Reducing traffic accidents is an important public safety challenge, therefore, accident analysis and prediction has been a topic of much research over the past few decades. Using small-scale datasets with limited coverage, being dependent on extensive set of data, and being not applicable for real-time purposes are the important shortcomings of the existing studies. To address these challenges, we propose a new solution for real-time traffic accident prediction using easy-to-obtain, but sparse data. Our solution relies on a deepneural-network model (which we have named DAP, for Deep Accident Prediction); which utilizes a variety of data attributes such as traffic events, weather data, points-of-interest, and time. DAP incorporates multiple components including a recurrent (for time-sensitive data), a fully connected (for time-insensitive data), and a trainable embedding component (to capture spatial heterogeneity). To fill the data gap, we have -through a comprehensive process of data collection, integration, and augmentation -created a large-scale publicly available database of accident information named US-Accidents. By employing the US-Accidents dataset and through an extensive set of experiments across several large cities, we have evaluated our proposal against several baselines. Our analysis and results show significant improvements to predict rare accident events. Further, we have shown the impact of traffic information, time, and pointsof-interest data for real-time accident prediction.
We present an usability study for a bi-modality Continuous Biometrics Authentication System (CBAS) that runs on the Windows platform. Our CBAS combines fingerprint and facial biometrics to authenticate users. As authentication is continuous, CBAS constantly contributes a computational overhead of up to 42% to the computer system. This usability study seeks to investigate (a) whether this overhead will have an impact on the performance of users to complete tasks; and (b) whether the users deem the responsiveness of the system to be acceptable. The results of our study are encouraging, indicating that the runtime cost of a CBAS system has no measurable statistical impact on the task completion by users. We found that user acceptance of CBAS to be good and they did not perceive the CBAS to degrade system response. This suggests that continuous biometrics for authentication is viablethe CBAS benefits outweighs system impact drawbacks.
In recent years, social networks have become very popular. Twitter, a micro-blogging service, is estimated to have about 200 million registered users and these users create approximately 65 million tweets a day. Twitter users usually show their opinion about topics of their interest. The challenge is that each tweet is limited in 140 characters, and is hence very short. It may contain slang and misspelled words. Thus, it is difficult to apply traditional NLP techniques which are designed for working with formal languages, into Twitter domain. Another challenge is that the total volume of tweets is extremely high, and it takes a long time to process. In this paper, we describe a large-scale distributed system for real-time Twitter sentiment analysis. Our system consists of two components: a lexicon builder and a sentiment classifier. These two components are capable of running on a large-scale distributed system since they are implemented using a MapReduce framework and a distributed database model. Thus, our lexicon builder and sentiment classifier are scalable with the number of machines and the size of data. The experiments also show that our lexicon has a good quality in opinion extraction, and the accuracy of the sentiment classifier can be improved by combining the lexicon with a machine learning technique.
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