1,721,046 research outputs found
Data Analysis and Modelling of Users' Behavior on the Web
Full text linkThe research developed during my PhD was driven by the need to understand how people interact with the web. This information gives ISPs and network managers better visibility and understanding of how users and web services change over time. Thanks to traces and logs of users' traffic, my work focuses on two complementary aspects: (i) data analytics, and (ii) user modelling.In this work, I show how to reconstruct users' online activity from passive measurements and to model their behaviour. I introduce machine learning approaches to identify the intentionally visited web-pages and web-sites. I highlight device usage evolution, the structure of the navigation and the interactions with social networks and search engines. I build users' profiles and then I show how to re-identify users in a future time thanks to their behavioural fingerprints. This is also instrumental for security applications. I next study the interaction with online ads, capturing the impact of the temporal dynamics of shown advertisement and improving revenues.I make available all the anonymized datasets and code for the community, to guarantee results reproducibility and foster further analyses
Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning
Full text linkCommunication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning delivers a comprehensive overview of the impact of artificial intelligence (AI) and machine learning (ML) on service and network management. Beginning with a fulsome description of ML and AI, the book moves on to discuss management models, architectures, and frameworks. The authors also explore how AI and ML can be used in service management functions like the generation of workload profiles, service provisioning, and more.
The book includes a handpicked selection of applications and case studies, as well as a treatment of emerging technologies the authors predict could have a significant impact on network and service management in the future. Statistical analysis and data mining are also discussed, particularly with respect to how they allow for an improvement of the management and security of IT systems and networks. Readers will also enjoy topics like:
A thorough introduction to network and service management, machine learning, and artificial intelligence
An exploration of artificial intelligence and machine learning for management models, including autonomic management, policy-based management, intent based management, and network virtualization-based management
Discussions of AI and ML for architectures and frameworks, including cloud systems, software defined networks, 5G and 6G networks, and Edge/Fog networks
An examination of AI and ML for service management, including the automatic generation of workload profiles using unsupervised learning
Perfect for information and communications technology educators, Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning will also earn a place in the libraries of engineers and professionals who seek a structured reference on how the emergence of artificial intelligence and machine learning techniques is affecting service and network management
Regular pattern and anomaly detection on corporate transaction time series
Full text linkBusiness applications make extensive usage of time series analysis for the most diverse tasks. By analyzing the development of any phenomena over time we gain some useful insights on the stock market forecast, analyze the risk related to investments, understand the behavior of a company on the market and so on. More specifically, in a corporate investment banking environment, analyzing the transaction history of a customer over the years is crucial to establish a fruitful relationship and adapt to its behavioural changes. In this environment we recognize three macro-categories of phenomena of interest: cyclic events, sudden and significant changes in trend, and isolated anomalous points. In this paper we present a framework to automatically spot these behaviors by means of simple - yet effective - machine learning techniques.We observe that cyclic behaviors and sudden changes can be easily targeted by means of adaptive threshold algorithms, while unsupervised machine learning techniques are the most reliable in detecting isolated anomalies. We design and test our algorithms on actual transactions collected in the past two years from more than 2,000 customers of UniCredit Bank, showing the efficiency of our solution. This work is tested to serve as a decision aid tool for corporate investment banking employees to facilitate the inspection of years of transactions and ease the visualization of interesting events in the customer history
Largescale available bandwidth measurements: Shortcomings of current techniques
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Fault Prediction in Resistance Spot Welding: A Comparison of Machine Learning Approaches
Full text linkResistance spot welding is widely adopted in manufacturing and is characterized by high reliability and simple automation in the production line. The detection of defective welds is a difficult task that requires either destructive or expensive and slow non-destructive testing (e.g., ultrasound). The robots performing the welding automatically collect contextual and process-specific data. In this paper, we test whether these data can be used to predict defective welds. To do so, we use a dataset collected in a real industrial plant that describes welding-related data labeled with ultrasonic quality checks. We use these data to develop several pipelines based on shallow and deep learning machine learning algorithms and test the performance of these pipelines in predicting defective welds. Our results show that, despite the development of different pipelines and complex models, the machine-learning-based defect detection algorithms achieve limited performance. Using a qualitative analysis of model predictions, we show that correct predictions are often a consequence of inherent biases and intrinsic limitations in the data. We therefore conclude that the automatically collected data have limitations that hamper fault detection in a running production plant
Recommendation Systems in Libraries: an Application with Heterogeneous Data Sources
Full text linkThe Reading[&]Machine project exploits the support of digitalization to increase the attractiveness of libraries and improve
the users’ experience. The project implements an application that helps the users in their decision-making process, providing
recommendation system (RecSys)-generated lists of books the users might be interested in, and showing them through an
interactive Virtual Reality (VR)-based Graphical User Interface (GUI). In this paper, we focus on the design and testing of the
recommendation system, employing data about all users’ loans over the past 9 years from the network of libraries located in
Turin, Italy. In addition, we use data collected by the Anobii online social community of readers, who share their feedback
and additional information about books they read. Armed with this heterogeneous data, we build and evaluate Content Based
(CB) and Collaborative Filtering (CF) approaches. Our results show that the CF outperforms the CB approach, improving
by up to 47% the relevant recommendations provided to a reader. However, the performance of the CB approach is heavily
dependent on the number of books the reader has already read, and it can work even better than CF for users with a large
history. Finally, our evaluations highlight that the performances of both approaches are significantly improved if the system
integrates and leverages the information from the Anobii dataset, which allows us to include more user readings (for CF) and
richer book metadata (for CB)
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