Miracle-dz/UniFAR-FTUs huggingface dataset:
On Requirements for Federated Data Integration as a Compilation Process

Data integration problems are commonly viewed as interoperability issues, where the burden of reaching a common ground for exchanging data is distributed across the peers involved in the process. While apparently an effective approach towards standardization and interoperability, it poses a constraint to data providers who, for a variety of reasons, require backwards compatibility with proprietary or non-standard mechanisms. Publishing a holistic data API is one such use case, where a single peer performs most of the integration work in a many-to-one scenario. Incidentally, this is also the base setting of software compilers, whose operational model is comprised of phases that perform analysis, linkage and assembly of source code and generation of intermediate code. There are several analogies with a data integration process, more so with data that live in the Semantic Web, but what requirements would a data provider need to satisfy, for an integrator to be able to query and transform its data effectively, with no further enforcements on the provider? With this paper, we inquire into what practices and essential prerequisites could turn this intuition into a concrete and exploitable vision, within Linked Data and beyond.
Theoretical study of a new kind HTS motor

This paper describes the design of a new kind HTS motor. Superconducting plates used as magnetic screen that cause a spatial variation of the flux density. It is theoretically possible to obtain an air gap flux density variation greater than 3 T. We perform a dimensionless study of the device showing that the torque depends on shape parameters and is limited by the maximum field on the superconducting wire. In the last section, we discuss the performance of the device in term of torque density.
Behavior of Sandwich Plates Reinforced with Polyurethane/Polyurea Interlayers under Blast Loads

In this study, the behavior of conventional and modified sandwich plate designs subjected to blast loads is examined with the finite element method. The conventional sandwich design consists of thin outer (loaded side) and inner facesheets made of fibrous laminates, separated by a layer of structural foam core. In the modified designs, a thin ductile interlayer is inserted between the outer facesheet and the foam core. Two materials are selected for the interlayers; one is rate-independent, hyperelastic (polyurethane, PUR), and the other is rate and pressure-dependent, elastic—plastic (polyurea). A comparison is made between the two enhanced designs and the conventional design during an extended time period of 5.0 ms under an exponential pressure impulse lasting for 0.05 ms, and has a peak pressure of 100 MPa. Results show that utilizing PUR or polyurea to separate the outer facesheet and the foam core leads to a much reduced core compression, facesheet vibration, and overall deflection compared to the conventional design. Similar reductions are found in the kinetic energy and the stored and dissipated strain energy. This is attributed to the ability of the interlayers to store energy and shield the inner foam by exhibiting great stiffness under pressure. Although strain rates as high as 104 s—1 are produced by the blast pressure impulse in the sandwich plates, the strains in both the PUR and the polyurea interlayers are relatively small. This leads to generally similar responses for the two interlayer types, with the initially stiffer PUR providing a slightly better protection for the foam core.
Macrophages in the development of protective immunity against experimental Brugia malayi infection

The present report compares the macrophage function in rodent hosts susceptible and resistant to the human lymphatic filariid Brugia malayi. Macrophages from both mastomys (resistant) and gerbil (susceptible) infected intraperitoneally (i.p.) with the infective larvae (L3) of B. malayi were isolated from peritoneal lavage at different time-intervals and formation rate of NO, H2O2, O2−, TNF-α, glutathione peroxidase and reductase was assayed. NO release was found to be significantly increased in resistant mastomys as compared to gerbils and the release was markedly suppressed by i.p. administration of the NOS inhibitor aminoguanidine (AG). The AG-treated mastomys also demonstrated significantly greater establishment of larvae which correlated well with suppressed formation of NO. Nitric oxide synergizes with superoxide to form peroxynitrite radical (potent oxidant), which is known to be more toxic per se than NO. Results indicate the possible involvement of peroxynitrite in the rapid killing of larvae in the peritoneal cavity of mastomys. In contrast, the production of H2O2 was found to be enhanced in both species indicating that B. malayi L3 could withstand the toxic effects of H2O2. The higher level of glutathione peroxidase and reductase, as observed in mastomys compared with the gerbil after larval introduction, possibly protects the cell against the injurious effect of H2O2. The TNF-α level remained virtually unchanged in both the hosts, suggesting an insignificant role for this cytokine in parasite establishment.
FMR image registration using the Mumford-Shah functional and Shape information

Motion can have a significant impact on signal changes in functional MR images, and affect the detection of real functional activation. To minimize the effect of motion on the fMRI signal, we propose a simultaneous segmentation and registration model. Due to T2 weighted signal loss, decreased resolution, and low contrast in fMR images, the feature to be used for realignment can't be detected reliably by using only image information. Our approach uses prior shape information in the Mumford-Shah segmentation [MS] technique to find the feature in a lowresolution image. In addition to this, the spatial transform that maps the segmented contour to the given shape is found. Given a time serier of images, we use this method to find transformations to realign the images. In this model, we minimize an energy functional that depends on the Mumford-Shah energy functional and the shape of interest, so that the boundary of the object can be captured by the different intensity distributions inside and outside the feature and the prior knowledge of its shape. The model has been tested both on synthetic data and fMR brain image data. The experimental results showed the effectiveness of this model in feature determination and in time series image registration.
Social Network-based Swarm Optimization algorithm

We propose a new population-based optimization algorithm, named Social Network-based Swarm Optimization algorithm (SNSO), for solving unconstrained single-objective optimization problems. In SNSO, the population topology, neighborhood structure and individual learning behavior are used to improve the search performance of a swarm. Specifically, a social network model is introduced to adjust the population topology dynamically, so as to change the information flow among different individuals. Based on the new topology, an extended neighborhood strategy is provided to build a neighborhood for each individual. Different form other forms of neighborhoods, the new structure includes some real individuals connected to the current one and some virtual individuals having better fitness in history, which could bring to more useful information to individuals for avoiding invalid attempts. Furthermore, we propose a new learning framework that defines two different position update methods for two types of individuals with the aim of enhancing the diversity and search ability of the swarm. The performance of SNSO is compared with seven other swarm algorithms on twelve well-known benchmark functions. The experimental results show that SNSO has a better performance than the selected algorithms.
Lubrication at Physiological Pressures by Polyzwitterionic Brushes

The very low sliding friction at natural synovial joints, which have friction coefficients of μ < 0.002 at pressures up to 5 megapascals or more, has to date not been attained in any human-made joints or between model surfaces in aqueous environments. We found that surfaces in water bearing polyzwitterionic brushes that were polymerized directly from the surface can have μ values as low as 0.0004 at pressures as high as 7.5 megapascals. This extreme lubrication is attributed primarily to the strong hydration of the phosphorylcholine-like monomers that make up the robustly attached brushes, and may have relevance to a wide range of human-made aqueous lubrication situations.
Enhanced stiffness modeling of serial and parallel manipulators for robotic-based processing of high performance materials

The thesis focuses on the enhancement of stiffness modeling of serial and parallel manipulators with passive joints under an essential loading. The developed technique takes into account different types of loadings: external force/torque applied to the end-point, internal preloading in the joints and auxiliary forces/torques applied to intermediate points. In contrast to previous works, the proposed technique includes computing an equilibrium configuration, which exactly corresponds to the loading. This allows to obtain the full-scale force-deflection relation for any given workspace point and to linearise it taking into account variation of the manipulator Jacobian because of the external force/torque. The proposed approach also enables designer to evaluate critical forces that may provoke non-linear behaviours of the manipulators, such as sudden failure due to elastic instability (buckling), which has not been previously studied in robotics. In the frame of this work, it is assumed that the manipulator elasticity is described by a multidimensional lumped-parameter model, which corresponds to a set of rigid bodies connected by 6-dof virtual springs. Each of these springs characterize flexibility of the corresponding link or actuated joint and takes into account both their compliance and joint particularities. To increase the model accuracy, the stiffness parameters of the spring are evaluated using FEA-based virtual experiments and dedicated identification technique developed in the thesis. This gives almost the same accuracy as FEA but essentially reduces the computational effort, eliminating repetitive re-meshing through the workspace.
Effect of experimental parameters on the formation of alginate–chitosan nanoparticles and evaluation of their potential application as DNA carrier

This study introduces a new procedure to prepare alginate–chitosan nanoparticles and examines several experimental parameters in relation to their formation and characteristics. Using DLS and TEM analysis, nanoparticle formation was shown to be predominantly affected by the ratio of alginate to chitosan, the molecular weight of the biopolymers and the solution pH. We report a method that results in spherical particles with mean diameters ranging from 323 nm to 1.6 μm, depending on the preparation conditions. The smallest particles were formed using lower molecular weight polymers with pH between 5.0 and 5.6 and having an alginate/chitosan weight ratio of 1 : 1.5. We have shown that DNA can be loaded with 60% association efficiency. Our system demonstrates suitable size, loading and release characteristics for application in drug- and gene-delivery systems.
A Human-Markov Chain Monte Carlo Method For Investigating Facial Expression Categorization

This paper demonstrates how a human-Markov Chain Monte Carlo (MCMC) method can be used to investigate models of facial expression categorization. Data were collected from four participants. At each step participants were asked to select a representation from a pair, that most resembled a particular emotional state; this was repeated iteratively. As such, they formed a component in the MCMC process. The representations were line drawn facial images with 10 nodes and four degrees of freedom. The judgements formed samples for a set of interleaved Markov Chains. These were mapped to a twodimensional plane using Generalized Discriminant Analysis. We contrast the results of the MCMC task with those of a second discrimination task. Estimates of the distributions along each of the four dimensions showed that for the outer eyebrow and lip corner variables one of the categories could be discriminated with confidence. The average examples from both MCMC and discrimination tasks were both plausible. However, the MCMC method allowed for greater sampling from areas of high interest. Finally, we show that a naive Bayes classifier trained on the MCMC data can be used to successfully predict human classification in a discrimination task.
First Steps Towards Violin Performance Extraction using Genetic Programming

This paper outlines the outstanding problem of performance extraction from an audio signal of violin performance. It describes some preliminary programming runs that solve components of the larger problem and suggest that genetic programming is a viable aproach to solving the larger problem. Techniques for dealing with tness cases and evaluation of time based signals using GP are presented.
The effect of harmonized emissions on aerosol properties in global models – an AeroCom experiment

The effects of unified aerosol sources on global aerosol fields simulated by different models are examined in this paper. We compare results from two AeroCom experiments, one with different (ExpA) and one with unified emissions, injection heights, and particle sizes at the source (ExpB). Surprisingly, harmonization of aerosol sources has only a small impact on the simulated inter-model diversity of the global aerosol burden, and consequently global optical properties, as the results are largely controlled by model-specific transport, removal, chemistry (leading to the formation of secondary aerosols) and parameterizations of aerosol microphysics (e.g., the split between deposition pathways) and to a lesser extent by the spatial and temporal distributions of the (precursor) emissions. The burdens of black carbon and especially sea salt become more coherent in ExpB only, because the large ExpA diversities for these two species were caused by a few outliers. The experiment also showed that despite prescribing emission fluxes and size distributions, ambiguities in the implementation in individual models can lead to substantial differences. These results indicate the need for a better understanding of aerosol life cycles at process level (including spatial dispersal and interaction with meteorological parameters) in order to obtain more reliable results from global aerosol simulations. This is particularly important as such model results are used to assess the consequences of specific air pollution abatement strategies.
USING FAHP TO EVALUATE NON-STORE RETAILING CHANNEL ALTERNATIVES

Past studies pointed to the fact that consumers associate a higher level of risk with non-store shopping than in-store shopping. However, with the advent of the information technology and e-commerce, shoppers are now increasingly likely to shop across multiple channels. Multi-channel integration continues to be a focus and will be a key factor driving growth onward. This paper describes the Fuzzy Analytic Hierarchical Process (FAHP) to determine the weights of the perceived benefits and risks of various non-store retailing channels in a Fuzzy Multiple Criteria Decision-Making (FMCDM) environment and then to evaluate the performance of those alternatives. We find that the importance of non-store retailing channel decision-making main-dimension can be ranked in the sequence of price, merchandise, information service, purchasing atmosphere, convenience, and perceived risks. Consequently, the ranking order of the synthetic performance values of the non-store retailing channels obtained in this case is online shopping, TV home shopping, catalog shopping, newspaper shopping, and broadcasting shopping.
Effective CU Size Decision for HEVC Intracoding

In high efficiency video coding (HEVC), the tree structured coding unit (CU) is adopted to allow recursive splitting into four equally sized blocks. At each depth level (or CU size), it enables up to 35 intraprediction modes, including a planar mode, a dc mode, and 33 directional modes. The intraprediction via exhaustive mode search exploited in the test model of HEVC (HM) effectively improves coding efficiency, but results in a very high computational complexity. In this paper, a fast CU size decision algorithm for HEVC intracoding is proposed to speed up the process by reducing the number of candidate CU sizes required to be checked for each treeblock. The novelty of the proposed algorithm lies in the following two aspects: 1) an early determination of CU size decision with adaptive thresholds is developed based on the texture homogeneity and 2) a novel bypass strategy for intraprediction on large CU size is proposed based on the combination of texture property and coding information from neighboring coded CUs. Experimental results show that the proposed effective CU size decision algorithm achieves a computational complexity reduction up to 67%, while incurring only 0.06-dB loss on peak signal-to-noise ratio or 1.08% increase on bit rate compared with that of the original coding in HM.
Baculovirus is an efficient vector for the transduction of the eye: comparison of baculovirus‐ and adenovirus‐mediated intravitreal vascular endothelial growth factor D gene transfer in the rabbit eye

The present study aimed to determine the efficiency and safety of baculovirus‐mediated intravitreal gene transfer in rabbit eye and to compare its efficiency with adenovirus. We also studied how an intravitreal injection of vectors producing vascular endothelial growth factor D (VEGF‐D) impacts the vasculature of rabbit eye.
Negative induction effect of graphite N on graphene quantum dots: tunable band gap photoluminescence

We synthesized nitrogen-doped graphene quantum dots (N-GQDs) under a high temperature range of 800–1200 °C and high pressure of 4.0 GPa through a solid-to-solid process. The graphite N in N-GQDs has a strong negative induction effect on the band gap. Without the interference of surface groups, the direct band gap of these N-GQDs increased with increased nitrogen doping, resulting in tunable photoluminescence (PL) with a high PL efficiency. Based on the recognized PL rules, we synthesised N-GQDs with a higher doping concentration and near ultraviolet light-emittance.
Probabilistic lexical modeling and unsupervised training for zero-resourced ASR

Standard automatic speech recognition (ASR) systems rely on transcribed speech, language models, and pronunciation dictionaries to achieve state-of-the-art performance. The unavailability of these resources constrains the ASR technology to be available for many languages. In this paper, we propose a novel zero-resourced ASR approach to train acoustic models that only uses list of probable words from the language of interest. The proposed approach is based on Kullback-Leibler divergence based hidden Markov model (KL-HMM), grapheme subword units, knowledge of grapheme-to-phoneme mapping, and graphemic constraints derived from the word list. The approach also exploits existing acoustic and lexical resources available in other resource rich languages. Furthermore, we propose unsupervised adaptation of KL-HMM acoustic model parameters if untranscribed speech data in the target language is available. We demonstrate the potential of the proposed approach through a simulated study on Greek language.
Leader-Based Rate Adaptive Multicasting for Wireless LANs

Multicasting is useful for various applications such as multimedia broadcasting. In current 802.11, multicast frames are sent as broadcast frames at a low transmission rate without any acknowledgement or binary exponential backoff. This naive multicasting mechanism degrades the performance of not only multicast flows but also unicast flows. In this paper, we propose a new multicasting mechanism based on the leader- based approach to improve the legacy multicast transmissions, maintaining coexistence with legacy 802.11 devices. Simulations show that our protocol achieves well-balanced performance in terms of reliability, latency, goodput, and transmission fairness in comprehensive environments.
Secured Service Provider selection methods in Cloud

Cloud computing is an important trend and experienced exponential growth over the last few years. Almost sixty percent of current small-to-medium businesses (SMB) use cloud services. The growth is only expected to increase over the next few years. However, the control of data over the cloud is less for the customers. Hence we lack in trust management which is near about two percentages of the total services provided. Prefer a cloud service provider among numerous vendors based on qualitative and quantitative manner is a challenge for the cloud customer. This paper on the theme of brief examine in existing models which are used for cloud service provider selection in cloud environment and we analysed the criteria which can be improved to provide the best selection.
Random Neural Networks with Synchronized Interactions

Large-scale distributed systems, such as natural neuronal and artificial systems, have many local interconnections, but they often also have the ability to propagate information very fast over relatively large distances. Mechanisms that enable such behavior include very long physical signaling paths and possibly saccades of synchronous behavior that may propagate across a network. This letter studies the modeling of such behaviors in neuronal networks and develops a related learning algorithm. This is done in the context of the random neural network (RNN), a probabilistic model with a well-developed mathematical theory, which was inspired by the apparently stochastic spiking behavior of certain natural neuronal systems. Thus, we develop an extension of the RNN to the case when synchronous interactions can occur, leading to synchronous firing by large ensembles of cells. We also present an O(N3) gradient descent learning algorithm for an N-cell recurrent network having both conventional excitatory-inhibitory interactions and synchronous interactions. Finally, the model and its learning algorithm are applied to a resource allocation problem that is NP-hard and requires fast approximate decisions.
Development of a Novel Portable Cooling Device for Inducing Mild Hypothermia

Therapeutic hypothermia is rapidly becoming an integral part of post-resuscitative care for post-cardiac arrest patients, with cooling increasingly being initiated in the pre-hospital setting in order to improve patient outcome. However, commercially available devices are not sufficiently portable or do not provide enough cooling power. Additionally, despite the significant impact of thermoregulation on core temperature change during rapid cooling, current mathematical models for thermoregulation have not been validated for hypothermic conditions. In the present study, a novel portable cooling device using adsorption cooling has been proposed, and a prototype was developed to prove that the concept is feasible. Additionally, a geometrically accurate 3D model of an upper leg was developed in order to further understand heat transfer in the human body and to validate thermoregulation models from literature. There was good agreement between simulation results and experimental data at 18°C water immersion, however, significant discrepancy was observed at lower temperature.