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11th International conference on Interactive Mobile Communication Technologies and Learning (IMCL)

作者： Vargic, Radoslav Medvecky, Martin Londak, Juraj Podhradsky, Pavol Slovak Univ Technol Bratislava Ilkovicova 3 Bratislava 81219 Slovakia

ISBN: (纸本)9783319751757;9783319751740

In this paper, we present current state and aims of our ongoing research aimed at advanced interactive multimedia and mulsemedia delivery in 5G networks. We summarize the underlying and necessary properties of 5G networks, architecture of SDN/NFV and their usage in the multimedia and mulsemedia delivery. Advanced topics as content adaptation and user identification are discussed. Preliminary results regarding algorithms for content adaptation are presented. We show the advance of usage of AR/VR headsets as effective terminal for immersive application for this kind of delivery and discuss the advantages and disadvantages. We propose corresponding system architecture for advanced interactive multimedia delivery in 5G networks.

关键词： Interactive multimedia 5G networks content adaptation

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A semantic indexing approach of multimedia documents content based partial transcription 2

A semantic indexing approach of multimedia documents content...

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2nd International conference on Natural Language and Speech Processing (ICNLSP)

作者： Bendib, Issam Laouar, Mohammed Ridda Univ Larbi Tebessi Tebessa LAMIS Lab Route Canstantine Tebessa 12002 Algeria

ISBN: (纸本)9781538645437

The complexity of searching and indexing spoken document retrieval depends closely on content structure and access strategies. However, Spoken Document Retrieval (SDR) approaches based on the transcription of content by LVCSR systems must treat the impact of recognition errors on the performance of the information retrieval system. Currently, several multimedia resources (spoken document) are unexploited, because the errors generated in the transcription process, decrease the performance of the systems of searches for spoken documents. Our contribution in this paper is the proposition a novel approach semantic based indexing content of multimedia documents by using the results of a partial automatic transcription to ride out research and indexing problems on these resources. In this context, the main hypothesis is developed around the following question: is it possible to identify and retrieve spoken document by using a semantic content indexing system based on his partial transcription? Also, does the partial content transcription of a spoken document is sufficient for his indexing? First, we are interested to look for a linguistic syntactic representation of the whole of the content spoken document. Then, it will be used to define the most discriminating indexing terms for his content. Nevertheless, in our approach, we have an assumption that the use of segments of a spoken document with an efficient semantic enrichment process instead of the whole spoken document content is sufficient. Also, with this strategy, we can resolve OOV problems, recognition errors and technical terms. Finally, in validation and experimentation phase, we tested the different modules proposed on the TED-LIUM corpora. The results obtained are interesting and encourage us to lead off our future perspectives.

关键词： Semantic content Indexing Spoken Term detection LVCSR Keyword Spotting Spoken Document WordNet Ontology similarity measures

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Recommender systems based on detection community in academic social network

Recommender systems based on detection community in academic...

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“Organization of Knowledge and Advanced Technologies” (OCTA), International Multi-conference on:

作者： Smail Boussaadi Hassina Aliane Ouahabi Abdeldjalil Dihia Houari Malika Djoumagh Laboratory LIMPAF University of Bouira Algeria Information Sciences R&D Laboratory Natural Language Processing and Digital Content Team. Cerist Algiers Algeria University of Bouira LIMPAF Bouira Algeria University of Bouira Algeria

ISBN: (数字)9781728164038

ISBN: (纸本)9781728164045

The speed with which new scientific articles are published and shared on academic social networks generated a situation of cognitive overload and the targeted access to the relevant information represents a major challenge for researchers. In this context, we propose a scientific article recommendation approach based on the discovery of thematic community structures, it focuses on the topological structure of the network combined with the analysis of the content of the social object (scientific article), a strategy that aims to mitigate the cold start problems and sparcity data in scoring matrix. A key element of our approach is the modeling of the researcher's thematic centers of interest derived from his corpus (a set of articles that interested him). In this perspective we use the technique of semantic exploration and extraction of latent topics in document corpora, LDA(Latent DirichletAllocation), an unsupervised learning method which offers the best solution of scalability problemcompared to other techniques of topic modeling. this technique allows us to build a profile model in the form of vectors in which the components are the probabilistic distributions on topics that reflect the interests of the researcher. The profile models thus constructed will be grouped into thematic clusters based on dominant topics using the fuzzy clustering algorithm, since the same topic can be treated in different scientific fields. Will follow a step of detection of community structures in thematic clusters to identify significant communities, the aim of this step is to project the recommendation process in a small space allowing better performance by reducing the computation time and the storage space for researcher/article data. The preliminary results of the experience of our approach on a population of 13 researchers and 60 articles shows that the articles generated by the recommendation process are very relevant to the target researcher or his community.

关键词： Social network services Filtering Collaboration Probabilistic logic Clustering algorithms Data models Semantics

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Optimal k-Nearest-Neighbor Query Processing via Multiple Lower Bound Approximations

Optimal k-Nearest-Neighbor Query Processing via Multiple Low...

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IEEE International conference on Big Data (Big Data)

作者： Beecks, Christian Berrendorf, Max Univ Munster Munster Germany Ludwig Maximilians Univ Munchen Munich Germany

ISBN: (纸本)9781538650356

Given a very large multimedia database, how to process k-nearest-neighbor queries efficiently? While the sequential scan is one of the most obvious solutions for small-to-moderate multimedia databases, it becomes practically infeasible when the database size grows. Concomitant with the volume and velocity of data, multimedia databases are frequently endowed with a complex distance-based similarity model that supports content-based data access in an adjustable and adaptive manner. Typical for many state-of-the-art distance-based similarity models is an at least quadratic computation time complexity for a single distance evaluation between two multimedia objects. Thus the search for the most query-like multimedia objects is still one of the major challenges. In this paper, we address the problem of optimal k-nearest-neighbor query processing via multiple lower bound approximations in very large multimedia databases. To this end, we propose the concepts of filter-optimality and refinement-optimality and present the Cascading Multi-Step Algorithm and the Interleaved Multi-Step Algorithm for fast query processing. Besides the algorithms' properties, we study their query processing performance with respect to the number of CPU and I/O operations on large-scale benchmark multimedia databases. Our performance analysis shows how to process k-nearest-neighbor queries in multimedia databases efficiently and provides a guide for further research.

关键词： k-Nearest-Neighbor Query Query Processing Lower Bound Approximations multimedia Databases

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content-Aware Cognitive Interference Control for Urban IoT systems

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2018年第3期4卷 500-512页

作者： Baidya, Sabur Levorato, Marco Univ Calif Irvine Donald Bren Sch Informat & Comp Sci Irvine CA 92617 USA

A novel cognitive interference control framework for heterogeneous local access networks supporting computing and data processing in urban Internet of Things (IoT) systems is presented. The notion of cognitive content-aware interference control is introduced, where the transmission pattern of cognitive nodes is dynamically adapted to the state of "protected" IoT data streams. The state describes the performance degradation that interference would cause to algorithms processing the data if the cognitive nodes chose to transmit in the corresponding time period. The framework is instantiated for a case-study scenario, where device-to-device (D2D) and long-term evolution communications coexist on the same channel resource. A city-monitoring application is considered, where the state captures the type of frames within a multimedia video stream processed at the edge of the network to detect and track objects. Numerical results show that the proposed cognitive transmission strategy enables a significant throughput increase of local D2D communications for a target accuracy of the monitoring application.

关键词： Device-to-device communications LTE urban IoT video streaming spectrum sharing

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17th IFIP WG 6.2 International conference on Wired/Wireless Internet Communications, WWIC 2019

17th IFIP WG 6.2 International Conference on Wired/Wireless ...

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17th IFIP WG 6.2 International conference on Wired/Wireless Internet Communications, WWIC 2019

ISBN: (纸本)9783030305222

The proceedings contain 20 papers. The special focus in this conference is on Wired/Wireless Internet Communications. The topics include: Performance of mmwave-based mesh networks in indoor environments with dynamic blockage;opportunistic D2D-aided uplink communications in 5G and beyond networks;ECN-enhanced codel AQM;on the significance of layer-3 traffic forwarding;delivering multicast content through secure D2D communications in the internet of things;Design and implementation of integrated ICN and CDN as a video streaming service;improving video delivery with fourier analysis of traffic in multi-access edge computing;analysis of distributed real-time control systems with shared network infrastructures;the effect of hardware/software features on the performance of an open–source network emulator;Adaptive guard time for energy-efficient IEEE 802.15.4 TSCH networks;Energy and quality aware multi-UAV flight path design through Q-learning algorithms;Assessment and hardening of IOT development boards;IEEE 802.11 latency modeling with non-IEEE 802.11 interfering source;ChoKIFA: A new detection and mitigation approach against interest flooding attacks in NDN;application-level traceroute: Adopting mimetic mechanisms to increase discovery capabilities;A NAT based seamless handover for software defined enterprise WLANs;proportional fair information freshness under jamming;optimal placement of user plane functions in 5G networks.

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multimedia content Popularity: Learning and Recommending a Prediction Method

Multimedia Content Popularity: Learning and Recommending a P...

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2018 IEEE Global Communications conference, GLOBECOM 2018

作者： Nguyen-Thanh, Nhan Marinca, Dana Khawam, Kinda Martin, Steven Boukhatem, Lila LRI CNRS - Univ. of Paris Saclay Univ. of Paris-Sud France David Lab. Univ. of Paris Saclay UVSQ France

ISBN: (纸本)9781538647288;9781538647271

In 5G networks, Mobile Edge Computing (MEC) has been proposed to enable computation and storage capabilities at the edge of Radio access Networks. Proactive content caching in MEC is crucial to guarantee users' Quality of Experience thanks to the reduction of traffic latency. Predicting content popularity plays a key role in the effectiveness of proactive caching. In this paper, we propose a generic and flexible recommendation framework which allows recommending suitable learning and prediction algorithms among available ones, in order to predict content popularity. The investigated algorithms are categorized into two main classes: tree-based regressors and recurrent neural networks. Through the study case of YouTube video solicitation profiles, our proposed method, called Imputation-Boosted Collaborative-Filtering based Recommending Prediction Method (IBCF-RPM) shows its effectiveness in the prediction of content popularity for various popularity profiles. By running only 30% of the prediction algorithms, randomly chosen, on a given content profile, the proposed recommending method is able to estimate the accuracy of the other predictors and recommend a well-suited predictor for content popularity. © 2018 IEEE.

关键词： Collaborative filtering

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The Comparison Between Geo-magnetism and WiFi for Indoor Positioning System for Public Places

The Comparison Between Geo-magnetism and WiFi for Indoor Pos...

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IEEE International conference on Robotics, Biomimetics, and Intelligent Computational systems (Robionetics)

作者： Haryanto, Dhanny Kurniawan Karyono, Kanisius Hutagalung, Samuel Univ Multimedia Nusantara Comp Engn Tangerang Indonesia Univ Multimedia Nusantara Elect Engn Tangerang Indonesia

ISBN: (纸本)9781538660867

The Indoor Positioning systems (IPS) can be built based on several methods. This work compares two IPS methods which are Geo-magnetism and WiFi. This work compares the accuracy of the methods in public places. The Geo-magnetism method uses Earth's Magnetic Fingerprint which is generated from HCM8533L sensor. The WiFi method uses the Received Signal Strength (RSS) from NodeMCU device. The position references are using existing working access points around the location. All of the noises and disturbances are real noises generated by the traffic. The algorithms for magnetic fingerprint are K-Nearest Neighbors (KNN) and first position. The WiFi method uses multilateration algorithm. Geo-magnetism IPS uses 36 fingerprint data every 1.44m(2). This approach results in 4.08m deviation with first position algorithm and 4.09m deviation with KNN. The WiFi RSS IPS uses multiple n values and multiple brand of access points. The n value of 1.8 results in 10.7m deviation and the value of 2 give 10.92m deviation. Based on these real world environment test, the Geo-magnetism IPS has a higher position accuracy than WiFi RSS IPS.

关键词： Indoor Positioning System Geo-magnetic WiFi Received Signal Strength K-Nearest Neighbors Multilateration

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Attentive Interactive Convolutional Matching for Community Question Answering in Social multimedia 18

Attentive Interactive Convolutional Matching for Community Q...

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26th ACM multimedia conference (MM)

作者： Hu, Jun Qian, Shengsheng Fang, Quan Xu, Changsheng Hefei Univ Technol Hefei Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781450356657

Nowadays, community-based question answering (CQA) services have accumulated millions of users to share valuable knowledge. An essential function in CQA tasks is the accurate matching of answers w.r.t given questions. Existing methods usually ignore the redundant, heterogeneous, and multi-modal properties of CQA systems. In this paper, we propose a multi-modal attentive interactive convolutional matching method (MMAICM) to model the multi-modal content and social context jointly for questions and answers in a unified framework for CQA retrieval, which explores the redundant, heterogeneous, and multi-modal properties of CQA systems jointly. A well-designed attention mechanism is proposed to focus on useful word-pair interactions and neglect meaningless and noisy word-pair interactions. Moreover, a multi-modal interaction matrix method and a novel meta-path based network representation approach are proposed to consider the multi-modal content and social context, respectively. The attentive interactive convolutional matching network is proposed to infer the relevance between questions and answers, which can capture both the lexical and the sequential information of the contents. Experiment results on two real-world datasets demonstrate the superior performance of MMAICM compared with other state-of-the-art algorithms.

关键词： Question-answering Attention Multi-Modal Social multimedia

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Improving Efficency of Mathematical Functions in Image Processing by Loop Fusion 5

Improving Efficency of Mathematical Functions in Image Proce...

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5th International conference on Electrical and Electronics Engineering (ICEEE)

作者： Bigdello, Shapour Joudi Bigdello, Manoochehr Joodi Mohammadzadeh, Hekmat Islamic Azad Univ Dept Comp Engn Parsabad Moghan Branch Parsabad Iran Guilan Univ Software Engn Rasht Iran Gazi Univ Comp Engn Ankara Turkey

ISBN: (纸本)9781538663929

Compiler optimization, reducing data load from memory and not using off-line data are issues that effective in enhancing energy retention of multimedia, embedded and general-purpose systems. In these systems most programs are comprised from loops, and this shows the importance of compiler optimization. In this paper we have carried out loop reduction using calculation and fusing two loops and transforming them to one loop that caused data locality increase and reusability, and decreased loading data from memory, thus increase speedup. We have applied this technique on several feature extraction algorithms, such as mean and variance and some similar algorithms like covariance and third moment that are used in content-based image retrieval systems. This technique approximately increases efficiency of two loops method twice. In addition, we have calculated the combination of these three functions. In this way, we have observed that the combined version also increases the efficiency twice.

关键词： compilers optimization embedded systems locality reusability loop fusion

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