Exploring Cyberbullying and Other Toxic Behavior in Team Competition Online Games

In this work we explore cyberbullying and other toxic behavior in team competition online games. Using a dataset of over 10 million player reports on 1.46 million toxic players along with corresponding crowdsourced decisions, we test several hypotheses drawn from theories explaining toxic behavior. Besides providing large-scale, empirical based understanding of toxic behavior, our work can be used as a basis for building systems to detect, prevent, and counter-act toxic behavior.Comment: CHI'1

Fast Video Classification via Adaptive Cascading of Deep Models

Recent advances have enabled "oracle" classifiers that can classify across many classes and input distributions with high accuracy without retraining. However, these classifiers are relatively heavyweight, so that applying them to classify video is costly. We show that day-to-day video exhibits highly skewed class distributions over the short term, and that these distributions can be classified by much simpler models. We formulate the problem of detecting the short-term skews online and exploiting models based on it as a new sequential decision making problem dubbed the Online Bandit Problem, and present a new algorithm to solve it. When applied to recognizing faces in TV shows and movies, we realize end-to-end classification speedups of 2.4-7.8x/2.6-11.2x (on GPU/CPU) relative to a state-of-the-art convolutional neural network, at competitive accuracy.Comment: Accepted at IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

Efficient Security and Privacy Enhancing Solutions in Untrusted Environments

Thesis (Ph.D.)--University of Washington, 2016-08Today, it is common to connect to Internet-based services through a variety of devices. While using the Internet, a user's personal information is exposed to untrusted or unreliable environments, from the applications they are using, to the networks delivering packets, to cloud-based remote services. As personal information increases in value, the incentives for remote services to collect as much of it as possible increase as well. On the other hand, users do not have much control over information exposure, while the risk is high as it is irreversible once it occurs. Despite the increasing security and privacy risk and much attention from research community and developers, many privacy issues remain unsolved. This dissertation explores the answers to the question: Can we design security and privacy enhancing systems in the current untrusted environment? In answering the question, my dissertation considers and tackles two key challenges---untrusted cloud services and linkability of user behavior by providing users with control over how and which of their information is exposed to other parties. It presents the solutions with two systems: MetaSync, a secure and reliable file synchronization service across multiple untrusted service providers, and the IPv6 pseudonym abstraction, a cross-layer architecture allowing users to have flexible control of linkability

Accelerating SSL with GPUs

SSL/TLS is a standard protocol for secure Internet communication. Despite its great success, today’s SSL deployment is largely limited to security-critical domains. The low adoption rate of SSL is mainly due to high computation overhead on the server side. In this paper, we propose Graphics Processing Units (GPUs) as a new source of computing power to reduce the server-side overhead. We have designed and implemented an SSL proxy that opportunistically offloads cryptographic operations to GPUs. The evaluation results show that our GPU implementation of cryptographic operations, RSA, AES, and HMAC-SHA1, achieves high throughput while keeping the latency low. The SSL proxy significantly boosts the throughput of SSL transactions, handling 25.8K SSL transactions per second, and has comparable response time even when overloaded

Sensitivity of the Gravity Model and Orbital Frame for On-board Real-Time Orbit Determination: Operational Results of GPS-12 GPS Receiver

This paper describes the sensitivity of both the orbital frame domain selection and the gravity model on the performance of on-board real-time orbit determination. Practical error sources, which affect the navigation solution of spaceborne global positioning system (GPS) receivers, are analyzed first. Then, a reasonable orbital frame (radial, in-track, cross-track (RIC)) is proposed to clearly represent the characteristics of the error in order to improve the performance of the orbit determination (OD) logic. In addition, the sensitivity of the gravity model affecting the orbit determination logic is analyzed by comparison with the precise orbit ephemeris (POE) of the Challenging Minisatellite Payload (CHAMP) satellite, and it is confirmed that the Gravity Recovery And Climate Experiment (GRACE) Gravity Model 03 (GGM03) outperforms the Earth Gravity Model 1996 (EGM96). The effects of both proposed orbit frames and the gravity model on the orbit determination logic are verified using a GPS simulator and observation data from the CHAMP satellite. Moreover, the practical performance of on-board real-time orbit determination logic is verified by updating the software of the spaceborne GPS receiver, GPS-12, on DubaiSat-2 operating at low Earth orbit (LEO). The results show that the position accuracy of on-board real-time orbit determination logic in GPS-12 is improved by 59%, from 12.6 m (1 σ) to 5.1 m (1 σ), after applying the proposed methods. The velocity accuracy is also improved by 57%, from 13.7 mm/s (1 σ) to 5.9 mm/s (1 σ)