Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems.

With the proliferation of IoT devices, ensuring the security and privacy of these devices and their associated data has become a critical challenge. In this paper, we propose a federated sampling and lightweight intrusion-detection system for IoT networks that use K-meansfor sampling network traffic and identifying anomalies in a semi-supervised way. The system is designed to preserve data privacy by performing local clustering on each device and sharing only summary statistics with a central aggregator.

Spatio-temporal dataset of building occupants.

The paper presents spatio-temporal dataset of building occupants captured using 200 Bluetooth Low Energy (BLE) beacons installed on different locations in two buildings. It contains 8426 data points of 11 building occupants collected with a sampling rate of 5 seconds during different times in a 12 days' interval. Each spatio-temporal data point comprises location and time components correspond to a building location which can be visualized using an OpenStreetMap (OSM) file of a building.

Embedded Implementation of VHR Satellite Image Segmentation.

Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc.

Architecture-Driven Level Set Optimization: From Clustering to Subpixel Image Segmentation.

Thanks to their effectiveness, active contour models (ACMs) are of great interest for computer vision scientists. The level set methods (LSMs) refer to the class of geometric active contours. Comparing with the other ACMs, in addition to subpixel accuracy, it has the intrinsic ability to automatically handle topological changes.

Design, characterization and analysis of a 0.35 μm CMOS SPAD.

Most of the works about single-photon detectors rely on Single Photon Avalanche Diodes (SPADs) designed with dedicated technological processes in order to achieve single-photon sensitivity and excellent timing resolution. Instead, this paper focuses on the implementation of high-performance SPADs detectors manufactured in a standard 0.35-micron opto-CMOS technology provided by AMS.

Multiple representations and mechanisms for visuomotor adaptation in young children.

In this study, we utilized transformed spatial mappings to perturb visuomotor integration in 5-yr-old children and adults. The participants were asked to perform pointing movements under five different conditions of visuomotor rotation (from 0° to 180°), which were designed to reveal explicit vs. implicit representations as well as the mechanisms underlying the visual-motor mapping.

gSRT-Soft: a generic software application and some methodological guidelines to investigate implicit learning through visual-motor sequential tasks.

Serial reaction time tasks and, more generally, the visual-motor sequential paradigms are increasingly popular tools in a variety of research domains, from studies on implicit learning in laboratory contexts to the assessment of residual learning capabilities of patients in clinical settings. A consequence of this success, however, is the increased variability in paradigms and the difficulty inherent in respecting the methodological principles that two decades of experimental investigations have made more and more stringent. The purpose of the present article is to address those problems.

Implicit learning of a repeated segment in continuous tracking: a reappraisal.

Several prior studies (e.g., Shea, Wulf, Whitacre, & Park, 2001; Wulf & Schmidt, 1997) have apparently demonstrated implicit learning of a repeated segment in continuous-tracking tasks.