Multimedia search without visual analysis: the value of linguistic and contextual information

This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features

Systematic Review on Security and Privacy Requirements in Edge Computing: State of the Art and Future Research Opportunities

Edge computing is a promising paradigm that enhances the capabilities of cloud computing. In order to continue patronizing the computing services, it is essential to conserve a good atmosphere free from all kinds of security and privacy breaches. The security and privacy issues associated with the edge computing environment have narrowed the overall acceptance of the technology as a reliable paradigm. Many researchers have reviewed security and privacy issues in edge computing, but not all have fully investigated the security and privacy requirements. Security and privacy requirements are the objectives that indicate the capabilities as well as functions a system performs in eliminating certain security and privacy vulnerabilities. The paper aims to substantially review the security and privacy requirements of the edge computing and the various technological methods employed by the techniques used in curbing the threats, with the aim of helping future researchers in identifying research opportunities. This paper investigate the current studies and highlights the following: (1) the classification of security and privacy requirements in edge computing, (2) the state of the art techniques deployed in curbing the security and privacy threats, (3) the trends of technological methods employed by the techniques, (4) the metrics used for evaluating the performance of the techniques, (5) the taxonomy of attacks affecting the edge network, and the corresponding technological trend employed in mitigating the attacks, and, (6) research opportunities for future researchers in the area of edge computing security and privacy

Deeply Smile Detection Based on Discriminative Features with Modified LeNet-5 Network

Facial expressions are caused by specific movements of the face muscles; they are regarded as a visible manifestation of a person\u27s inner thought process, internal emotional states, and intentions. A smile is a facial expression that often indicates happiness, satisfaction, or agreement. Many applications use smile detection such as automatic image capture, distance learning systems, interactive systems, video conferencing, patient monitoring, and product rating. The smile detection system is divided into two stages: feature extraction and classification. As a result, the accuracy of smile detection is dependent on both phases. In recent years, numerous researchers and scholars have identified various approaches to smile detection, however, their accuracy is still under the desired level. To this end, we propose an effective Convolutional Neural Network (CNN) architecture based on modified LeNet-5 Network (MLeNet-5) for detecting smiles in images. The proposed system generates low-level face identifiers and detect smiles using a strong binary classifier. In our experiments, the proposed MLenet-5 system used the SMILEsmilesD and (GENKI-4 K) databases in which the smile detection rate of the proposed method improves the accuracy by 2% on SMILEsmilesD database and 5% on GENKI-4 K database relative to LeNet-5-based CNN network. In addition, the proposed system decreases the number of parameters compared to LeNet-5-based CNN network and most of the existing models while maintaining the robustness and effectiveness of the results

An Open-Source Web Platform for 3D Documentation and Storytelling of Hidden Cultural Heritage

The rapid evolution of the urban landscape highlights the need to digitally document the state and historical transformations of heritage sites in densely urbanised areas through the combination of different geomatics survey approaches. Moreover, it is necessary to raise awareness of sites by developing strategies for their dissemination to a diverse audience through engaging, interactive, and accessible 3D web platforms. This work illustrates a methodology for the digital documentation and narration of a cultural heritage site through the implementation of a lightweight and replicable 3D navigation platform based on open-source technologies. Such a solution aims to be an easy-to-implement low-cost approach. The methodology is applied to the case study of the Farnese Castle in Piacenza (Italy), describing the data collection and documentation carried out with an in situ survey and illustrating how the resulting products were integrated into the web platform. The exploration functionalities of the platform and its potential for different types of audiences, from experts to users not familiar with 3D objects and geomatics products, were evaluated and documented on a ReadTheDocs website, allowing interested users to reproduce the project for other applications thanks to the template code available on GitHub

Deeply Smile Detection Based on Discriminative Features with Modified LeNet-5 Network

Facial expressions are caused by specific movements of the face muscles; they are regarded as a visible manifestation of a person\u27s inner thought process, internal emotional states, and intentions. A smile is a facial expression that often indicates happiness, satisfaction, or agreement. Many applications use smile detection such as automatic image capture, distance learning systems, interactive systems, video conferencing, patient monitoring, and product rating. The smile detection system is divided into two stages: feature extraction and classification. As a result, the accuracy of smile detection is dependent on both phases. In recent years, numerous researchers and scholars have identified various approaches to smile detection, however, their accuracy is still under the desired level. To this end, we propose an effective Convolutional Neural Network (CNN) architecture based on modified LeNet-5 Network (MLeNet-5) for detecting smiles in images. The proposed system generates low-level face identifiers and detect smiles using a strong binary classifier. In our experiments, the proposed MLenet-5 system used the SMILEsmilesD and (GENKI-4 K) databases in which the smile detection rate of the proposed method improves the accuracy by 2% on SMILEsmilesD database and 5% on GENKI-4 K database relative to LeNet-5-based CNN network. In addition, the proposed system decreases the number of parameters compared to LeNet-5-based CNN network and most of the existing models while maintaining the robustness and effectiveness of the results

A fault diagnosis framework for centrifugal pumps by scalogram-based imaging and deep learning.

Centrifugal pumps are the most vital part of any process industry. A fault in centrifugal pump can affect imperative industrial processes. To ensure reliable operation of the centrifugal pump, this paper proposes a novel automated health state diagnosis framework for centrifugal pump that combines a signal to time-frequency imaging technique and an Adaptive Deep Convolution Neural Network model (ADCNN). First, the vibration signals corresponding to different health conditions of the centrifugal pump are acquired. Vibration signals obtained from the centrifugal pump carry a great deal of information and generally, statistical features are extracted from the vibration signals to retain meaningful fault information. However, these features are either insensitive to weak incipient faults or unsuitable for tracking severe faults, thus, decreasing the fault classification accuracy. To tackle this problem, a signal to time-frequency imaging technique is applied to the pump vibration signals. For this purpose, Continuous Wavelet Transform (CWT) is applied to decompose the vibration signals over different time-frequency scales and extract the pump fault information in both the time and frequency domains. The CWT scales form two-dimensional time-frequency images commonly referred to as scalograms. The CWT scalograms are then converted into grayscale images (SGI). Over the past few decades, CNN models have been established as an effective practice to process images for classification and pattern recognition. Consequently, the extracted CWTSGIs are finally provided as inputs to the proposed ADCNN architecture to achieve feature extraction and classification for centrifugal pump faults. The performance of the proposed diagnostic framework (CWTSGI + ADCNN) is validated with a vibration dataset collected from a testbed specifically designed for centrifugal pump diagnosis. The experimental results suggest that the proposed technique based on CWTSGI and ADCNN outperformed existing methods with an average performance improvement of 4.7 - 15.6%

Ontwerp en evaluatie van content distributie netwerken voor multimediale streaming diensten.

Traditionele Internetgebaseerde diensten voor het verspreiden van bestanden, zoals Web browsen en het versturen van e-mails, worden aangeboden via één centrale server. Meer recente netwerkdiensten zoals interactieve digitale televisie of video-op-aanvraag vereisen echter hoge kwaliteitsgaranties (QoS), zoals een lage en constante netwerkvertraging, en verbruiken een aanzienlijke hoeveelheid bandbreedte op het netwerk. Architecturen met één centrale server kunnen deze garanties moeilijk bieden en voldoen daarom niet meer aan de hoge eisen van de volgende generatie multimediatoepassingen. In dit onderzoek worden daarom nieuwe netwerkarchitecturen bestudeerd, die een dergelijke dienstkwaliteit kunnen ondersteunen. Zowel peer-to-peer mechanismes, zoals bij het uitwisselen van muziekbestanden tussen eindgebruikers, als servergebaseerde oplossingen, zoals gedistribueerde caches en content distributie netwerken (CDN's), komen aan bod. Afhankelijk van de bestudeerde dienst en de gebruikte netwerktechnologieën en -architectuur, worden gecentraliseerde algoritmen voor netwerkontwerp voorgesteld. Deze algoritmen optimaliseren de plaatsing van de servers of netwerkcaches en bepalen de nodige capaciteit van de servers en netwerklinks. De dynamische plaatsing van de aangeboden bestanden in de verschillende netwerkelementen wordt aangepast aan de heersende staat van het netwerk en aan de variërende aanvraagpatronen van de eindgebruikers. Serverselectie, herroutering van aanvragen en het verspreiden van de belasting over het hele netwerk komen hierbij ook aan bod

Context-Aware Data-Driven Intelligent Framework for Fog Infrastructures in Internet of Vehicles

Internet of Vehicles (IoV) is the evolution of VANET (Vehicular Ad-hoc Networks) and Intelligent Transportation Systems (ITS) focused on reaping the benefits of data generated by various sensors within these networks. The IoV is further empowered by a centralized cloud and distributed fog-based infrastructure. The myriad amounts of data generated by the vehicles and the environment have the potential to enable diverse services. These services can benefit from both variety and velocity of the generated data. This paper focuses on the data at the edge nodes to enable fog-based services that can be consumed by various IoV safety and non-safety applications. The paper emphasizes the challenges involved in offering the context-aware services in a IoV environment. In order to overcome these challenges, the paper proposes a data analytics framework for fog infrastructures at the fog layer of traditional IoV architecture that offers context-aware real time, near real-time and batch services at the edge of a network. Finall