A QUIC Implementation for ns-3

Quick UDP Internet Connections (QUIC) is a recently proposed transport protocol, currently being standardized by the Internet Engineering Task Force (IETF). It aims at overcoming some of the shortcomings of TCP, while maintaining the logic related to flow and congestion control, retransmissions and acknowledgments. It supports multiplexing of multiple application layer streams in the same connection, a more refined selective acknowledgment scheme, and low-latency connection establishment. It also integrates cryptographic functionalities in the protocol design. Moreover, QUIC is deployed at the application layer, and encapsulates its packets in UDP datagrams. Given the widespread interest in the new QUIC features, we believe that it is important to provide to the networking community an implementation in a controllable and isolated environment, i.e., a network simulator such as ns-3, in which it is possible to test QUIC's performance and understand design choices and possible limitations. Therefore, in this paper we present a native implementation of QUIC for ns-3, describing the features we implemented, the main assumptions and differences with respect to the QUIC Internet Drafts, and a set of examples.Comment: 8 pages, 4 figures. Please cite it as A. De Biasio, F. Chiariotti, M. Polese, A. Zanella, M. Zorzi, "A QUIC Implementation for ns-3", Proceedings of the Workshop on ns-3 (WNS3 '19), Firenze, Italy, 201

Retail Shelf Analytics Through Image Processing and Deep Learning

The present thesis promotes an innovative approach based on modern deep learning and image processing techniques for retail shelf analytics within an actual business context. To achieve this goal, the research focused on recent developments in computer vision while maintaining a business-oriented approach. The project involved the full-stack software development of a product to analyze structured and unstructured data and provide business intelligence services for retail systems

Graph-based Explainable Recommendation Systems: Are We Rigorously Evaluating Explanations? A Position Paper

In recent years, we have witnessed an increase in the amount of published research in the field of Explainable Recommender Systems. These systems are designed to help users find the items of the most interest by providing not only suggestions but also the reasons behind those recommendations. Research has shown that there are many advantages to complementing a recommendation with a convincing explanation. For example, such an explanation can often lead to an increase in user trust, which in turn can improve recommendation effectiveness and system adoption. In particular, for this reason, many research works are studying explainable recommendation algorithms based on graphs, e.g., exploiting knowledge graphs or graph neural networks based methods. The use of graphs is very promising since algorithms can, in principle, combine the benefits of personalization and graph reasoning, thus potentially improving the effectiveness of both recommendations and explanations. However, although graph-based algorithms have been repeatedly shown to bring improvements in terms of ranking quality, not much literature has yet studied how to properly evaluate the quality of the corresponding explanations. In this position paper, we focus on this problem, examining in detail how the explanations of explainable recommenders based on graphs are currently evaluated and discussing how they could be evaluated in the future in a more quantitative and comparable way in compliance with the well-known Explainable Recommender Systems guidelines