Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Study of MFCC Feature with Different Machine Learning Techniques in Acoustic Scene Classification

The task of labeling the audio sample in outdoor condition or indoor condition is called Acoustic Scene Classification (ASC). The ASC use acoustic information to imply about the context of the recorded environment. Since ASC can only applied in indoor environment in real world, a new set of strategies and classification techniques are required to consider for outdoor environment. In this paper, we present the comparative study of different machine learning classifiers with Mel-Frequency Cepstral Coefficients (MFCC) feature. We used DCASE Challenge 2016 dataset to show the properties of machine learning classifiers. There are several classifiers to address the ASC task. In this paper, we compare the properties of different classifiers: K-nearest neighbors (KNN), Support Vector Machine (SVM), Decision Tree (ID3) and Linear Discriminant Analysis by using MFCC feature. The best of classification methodology and feature extraction are essential for ASC task. In this comparative study, we extract MFCC feature from acoustic scene audio and then extracted feature is applied in different classifiers to know the advantages of classifiers for MFCC feature. This paper also proposed the MFCC-moment feature for ASC task by considering the statistical moment information of MFCC feature.
Architectural stability of self-adaptive software systems

This thesis studies the notion of stability in software engineering with the aim of understanding its dimensions, facets and aspects, as well as characterising it. The thesis further investigates the aspect of behavioural stability at the architectural level, as a property concerned with the architecture's capability in maintaining the achievement of expected quality of service and accommodating runtime changes, in order to delay the architecture drifting and phasing-out as a consequence of the continuous unsuccessful provision of quality requirements. The research aims to provide a systematic and methodological support for analysing, modelling, designing and evaluating architectural stability. The novelty of this research is the consideration of stability during runtime operation, by focusing on the stable provision of quality of service without violations. As the runtime dimension is associated with adaptations, the research investigates stability in the context of self-adaptive software architectures, where runtime stability is challenged by the quality of adaptation, which in turn affects the quality of service. The research evaluation focuses on the effectiveness, scale and accuracy in handling runtime dynamics, using the self-adaptive cloud architectures.
Fault-tolerant Control of a Class of Uncertain Systems with Actuator Failure

The fault-tolerant control of uncertain networked control systems with actuator failure and packet loss is studied. In the event of a failure of the actuator component, there is a problem of packet loss. The uncertain control system with actuator failure and packet loss is modeled as a Markov jump linear system based on the different locations of packet loss during execution. Then, analysing the stability of the fault system by using Lyapunov stability theory and linear matrix inequalities technology, the adequacy of the random stability of the closed-loop fault system is proved. At the same time, the mature cone repair linearization method is adopted for the controller design. Finally, the feasibility of the design method was verified by using the example of a double rotor helicopter simulation model.
Self-assembly, buckling and density-invariant growth of three-dimensional vascular networks

The experimental actualization of organoids modelling organs from brains to pancreases has revealed that much of the diverse morphologies of organs are emergent properties of simple intercellular ‘rules’ and not the result of top-down orchestration. In contrast to other organs, the initial plexus of the vascular system is formed by aggregation of cells in the process known as vasculogenesis. Here we study this self-assembling process of blood vessels in three dimensions through a set of simple rules that align intercellular apical–basal and planar cell polarity. We demonstrate that a fully connected network of tubes emerges above a critical initial density of cells. Through planar cell polarity, our model demonstrates convergent extension, and this polarity furthermore allows for both morphology-maintaining growth and growth-induced buckling. We compare this buckling with the special vasculature of the islets of Langerhans in the pancreas and suggest that the mechanism behind the vascular density-maintaining growth of these islets could be the result of growth-induced buckling.
Towards a Predictive Modelling Capacity for DT Plasmas. European Transport Simulator (ETS) Verification and Validation

The European Transport Simulator (ETS) has been developed under the EUROfusion Integrated Modelling (EU-IM) effort to meet the requirements for scenario development of burning plasmas. The ETS focuses both on interpretive and predictive modelling and is now being deployed for broader exploitation, e.g. on the JET computing infrastructure for close integration and use in support of JET campaigns. Recently, developments have been undertaken to enhance the ETS modelling capabilities for H, D and T plasmas, with special emphasis on DT in view of the upcoming experiment. A coherent inclusion of fast particle physics effects as well as a consistent approach for multispecies plasmas have been implemented. Major features are the capability for separate modelling of the different hydrogen isotope channels as well as light and heavy impurities in all their charge states and a set of advanced heating and current drive modules. The ICRH modeling is done interfacing 2D full-wave equation codes to a fast Fokker-Planck solver capable of describing the RF power absorbed by the minority as well as majority ions, all of which having non-Maxwellian distributions and requiring accounting for the fully non-linear collision operator as well as the quasi-linear RF diffusion operator. Here we describe the extensions to the ETS workflow undertaken to enhance the modelling capabilities for DT plasmas and discuss a detailed verification and validation activity on effects of different hydrogenic species.
System Level Simulation of Scheduling Schemes for C-V2X Mode-3

The 3rd Generation Partnership Project (3GPP) introduced Cellular Vehicle--to--Everything (C-V2X) as a novel technology to support sidelink vehicular communications. While a distributed scheduling approach has been proposed by 3GPP for the out--of--coverage scenario, i.e. C-V2X \textit{mode-4}, there is no standardized scheme for centralized systems in C-V2X \textit{mode-3}. In this paper, we propose two scheduling approaches for C-V2X \textit{mode-3}. One of them is based on the minimization of the overall power perceived by the vehicles. The second approach is based on the maximization of the subchannels re-usage distance. The two centralized schemes are compared against the distributed approach. Through simulations we show that the proposed schemes outperform C-V2X \textit{mode-4} as the subchannels are assigned in a more efficient manner with mitigated interference.
PPGAN: Privacy-Preserving Generative Adversarial Network

Generative Adversarial Network (GAN) and its variants serve as a perfect representation of the data generation model, providing researchers with a large amount of high-quality generated data. They illustrate a promising direction for research with limited data availability. When GAN learns the semantic-rich data distribution from a dataset, the density of the generated distribution tends to concentrate on the training data. Due to the gradient parameters of the deep neural network contain the data distribution of the training samples, they can easily remember the training samples. When GAN is applied to private or sensitive data, for instance, patient medical records, as private information may be leakage. To address this issue, we propose a Privacy-preserving Generative Adversarial Network (PPGAN) model, in which we achieve differential privacy in GANs by adding well-designed noise to the gradient during the model learning procedure. Besides, we introduced the Moments Accountant strategy in the PPGAN training process to improve the stability and compatibility of the model by controlling privacy loss. We also give a mathematical proof of the differential privacy discriminator. Through extensive case studies of the benchmark datasets, we demonstrate that PPGAN can generate high-quality synthetic data while retaining the required data available under a reasonable privacy budget.
Long‐Term Regularity of 3D Gravity Water Waves

We study a fundamental model in fluid mechanics--the 3D gravity water wave equation, in which an incompressible fluid occupying half the 3D space flows under its own gravity. In this paper we show long-term regularity of solutions whose initial data is small but not localized. Our results include: almost global wellposedness for unweighted Sobolev initial data and global wellposedness for weighted Sobolev initial data with weight $|x|^\alpha$, for any $\alpha > 0$. In the periodic case, if the initial data lives on an $R$ by $R$ torus, and $\epsilon$ close to the constant solution, then the life span of the solution is at least $R/\epsilon^2(\log R)^2$.
Analytical Performance Evaluation of THz Wireless Fiber Extenders

This paper presents the theoretical framework for the performance evaluation of terahertz (THz) wireless fiber extender in the presence of misalignment and multipath fading. In more detail, after providing the appropriate system model that incorporates the different operation, design, and environmental parameters, such as the operation frequency, transceivers antenna gains, the level of misalignment as well as the stochastic behavior of the channel, we extract novel closed-form expressions for the ergodic capacity. These expressions are expected to be used as useful tools for the analysis and design of such systems. Moreover, several insightful scenarios are simulated. Their results highlight the importance of taking into account the impact of misalignment fading when analyzing the performance of the THz wireless fiber extender.
Online repeated posted price auctions with a demand side platform

We consider an online ad network problem in which an ad exchange auctions ad slots and intermediaries called demand side platforms (DSPs) buy these ad slots for their clients (advertisers). An intermediary represents multiple advertisers. Different types of ad slots are auctioned by the ad exchange, e.g., video ad, banner ad etc. We study repeated posted price auctions for homogeneous and heterogeneous items when there is an intermediary. In a posted price auction, the auctioneer sets a fixed reserve price. The buyer can accept the price and win the ad slot or reject the price. We analyze the system from the auctioneer's perspective and show that the optimal reserve price is dynamic for heterogeneous items. We also investigate system from intermediary's perspective and devise algorithms for scheduling advertisers. Often the advertisers have budget constraints and impression constraints. We formulate a revenue optimization problem at the intermediary and also consider the problem of scheduling advertisers with budget and impression constraints. Finally, we present a numerical study for the single seller and advertiser model which considers various valuation distributions such as uniform, exponential and lognormal.
SMP Challenge: An Overview of Social Media Prediction Challenge 2019

"SMP Challenge" aims to discover novel prediction tasks for numerous data on social multimedia and seek excellent research teams. Making predictions via social multimedia data (e.g. photos, videos or news) is not only helps us to make better strategic decisions for the future, but also explores advanced predictive learning and analytic methods on various problems and scenarios, such as multimedia recommendation, advertising system, fashion analysis etc. In the SMP Challenge at ACM Multimedia 2019, we introduce a novel prediction task Temporal Popularity Prediction, which focuses on predicting future interaction or attractiveness (in terms of clicks, views or likes etc.) of new online posts in social media feeds before uploading. We also collected and released a large-scale SMPD benchmark with over 480K posts from 69K users. In this paper, we define the challenge problem, give an overview of the dataset, present statistics of rich information for data and annotation and design the accuracy and correlation evaluation metrics for temporal popularity prediction to the challenge.
Searching for solar siblings in APOGEE and Gaia DR2 with N-body simulations

We make use of APOGEE and $Gaia\,$ data to identify stars that are consistent with being born in the same association or star cluster as the Sun. We limit our analysis to stars that match solar abundances within their uncertainties, as they could have formed from the same giant molecular cloud (GMC) as the Sun. We constrain the range of orbital actions that solar siblings can have with a suite of simulations of solar birth clusters evolved in static and time-dependent tidal fields. The static components of each galaxy model are the bulge, disc, and halo, while the various time-dependent components include a bar, spiral arms, and GMCs. In galaxy models without GMCs, simulated solar siblings all have JR < 122 km $\rm s^{-1}$ kpc, 990 < Lz < 1986 km $\rm s^{-1}$ kpc, and 0.15 < Jz < 0.58 km $\rm s^{-1}$ kpc. Given the actions of stars in APOGEE and $Gaia\,$, we find 104 stars that fall within this range. One candidate in particular, Solar Sibling 1, has both chemistry and actions similar enough to the solar values that strong interactions with the bar or spiral arms are not required for it to be dynamically associated with the Sun. Adding GMCs to the potential can eject solar siblings out of the plane of the disc and increase their Jz, resulting in a final candidate list of 296 stars. The entire suite of simulations indicate that solar siblings should have JR < 122 km $\rm s^{-1}$ kpc, 353 < Lz < 2110 km $\rm s^{-1}$ kpc, and Jz < 0.8 km $\rm s^{-1}$ kpc. Given these criteria, it is most likely that the association or cluster that the Sun was born in has reached dissolution and is not the commonly cited open cluster M67.
Type IIP Supernova Progenitors and Their Explodability. I. Convective Overshoot, Blue Loops, and Surface Composition

We present the evolution of massive star progenitors of supernovae of type IIP. We take the example of the nearby and well-studied SN 2013ej. We explore how convective overshoot affects the stellar structure, surface abundances, and effective temperature of massive stars, using the Modules for Experiments in Stellar Astrophysics. In particular, models with moderate overshoot (f = 0.02–0.031) show the presence of blue loops in the Hertzsprung–Russell diagram with a red to blue excursion (log10[Teff/K] from <3.6 to >4.0) and transition back to red, during the core helium-burning phase. Models with overshoot outside this range of f values kept the star in the red supergiant state throughout the post-helium-ignition phases. The surface CNO abundance shows enrichment post-main-sequence and again around the time when helium is exhausted in the core. These evolutionary changes in surface CNO abundance are indistinguishable in the currently available observations due to large observational uncertainties. However, these observations may distinguish between the ratio of surface nitrogen to oxygen at different evolutionary stages of the star. We also compare the effects of convective overshoot on various parameters related to likelihood of explosion of a star as opposed to collapse to a black hole. These parameters are the compactness parameter, M4, and μ4. The combination μ4 × M4, and μ4 have similar variations with f and both peak at f = 0.032. We find that all of our 13 models are likely to explode.
Low-Temperature Electron–Phonon Interaction of Quantum Emitters in Hexagonal Boron Nitride

Quantum emitters based on atomic defects in layered hexagonal Boron Nitride (hBN) have emerged as promising solid state 'artificial atoms' with atom-like photophysical and quantum optoelectronic properties. Similar to other atom-like emitters, defect-phonon coupling in hBN governs the characteristic single-photon emission and provides an opportunity to investigate the atomic and electronic structure of emitters as well as the coupling of their spin- and charge-dependent electronic states to phonons. Here, we investigate these questions using photoluminescence excitation (PLE) experiments at T=4K on single photon emitters in multilayer hBN grown by chemical vapor deposition. By scanning up to 250 meV from the zero phonon line (ZPL), we can precisely measure the emitter's coupling efficiency to different phonon modes. Our results show that excitation mediated by the absorption of one in-plane optical phonon increases the emitter absorption probability ten-fold compared to that mediated by acoustic or out-of-plane optical phonons. We compare these measurements against theoretical predictions by first-principles density-functional theory of four defect candidates, for which we calculate prevalent charge states and their spin-dependent coupling to bulk and local phonon modes. Our work illuminates the phonon-coupled dynamics in hBN quantum emitters at cryogenic temperature, with implications more generally for mesoscopic quantum emitter systems in 2D materials and represents possible applications in solid-state quantum technologies.
Energy efficiency maximization in mmWave wireless networks with 3D beamforming

In this paper, we address the problem of 3D beamforming (3DBF) in millimeter wave (mmWave) wireless networks. In particular, we study the impact of base station (BS) antenna tilt angle optimization on the energy efficiency (EE) of mmWave networks under two different scenarios: a homogeneous network consisting of multiple macro base stations (MBSs), and a heterogeneous network where several femto base stations are added within the coverage areas of the MBSs. First, by adopting a stochastic geometry approach, we analyze the coverage probability of both scenarios that incorporate 3DBF. Then, we derive the EE of the networks as a function of the MBS antenna tilt angle. Next, optimization problems are formulated to maximize the EE of the networks by optimizing the tilt angle. Since the computational complexity of the optimal solution is very high, near-optimal low-complexity methods are proposed for solving the optimization problems. Simulation results show that in the mmWave networks, the 3DBF technique with optimized tilt angle can considerably improve the EE of the network. Also, the proposed low complexity approach presents a performance close to the optimal solution but with a significant reduced complexity.
The Influence of Person-Specific Biometrics in Improving Generic Stress Predictive Models

Because stress is subjective and is expressed differently from one person to another, generic stress prediction models (i.e., models that predict the stress of any person) perform crudely. Only person-specific ones (i.e., models that predict the stress of a preordained person) yield reliable predictions, but they are not adaptable and costly to deploy in real-world environments. For illustration, in an office environment, a stress monitoring system that uses person-specific models would require collecting new data and training a new model for every employee. Moreover, once deployed, the models would deteriorate and need expensive periodic upgrades because stress is dynamic and depends on unforeseeable factors. We propose a simple, yet practical and cost effective calibration technique that derives an accurate and personalized stress prediction model from physiological samples collected from a large population. We validate our approach on two stress datasets. The results show that our technique performs much better than a generic model. For instance, a generic model achieved only a 42.5% accuracy. However, with only 100 calibration samples, we raised its accuracy to 95.2% We also propose a blueprint for a stress monitoring system based on our strategy, and we debate its merits and limitation. Finally, we made public our source code and the relevant datasets to allow other researchers to replicate our findings.
Transplantation

'Recent Advances in Physiology'. Edited by R. Creese, PH.D., M.B. , B.S. Eighth edition. Pp. viii + 471, with 120 illustrations. London: J. & A. Churchill. I963. 55s. 'Drugs, Medicine and Man'. By Harold Burn, M.D., F.R. S. Second edition. Pp. 235, illustrated. London: Unwin University Books. I963. i8s. 'Handbook of Paediatrics'. Fifth edition. By H. K. Silver, C. H. Kempe and H. B. Bruyn. Pp. 602. Los Altos, California: Lange Medical Publications. I 963. $4.
Extending Gaussian process emulation using cluster analysis and artificial neural networks to fit big training sets

ABSTRACT Gaussian process (GP) emulation is a relatively recent statistical technique that provides a fast-running approximation to a complex computer model, given training data generated by the considered model. Despite its sound theoretical foundation, GP emulation falls short in practical applications where the training dataset is very large, due to numerical instabilities in inverting the correlation matrix. We show how GP emulation can be extended to handle large training sets by first dividing the training set into smaller subsets using cluster analysis, then training an emulator for each subset, and finally combining the emulators using an artificial neural network (ANN). Our work has also conceptual relevance, as it shows how to solve a big data problem by introducing a local level in input space, where each emulator specialises in a certain subregion, and a global level, where the identified local features of the computer model are combined into a global view.
A Truthful Reverse-Auction Mechanism for Computation Offloading in Cloud-Enabled Vehicular Network

The growth of smart vehicles and computation-intensive applications poses new challenges in providing reliable and efficient vehicular services. Offloading such applications from vehicles to mobile edge cloud servers has been considered as a remedy, although resource limitations and coverage constraints of the cloud service may still result in unsatisfactory performance. Recent studies have shown that exploiting the unused resources of nearby vehicles for application execution can augment the computational capabilities of application owners while alleviating heavy on-board workloads. However, encouraging vehicles to share resources or execute applications for others remains a sensitive issue due to user selfishness. To address this issue, we establish a novel computation offloading marketplace in vehicular networks where a Vickrey–Clarke–Groves based reverse auction mechanism utilizing integer linear programming (ILP) problem is formulated while satisfying the desirable economical properties of truthfulness and individual rationality. As ILP has high computation complexity which brings difficulties in implementation under larger and fast changing network topologies, we further develop an efficient unilateral-matching-based mechanism, which offers satisfactory suboptimal solutions with polynomial computational complexity, truthfulness and individual rationality properties as well as matching stability. Simulation results show that, as compared with baseline methods, the proposed unilateral-matching-based mechanism can greatly improve the system efficiency of vehicular networks in all traffic scenarios.