Miracle-dz/UniFAR-FTUs huggingface dataset:
Music genre recognition using convolutional recurrent neural network architecture

The genre is an abstract feature, but still, it is considered to be one of the important characteristics of music. Genre recognition forms an essential component for a large number of commercial music applications. Most of the existing music genre recognition algorithms are based on manual feature extraction techniques. These extracted features are used to develop a classifier model to identify the genre. However, in many cases, it has been observed that a set of features giving excellent accuracy fails to explain the underlying typical characteristics of music genres. It has also been observed that some of the features provide a satisfactory level of performance on a particular dataset but fail to provide similar performance on other datasets. Hence, each dataset mostly requires manual selection of appropriate acoustic features to achieve an adequate level of performance on it. In this paper, we propose a genre recognition algorithm that uses almost no handcrafted features. The convolutional recurrent neural network‐based model proposed in this study is trained on melspectrogram extracted from 3‐s duration audio clips taken from GTZAN dataset. The proposed model provides an accuracy of 85.36% on 10‐class genre classification. The same model has been trained and tested on 10 genres of MagnaTagATune dataset having 18,476 clips of 29‐s duration. The model has yielded an accuracy of 86.06%. The experimental results suggest that the proposed architecture with melspectrogram as input feature is capable of providing consistent performances across the different datasets
Designing a Logic Model to Inform Montessori Research

Montessori education has a long history, but its recent growth in American public schools has led to increased interest in research efforts, particularly in exploring the potential of the Montessori experience to moderate the effects of poverty and in gathering data to evaluate public investment in Montessori schools. To assist research efforts, this paper introduces a comprehensive visual model, or logic model, that depicts the core components, underlying assumptions, and intended outcomes of the Montessori approach. Logic modeling, which results in a visual representation depicting the connections among a program’s inputs, primary activities, and outcomes, is often used in program planning and research to provide a common framework from which to work. Developed over a 3-year period by a collaborative group of experienced Montessori researchers and practitioners, the Logic Model for Montessori Education presented in this paper is a valuable tool for researchers with the potential to lay a foundation across disciplines for future research that is both rigorous and systematic in its measurement of Montessori processes and outcomes.
Four-Wheeled Dead-Reckoning Model Calibration using RTS Smoothing

Localization is one of the main challenges to be addressed to develop autonomous vehicles able to perform complex maneuvers on roads opened to public traffic. Having an accurate dead-reckoning system is an essential step to reach this objective. This paper presents a dead-reckoning model for car-like vehicles that performs the data fusion of complementary and redundant sensors: wheel encoders, yaw rate gyro and steering wheel measurements. In order to get an accurate dead-reckoning system with a drift reduced to the minimum, the parameters have to be well calibrated and the procedure has to be simple and efficient. We present a method able to accurately calibrate the parameters without knowing the ground truth by using a Rauch-Tung-Striebel smoothing scheme which enables to obtain state estimates as close to the ground truth as possible. The smoothed estimates are then used within a optimization process to calibrate the model parameters. The method has been tested using data recorded from an experimental vehicle on public roads. The results show a significant diminution of the dead-reckoning drift compared to a commonly used calibration method. We evaluate finally the average distance a vehicle can navigate without exteroceptive sensors by using the proposed four-wheeled dead reckoning system.
Guaranteed Matrix Completion Under Multiple Linear Transformations

Low-rank matrix completion (LRMC) is a classical model in both computer vision (CV) and machine learning, and has been successfully applied to various real applications. In the recent CV tasks, the completion is usually employed on the variants of data, such as "non-local" or filtered, rather than their original forms. This fact makes that the theoretical analysis of the conventional LRMC is no longer suitable in these applications. To tackle this problem, we propose a more general framework for LRMC, in which the linear transformations of the data are taken into account. We rigorously prove the identifiability of the proposed model and show an upper bound of the reconstruction error. Furthermore, we derive an efficient completion algorithm by using augmented Lagrangian multipliers and the sketching trick. In the experiments, we apply the proposed method to the classical image inpainting problem and achieve the state-of-the-art results.
The Lincoln tied-mixture HMM continuous speech recognizer

The Lincoln robust HMM (hidden Markov model) recognizer has been converted from a single Gaussian or Gaussian mixture PDF per state to tied mixtures in which a single set of Gaussians is shared between all states. There were initial difficulties caused by the use of mixture pruning but these were cured by using observation pruning. Fixed weight smoothing of the mixture weights allowed the use of word-boundary-context-dependent triphone models for both speaker-dependent (SD) and speaker-independent (SI) recognition. A second-differential observation stream further improved SI performance but not SD performance. A novel form of phonetic context model, the semiphone, is also introduced. This model significantly reduces the number of states required to model a vocabulary and unifies triphone and diphone modeling. <<ETX>>
Analysis and Design of Tri-Gate MOSFET with High Dielectrics Gate

The scaling of simple gate transistors requires the scaling and transistor elements like source/drain junction became difficult to scale further after a limit due to adverse effect of electrostatic and short-channel performance. The solution of the problem is tri-gate where we can increase the performance without increasing the width and without scaling. In this paper we have described the parameter of tri-gate and taking the high dielectric as substrate. Index Terms—Tri-gate MOSFET; Radio frequency; HfO2; High dielectric constant; CMOS; VLSI.
Clustering‐based real‐time anomaly detection—A breakthrough in big data technologies

Off late, the ever increasing usage of a connected Internet‐of‐Things devices has consequently augmented the volume of real‐time network data with high velocity. At the same time, threats on networks become inevitable; hence, identifying anomalies in real time network data has become crucial. To date, most of the existing anomaly detection approaches focus mainly on machine learning techniques for batch processing. Meanwhile, detection approaches which focus on the real‐time analytics somehow deficient in its detection accuracy while consuming higher memory and longer execution time. As such, this paper proposes a novel framework which focuses on real‐time anomaly detection based on big data technologies. In addition, this paper has also developed streaming sliding window local outlier factor coreset clustering algorithms (SSWLOFCC), which was then implemented into the framework. The proposed framework that comprises BroIDS, Flume, Kafka, Spark streaming, SparkMLlib, Matplot and HBase was evaluated to substantiate its efficacy, particularly in terms of accuracy, memory consumption, and execution time. The evaluation is done by performing critical comparative analysis using existing approaches, such as K‐means, hierarchical density‐based spatial clustering of applications with noise (HDBSCAN), isolation forest, spectral clustering and agglomerative clustering. Moreover, Adjusted Rand Index and memory profiler package were used for the evaluation of the proposed framework against the existing approaches. The outcome of the evaluation has substantially proven the efficacy of the proposed framework with a much higher accuracy rate of 96.51% when compared to other algorithms. Besides, the proposed framework also outperformed the existing algorithms in terms of lesser memory consumption and execution time. Ultimately the proposed solution enable analysts to precisely track and detect anomalies in real time.
Hierarchical Game-Theoretic and Reinforcement Learning Framework for Computational Offloading in UAV-Enabled Mobile Edge Computing Networks With Multiple Service Providers

We present a novel game-theoretic (GT) and reinforcement learning (RL) framework for computational offloading in the mobile edge computing (MEC) network operated by multiple service providers (SPs). The network is formed by MEC servers installed at stationary base stations (BSs) and unmanned aerial vehicles (UAVs) deployed as quasi-stationary BSs. Since computing powers of MEC servers are limited, the BSs in proximity can form coalitions with shared data processing resources to serve their users more efficiently. However, as BSs can be privately owned or controlled by different SPs, in any coalition, the BSs: 1) take only the actions that maximize their long-term payoffs and 2) do not coordinate their actions with other BSs in the coalition. That is, inside each coalition, BSs act in an independent and self-interested manner. Therefore, the interactions among BSs cannot be described by conventional coalitional games. Instead, the network operation is modeled by a two-level hierarchical model. The upper level is a cooperative game that defines the process of coalition formation. The lower level comprises the set of noncooperative subgames to represent a self-interested and independent behavior of BSs in coalitions. To enable each BS to select a coalition and decide on its action maximizing its long-term payoff, we propose two algorithms that combine coalition formation with RL and prove that these algorithms converge to the states where the coalitional structure is strongly stable and the strategies of BSs are in the mixed-strategy Nash equilibrium (NE).
Contextual Models for Sequential Recommendation

Recommender systems aim to capture the interests of users in order to provide them with tailored recommendations for items or services they might like. User interests are often unique and depend on many unobservable factors including internal moods or external events. This phenomenon creates a broad range of tasks for recommendation systems that are difficult to address altogether. Nevertheless, analyzing the historical activities of users sheds light on the characteristic traits of individual behaviors in order to enable qualified recommendations. In this thesis, we deal with the problem of comprehending the interests of users, searching for pertinent items, and ranking them to recommend the most relevant items to the users given different contexts and situations. We focus on recommendation problems in sequential scenarios, where a series of past events influences the future decisions of users. These events are either the developed preferences of users over a long span of time or highly influenced by the zeitgeist and common trends. We are among the first to model recommendation systems in a sequential fashion via exploiting the short-term interests of users in session-based scenarios. We leverage reinforcement learning techniques to capture underlying short- and long-term user interests in the absence of explicit feedback and develop novel contextual approaches for sequential recommendation systems. These approaches are designed to efficiently learn models for different types of recommendation tasks and are extended to continuous and multi-agent settings. All the proposed methods are empirically studied on large-scale real-world scenarios ranging from e-commerce to sport and demonstrate excellent performance in comparison to baseline approaches.
Animal protein and bone growth Scientific substantiation of a health claim related to animal protein and bone growth pursuant to Article 14 of Regulation (EC) No 1924/2006 1 Scientific Opinion of the Panel on Dietetic Products, Nutrition and Allergies

SUMMARY Following an application from the Association de la Transformation Laitiere Francaise (ATLA) submitted pursuant to Article 14 of Regulation (EC) No 1924/2006 via the Competent Authority of France, the Panel on Dietetic Products, Nutrition and Allergies was asked to deliver an opinion on the scientific substantiation of a health claim related to proteins of animal origin and bone growth. The scope of the application was proposed to fall under claims referring to children’s development and health. The health claim relates to food products containing proteins of animal origin. The applicant has proposed that the claim shall apply to all foods providing at least 12% of their energy value as protein. It is not stated whether this minimum value concerns protein of animal origin only. The Panel considers that the constituent (animal protein) which is the subject of the claim is sufficiently characterised.
Manifold learning and convergence of Laplacian eigenmaps

In this thesis, we investigate the problem of obtaining meaningful low dimensional representation of high dimensional data, often referred to as manifold learning. We examine classical methods for manifold learning such as PCA and cMDS as well as some modern techniques of manifold learning namely Isomap, Locally Linear Embedding and Laplacian Eigenmaps. The algorithms for these individual methods are presented in mathematically consistent, concise and easy to understand fashion so that people with no computer science background can use the methods presented in their own research. Motivations and justifications of these manifold learning methods are provided. Finally we prove the convergence of Laplacian Eigenmaps method in a self contained and compact fashion following the work of Mikhail Belkin and Partha Niyogi. %%%%Dans cette these, on aborde le probleme de l'apprentissage de variete afin de reduire la dimensionalite d'ensembles de donnees pour obtenir une representation significative de basse dimension. On examine les methodes classiques d'apprentissage de variete telles que l'analyse en composantes principales (PCA) et le positionnement multidimensionnel classique (cMDS). On presente, de plus, trois techniques modernes pour resoudre ce probleme: les methodes «Isomap», «Locally Linear Embedding» et «Laplacian Eigenmaps». On expose les details mathematiques de ces algorithmes en termes concis et simples tout en preservant leur coherence mathematique. Ces developpements aideront sans doute d'autres chercheurs sans experience en informatique a utiliser ces methodes. Par la suite, on justifie l'applicabilite de ces techniques pour resoudre le probleme de reduction dimensionelle. Finalement, on montre la convergence de la methode «Laplacian Eigenmaps» d'une facon compacte en suivant l'approche de Mikhail Belkin et de Partha Nyogi.
Implications of Model Complexity for the Simulated Thermal Behavior of a Casement Window

Windows have been a subject of interest for research in building industry due to their multifaceted and significant implications for indoor environmental quality and energy use. In this context, the present contribution explores the retrofit opportunity toward retrofit of casement windows via application of vacuum glazing. The first scenario included conductive and convective heat transfer processes. The second, more detailed scenario took, in addition, the effects of (long-wave) radiation phenomena into account. The main objective of the present contribution is thus to contrast the results obtained from primarily conductive and convective computations with those that involve a detailed coupled conductive, convective, and radiation simulation. The study benefits from a CFD (Computational Fluid Dynamics) model to evaluate the airflow patterns (temperature and velocity) within the window’s interstitial space by assuming isothermal boundary conditions. Based on simulation results, the thermal performance of the retrofitted casement window (with vacuum glass applied as external pane) can be compared to the conventional construction. Thus, benefits in terms of reduced energy use and surface condensation risk can be documented.
Development of a dual-frequency GPS/GLONASS receiver for space application

Laboratory breadboards of a high precision dual-frequency Global Navigation Satellite System (GNSS) receiver have been developed at the Institute of Satellite Navigation at the University of Leeds (ISN) under a European Space Agency (ESA) programme. The main objective of these breadboards is to serve as development tools for receiver signal processing research in view of the scientific applications of GNSS. Some of these applications will be realised by means of receivers embarked on satellites in low Earth orbit (LEO) as part of on-going and future ESA programmes and will include precise orbit determination, radio occultation measurements and measurements of the parameters characterising GNSS signals reflected at the sea surface. During an occultation measurement the GNSS signal propagation encounters a challenging environment. Not only substantial Doppler dynamics occur during a measurement due to the effect of gradients of the refraction index, but also attenuation and deep fading caused by multipath propagation. The signal tracking requirements for space applications are analysed and applied to the hardware and software tasks. Resultant enhancements to the receiver digital processing are presented along with their impact on the intended application. The areas of concern are the acquisition and tracking of signals in high dynamics and at low carrier-to-noise ratios (CNR), and on improving the performance of tracking the GPS P code without full knowledge of the spreading code.
Adaptive Media Streaming over Emerging Protocols

The emerging MPEG standard Dynamic Adaptive Streaming over HTTP (MPEG-DASH) is designed for media delivery over the top of existing infrastructures and enables smooth multimedia streaming towards heterogeneous devices including both wired and wireless environments. The MPEG-DASH standard was designed to work with HTTP-URLs but mandates neither the actual version nor which underlying protocols to be used. This paper will provide a detailed introduction into emerging protocols (HTTP/2.0 and beyond) to be used in the context of adaptive media streaming, specifically DASH.
Unsupervised Information Extraction: Regularizing Discriminative Approaches with Relation Distribution Losses

Unsupervised relation extraction aims at extracting relations between entities in text. Previous unsupervised approaches are either generative or discriminative. In a supervised setting, discriminative approaches, such as deep neural network classifiers, have demonstrated substantial improvement. However, these models are hard to train without supervision, and the currently proposed solutions are unstable. To overcome this limitation, we introduce a skewness loss which encourages the classifier to predict a relation with confidence given a sentence, and a distribution distance loss enforcing that all relations are predicted in average. These losses improve the performance of discriminative based models, and enable us to train deep neural networks satisfactorily, surpassing current state of the art on three different datasets.
Performance and energy aware robust specification of control execution patterns under dropped samples

Embedded control systems are prevalent in a multitude of domains such as automotive, avionics, industrial control etc. For such systems, robustness against non-idealities of the compute platform created by situations such as hardware level transient faults (memory errors, sensor reading errors), network packet drops, late arrival of messages etc., is needed to be ensured at the design level. In traditional regular periodic execution of control loops, such guarantees are obtained by oversampling the plant, which requires extra rounds of sensing, control law computation, and actuation. One possible measure for reducing such over-provisioning of computing and communication resources is allowing occasional drops in the execution leading to better resource management as well as energy efficiency. This work showcases a methodology for deriving window-based bounds on possible drops in control loop executions while assuring formal performance guarantees even in the presence of platform-level non-idealities. Derivation of such relaxation bounds in terms of control loop executions help in deciding energy efficient scheduling solutions for low-power resource-constrained embedded control platforms while retaining control performance. The present work provides a structured methodology for deriving performance-aware control execution patterns given an energy-budget and uncertainty specification of the platform executing a set of embedded control loops.
Realization of BCD adder using Reversible Logic

This paper proposes novel carry select and carry look-ahead BCD adders using reversible logic. Reversible logic gates are widely known to be compatible with future computing technologies which virtually dissipate zero heat. Adders are fundamental building blocks in many computational units. For this reason, we have simulated several adder circuits using the reversible gates. Among all the other adders, the main virtue of BCD adders is that it allows easy conversion to decimal digits for printing or display and faster decimal calculations. These reversible BCD circuits are basis of the decimal ALU of primitive Quantum CPU. The proposed BCD adders have been simulated in VLSI and static timing report was analyzed. Everyday new technology which is faster, smaller and more complex than its predecessor is being developed. The increase in clock frequency to achieve greater speed and increase in number of transistors packed onto a chip to achieve complexity of a conventional system results in increased power consumption. Almost all the millions of gates used to perform logical operations in a conventional computer are irreversible. That is, every time a logical operation is performed some information about the input is erased or lost and is dissipated as heat. As per Landauer (1), for irreversible logic, each bit of information lost generates kTln2 Joules of heat energy, where k is Boltzmann's constant and T is absolute temperature at which the computation is performed. For room temperature T, the amount of heat dissipated for one bit is small i.e. 2.9×10-21 J (2). The current processors, first of all dissipate 500 times this amount of heat every time a bit is lost. Secondly, assuming every transistor out of more than 4×107 dissipates heat at the processor frequency of 2GHz, the figure becomes 4×1019*kTln2 J/sec. Although, it is around 0.1W at 400 degree Kelvin, Moore's law predicts exponential growth of heat generated due to information loss which will be an intolerable amount in the next decade. This heat dissipation dramatically reduces the performance and lifetime of the circuits. The solution is to use revolutionary technology which enables extremely low power consumption and heat dissipation in computing. Reversible logic, which is an actively researched area, is considered as an apt alternative. The employment of