Optimization of information retrieval for cross media contents in a best practice network

Bellini, Pierfrancesco; Cenni, Daniele; Nesi, Paolo

doi:10.1007/s13735-014-0058-8

Cited by 6 publications

(10 citation statements)

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“…The learning is carried out for the multiple linear transformation U and V of the input, which maps the same output, instead of the single learn transformation used in the previous methods. Let U ∈ R d x * x , V ∈ R d y * c be the distance parameter matrices for X ∈ R d x * m and Y ∈ R d y * n respectively, here d x ,d y are the dimensions of the original media types, c is the dimension of the mapped space, m and n are the count of media objects of the media X and media Y respectively [22], [23]. The heterogeneous distance measure technique is given as follows:…”

Section: Heterogeneous Metric Learningmentioning

confidence: 99%

Joint graph regularization based semantic analysis for cross-media retrieval: a systematic review

Ayyavaraiah¹,

Venkateswarlu²

2018

IJET

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The large number of heterogeneous data are rapidly increasing in the internet and most data consist of audio, video, text and images. The searching of the required data from the large database is difficult and time taking process. The single media retrieval is used to get the needed data from the large dataset and it has the drawback, it can only retrieve the single media only. If the query is given as the text and acquired result are present in text. The users demand the cross-media retrieval for their queries and it is very consistent in providing the result. This helps the users to get more information regarding to their queries. Finding the similarities between the heterogeneous data is very complex. Many research is done on the cross-media retrieval with different methods and provide the different result. The aim is to analysis the different cross-media retrieval with the joint graph regularization (JGR) to understand the various technique. The most of researches are using the parameter of MAP, precision and recall for their research.

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Section: Heterogeneous Metric Learningmentioning

confidence: 99%

Joint graph regularization based semantic analysis for cross-media retrieval: a systematic review

Ayyavaraiah¹,

Venkateswarlu²

2018

IJET

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“…Well known solutions and techniques produce suggestions and recommendations guessing "what the user would like", by similarity [9], by indexing [5], by filtering, etc., may be according to the advertiser strategies The engagement should be more capable to understand the story of the user behavior, and predicting possible new future attitudes [10] according to the city strategies. The context of smart city is much more complex than the simple media advertising since spatial reasoning is added to the classical temporal reasoning [8].…”

Section: A Related Workmentioning

confidence: 99%

“…The solution is grounded on assessing user behavior, deriving contextual information, computing conditions of users, detecting city users changes, providing engagements, collecting feedbacks and follow ups to perform analysis. The whole approach has been developed and enforced into Sii-Mobility Smarty City National project on mobility and transport of the MIUR (Italian Ministry of University and Research) (http://www.sii-mobility.org), with its mobile App and smart city infrastructure based on the Km4City ontological model (http://www.km4city.org) [5] and Smart City API [6].…”

Section: B Paper Organizationmentioning

confidence: 99%

“…The defined Strategies are automatically translated in Conditions to be assessed/detected and Actions propagated to the final City Users based on User Profile Evolution directly observed from the Mobile App, from User Behavior Data Analytics and from the city context which can be directly accessible on Km4City [5]. The engagement rules are defined using the Drools Workbench, the web User Interface of the Drools tools-suite.…”

Section: Figure 1 -Sii-mobility User Engagement Architecturementioning

confidence: 99%

“…The further step consists in providing smart city Application Programming Interface (API) to access at single data set or data aggregations. For example: CitySDK makes transformations on each dataset in order to obtain and manage uniform data [2]; Transport.API provides aggregated open data in the UK about transport information [3]; Navitia.io is an open source project exploiting OpenDataSoft for open data aggregation in France [4]; Km4City Smart City API provides semantically integrated information for app and services [5] with its API [6]. The successive step in Smart City implementation consists in deeply understand the city usage and thus the user behavior [7], [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

User Engagement Engine for Smart City Strategies

Badii¹,

Bellini²,

Cenni³

et al. 2017

2017 IEEE International Conference on Smart Computing (SMARTCOMP)

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Abstract-The new challenges in the smart city context are mainly related to the stimulation of the city users towards taking more sustainable behaviors, in mobility and energy. The state of the art in this case is mainly focused on classical smart city solution for informing the city users and or for engaging them with specific wired rules toward virtuous models. And not using flexible languages and predictive models, pushing them towards a larger range of virtuous habits. On this regards, the main problems are the computation of user behavior via data analytic (semantic computing, machine learning), as well as the formalization of strategies via simple and well formalized language for producing engagements to the city users, which can be understood by city operators. In this paper, a solution for city users engagement is studied and implemented for Sii-Mobility Smart city national project in Italy has been presented. The solution has been implemented thanks to the exploitation of Km4City model and semantic computing. The paper also presents the validation of results about the effective usage of the solution by providing some statistical evidence about the efficient assessment of user behavior and of engagement rules acceptance rate.

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Classification of users’ transportation modalities from mobiles in real operating conditions

Badii

Difino

Nesi

et al. 2021

Multimed Tools Appl

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The modern mobile phones and the complete digitalization of the public and private transport networks have allowed to access useful information to understand the user’s mean of transportation. This enables a plethora of old and new applications in the fields of sustainable mobility, smart transportation, assistance, and e-health. The precise understanding of the travel means is at the basis of the development of a large range of applications. In this paper, a number of metrics has been identified to understand whether an individual on the move is stationary, walking, on a motorized private or public transport, with the aim of delivering to city users personalized assistance messages for: sustainable mobility, health, and/or for a better and enjoyable life, etc. Differently from the state-of-the-art solutions, the proposed approach has been designed to provide results, and thus collect metrics, in real operating conditions (imposed on the mobile phones as: a range of different mobile phone kinds, operating system constraints managing Applications, active battery consumption manager, etc.). The paper reports the whole experimentations and results. The solution has been developed in the context of Sii-Mobility Km4City Research Project infrastructure and tools, performed with the collaboration of public transport operators, and GDPR compliant. The same solution has been used in Snap4City mobile Apps with experiments performed in Antwerp and Helsinki.

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Optimization of information retrieval for cross media contents in a best practice network

Cited by 6 publications

References 50 publications

Joint graph regularization based semantic analysis for cross-media retrieval: a systematic review

Joint graph regularization based semantic analysis for cross-media retrieval: a systematic review

User Engagement Engine for Smart City Strategies

Classification of users’ transportation modalities from mobiles in real operating conditions

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