Browsing by Subject "Distributed Computing"

Now showing 1 - 3 of 3
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    Multilingual Cyberbullying Detection System
    (2019-05) Pawar, Rohit S.; Raje, Rajeev R.; Tuceryan, Mihran; Durresi, Arjan
    Since the use of social media has evolved, the ability of its users to bully others has increased. One of the prevalent forms of bullying is Cyberbullying, which occurs on the social media sites such as Facebook©, WhatsApp©, and Twitter©. The past decade has witnessed a growth in cyberbullying – is a form of bullying that occurs virtually by the use of electronic devices, such as messaging, e-mail, online gaming, social media, or through images or mails sent to a mobile. This bullying is not only limited to English language and occurs in other languages. Hence, it is of the utmost importance to detect cyberbullying in multiple languages. Since current approaches to identify cyberbullying are mostly focused on English language texts, this thesis proposes a new approach (called Multilingual Cyberbullying Detection System) for the detection of cyberbullying in multiple languages (English, Hindi, and Marathi). It uses two techniques, namely, Machine Learning-based and Lexicon-based, to classify the input data as bullying or non-bullying. The aim of this research is to not only detect cyberbullying but also provide a distributed infrastructure to detect bullying. We have developed multiple prototypes (standalone, collaborative, and cloud-based) and carried out experiments with them to detect cyberbullying on different datasets from multiple languages. The outcomes of our experiments show that the machine-learning model outperforms the lexicon-based model in all the languages. In addition, the results of our experiments show that collaboration techniques can help to improve the accuracy of a poor-performing node in the system. Finally, we show that the cloud-based configurations performed better than the local configurations.
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    Real-time road traffic events detection and geo-parsing
    (2018-08-08) Kumar, Saurabh; Koskie, Sarah
    In the 21st century, there is an increasing number of vehicles on the road as well as a limited road infrastructure. These aspects culminate in daily challenges for the average commuter due to congestion and slow moving traffic. In the United States alone, it costs an average US driver $1200 every year in the form of fuel and time. Some positive steps, including (a) introduction of the push notification system and (b) deploying more law enforcement troops, have been taken for better traffic management. However, these methods have limitations and require extensive planning. Another method to deal with traffic problems is to track the congested area in a city using social media. Next, law enforcement resources can be re-routed to these areas on a real-time basis. Given the ever-increasing number of smartphone devices, social media can be used as a source of information to track the traffic-related incidents. Social media sites allow users to share their opinions and information. Platforms like Twitter, Facebook, and Instagram are very popular among users. These platforms enable users to share whatever they want in the form of text and images. Facebook users generate millions of posts in a minute. On these platforms, abundant data, including news, trends, events, opinions, product reviews, etc. are generated on a daily basis. Worldwide, organizations are using social media for marketing purposes. This data can also be used to analyze the traffic-related events like congestion, construction work, slow-moving traffic etc. Thus the motivation behind this research is to use social media posts to extract information relevant to traffic, with effective and proactive traffic administration as the primary focus. I propose an intuitive two-step process to utilize Twitter users' posts to obtain for retrieving traffic-related information on a real-time basis. It uses a text classifier to filter out the data that contains only traffic information. This is followed by a Part-Of-Speech (POS) tagger to find the geolocation information. A prototype of the proposed system is implemented using distributed microservices architecture.
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    Using Non-Intrusive Instrumentation to Analyze any Distributed Middleware in Real-Time
    (2021-05) Lui, Nyalia; Hill, James H.; Raje, Rajeev; Song, Fengguang
    Dynamic Binary Instrumentation (DBI) is one way to monitor a distributed system in real-time without modifying source code. Previous work has shown it is possible to instrument distributed systems using standards-based distributed middleware. Existing work, however, only applies to a single middleware, such as CORBA. This thesis therefore presents a tool named the Standards-based Distributed Middleware Monitor (SDMM), which generalizes two modern standards-based distributed middleware, the Data Distribution Service (DDS) and gRemote Procedure Call (gRPC). SDMM uses DBI to extract values and other data relevant to monitoring a distributed system in real-time. Using dynamic instrumentation allows SDMM to capture information without a priori knowledge of the distributed system under instrumentation. We applied SDMM to systems created with two DDS vendors, RTI Connext DDS and OpenDDS, as well as gRPC which is a complete remote procedure call framework. Our results show that the data collection process contributes to less than 2% of the run-time overhead in all test cases.