A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Context-Based Cultural Visits

Over the last two decades, there have been tremendous advances in mobile technologies, which have increased the interest in studying and developing mobile augmented reality systems, especially in the field of Cultural Heritage. Nowadays, people rely even more on smartphones, for example, when visiting a new city to search for information about monuments and landmarks, and the visitor expects precise and tailored information to his needs. Therefore, researchers started to investigate innovative approaches for presenting and suggesting digital content related to cultural and historical places around the city, incorporating contextual information about the visitor and his needs. This document presents a novel mobile augmented reality application, NearHeritage, that was developed within the scope of the master's thesis on Electrical and Computers Engineering from the Faculty of Engineering of Porto University (FEUP), in collaboration with INESC TEC. The research carried out was focused on the importance of utilising modern technologies to assist the visitors in finding and exploring Cultural Heritage. In this way, it is provided not only the nearby points-of-interest of a city but also detailed information about each POI. The solution presented uses built-in sensors and hardware of Android devices and takes advantage of various APIs (Foursquare API, Google Maps API and IntelContextSensing) to retrieve information about the landmarks and the visitor context. Also, these are crucial hardware components for implementing the full potential of augmented reality tools to create innovative contents that increase the overall user experience. All the experiments were conducted in Porto, Portugal, and the final results showcase that the concept of a MAR application can improve the user experience in discovering and learning more about Cultural Heritage around the world, creating an interactive, enjoyable and unforgettable adventure

Implicit personalization in driving assistance: State-of-the-art and open issues

In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community</h2

Optimal Real-Time Bidding for Display Advertising

Real-Time Bidding (RTB) is revolutionising display advertising by facilitating a real-time auction for each ad impression. As they are able to use impression-level data, such as user cookies and context information, advertisers can adaptively bid for each ad impression. Therefore, it is important that an advertiser designs an effective bidding strategy which can be abstracted as a function - mapping from the information of a specific ad impression to the bid price. Exactly how this bidding function should be designed is a non-trivial problem. It is a problem which involves multiple factors, such as the campaign-specific key performance indicator (KPI), the campaign lifetime auction volume and the budget. This thesis is focused on the design of automatic solutions to this problem of creating optimised bidding strategies for RTB auctions: strategies which are optimal, that is, from the perspective of an advertiser agent - to maximise the campaign's KPI in relation to the constraints of the auction volume and the budget. The problem is mathematically formulated as a functional optimisation framework where the optimal bidding function can be derived without any functional form restriction. Beyond single-campaign bid optimisation, the proposed framework can be extended to multi-campaign cases, where a portfolio-optimisation solution of auction volume reallocation is performed to maximise the overall profit with a controlled risk. On the model learning side, an unbiased learning scheme is proposed to address the data bias problem resulting from the ad auction selection, where we derive a "bid-aware'' gradient descent algorithm to train unbiased models. Moreover, the robustness of achieving the expected KPIs in a dynamic RTB market is solved with a feedback control mechanism for bid adjustment. To support the theoretic derivations, extensive experiments are carried out based on large-scale real-world data. The proposed solutions have been deployed in three commercial RTB systems in China and the United States. The online A/B tests have demonstrated substantial improvement of the proposed solutions over strong baselines

Goal-driven Collaborative Filtering

Recommender systems aim to identify interesting items (e.g. movies, books, websites) for a given user, based on their previously expressed preferences. As recommender systems grow in popularity, a notable divergence emerges between research practices and the reality of deployed systems: when recommendation algorithms are designed, they are evaluated in a relatively static context, mainly concerned about a predefined error measure. This approach disregards the fact that a recommender system exists in an environment where there are a number of factors that the system needs to satisfy, some of these factors are dynamic and can only be tackled over time. Thus, this thesis intends to study recommender systems from a goal-oriented point of view, where we define the recommendation goals, their associated measures and build the system accordingly. We first start with the argument that a single fixed measure, which is used to evaluate the system’s performance, might not be able to capture the multidimensional quality of a recommender system. Different contexts require different performance measures. We propose a unified error minimisation framework that flexibly covers various (directional) risk preferences. We then extend this by simultaneously optimising multiple goals, i.e., not only considering the predicted preference scores (e.g. ratings) but also dealing with additional operational or resource related requirements such as the availability, profitability or usefulness of a recommended item. We demonstrate multiple objectives through another example where a number of requirements, namely, diversity, novelty and serendipity are optimised simultaneously. At the end of the thesis, we deal with time-dependent goals. To achieve complex goals such as keeping the recommender model up-to-date over time, we consider a number of external requirements. Generally, these requirements arise from the physical nature of the system, such as available computational resources or available storage space. Modelling such a system over time requires describing the system dynamics as a combination of the underlying recommender model and its users’ behaviour. We propose to solve this problem by applying the principles of Modern Control Theory to construct and maintain a stable and robust recommender system for dynamically evolving environments. The conducted experiments on real datasets demonstrate that all the proposed approaches are able to cope with multiple objectives in various settings. These approaches offer solutions to a variety of scenarios that recommender systems might face

HIGH-BANDWIDTH IDENTIFICATION AND COMPENSATION OF HYSTERETIC DYNAMICS

Ph.DDOCTOR OF PHILOSOPH

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl