Building a P2P RDF Store for Edge Devices

The Semantic Web technologies have been used in the Internet of Things (IoT) to facilitate data interoperability and address data heterogeneity issues. The Resource Description Framework (RDF) model is employed in the integration of IoT data, with RDF engines serving as gateways for semantic integration. However, storing and querying RDF data obtained from distributed sources across a dynamic network of edge devices presents a challenging task. The distributed nature of the edge shares similarities with Peer-to-Peer (P2P) systems. These similarities include attributes like node heterogeneity, limited availability, and resources. The nodes primarily undertake tasks related to data storage and processing. Therefore, the P2P models appear to present an attractive approach for constructing distributed RDF stores. Based on P-Grid, a data indexing mechanism for load balancing and range query processing in P2P systems, this paper proposes a design for storing and sharing RDF data on P2P networks of low-cost edge devices. Our design aims to integrate both P-Grid and an edge-based RDF storage solution, RDF4Led for building an P2P RDF engine. This integration can maintain RDF data access and query processing while scaling with increasing data and network size. We demonstrated the scaling behavior of our implementation on a P2P network, involving up to 16 nodes of Raspberry Pi 4 devices.Comment: Accepted to IoT Conference 202

Asymptotically autonomous robustness of random attractors for a class of weakly dissipative stochastic wave equations on unbounded domains

This paper is concerned with the asymptotic behavior of solutions to a class of non-autonomous stochastic nonlinear wave equations with dispersive and viscosity dissipative terms driven by operator-type noise defined on the entire space Rn. The existence, uniqueness, time-semi-uniform compactness and asymptotically autonomous robustness of pullback random attractors are proved in H1(Rn) _ H1(Rn) when the growth rate of the nonlinearity has a subcritical range, the density of the noise is suitably controllable, and the time-dependent force converges to a time-independent function in some sense. The main difficulty to establish the time-semi-uniform pullback asymptotic compactness of the solutions in H1(Rn) _ H1(Rn) is caused by the lack of compact Sobolev embeddings on Rn, as well as the weak dissipativeness of the equations is surmounted at light of the idea of uniform tail-estimates and a spectral decomposition approach. The measurability of random attractors is proved by using an argument which considers two attracting universes developed by Wang and Li (Phys. D 382: 46-57, 2018)

A comparative study of discrete velocity methods for low-speed rarefied gas flows

In the study of rarefied gas dynamics, the discrete velocity method (DVM) has been widely employed to solve the gas kinetic equations. Although various versions of DVM have been developed, their performance, in terms of modeling accuracy and computational efficiency, is yet to be comprehensively studied in all the flow regimes. Here, the traditional third-order time-implicit Godunov DVM (GDVM) and the recently developed discrete unified gas-kinetic scheme (DUGKS) are analysed in finding steady-state solutions of the low-speed force-driven Poiseuille and lid-driven cavity flows. With the molecular collision and free streaming being treated simultaneously, the DUGKS preserves the second-order accuracy in the spatial and temporal discretizations in all flow regimes. Towards the hydrodynamic flow regime, not only is the DUGKS faster than the GDVM when using the same spatial mesh, but also requires less spatial resolution than that of the GDVM to achieve the same numerical accuracy. From the slip to free molecular flow regimes, however, the DUGKS is slower than the GDVM, due to the complicated flux evaluation and the restrictive time step which is smaller than the maximum effective time step of the GDVM. Therefore, the DUGKS is preferable for problems involving different flow regimes, particularly when the hydrodynamic flow regime is dominant. For highly rarefied gas flows, if the steady-state solution is mainly concerned, the implicit GDVM, which can boost the convergence significantly, is a better choice

Exploring cross-modality utilization in recommender systems

National Research Foundation (NRF) Singapore under NRF Fellowship Programm

Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs

Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a list of non-discrete attributes for each entity. Intuitively, these attributes such as height, price or population count are able to richly characterize entities in knowledge graphs. This additional source of information may help to alleviate the inherent sparsity and incompleteness problem that are prevalent in knowledge graphs. Unfortunately, many state-of-the-art relational learning models ignore this information due to the challenging nature of dealing with non-discrete data types in the inherently binary-natured knowledge graphs. In this paper, we propose a novel multi-task neural network approach for both encoding and prediction of non-discrete attribute information in a relational setting. Specifically, we train a neural network for triplet prediction along with a separate network for attribute value regression. Via multi-task learning, we are able to learn representations of entities, relations and attributes that encode information about both tasks. Moreover, such attributes are not only central to many predictive tasks as an information source but also as a prediction target. Therefore, models that are able to encode, incorporate and predict such information in a relational learning context are highly attractive as well. We show that our approach outperforms many state-of-the-art methods for the tasks of relational triplet classification and attribute value prediction.Comment: Accepted at CIKM 201

1,1′-[o-Phenyl­enebis(nitrilo­methyl­idyne)]di-2-naphthol ethanol hemisolvate

The asymmetric unit of the title compound, C28H20N2O2·0.5C2H5OH, contains two independent mol­ecules of 1,1′-[o-phenyl­enebis(nitrilo­methyl­idyne)]di-2-naphthol, denoted A and B, and one ethanol solvent mol­ecule. The hydr­oxy groups are involved in intra­molecular O—H⋯N hydrogen bonds influencing the mol­ecular conformations, which are slightly different in mol­ecules A and B, where the two bicyclic systems form dihedral angles of 51.93 (9) and 58.52 (9)°, respectively. In the crystal structure, a number of short inter­molecular C⋯C contacts with distances of less than 3.5 Å suggest the existence of π–π inter­actions, which contribute to the stability of the crystal packing

Fiber Optic Refractive Index Distributed Multi-Sensors by Scattering-Level Multiplexing With MgO Nanoparticle-Doped Fibers

© 2020 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] In this work, we present the architecture of a multiplexed refractive index (RI) sensing system based on the interrogation of Rayleigh backscattering. The RI sensors are fabricated by fiber wet-etching of a high-scattering MgO nanoparticle-doped fiber, without the need for a reflector or plasmonic element. Interrogation is performed by means of optical backscatter reflectometry(OBR), which allows a detection with a millimeter-level spatial resolution. Multiplexing consists of a simultaneous scan of multiple fibers, achieved by means of scattering-level multiplexing (SLMux) concept, which uses the backscattered power level in each location as a diversity element. The sensors fabricated have sensitivity in the order of 0.473-0.568 nm/RIU (in one sensing point) and have been simultaneously detected together with a distributed temperature sensing element for multi-parameter measurement. An experimental setup has been prepared to demonstrate the capability of each sensing region to operate without cross-talk, while operating multi-fiber detection.This work was supported in part by the ORAU Programme at Nazarbayev University (LIFESTART and FOSTHER Grants), in part by the Agence Nationale de la Recherche (ANR) Project NanoSlim under Grant ANR-17-17-CE08-0002, in part by the National Natural Science Foundation for Excellent Youth Foundation of China under Grant 61722505, in part by the Key Program of Guangdong Natural Science Foundation under Grant 2018B030311006, and in part by The Spanish Ministry of Economy and Competitiveness under Grant DIMENSION TEC2017 88029-R. The associate editor coordinating the review of this article and approving it for publication was Prof. Marco Petrovich.Ayupova, T.; Shaimerdenova, M.; Korganbayev, S.; Sypabekova, M.; Bekmurzayeva, A.; Blanc, W.; Sales Maicas, S.... (2020). Fiber Optic Refractive Index Distributed Multi-Sensors by Scattering-Level Multiplexing With MgO Nanoparticle-Doped Fibers. IEEE Sensors Journal. 20(5):2504-2510. https://doi.org/10.1109/JSEN.2019.2953231S2504251020