A content dissemination framework for vehicular networking

Vehicular Networks are a peculiar class of wireless mobile networks in which vehicles are equipped with radio interfaces and are, therefore, able to communicate with fixed infrastructure (if available) or other vehicles. Content dissemination has a potential number of applications in vehicular networking, including advertising, traffic warnings, parking notifications and emergency announcements. This thesis addresses two possible dissemination strategies: i) Push-based that is aiming to proactively deliver information to a group of vehicles based on their interests and the level of matching content, and ii) Pull-based that is allowing vehicles to explicitly request custom information. Our dissemination framework is taking into consideration very specific information only available in vehicular networks: the geographical data produced by the navigation system. With its aid, a vehicle's mobility patterns become predictable. This information is exploited to efficiently deliver the content where it is needed. Furthermore, we use the navigation system to automatically filter information which might be relevant to the vehicles. Our framework has been designed and implemented in .NET C# and Microsoft MapPoint. It was tested using a small number of vehicles in the area of Cambridge, UK. Moreover, to prove the correctness of our protocols, we further evaluated it in a large-scale network simulation over a number of realistic vehicular trace-based scenarios. Finally, we built a test-case application aiming to prove that vehicles can gain from such a framework. In this application every vehicle collects and disseminates road traffic information. Vehicles that receive this information can individually evaluate the traffic conditions and take an alternative route, if needed. To evaluate this approach, we collaborated with UCLA's Network Research Lab (NRL), to build a simulator that combines network and dynamic mobility emulation simultaneously. When our dissemination framework is used, the drivers can considerably reduce their trip-times

EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices

In recent years, advances in deep learning have resulted in unprecedented leaps in diverse tasks spanning from speech and object recognition to context awareness and health monitoring. As a result, an increasing number of AI-enabled applications are being developed targeting ubiquitous and mobile devices. While deep neural networks (DNNs) are getting bigger and more complex, they also impose a heavy computational and energy burden on the host devices, which has led to the integration of various specialized processors in commodity devices. Given the broad range of competing DNN architectures and the heterogeneity of the target hardware, there is an emerging need to understand the compatibility between DNN-platform pairs and the expected performance benefits on each platform. This work attempts to demystify this landscape by systematically evaluating a collection of state-of-the-art DNNs on a wide variety of commodity devices. In this respect, we identify potential bottlenecks in each architecture and provide important guidelines that can assist the community in the co-design of more efficient DNNs and accelerators.Comment: Accepted at MobiSys 2019: 3rd International Workshop on Embedded and Mobile Deep Learning (EMDL), 201

The architecture of innovation: Tracking face-to-face interactions with UbiComp technologies

The layouts of the buildings we live in shape our everyday lives. In office environments, building spaces affect employees' communication, which is crucial for productivity and innovation. However, accurate measurement of how spatial layouts affect interactions is a major challenge and traditional techniques may not give an objective view.We measure the impact of building spaces on social interactions using wearable sensing devices. We study a single organization that moved between two different buildings, affording a unique opportunity to examine how space alone can affect interactions. The analysis is based on two large scale deployments of wireless sensing technologies: short-range, lightweight RFID tags capable of detecting face-to-face interactions. We analyze the traces to study the impact of the building change on social behavior, which represents a first example of using ubiquitous sensing technology to study how the physical design of two workplaces combines with organizational structure to shape contact patterns.This is the author accepted manuscript. The final version is available at http://dl.acm.org/citation.cfm?id=2632056&CFID=528294814&CFTOKEN=36484024

Detecting cyberbullying and cyberaggression in social media

Cyberbullying and cyberaggression are increasingly worrisome phenomena affecting people across all demographics. More than half of young social media users worldwide have been exposed to such prolonged and/or coordinated digital harassment. Victims can experience a wide range of emotions, with negative consequences such as embarrassment, depression, isolation from other community members, which embed the risk to lead to even more critical consequences, such as suicide attempts. In this work, we take the first concrete steps to understand the characteristics of abusive behavior in Twitter, one of today’s largest social media platforms. We analyze 1.2 million users and 2.1 million tweets, comparing users participating in discussions around seemingly normal topics like the NBA, to those more likely to be hate-related, such as the Gamergate controversy, or the gender pay inequality at the BBC station. We also explore specific manifestations of abusive behavior, i.e., cyberbullying and cyberaggression, in one of the hate-related communities (Gamergate). We present a robust methodology to distinguish bullies and aggressors from normal Twitter users by considering text, user, and network-based attributes. Using various state-of-the-art machine-learning algorithms, we classify these accounts with over 90% accuracy and AUC. Finally, we discuss the current status of Twitter user accounts marked as abusive by our methodology and study the performance of potential mechanisms that can be used by Twitter to suspend users in the future

Kek, Cucks, and God Emperor Trump: A Measurement Study of 4chan's Politically Incorrect Forum and its Effects on the Web

The discussion-board site 4chan has been part of the Internet's dark underbelly since its inception, and recent political events have put it increasingly in the spotlight. In particular, /pol/, the “Politically Incorrect'” board, has been a central figure in the outlandish 2016 US election season, as it has often been linked to the alt-right movement and its rhetoric of hate and racism. However, 4chan remains relatively unstudied by the scientific community: little is known about its user base, the content it generates, and how it affects other parts of the Web. In this paper, we start addressing this gap by analyzing /pol/ along several axes, using a dataset of over 8M posts we collected over two and a half months. First, we perform a general characterization, showing that /pol/ users are well distributed around the world and that 4chan's unique features encourage fresh discussions. We also analyze content, finding, for instance, that YouTube links and hate speech are predominant on /pol/. Overall, our analysis not only provides the first measurement study of /pol/, but also insight into online harassment and hate speech trends in social media

Finding Dory in the Crowd: Detecting Social Interactions using Multi-Modal Mobile Sensing

21 pages, 6 figures, conference paper21 pages, 6 figures, conference pape

Idiopathic portal hypertension complicating systemic sclerosis: a case report

BACKGROUND: Patients with systemic sclerosis may develop mild abnormalities of liver function tests. More serious hepatic involvement has been well documented but is rare. Idiopathic portal hypertension had been reported only in a few female patients with systemic sclerosis. CASE PRESENTATION: An 82-year-old man with known systemic sclerosis presented with melaena. Urgent gastroscopy revealed oesophageal varices, which re-started bleeding during the procedure and were treated ensocopically, with Sengstaken tube and glypressin. Liver function tests and coagulation were normal. Non-invasive liver screen (including hepatitis viral serology and autoantibodies) was negative. Ultrasound scan of the abdomen revealed a small liver with coarse texture and no focal lesion. Hepato-portal flow was demonstrated in the portal vein. The spleen was enlarged. A moderate amount of free peritoneal fluid was present. A CT scan confirmed the absence of portal vein thrombosis. One month following discharge the patient had a liver biopsy. Histological examination showed essentially normal liver tissue; there was no evidence of any excess inflammation and no features to suggest cirrhosis or drug-induced liver disease. Taking into account the above evaluation we concluded that the patient had idiopathic portal hypertension. CONCLUSION: Both male and female patients with systemic sclerosis may – rarely – develop idiopathic portal hypertension