we combine self-regulation indicators with digital footprints to understand how students learn? This talk describes a case study with a first year engineering course exploring this problem.
Data2U: Scalable Real time Student Feedback in Active Learning EnvironmentsAbelardo Pardo
Active learning environments require sustained student engagement in learning scenarios. Can we use data to provide feedback in real time about this participation?
Active learning methods are known to improve academic achievement. Flipped learning takes advantage of preparation activities to increase student engagement. But how do we approach the design of such experiences?
Scaling the provision of feedback from formative assessmentAbelardo Pardo
Informal notes about a presentation in the New South Wales Learning Analytics Work group about how to send meaningful feedback to a large student cohort using learning analytics and semi-automatic processing.
Using data to support active learning experiencesAbelardo Pardo
How can you leverage the use of data to improve a learning experience? Learning analytics helps increase the accuracy of how we perceive the complexity of a learning scenario. In this talk I present some suggestions and an example of how to achieve this.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
2. Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
StateRecordsNSWFlickr
Lecturer at School of EIE
Two courses
Grad/Undergrad
Active learning
Use of technology
Tech to empower individuals
and communities
Behavioral analytics
User validation
3. Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
APrincessflickr.com
Observe Process Act
4. Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
TulanePublicRelationsflickr.com
Why observation now?
5. Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
www.cooking-hacks.com
Sensors are becoming a commodity
6. Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
mrbillflickrflickr.com
Emerging a community of hobbyists
7. Mind Sights: Original Visual Illusions, Ambiguities, and Other Anomalies, With a Commentary on the Play of Mind in Perception and Art, Roger N. Shepard, 1990
Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
Humans are highly subjective
8. Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
Derableflickr.com
Maximize Performance
10. Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
qmnonicflickr.com
Observe subjects while
performing a task
Collect events
Process and relay back to subjects
11. Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
QuinnAnyaflickr.com
Detect and analyse gestures
12. Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
ninahaleflickr.com
Comply with procedures
13. Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
AnthonyJBentleyflickr.com
High yield environments
15. Facial Expressions
Detect facial features
Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
(Monkaresi et al., 2012)
16. Engagement
Engagement while writing a document
Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
(Monkaresi et al., 2013 Under review)
17. Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
dklimkeFlickr.com
Erratic
engagement
18. Wireless sensing
Heart rate through video capture
Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
(Monkaresi et al., 2013 Under review)
19. Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
Sergejfflickr.com
Identify missteps
Motivate for improvement
Modify activities
20. Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
http://hdguru.com/is-your-new-hdtv-watching-you/7643(lastaccessedMarch2013)
21. Context Integrity: Framework to provide
guidance to solve privacy related conflicts.
Abelardo Pardo Behavioral Analytics to Foster Long Term Changes
ContextTravelflickr.com
Appropriateness & Distribution
(Nissenbaum, H., 2004, Privacy as Contextual Integrity, Washington Law Review, 79(1))
23. References
Monkaresi, H., Hussain, M. S., Calvo, R., 2012
A dynamic approach for detecting naturalistic affective states from facial videos during HCI
Australasian Joint Conference on Artificial Intelligence, Sydney, Australia. LNAI, Springer 2012, pp.170-181
Monkaresi, H., Calvo, R., Robinson, P., Martin, A. 2013
Facial expressions as a measure of engagement with online writing activities.
Manuscript under review.
Abelardo Pardo Behavioral Analytics to Foster Long Term Changes