Traffic event detection framework using social media

This is an accepted manuscript of an article published by IEEE in 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC) on 18/09/2017, available online: https://ieeexplore.ieee.org/document/8038595 The accepted version of the publication may differ from the final published version.© 2017 IEEE. Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.Published versio

The Pulse of News in Social Media: Forecasting Popularity

News articles are extremely time sensitive by nature. There is also intense competition among news items to propagate as widely as possible. Hence, the task of predicting the popularity of news items on the social web is both interesting and challenging. Prior research has dealt with predicting eventual online popularity based on early popularity. It is most desirable, however, to predict the popularity of items prior to their release, fostering the possibility of appropriate decision making to modify an article and the manner of its publication. In this paper, we construct a multi-dimensional feature space derived from properties of an article and evaluate the efficacy of these features to serve as predictors of online popularity. We examine both regression and classification algorithms and demonstrate that despite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84% accuracy. Our study also serves to illustrate the differences between traditionally prominent sources and those immensely popular on the social web

Location Reference Recognition from Texts: A Survey and Comparison

A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching–based, statistical learning-–based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs

Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences

In this survey, we first briefly review the current state of cyber attacks, highlighting significant recent changes in how and why such attacks are performed. We then investigate the mechanics of malware command and control (C2) establishment: we provide a comprehensive review of the techniques used by attackers to set up such a channel and to hide its presence from the attacked parties and the security tools they use. We then switch to the defensive side of the problem, and review approaches that have been proposed for the detection and disruption of C2 channels. We also map such techniques to widely-adopted security controls, emphasizing gaps or limitations (and success stories) in current best practices.Comment: Work commissioned by CPNI, available at c2report.org. 38 pages. Listing abstract compressed from version appearing in repor

International overview on the legal framework for highly automated vehicles

The evolution of Autonomous and automated technologies during the last decades has been constant and maintained. All of us can remember an old film, in which they shown us a driverless car, and we thought it was just an unreal object born of filmmakers imagination. However, nowadays Highly Automated Vehicles are a reality, even not in our daily lives. Hardly a day we don’t have news about Tesla launching a new model or Google showing the new features of their autonomous car. But don’t have to travel far away from our borders. Here in Europe we also can find different companies trying, with more or less success depending on with, not to be lagged behind in this race. But today their biggest problem is not only the liability of their innovative technology, but also the legal framework for Highly Automated Vehicles. As a quick summary, in only a few countries they have testing licenses, which not allow them to freely drive, and to the contrary most nearly ban their use. The next milestone in autonomous driving is to build and homogeneous, safe and global legal framework. With this in mind, this paper presents an international overview on the legal framework for Highly Automated Vehicles. We also present de different issues that such technologies have to face to and which they have to overcome in the next years to be a real and daily technology

Automatic Extraction and Assessment of Entities from the Web

The search for information about entities, such as people or movies, plays an increasingly important role on the Web. This information is still scattered across many Web pages, making it more time consuming for a user to find all relevant information about an entity. This thesis describes techniques to extract entities and information about these entities from the Web, such as facts, opinions, questions and answers, interactive multimedia objects, and events. The findings of this thesis are that it is possible to create a large knowledge base automatically using a manually-crafted ontology. The precision of the extracted information was found to be between 75–90 % (facts and entities respectively) after using assessment algorithms. The algorithms from this thesis can be used to create such a knowledge base, which can be used in various research fields, such as question answering, named entity recognition, and information retrieval

Retrieving information from heterogeneous freight data sources to answer natural language queries

textThe ability to retrieve accurate information from databases without an extensive knowledge of the contents and organization of each database is extremely beneficial to the dissemination and utilization of freight data. The challenges, however, are: 1) correctly identifying only the relevant information and keywords from questions when dealing with multiple sentence structures, and 2) automatically retrieving, preprocessing, and understanding multiple data sources to determine the best answer to user’s query. Current named entity recognition systems have the ability to identify entities but require an annotated corpus for training which in the field of transportation planning does not currently exist. A hybrid approach which combines multiple models to classify specific named entities was therefore proposed as an alternative. The retrieval and classification of freight related keywords facilitated the process of finding which databases are capable of answering a question. Values in data dictionaries can be queried by mapping keywords to data element fields in various freight databases using ontologies. A number of challenges still arise as a result of different entities sharing the same names, the same entity having multiple names, and differences in classification systems. Dealing with ambiguities is required to accurately determine which database provides the best answer from the list of applicable sources. This dissertation 1) develops an approach to identify and classifying keywords from freight related natural language queries, 2) develops a standardized knowledge representation of freight data sources using an ontology that both computer systems and domain experts can utilize to identify relevant freight data sources, and 3) provides recommendations for addressing ambiguities in freight related named entities. Finally, the use of knowledge base expert systems to intelligently sift through data sources to determine which ones provide the best answer to a user’s question is proposed.Civil, Architectural, and Environmental Engineerin