The Discreet Charm of Networking: From the Internet to the Brain - - PowerPoint PPT Presentation

The Discreet Charm of Networking: From the Internet to the Brain

UNIVERSITY OF CRETE

Computer Science Department Professor Maria Papadopouli Department of Computer Science - University of Crete Institute of Computer Science - Foundation for Research & Technology – Hellas (FORTH)

http://www.ics.forth.gr/tnl http://www.ics.forth.gr/mobile http://www.ics.forth.gr/neuronxnet

Telecommunications & Networks Laboratory ry

• Measuring & Mitigating Internet Routing Attacks

3 ERC grants (1 StG & 2 PoC)

• Quality of Experience in the Internet
• Internet of Things
• Communication optimization
• Secure over-the-air programming as a service
• Remote compilation & monitoring
• Healthcare & environmental applications
• Supply chain traceability
• Smart City Backbone development in real environments:

Heraklion & Larnaca

• Brain Network Analysis

Some Recent Research Activities

• Internet of Things, Sensor Networks
• Networks & Mobile Computing in Health-Care
• Network Paradigms for better Quality of Experience in Video Streaming & VR/AR
• Brain networks Analysis & Modeling
• Complex real-world networks
• Interdisciplinarity
• Use of sophisticated machine-learning & statistical analysis algorithms (e.g., RQA, GANs)

2) GestureKeeper Gesture Recognition for Controlling Devices in IoT Environments

Vasileios Sideridis, Andrew Zacharakis, George Tzagkarakis, Maria Papadopouli. 27th European Signal Processing Conference EUSIPCO 2019

3) DysLexML Robust & accurate screening tool for dyslexia with data augmentation using GANs

Thomais Asvestopoulou, Victoria Manousaki, Antonis Psistakis, Erjona Nikolli, Vassilios Andreadakis, Ioannis M. Aslanides, Yannis Pantazis, Ioannis Smyrnakis, Maria Papadopouli. 19th IEEE International Conference on BioInformatics & BioEngineering BIBE 2019

4) neuronXnet Brain networks analysis & Modeling

Andreas Zacharakis, Manthos Kampourakis, Orestis Mousouros, Ganna Palagina, Jochen Meyer, Stelios Manolis Smirnakis, Ioannis Smirnakis, Maria Papadopouli. Functional Network Connectivity Analysis in Absence Epilepsy Using Stargazer Mice. IEEE 19th International Conference on BioInformatics & BioEngineering, BIBE 2019

1) New Network Paradigms for Improving QoE for VR/AR

Network Challenges in AR/VR: Severe Latency Constraints

The 1ms round-trip delay is still challenging due to the backhaul bottleneck that cloud servers face in the Internet Edge Computing

• A three-tier architecture of cloud servers, edge nodes & end user devices or IoT nodes
• Edge: small cells with ample resources of storage, computation and communication
• Prediction of full-body movement
• Rendering
• Prefetching of relevant content
• Takes advantage of the spatial locality of information to serve requests locally, avoiding

directing them to the cloud → Significant improvement of the user experience and cost effectiveness

Approaches for Gesture Recognition f for Supporting an Io IoT Envir ironment

1) Camera-based

• High recognition accuracy
• High computational cost & sensitivity in environmental conditions

2) Sensor-based

• Practical considerations (e.g., smaller sensor size, efficient, low cost)
• Energy constrains

3) Wireless Access-Point based

• Practical considerations. No intervention
• Sensitivity in dynamic environmental conditions

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GestureKeeper

Hand-gesture identification & recognition based on wearable inertial measurements unit (IMU) Identify the start of gesture by exploiting underlying dynamics of collected time-series

• First automatic hand-gesture identification system based only on accelerometers

Recognize accurately a dictionary of 12 hand-gestures

• Wearable sensor sends periodically collected measurements to server
• Server performs gesture identification & recognition

Long-term objective: Allow user to control devices through hand gestures

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GestureKeeper: Aim ims to recogniz ize each perf rformed gesture

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Shimmer 3

• Includes 3-axis accelerometer,

gyroscope, magnetometer

• Small detection range for the

accelerometer

• Sampling frequency: 50 Hz
• Data streaming & logging

Id Identification: Aim ims to

• id

identify fy tim time win indows con

• ntain

inin ing gestures & & act activit ities of

• f dail

aily livi living (A (ADLs)

• Feature vectors produced from transformations on acceleration data
• Classifies the data in two classes: gestures & ADL

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Recurrence Quantifi fication Analysis (R (RQA)

• Powerful tool that uses theory of non-linear dynamics based on the topological analysis of the

phase space of the underlying dynamics

• Enables the understanding of the behavior of a complex dynamic system
• Does not make any assumption about the model that governs the system or the data
• Can handle short time-series, non-stationary data
• Is robust to outliers

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Towards a robust and accurate screening tool for dyslexia with data augmentation using GANs

Thomais Asvestopoulou 1,2 , Victoria Manousaki 1,2, Antonis Psistakis 1,2, Erjona Nikolli 1,2, Vassilios Andreadakis 3, Ioannis M. Aslanides4, Yannis Panatazis 5 , Ioannis Smyrnakis 2,3 and Maria Papadopouli 1,2

1Department of Computer Science, University of Crete, Heraklion, Greece 2Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece 3Optotect Ltd., Heraklion, Greece 4 Emmetropia Eye Institute, Heraklion, Greece, 5Institute of Applied and Computational Mathematics, Heraklion, Greece 13

Dyslexia is a reading disorder characterized by difficulties with accurate and/or fluent word recognition and by poor spelling and decoding abilities.

• G. R. Lyon, S. E. Shaywitz, B. A. Shaywitz, “A definition of dyslexia”, Annals of Dyslexia, vol. 53, pp 1-14, 2003

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Early intervention can be effective in alleviating the symptoms of the disability. However, screening large populations of children is rather time consuming and expensive. Is it possible to build a screening tool that can reliably identify dyslexic readers by analyzing

• culomotor patterns collected during reading and is robust under noise?

Could we address the lack of large-size datasets?

Typical Reader Dyslexic Reader

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Dyslexic readers manifest:

• longer and more frequent fixations
• shorter saccade lengths
• more backward refixations

test set training set LASSO regression Dominant features selection Build classifier classification accuracy Extracting dominant features test classifier Cross- validation Feature extraction fixation points

DysLexML

Feature selection

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Classification Algorithms

• 1. Support Vector Machines (linear, Gaussian, polynomial)
• 2. Naϊve Bayes (continuous values, Gaussian distribution)
• 3. K-means (variation to fit classification, aiming to catch possible sub-classes)

Leave One Out Cross Validation (LOOCV) for calculating the mean accuracy

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Dominant Features

• Number of fixations
• Number of short forward

movements

• Fixation median duration
• Median length of medium

forward movements

• Number of multiply fixated

words

• Age

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1st field study custom made eye-tracker use of chin rest age span 8.5- 12.5 years old 69 participants (39 typical, 32 dyslexic)

All participants were native Greek speakers

2nd field study Tobii 4C eye-tracker Non-invasive procedure age span 7-17 years old 152 participants (80 typical, 72 dyslexic)

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Recording images up to 60Hz, resolution of 1600 x 1200 pixels Recording images up to 90Hz (no chin rest was used) Easy text: 143 words, mostly of 1 or 2 syllables Difficult text: 181 words, many multi-syllable

DysLexML: Findings

• Dominant features are interpretable and accepted by the community
• For noise levels smaller than 30 pixels, system performs robustly

Synthetic data generation for feeding more data-greedy & sophisticated classification

methods to increase the accuracy This work sets the basis for developing screening systems that can reach larger more diverse populations, in less controlled environments, for early intervention and potentially larger social impact

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Brain Networks Analysis & Modeling

• Network analysis of visual cortex during learning
• Encoding and adaptation in primary visual cortex
• Neural networks in neurological diseases
• Deciphering the role of neuronal activity patterns in epilepsy
• Developing biomarkers for the progression of Alzheimer’s Disease

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Collaborator /co-Investigator Prof Stelios Smirnakis Department of Neurology Harvard Medical School

https://www.ics.forth.gr/tnl/