Eye tracking-based Sedentary Activity Recognition with Conventional Machine Learning Algorithms

Sedentary activity recognition is an important research field due to its various positive implications in people’s life. This study builds upon previous research which is based on low level features extracted from the gaze signals using a fixation filter and uses a dataset of 24 participants performing 8 different sedentary activities. The main...

SCA Strikes Back: Reverse Engineering Neural Network Architectures using Side Channels

This paper was selected for Top Picks in Hardware and Embedded Security 2020 and it presents a physical side-channel attack aiming at reverse engineering neural networks implemented on an edge device. The attack does not need access to training data and allows for neural network recovery by feeding known random inputs. We successfully reverse...

Temporal Network Prediction and Interpretation

Temporal networks refer to networks like physical contact networks whose topology changes over time. Predicting future temporal network is crucial e.g., to forecast the epidemics. Existing prediction methods are either relatively accurate but black-box, or white-box but less accurate. The lack of interpretable and accurate prediction methods...

Three Symmetries for Data-Driven Pedestrian Inertial Navigation

The last years have seen a growing body of literature on data-driven pedestrian inertial navigation. However, despite this, it is still unclear how to efficiently combine classical models and other a priori information with existing machine learning frameworks. In this paper, we first categorize existing approaches to data-driven pedestrian...

Birkhoff's Decomposition Revisited: Sparse Scheduling for High-Speed Circuit Switches

Data centers are increasingly using high-speed circuit switches to cope with the growing demand and reduce operational costs. One of the fundamental tasks of circuit switches is to compute a sparse collection of switching configurations to support a traffic demand matrix. Such a problem has been addressed in the literature with variations of the...

Demonstrating a Bayesian Online Learning for Energy-Aware Resource Orchestration in vRANs

Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We demonstrate a novel machine learning approach to solve resource orchestration...

SGD_Tucker: A Novel Stochastic Optimization Strategy for Parallel Sparse Tucker Decomposition

Sparse Tucker Decomposition (STD) algorithms learn a core tensor and a group of factor matrices to obtain an optimal low-rank representation feature for the High-Order, High-Dimension, and Sparse Tensor (HOHDST). However, existing STD algorithms face the problem of intermediate variables explosion which results from the fact that the...

Land subsidence susceptibility mapping using persistent scatterer SAR interferometry technique and optimized hybrid machine learning algorithms

In this paper, land subsidence susceptibility was assessed for Shahryar County in Iran using the adaptive neuro-fuzzy inference system (ANFIS) machine learning algorithm. Another aim of the present paper was to assess if ensembles of ANFIS with two meta-heuristic algorithms (imperialist competitive algorithm (ICA) and gray wolf optimization ...

Image shift due to atmospheric refraction: Prediction by numerical weather modeling and machine learning

We develop and study two approaches for the prediction of optical refraction effects in the lower atmosphere. Refraction can cause apparent displacement or distortion of targets when viewed by imaging systems or produce steering when propagating laser beams. Low-cost, time-lapse camera systems were deployed at two locations in New Mexico to...

Forecasting pedestrian movements using recurrent neural networks: An application of crowd monitoring data

Currently, effective crowd management based on the information provided by crowd monitoring systems is difficult as this information comes in at the moment adverse crowd movements are already occurring. Up to this moment, very little forecasting techniques have been developed that predict crowd flows a longer time period ahead. Moreover, most...

Predicting atmospheric refraction with weather modeling and machine learning

This work details the analysis of time-lapse images with a point-tracking image processing approach along with the use of an extensive numerical weather model to investigate image displacement due to refraction. The model is applied to create refractive profile estimates along the optical path for the days of interest. Ray trace analysis...

CMOS Image Sensor Design Methodology Applied to Optical Tomography and Neural Networks

Imaging sensors are remarkable devices which are able to capture moments and present them in a form available to us for years to come. With the use of material properties and photon particles, charges can be produced which, in combination with electric circuitry, transform into information understandable to the human eye and eventually the brain...

Ranking the Level of Fear from Voice using Nominal Classification Methods

To investigate human emotion, which is conveyed in human speech, methods which can achieve this need to be developed. One way to respond to it is to extract prosodic features from speech which are relevant in emotion research and feed this to a machine learning algorithm. This thesis discusses the encoding, decoding and inference processes of...