Report on the PhD Activities
Parvaneh Parvin
e-mail: parvaneh.parvin@di.unipi.it
25/02/2020
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Research Activities
Parvaneh Parvin’s PhD thesis is focused on the design of an intelligent platform able to monitor users’ behavior, detect the significant changes in their behavior and provide them with relevant and tailored health related information and quality of life-improving suggestions.
Our just-time adaptive anomaly detection and personalized health in-tervention framework, is an inin-tervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual’s chang-ing internal and contextual state. Therefore, our framework for JITAI design and personalization is an adaptive and personalized solution to support older adults by performing a daily activity verification and anomaly detection. More-over, our solution supports implementations for an automated, personalized and persuasive health intervention generation system that issues interventions based on the detected anomalous activity and the user preferences.
The proposed solution first, models the users’ daily routine activities using a task model specification and associates these activities with the events in user context. The user model serves as a personalized knowledge-base for detecting the users’ abnormal behavior. Second, it verifies the user activity using the in-formation received from the context manager (which detects relevant contextual events occurring in the older adults’ home environment) and associated data in the user model. Third, it performs an online anomaly detection algorithm to detect any significant changes in user routine. The deviations in user behavior will be classified in 11 categories regarding the activity type, location, order, time and the duration. Later, the system filters the anomalies to reduces false alarms using a Mamdani-type fuzzy rule-based system that as input takes the user context and detected anomalies and outputs the true anomalies with the level of intervention needed.
Finally, by a systematic validation through a system that automatically gen-erates wrong sequences of activities, we show that our online anomaly detection algorithm is able to find behavioral deviations from the expected behavior at different times by considering the extended classification of the possible devia-tions with good accuracy. We also presented some preliminary results based on the 2-weeks experiment with real-data in our lab testbed.
In addition, our system supports implementations to issue personalized in-terventions to users aiming to minimize their anomalous behavior. The per-sonalization part employs a reinforcement learning-based approach to
mize/personalize the intervention delivery concerning the frequency, type, and timing of interventions dynamically according to data aggregated for a person over time. To this aim, we propose the use of a sequential decision policy, implemented based on the contextual multi-armed bandit formalization to se-lect messages adopting distinct persuasive strategies for each individual so that compliance is maximized. We validated the personalized intervention delivery mechanism through a simulation in which deviations, interventions and per-sonas, with differentiating characteristics, are simulated. We present that the personalization algorithm is able to capture the rules associated with the simu-lated concepts, indicating its potential to be used in real-world settings.
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Training Activities Schools:
• 1st School on Foundations of Programming and Software systems- Prob-abilistic Programming in Braga, Portugal, May 29th -June 4th 2017. The goal of the school was to introduce attendants to theoretical and practical aspects of programming languages, and will propose courses that cover the following topics: semantics, analysis, verification, applications to machine learning, privacy, and security. Following are the courses pre-sented in this school:
– Empowering Spreadsheet Users with Probabilistic Programming – Foundations of Privacy and Quantitative information Flow – Classical and non-classical stochastic path problems
– Inference Compilation and Universal Probabilistic Programming – Semantics of Higher-Order Probabilistic Programs with Continuous
Distributions
– Verification of probabilistic infinite-state systems – Quantum Programming
– Equational reasoning about probabilistic programs
– Concentration of Measure Inequalities and Quantitative Analysis of Probabilistic Programs
– Machine Learning Meets Privacy
– Foundations of probabilistic programming: operational and denota-tional semantics, conservation laws, and duality
– Managing and Exploiting Uncertainty for Fast Approximate Compu-tations
• Data Science: Models,Algorithms, AI and Beyond in Lipari, Italy, July 19-25, 2019. This summer school has provided opportunities to collect experience with modern data analysis, in particular Big Data analytics. This includes subjects to mine data in the Internet of Things. Following are the courses presented in this school:
– Data and epidemic models
– Search and data mining on the web: from string matching to assistive exploration
– Biological Data: Algorithms, Models, and Biomedical Applications – The beneficial role of randomness: from physics to social systems – Networks analysis of innovation ecosystems
– Financial Data
– Learned indexes for Big Data – Explainable AI
– Data Science applications in Modern Media Services
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Seminars Cycles:
• Mauriana Pesaresi seminars • Service, cloud and fog computing
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PhD Courses’ Exam:
• Introduction to Network Science
• Distributed Computing and Large Graph Mining
• The Internet of Everything, Everywhere: Methods and Technologies for Internet working Land, Air, and Sea
• Algorithm (as a recovery course) • An introduction to Deep Learning
• Data Stream Processing from the Parallelism Perspective
• Continuous monitoring of health and well-being using wearable sensors • Mobile and cyber-physical systems
• Algorithmic
• Computability and Complexity
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Period Abroad
• Jheronimus Academy of Data Science, ’s-Hertogenbosch, Netherlands (2018)
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Publications
International Journals
• Parvin, Parvaneh, Stefano Chessa, Maurits Kaptein, and Fabio Paterno. ”Personalized real-time anomaly detection and health feedback for older adults.” Journal of Ambient Intelligence and Smart Environments 11, no. 5 (2019): 453-469
• Parvin, Parvaneh, Chessa Stefano, Manca Marco, Paterno Fabio, ”Real-Time Anomaly Detection in Elderly Behavior with the Support of Task Models.” Journal Proceedings of the ACM on Human- Computer Interac-tion, Vol. 2. EICS (2018): 15
International Conferences:
• Parvin, Parvaneh. ”Real-Time Anomaly Detection in Elderly Behavior.” Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems. ACM, 2018
• Parvin, Parvaneh, Fabio Paterno, and Stefano Chessa. ”Anomaly De-tection in Elderly Daily Behavior.” 2018 14th International Conference on Intelligent Environments (IE). IEEE, 2018 (Best doctoral colloquium paper)
• Manca Marco, Parvaneh Parvin, Fabio Paterno, and Carmen Santoro. ”Detecting anomalous elderly behavior in ambient assisted living.” In Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems, pp. 63-68. ACM, 2017
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Conference attended
• The 18th International Conference on Human-Computer Interaction with Mobile Devices and Services, 6-9 September 2016, Florence, Italy
• The 10 th ACM SIGCHI Symposium on Engineering Interactive Comput-ing Systems, 19-22 June 2018, Paris, France
• The 14 th International Conference on Intelligent Environments (IE). IEEE, 25-28 June 2018, Rome, Italy
• The 17th IFIP TC.13 International Conference on Human-Computer In-teraction - INTERACT 2019, 2 - 6 September 2019, Paphos, Cyprus
