Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.

In this paper, we develop a 2D and 3D segmentation pipelines for fully automated cardiac MR image segmentation using Deep Convolutional Neural Networks (CNN). Our models are trained end-to-end from scratch using the ACD Challenge 2017 dataset comprising of 100 studies, each containing Cardiac MR images in End Diastole and End Systole phase. We show that both our segmentation models achieve near state-of-the-art performance scores in terms of distance metrics and have convincing accuracy in terms of clinical parameters. A comparative analysis is provided by introducing a novel dice loss function and its combination with cross entropy loss. By exploring different network structures and comprehensive experiments, we discuss several key insights to obtain optimal model performance, which also is central to the theme of this challenge.

In the past few years, numerous privacy vulnerabilities have been discovered in the WiFi standards and their implementations for mobile devices. These vulnerabilities allow an attacker to collect large amounts of data on the device user, which could be used to infer sensitive information such as religion, gender, and sexual orientation. Solutions for these vulnerabilities are often hard to design and typically require many years to be widely adopted, leaving many devices at risk. In this paper, we present UNVEIL-an interactive and extendable platform to demonstrate the consequences of these attacks. The platform performs passive and active attacks on smartphones to collect and analyze data leaked through WiFi and communicate the analysis results to users through simple and interactive visualizations. The platform currently performs two attacks. First, it captures probe requests sent by nearby devices and combines them with public WiFi location databases to generate a map of locations previously visited by the device users. Second, it creates rogue access points with SSIDs of popular public WiFis (e.g. _Heathrow WiFi, Railways WiFi) and records the resulting internet traffic. This data is then analyzed and presented in a format that highlights the privacy leakage. The platform has been designed to be easily extendable to include more attacks and to be easily deployable in public spaces. We hope that UNVEIL will help raise public awareness of privacy risks of WiFi networks. CCS CONCEPTS • Security and privacy → Social aspects of security and privacy; • Human-centered computing → Information visualization.

Mobile phones and other ubiquitous technologies are generating vast amounts of high-resolution location data. This data has been shown to have a great potential for the public good, e.g. to monitor human migration during crises or to predict the spread of epidemic diseases. Location data is, however, considered one of the most sensitive types of data, and a large body of research has shown the limits of traditional data anonymization methods for big data. Privacy concerns have so far strongly limited the use of location data collected by telcos, especially in developing countries. In this paper, we introduce OPAL (for OPen ALgorithms), an open-source, scalable, and privacy-preserving platform for location data. At its core, OPAL relies on an open algorithm to extract key aggregated statistics from location data for a wide range of potential use cases. We first discuss how we designed the OPAL platform, building a modular and resilient framework for efficient location analytics. We then describe the layered mechanisms we have put in place to protect privacy and discuss the example of a population density algorithm. We finally evaluate the scalability and extensibility of the platform and discuss related work. The code will be open-sourced on GitHub upon publication.

Vehicle aerodynamics is a broad encompassing field that describes the forces acting on an object when moving through a fluid. When stationary, the exterior surfaces of an automobile experience one atmospheric pressure; the upper and lower surface as well as the front and rear surfaces all have the same pressures exerted and ultimately achieve equilibrium with the summation of forces being equal to zero. As the vehicle starts to move through the fluid, the pressures exerted on the exterior surfaces change proportional to the square of velocity. These pressure changes create forces acting on the surface of the vehicle that can drastically hinder the performance of the vehicle. Aerodynamicists study this natural phenomenon to try and minimize forces that inhibit motion and, in some cases, develop these forces and use them to improve performance and safety. Vehicle aerodynamicists are primarily concerned with two dominant forces that interact with a vehicle; lift and drag. The management of these forces is crucial to the performance of any vehicle however the philosophy of vehicle aerodynamics differ significantly depending on the application. In this work introducing multi element in the rear of the vehicle to maintain the lift force and computational fluid dynamics analysis carry out to measure the performance of the multielement wing.