Miracle-dz/UniFAR-FTUs huggingface dataset:
A French translation of the Goldsmiths Musical Sophistication Index, an instrument to assess self-reported musical skills, abilities and behaviours

ABSTRACT The aim of this study was to translate into French the Goldsmiths Musical Sophistication Index (Gold-MSI) (Müllensiefen, D., Gingras, B., Musil, J., & Stewart, L. (2014). The musicality of non-musicians: An index for assessing musical sophistication in the general population. PLOS One, 9(2), e89642) – an inventory to assess self-reported musical skills, abilities and behaviours -, to examine the internal consistency and to confirm the factor structure of this new French translation. The Gold-MSI has been translated into French in different steps and completed online by 795 persons, out of which data from 750 persons have been analysed. Measures of internal consistency reveal that the French version of the Gold-MSI has high levels of internal consistency (α = .80–.95; ω = .86–.96; Guttman λ6 = .80–.97). Additionally, confirmatory factor analysis shows a good structure index (RMSEA = .063; SRMR = .066; CFI = .884) and confirms the underlying factor structure. These findings support the use of the French version of the Gold-MSI as a reliable instrument to measure the level of different musical facets. The French translation, which is freely available in supplementary material, can contribute to research about musical skills and behaviours in French-speaking regions.
Mobile Video Learning for Improving Programming Competency

Programming competency is a must competency that every student who are studying computer science must have. Mostly of the student are struggling learning this skill especially for those who do not have any computer background. The objective of this research is to design and develop e-learning using videos and tutorial to provide content of programming skill that needed by the student to increase their coding skill. Student can watch this video many times to have a fully understanding about the topic. Research and Development method are used by this research to design the video learning that can be accessed using mobile phone. As the result, the programming skill of the student increased rapidly.
Quadratic Spline Wavelets with Short Support Satisfying Homogeneous Boundary Conditions

In the paper, we construct a new quadratic spline-wavelet basis on the interval and a unit square satisfying homogeneous Dirichlet boundary conditions of the first order. Wavelets have one vanishing moment and the shortest support among known quadratic spline wavelets adapted to the same type of boundary conditions. Stiffness matrices arising from a discretization of the second-order elliptic problems using the constructed wavelet basis have uniformly bounded condition numbers and the condition numbers are small. We present quantitative properties of the constructed basis. We provide numerical examples to show that the Galerkin method and the adaptive wavelet method using our wavelet basis requires smaller number of iterations than these methods with other quadratic spline wavelet bases. Moreover, due to the short support of the wavelets one iteration requires smaller number of floating point operations.
Sample Size for Assessing Agreement between Two Methods of Measurement by Bland−Altman Method

Abstract: The Bland–Altman method has been widely used for assessing agreement between two methods of measurement. However, it remains unsolved about sample size estimation. We propose a new method of sample size estimation for Bland–Altman agreement assessment. According to the Bland–Altman method, the conclusion on agreement is made based on the width of the confidence interval for LOAs (limits of agreement) in comparison to predefined clinical agreement limit. Under the theory of statistical inference, the formulae of sample size estimation are derived, which depended on the pre-determined level of α, β, the mean and the standard deviation of differences between two measurements, and the predefined limits. With this new method, the sample sizes are calculated under different parameter settings which occur frequently in method comparison studies, and Monte-Carlo simulation is used to obtain the corresponding powers. The results of Monte-Carlo simulation showed that the achieved powers could coincide with the pre-determined level of powers, thus validating the correctness of the method. The method of sample size estimation can be applied in the Bland–Altman method to assess agreement between two methods of measurement.
Efficient Acoustic Simulation for Immersive Media and Digital Fabrication

Efficient Acoustic Simulation for Immersive Media and Digital Fabrication Dingzeyu Li Sound is a crucial part of our life. Well-designed acoustic behaviors can lead to significant improvement in both physical and virtual interactions. In computer graphics, most existing methods focused primarily on improving the accuracy. It remained underexplored on how to develop efficient acoustic simulation algorithms for interactive practical applications. The challenges arise from the dilemma between expensive accurate simulations and fast feedback demanded by intuitive user interaction: traditional physics-based acoustic simulations are computationally expensive; yet, for end users to benefit from the simulations, it is crucial to give prompt feedback during interactions. In this thesis, I investigate how to develop efficient acoustic simulations for real-world applications such as immersive media and digital fabrication. To address the above-mentioned challenges, I leverage precomputation and optimization to significantly improve the speed while preserving the accuracy of complex acoustic phenomena. This work discusses three efforts along this research direction: First, to ease sound designer’s workflow, we developed a fast keypoint-based precomputation algorithm to enable interactive acoustic transfer values in virtual sound simulations. Second, for realistic audio editing in 360° videos, we proposed an inverse material optimization based on fast sound simulation and a hybrid ambisonic audio synthesis that exploits the directional isotropy in spatial audios. Third, we devised a modular approach to efficiently simulate and optimize fabrication-ready acoustic filters, achieving orders of magnitudes speedup while maintaining the simulation accuracy. Through this series of projects, I demonstrate a wide range of applications made possible by efficient acoustic simulations.
A New Quantum Mechanical Formalism Based on the Probability Representation of Quantum States

A new quantum mechanical formalism based on the probability representation of quantum states is proposed. This paper in particular deals with the special case of the measurement problem, known as Schrodinger’s cat paradox. We pointed out that Schrodinger’s cat demands to reconcile Born’s rule. Using new quantum mechanical formalism we find the collapsed state of the Schrodinger’s cat always shows definite and predictable outcomes even if cat also consists of a superposition. Using new quantum mechanical formalism the EPRB-paradox is considered successfully. We find that the EPRB-paradox can be resolved by nonprincipal and convenient relaxing of the Einstein’s locality principle.
Biologically Inspired Resource Allocation for Network Slices in 5G-Enabled Internet of Things

The fifth generation (5G) mobile communication system is regard as a key enabler in promoting the deployment of Internet of Things (IoT), which is accompanied by the increasing service demands such like high data rate, enormous connection, and low latency. To meet these demands, network slicing has been envisioned as an efficient technology to customize infrastructures and allocate resources for 5G IoT services. However, due to various application backgrounds and ubiquitous social interactions of IoT services, the heterogeneous and social-driven resource requirement of users should be carefully assessed in resource allocation for the sliced 5G wireless network. In this paper, a novel nature-inspired wireless resource allocation scheme with slice characteristic perception is proposed, which comprehensively analyzes the properties of slices and converts them into a network profit model of resource utilization. Specifically, personalized service preferences and evolutionary interest relationships of users are exploited to model the complex and dynamic network environment with cellular automaton, and a biologically inspired allocation strategy of virtual wireless resource is proposed on the requirements of continuously updated user groups. Simulation results show that the proposed scheme achieves favorable resource utilization and low computational complexity, which favors the dynamic IoT slicing architecture and improves the efficiency and flexibility of resource allocation.
Limited memory optimal filtering

Linear and nonlinear optimal filters with limited memory length are developed. The filter output is the conditional probability density function and, in the linear Gaussian case, is the conditional mean and covariance matrix where the conditioning is only on a fixed amount of most recent data. This is related to maximum-likelihood least-squares estimation. These filters have application in problems where standard filters diverge due to dynamical model errors. This is demonstrated via numerical simulations.
Nonlinear disturbance observer-based control for a class of nonlinear systems

This paper presents a design method of nonlinear disturbance observer based control (NLDOBC) for a class of nonlinear systems with the disturbance modeled by the nonlinear exosystem. First, the virtual disturbance measurement output is defined. Then, a NLDOBC scheme is constructed to estimate and compensate the modeled disturbance. Subsequently, an linear matrix inequality (LMI)-based design method of NLDOBC is developed such that the augmented system is exponentially stable and then the original closed-loop system is exponentially stable in the presence of the modeled disturbance. Finally, some simulation results are given to show its effectiveness of the proposed method.
Beilinson-Kato and Beilinson-Flach elements, Coleman-Rubin-Stark classes, Heegner points and the Perrin-Riou Conjecture

Our first goal in this note is to explain that a weak form of Perrin-Riou's conjecture on the non-triviality of Beilinson-Kato classes follows as an easy consequence of the Iwasawa main conjectures, and deduce its refined versions in the supersingular case from this fact and a variety of Gross-Zagier formulae. Our second goal is to set up a conceptual framework in the context of $\Lambda$-adic Kolyvagin systems to treat analogues of Perrin-Riou's conjectures for higher motives of higher rank. We apply this general discussion in order to establish a link between Heegner points on a general class of CM abelian varieties and the (conjectural) Coleman-Rubin-Stark elements we introduce here. This is a higher dimensional version of Rubin's results on rational points on CM elliptic curves.
A new approach to obtain a colour palette in thermographic images

ABSTRACT This work presents a methodology for automatic colour allocation in thermographic images which is based on two thermograms, one with an Iron colour palette for segmentation and the other in grayscale for thermal analysis through the thermal matrix. With the fusion of both procedures, a palette of colours image is obtained. The colour palette allows characterising the thermography in a controlled manner and gain a better interpretation of the colours. Likewise, the colour palette permits to work in temperature level. With this proposed methodology, it is possible to assign any colour desired. An experiment to induce human emotions (joy, disgust, anger, fear, and sadness) was carried out to prove the application of this proposed method. In a quantitative way, a thermal index of emotions that shows the emotion concerning the baseline was obtained, which correlates with the colours assigned to the new image. Each emotion was associated with one colour. The results attained from both the thermal index of emotions and the hue allocation show the applicability and validation of the proposed palette; thus, it could be employed in any application of thermographic analysis.
Adaptive Sequential Pattern Recognition Applied on Credit Card Fraud Detection in the Online Games Industry

— Online credit card fraud presents a significant challenge in the field of eCommerce. In 2012 alone, the total loss due to credit card fraud in the US amounted to $ 54 billion. Especially online games merchants have difficulties applying standard fraud detection algorithms to achieve timely and accurate detection. This paper describes the special constrains of this domain and highlights the reasons why conventional algorithms are not quite effective to deal with this problem. Our suggested solution for the problem originates from the fields of feature construction joined with the field of temporal sequence data mining. We present feature construction techniques, which are able to create discriminative features based on a sequence of transaction and are able to incorporate the time into the classification process. In addition to that, a framework is presented that allows for an automated and adaptive change of features in case the underlying pattern is changing.
Elephant detection using boundary sense deep learning (BSDL) architecture

ABSTRACT Elephant entry in human settlements causes a major threat to human–elephant conflict (HEC), but the rapid detection thereof while crossing boundary remains a difficult role, due to the lack of necessary boundary sensing infrastructure. A combination of wireless sensor networks and recent advances in deep learning has paved an effective way for elephant detection. This paper presents and evaluates a Boundary Sense Deep Learning architecture (BSDL) for automated detection of elephants, integrating, elephant image feature learning and elephant classification. A novelty in this approach enhances the deep learning Boundary Sense (BS) architecture to include a background subtraction module which highlights the visual region important for elephant recognition. The objective of the proposed BSDL architecture is validated by achieving better accuracy when compared with the raw image, time efficiency, simplifying and boosting up of Deep Convolution Neural Network (DCNN) by introducing preliminaries; elephant recognition with Multi view dataset dramatically improves the state of art and fine tuning using back-propagation. The dataset includes 1,28,069 images of various postures of elephants collected under different circumstances from different regions (Hosur, Bannerghatta, Shimoga (Sakrebailu) and Dubare). The trained model is designed for identifying only one target species, the elephants. The conclusions drawn from our work prove that the achievement percentage is 97% accuracy in the BSDL architecture, when compared with the existing deep learning algorithms using raw image.
A Survey Based on Opinion Classification Techniques.

The essential one is to collect the information as what the people think forever. Nowadays people can share everything in online Social network such as twitter, facebook. People articulates their views and opinions regarding products and services. People can easily access and understand the opinions of others via web resources like as blog, review, and forum. These opinions are individual information which represents user's opinions. The precise way for predicting opinions are to extract sentiments from the web, which could valuable for marketing research. Opinions are so vital that whenever need to make a decision before want to know others' opinions. Opinion is not only important for a user but is also useful for an organization. This survey is about various methods and techniques used to classify the user opinions like as positive, negative.
Hybrid wind instrument and electronic system used therein

A composition of acoustic saxophone (2) and the electronic system (4) obtained by mixing saxophone (1), the electronic system (4) comprises sensor keys (24b) for monitoring the key (11a) detects the position of the tongue motion sensor tongue (23) and blow sensor (23a) and the lip sensor (24), pieces of performance data for producing the electronic tones are transmitted to the electronic tone generator (4a, 4b); acoustic saxophone mouthpiece (20 ) replaced by another mouthpiece (20A), the other blowing mouthpiece will not be provided to reed (22a), and a rotary type air flow conditioner (26) is provided in the mouthpiece (20A), so that the performance this feels the acoustic playing saxophonist (2) are the same.
Experimental Evaluation of Varying Feedback of a Cognitive Agent System for UAV Mission Management

In this study we investigate on a cognitive delegation agent for UAV task-based mission management. Particularly, we advocate a specific high-level feedback provided by the agent to the human operator to enhance mission effectiveness. As extension to human supervisory control we suggest to introduce the concept of agent supervisory control where the agent is delegated by high-level operator commands and controls several sub-systems aboard the UAV fulfilling the mission in highly automated fashion. Results of our experimental human-in-the-loop study focusing on the effects of high-level feedback are presented. Therefore, two configurations are compared, one with basic feedback and one with full feedback. The results show that particularly during in-flight re-planning situations the full feedback is beneficial. Interaction times and task related activities are significantly lower. In the pre-flight mission preparation phase no significant effect were found. Results can be used to further develop highly automated (multi)-UAV mission management systems.
PARAMETERS FORECASTING OF LASER WELDING BY THE ARTIFICIAL INTELLIGENCE TECHNIQUES

Laser welding process is used in many industrial sectors. One of the most important aspects of the laser welding quality refers to the geometrical and mechanical properties of welding joints. In order to develop optimal conditions for the laser welding process it is desirable to know in advance which machining parameters to select. Though there are manuals which recommend specific parameters combinations for the desired laser welding quality it is difficult to cover all possible combinations because of the process nonlinearity. Therefore, in this study the main aim is to establish an algorithm for optimal parameters forecasting of the laser welding process. The algorithm is based on an artificial intelligence approach. The main goal is to forecast the geometrical parameters of the welding joints like front width, front heights, back width and back heights of the welding joints. Experimental process was performed in order to acquire training and testing data of the laser welding process. The obtained results could be of practical importance for engineers in industry.
Highly-Available, Atomic Technology

The improvement of Lamport clocks has deployed the lookaside buffer, and current trends suggest that the exploration of XML will soon emerge. After years of typical research into model checking, we demonstrate the development of DHCP, which em- bodies the essential principles of artificial intelli- gence. In order to achieve this mission, we examine how the World Wide Web can be applied to the synthesis of suffix trees.
A weighted value scheduling algorithm based on Hadoop computer platform

Although Hadoop at the core of computing systems , but the efficiency is very low, ,and due to the Map Reduce scheduling system is not well on the process for priority classification, This article from the efficiency point of view, allocate weight values of task priority, In the weighting process, According to the Container resource request amount, expect a resource located node, the number of Container, submitted to the four main aspects of time for modeling Container, Because the running priority, making the system to improve the operational efficiency.
Low-Cost Posture Recognition of Moving Hands by Profile-Mold Construction in Cluttered Background and Occlusion

In this paper, we propose a low-cost posture recognition scheme using a single webcam for the signaling hand with nature sways and possible oc-clusions. It goes for developing the untouchable low-complexity utility based on friendly hand-posture signaling. The scheme integrates the dominant temporal-difference detection, skin color detection and morphological filtering for efficient cooperation in constructing the hand profile molds. Those molds provide representative hand profiles for more stable posture recognition than accurate hand shapes with in effect trivial details. The resultant bounding box of tracking the signaling molds can be treated as a regular-type object-matched ROI to facilitate the stable extraction of robust HOG features. With such commonly applied features on hand, the prototype SVM is adequately capable of obtaining fast and stable hand postures recognition under natural hand movement and non-hand object occlusion. Experimental results demonstrate that our scheme can achieve hand-posture recognition with enough accuracy under background clutters that the targeted hand can be allowed with medium movement and palm-grasped object. Hence, the proposed method can be easily embedded in the mobile phone as application software.
Application of Otsu to Improve Image Edge Detection Method in Canny Algorithm

An improved Canny edge detection method is proposed due to the easily lost edge details when detected by the traditional Canny algorithm.The traditional Canny and its defects are analyzed in terms of mathematical morphology.Then the Gauss filter with scales for edge detection is applied to improve the traditional Gauss filter.The Otsu replaces the traditional dual-threshold algorithm to get the best threshold value which effectively balances the contradiction between the anti-noise function and the retaining of edge details.In order to solve the problems of the long-time ergodicity and the poor real-time capability,the minimum error estimation algorithm of Kalman filer is suggested to estimate the threshold range of subsequent image sequences in advance to facilitate the ergodicity process.Finally,through the experimental analysis,the algorithm is proved to detect the image edges more quickly and effectively.
Application of the 0-1 test algorithm for chaos to runoff time series

The commonly used methods for the identification of chaotic characteristic of runoff time series are the correlation dimension method,the largest Lyapunov exponent method and Kolmogorov entropy method,which are based on the phase space reconstruction.Recently,a novel test approach for chaos detection in time series named zero-one(0-1) test has been proposed.This method applies directly to time series data;therefore,the phase space reconstruction is not required.Moreover,the non-chaotic and chaotic motions can be decided by means of the parameters Kc approaching asymptotically either to zero or one.Case studies of Logistic map,daily runoff series of Jinsha River in China and Umpqua River in America are implemented.The chaotic characteristics are identified and verified by using the 0-1 test algorithm.Then,based on the phase space reconstruction,five nonlinear dynamic methods are employed: ① phase space reconstruction;② the false Nearest Neighbor(FNN) algorithm;③ correlation dimension method;④ Lyapunov exponent method;and,⑤ Kolmogorov entropy.The comparative results show the effectiveness and reliability of the 0-1 test algorithm.The results from these methods provide cross-verification and confirmation of the existence of a mild low-dimensional chaos in the two daily runoff time series.