In this paper, we propose an ontology schema towards linking semantified Twitter social analytics with the Linked Open Data cloud. The ontology is deployed over a publicly available service that measures how influenti...
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ISBN:
(纸本)9781467383967
In this paper, we propose an ontology schema towards linking semantified Twitter social analytics with the Linked Open Data cloud. The ontology is deployed over a publicly available service that measures how influential a Twitter account is by combining its social activity in Twitter. According to our knowledge this is the first work that combines social analytics with the Linked Open Data (LOD) cloud.
Hybrid acoustic-wave-lumped-element resonator (AWLR)-based bandpass filters (BPFs) with reconfigurable bandwidth (BW) and tunable out-of-band isolation (IS) are reported in this paper. They are based on a new BPF arch...
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ISBN:
(纸本)9781509006991
Hybrid acoustic-wave-lumped-element resonator (AWLR)-based bandpass filters (BPFs) with reconfigurable bandwidth (BW) and tunable out-of-band isolation (IS) are reported in this paper. They are based on a new BPF architecture in which the AWLRs are in-parallel cascaded to an all-pass network through variable lumped-element (LE) impedance inverters. In this manner, passbands with arbitrarily-large BW - i.e., no longer limited by the electromechanical coupling coefficient (k_t~2) of its constituent acoustic-wave resonators (AWRs)-can be created and continuously controlled whilst preserving the high-quality-factor (Q: order of 10,000) characteristics of the AWR. Furthermore, tuning of the out-of-band IS is obtained by adjusting the location of the AWLRs transmission zeros (TZs: 2N for an N-pole BPF) through variable LE capacitors. The operating principles of the devised AWLR-based tunable BPF concept are experimentally validated through a three-pole/six-TZ prototype at 418 MHz made up of commercially-available surface acoustic wave (SAW) resonators and LEs. It exhibits tunable BW between 0.16-0.49 MHz (0.5-1.5k_t~2), minimum in-band insertion loss (IL) between 3.3-1.2 dB (effective Q >10,000), and out-of-band IS reconfigurability.
We investigate video hyperlinking based on speech transcripts, leveraging a hierarchical topical structure to address two essential aspects of hyperlinking, namely, serendipity control and link justification. We propo...
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Fuzzy Description Logics (DLs) are a formalism for the representation of structured knowledge affected by imprecision or vagueness. A key factor in the practical success of fuzzy DLs is the availability of highly impl...
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Fuzzy Description Logics (DLs) are a formalism for the representation of structured knowledge affected by imprecision or vagueness. A key factor in the practical success of fuzzy DLs is the availability of highly implemented reasoners. This paper studies two optimisation techniques (ABox partitioning based on individual groups and optimisation problem partitioning) in the setting of the fuzzy ontology reasoner fuzzy DL. We study the applicability of these techniques in expressive fuzzy DL languages, proposing a new strategy, and perform an empirical evaluation proving that they are not helpful in practice so far.
This paper focuses on a new family of narrow-band bandpass filters, along with its coupling-matrix-based design approach which feature quasi-elliptic frequency response, effective quality factors (Qs eff ) of the orde...
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This paper focuses on a new family of narrow-band bandpass filters, along with its coupling-matrix-based design approach which feature quasi-elliptic frequency response, effective quality factors (Qs eff ) of the order of 1,000 and small form factors. Hybrid acoustic-wave-lumped-element resonator (AWLR) architectures are proposed as fundamental elements of these filters leading to low-loss passbands with fractional bandwidth (FBW) that is much broader (3.8-7.5 times) than in all acoustic wave (AW) filters and Qs eff that are 10-20 times larger than in traditional lumped-element filters. Experimental prototypes based on commercially-available surface acoustic wave (SAW) resonators were built and tested at 418 MHz. Measured Qs eff between 750-1550 and bandwidths ranging from 0.65 to 0.94 MHz (i.e., 2-3 times the electromechanical coupling coefficient of the AW resonator) are presented.
We study in this paper the visualization of large multidimensional datasets with a focus on Open Data. Starting from our early work in which we defined a visualization based on points of interest, we improve this meth...
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We study in this paper the visualization of large multidimensional datasets with a focus on Open Data. Starting from our early work in which we defined a visualization based on points of interest, we improve this method in several ways with the aim of dealing with larger datasets and especially Open datasets. We propose the parallelization, using CPU and GPU, of the most costly steps of our method, like the computation of the data layout. We improve the visualization with a density rendering so as to keep the display informative for large datasets and for Open Data. We propose a layered visualization with interactions that can support several users tasks such as data filtering and labeling. We show that, even with common hardware, the performances of our approach are such that any user graphical queries can be processed in a few seconds. We detail how we were able to visualize and explore a collection of 300,000 Open datasets from the French Open Data web site. With the resulting visualization, we were able to improve our previous results.
The exponential growth on the number of mobile devices and their capabilities are leveraging new possibilities of networking architectures for processing, storing, and exchanging of information. At a glance, existing ...
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ISBN:
(纸本)9789897581106
The exponential growth on the number of mobile devices and their capabilities are leveraging new possibilities of networking architectures for processing, storing, and exchanging of information. At a glance, existing architectures take advantage of these devices, the social behavior of their users, and/or the dynamicity on resource usage. Despite of the potential of existing initiatives, they do not interoperate which reduce their applications and deployment. As we walk towards a very dynamic world (regarding the user needs and characteristics, the information traversing the network, and the networking capability to adaptation at both users features and content of the demands levels), these architectures should merge into a solution that fits any type of scenario. In this paper, we specify an opportunistic, socially-driven, self-organizing, cloud networking architecture using a future Internet proposal named NovaGenesis. We highlight the requirements and solutions that NovaGenesis brings to accommodate the inherent challenges of today's dynamic networking scenario. Thus, we describe a convergent architecture, which integrates the new requirements with the already implemented NovaGenesis features.
Linked Data seem to play a seminal role in the establishment of the Semantic Web as the next-generation Web. This is even more important for digital object collections and educational institutions that aim not only to...
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Extreme Learning Machine (ELM) is a noniterative training method suited for Single Layer Feed Forward Neural Networks (SLFF-NN). Typically, a hardware neural network is trained before implementation in order to avoid ...
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Extreme Learning Machine (ELM) is a noniterative training method suited for Single Layer Feed Forward Neural Networks (SLFF-NN). Typically, a hardware neural network is trained before implementation in order to avoid additional on-chip occupation, delay and performance degradation. However, ELM provides fixed-time learning capability and simplifies the process of re-training a neural network once implemented in hardware. This is an important issue in many applications where input data are continuously changing and a new training process must be launched very often, providing self-adaptation. This work describes a general SLFF-NN design environment to assist in the definition of neural network hardware implementation parameters including real-time ELM training. The software design environment uses initial user-provided input data with information about the type of problem: sample dataset and validated results, input fields, accuracy; and, together with simulation tools, recommends the optimum configuration for the neural topology and automatically generates synthesizable code for the hardware implementation tool. This is possible due to the design of parameter-dependent synthesis code and optimal hardware architecture design for both neural network and ELM training. Results show all the steps required to follow a successful design flow from the software tool to the final running device and, as an application example, the FPGA implementation for realtime detection of brain area in electrode positioning during a Deep Brain Stimulation (DBS) surgery is shown.
Quality evaluation is a fundamental problem in the field of linguistic description of data. In this work, we analyze the concept of quality and study different approaches to measure quality. Although most of the appro...
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ISBN:
(纸本)9781467374293
Quality evaluation is a fundamental problem in the field of linguistic description of data. In this work, we analyze the concept of quality and study different approaches to measure quality. Although most of the approaches considered focused on time series data, that are one of the most frequent datasets in real application domains, they can be used for quality assessment of linguistic descriptions generated for any type of data.
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