5 research outputs found

    Efektivní algoritmy pro problémy se sociálním vlivem u velkých sítí

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    In recent years, the dizzying explosion of data and information results from social networks with millions to billions of users, such as Facebook, YouTube, Twitter, and LinkedIn. Users can use online social networks (OSNs) to quickly trade information, communicate with other users, and keep their information up-to-date. The challenge of spreading information on social networks that arises in practice requires effective information management solutions, such as disseminating useful information, maximizing the influence of information transmission, and preventing disinformation, rumors, and viruses from being disseminated. Motivated by the above issues, we investigate the problem of information diffusion on OSNs. We study this problem based on two models, Independent Cascade (IC) and Linear Threshold (LT), and classical Influence Maximization (IM) in online social networks. In addition, we investigate various aspects of IM problems, such as budget variations, topics of interest, multiple competitors, and others. Moreover, we also investigate and apply the theory of combinatorial optimization problems to solve one of the current concerns in social networks, maximizing the influence on the groups and topics in social networks. In general, the main goals of the Ph.D thesis proposal are as follows. 1. We investigate the Multi-Threshold problem for IM, which is a variant of the IM problem with threshold constraints. We propose an efficient algorithm that IM for multiple thresholds in the social network. In particular, we develop a novel algorithmic framework that can use the solution to a smaller threshold to find that of larger ones. 2. We study the Group Influence Maximization problem and introduce an efficient group influence maximization algorithm with more advantages than each node’s influence in networks, using a novel sampling technique to estimate the epsilon group function. We also devised an approximation algorithm to estimate multiple candidate solutions with theoretical guarantee. 3. We investigate an approach for Influence Maximization problem with k-topic under constraints in social network. More specifically, we also study a streaming algorithm that combines an optimization algorithm to improve the approximation algorithm and theoretical guarantee in terms of solution quality and running time.V posledních letech je závratná exploze dat a informací výsledkem sociálních sítí s miliony až miliardami uživatelů, jako jsou Facebook, YouTube, Twitter a LinkedIn. Uživatelé mohou využívat online sociální sítě (OSNs) k rychlému obchodování s informacemi, komunikaci s ostatními uživateli a udržování jejich informací v aktuálním stavu. Výzva šíření informací na sociálních sítích, která se v praxi objevuje, vyžaduje efektivní řešení správy informací, jako je šíření užitečných informací, maximalizace vlivu přenosu informací a zabránění šíření dezinformací, fám a virů. Motivováni výše uvedenými problémy zkoumáme problém šíření informací na OSN. Tento problém studujeme na základě dvou modelů, Independent Cascade (IC) a Linear Threshold (LT) a klasické Influence Maximization (IM) v online sociálních sítích. Kromě toho zkoumáme různé aspekty problémů s rychlým zasíláním zpráv, jako jsou změny rozpočtu, témata zájmu, více konkurentů a další. Kromě toho také zkoumáme a aplikujeme teorii kombinatorických optimalizačních problémů k vyřešení jednoho ze současných problémů v sociálních sítích, maximalizujeme vliv na skupiny a témata v sociálních sítích. Obecně lze říci, že hlavní cíle Ph.D. návrh diplomové práce je následující. 1. Zkoumáme problém Multi-Threshold pro IM, což je varianta problému IM s prahovými omezeními. Navrhujeme účinný algoritmus, který IM pro více prahů v sociální síti. Zejména vyvíjíme nový algoritmický rámec, který může použít řešení pro menší práh k nalezení prahu většího. 2. Studujeme problém maximalizace vlivu skupiny a zavádíme účinný algoritmus maxima- lizace vlivu skupiny s více výhodami, než je vliv každého uzlu v sítích, pomocí nové vzorkovací techniky k odhadu funkce skupiny epsilon. Navrhujeme také aproximační algoritmus pro odhad více kandidátních řešení s teoretickou zárukou. 3. Zkoumáme přístup pro maximalizaci vlivu s k-téma pod omezeními v rozsáhlé síti. Konkrétněji budeme studovat novou metriku, která kombinuje optimalizační algoritmus pro zlepšení aproximačního algoritmu z hlediska kvality řešení a doby běhu na základě kliky a komunity v komplexních sítích.460 - Katedra informatikyvyhově

    A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

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    A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques

    Data science in economics: Comprehensive review of advanced machine learning and deep learning methods

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    © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models

    Low Cost Eye Tracking : The Current Panorama

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    Altres ajuts: Consolider 2010 MIPRCV, Universitat Autonoma de Barcelona i Google Faculty AwardDespite the availability of accurate, commercial gaze tracker devices working with infrared (IR) technology, visible light gaze tracking constitutes an interesting alternative by allowing scalability and removing hardware requirements. Over the last years, this field has seen examples of research showing performance comparable to the IR alternatives. In this work, we survey the previous work on remote, visible light gaze trackers and analyze the explored techniques from various perspectives such as calibration strategies, head pose invariance, and gaze estimation techniques. We also provide information on related aspects of research such as public datasets to test against, open source projects to build upon, and gaze tracking services to directly use in applications. With all this information, we aim to provide the contemporary and future researchers with a map detailing previously explored ideas and the required tools

    Low Cost Eye Tracking: The Current Panorama

    Get PDF
    Despite the availability of accurate, commercial gaze tracker devices working with infrared (IR) technology, visible light gaze tracking constitutes an interesting alternative by allowing scalability and removing hardware requirements. Over the last years, this field has seen examples of research showing performance comparable to the IR alternatives. In this work, we survey the previous work on remote, visible light gaze trackers and analyze the explored techniques from various perspectives such as calibration strategies, head pose invariance, and gaze estimation techniques. We also provide information on related aspects of research such as public datasets to test against, open source projects to build upon, and gaze tracking services to directly use in applications. With all this information, we aim to provide the contemporary and future researchers with a map detailing previously explored ideas and the required tools
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