The Effect of Network and Infrastructural Variables on SPDY's Performance

HTTP is a successful Internet technology on top of which a lot of the web resides. However, limitations with its current specification, i.e. HTTP/1.1, have encouraged some to look for the next generation of HTTP. In SPDY, Google has come up with such a proposal that has growing community acceptance, especially after being adopted by the IETF HTTPbis-WG as the basis for HTTP/2.0. SPDY has the potential to greatly improve web experience with little deployment overhead. However, we still lack an understanding of its true potential in different environments. This paper seeks to resolve these issues, offering a comprehensive evaluation of SPDY's performance using extensive experiments. We identify the impact of network characteristics and website infrastructure on SPDY's potential page loading benefits, finding that these factors are decisive for SPDY and its optimal deployment strategy. Through this, we feed into the wider debate regarding HTTP/2.0, exploring the key aspects that impact the performance of this future protocol

Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting

Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems to relieve the increasing amount of vehicular traffic congestion and incidents. Existing incident detection techniques are limited to the use of sensors in the transportation network and hang on human-inputs. Despite of its data abundance, social media is not well-exploited in such context. In this paper, we develop an automated traffic alert system based on Natural Language Processing (NLP) that filters this flood of information and extract important traffic-related bullets. To this end, we employ the fine-tuning Bidirectional Encoder Representations from Transformers (BERT) language embedding model to filter the related traffic information from social media. Then, we apply a question-answering model to extract necessary information characterizing the report event such as its exact location, occurrence time, and nature of the events. We demonstrate the adopted NLP approaches outperform other existing approach and, after effectively training them, we focus on real-world situation and show how the developed approach can, in real-time, extract traffic-related information and automatically convert them into alerts for navigation assistance applications such as navigation apps.Comment: This paper is accepted for publication in IEEE Technology Engineering Management Society International Conference (TEMSCON'20), Metro Detroit, Michigan (USA

Progressive failure analysis of fibrous composite materials and structures

A brief description is given of the modifications implemented in the PAFAC finite element program for the simulation of progressive failure in fibrous composite materials and structures. Details of the memory allocation, input data, and the new subroutines are given. Also, built-in failure criteria for homogeneous and fibrous composite materials are described