Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1941

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    Quality-Aware Wi-Fi Offload: Analysis, Design and Integration Perspectives
    (2018) Mester, Yavuz; Buyruk, Hasan; Zeydan, Engin; Tan, A. Serdar
    The rapid spread of smart wireless devices and expansion of mobile data traffic have increased the interest for efficient traffic offloading techniques in next-generation communication technologies. Wi-Fi offloading uses ubiquitous Wi-Fi technology in order to satisfy the ever increasing demand for mobile bandwidth and therefore is an appropriate methodology for mobile operators. As a matter of fact, design and integration of an offloading technology inside mobile network operators' infrastructures is a challenging task due to convergence issues between the The 3rd Generation Partnership Project (3GPP) and non-3GPP networks. Therefore, a connectivity management platform is a key element for integrated heterogeneous mobile network operators in order to enable smart and effective offloading. In this paper, analysis, design and integration of a connectivity management platform that uses a Multiple Attribute Decision Making (MADM) algorithm for efficient Wi-Fi Offloading in heterogeneous wireless networks is presented. In order to enhance the end-user's quality-of-experience (QoE), we have especially concentrated on the benefits that can be achieved by exploiting the presence of integrated service provider platform. Hence, the proposed platform can collect several User Equipment (UE) and network-based attributes via infrastructure and client Application Programming Interfaces (APIs) and decides on the best network access technology (i.e. 3GPP and non-3GPP) to connect to for requested users. We have also proposed multi-user extensions of the MADM algorithms for offloading. Through simulations and experiments, we provide details of the comparisons of the proposed algorithms as well as the sensitivity analysis of the MADM algorithm through an experimental set-up.
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    Citation - WoS: 1
    Citation - Scopus: 4
    Turkish Data-To Generation Using Sequence-To Neural Networks
    (Assoc Computing Machinery, 2023) Demir, Şeniz
    End-to-end data-driven approaches lead to rapid development of language generation and dialogue systems. Despite the need for large amounts of well-organized data, these approaches jointly learn multiple components of the traditional generation pipeline without requiring costly human intervention. End-to-end approaches also enable the use of loosely aligned parallel datasets in system development by relaxing the degree of semantic correspondences between training data representations and text spans. However, their potential in Turkish language generation has not yet been fully exploited. In this work, we apply sequenceto-sequence (Seq2Seq) neural models to Turkish data-to-text generation where the input data given in the form of a meaning representation is verbalized. We explore encoder-decoder architectures with attention mechanism in unidirectional, bidirectional, and stacked recurrent neural network (RNN) models. Our models generate one-sentence biographies and dining venue descriptions using a crowdsourced dataset where all field value pairs that appear in meaning representations are fully captured in reference sentences. To support this work, we also explore the performances of our models on a more challenging dataset, where the content of a meaning representation is too large to fit into a single sentence, and hence content selection and surface realization need to be learned jointly. This dataset is retrieved by coupling introductory sentences of person-related Turkish Wikipedia articles with their contained infobox tables. Our empirical experiments on both datasets demonstrate that Seq2Seq models are capable of generating coherent and fluent biographies and venue descriptions from field value pairs. We argue that the wealth of knowledge residing in our datasets and the insights obtained fromthis study hold the potential to give rise to the development of new end-to-end generation approaches for Turkish and other morphologically rich languages.