Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanocomposites of ferroelectric liquid crystals and FeCo nanoparticles: towards a magnetic response via the application of a small electric field

ABSTRACT We study a nanocomposite consisting of a ferroelectric liquid crystal and a magnetic nanoparticle in order to explore the possibility of using it as a magnetic resonant imaging contrast agent which will measure a field of 20 V/m. To achieve this we use the ferroic properties exhibited by the nanocomposite. We used the ferroelectric liquid crystal 2-(4-((2-fluorooctyl)oxy)phenyl)-5-(octyloxy)pyrimidine mixed with FeCo nanoparticles nominally 2–3 nm in diameter in concentrations of 0.56, 4.3 and 10.8 wt%. The 10.8 wt% sample was chosen for our study because the nanoparticles acted as a lubricant for the ferroelectric liquid crystal. This concentration yields nanoparticle clusters in about 5 − 10 μm diameter spherulites. An electric field as low as 5V/cm is enough to turn and realign the spherulites where the particles are contained. We estimate the value of the magnetic in a spehrulite and associate it to the number of spherulites aligned as a function of electric field. We find thus that we can achieve low electric fields. Graphical Abstract
Wireless Sensor Network Based on ZigBee with Applications for Smart Building

Zigbee is a new wireless network protocol stack of IEEE 802.15. 4. In our experiment, Enhanced Dijkstra algorithm is introduced as the whole network shortest routing method. For each node, Location Table and Distance Table which are the input parameters of Dijkstra algorithm is achieved by GPS, and the result is the shortest packet transmission paths, by which the whole wireless network system is improved.
ALMA Observations of a Massive and Dense Cold Clump: G305.137+0.069

We report observations, made with the Atacama Large Millimeter/submillimeter Array, of 3 mm dust continuum emission and molecular line emission in HCO+, CS, and N2H+, toward G305.137+0.069, a massive, dense, and cold clump, in order to characterize its small-scale (∼2000 au) structure. The dust continuum observations reveal the presence of twelve compact structures (cores) with masses ranging from 3.3 to 50.6 M⊙, radii from 1800 to 5300 au, and densities from 3.1 × 106 to 3.1 × 107 cm−3. The line observations show that the molecular emission arises from a bright central region, with an angular size of ∼12″, and an extended, weaker envelope best seen in the HCO+ line. The N2H+ emission is best correlated with the continuum emission and hence with the cores. For cores with single line profiles, the line widths range from 1.9 to 3.1 km s−1 with an average value of 2.6 km s−1, indicating that they are dominated by nonthermal motions, either due to random turbulence or core-scale motions. The virial parameter of the three most massive cores are smaller than one, suggesting that they are undergoing collapse. We find that in the regime of masses probed by our observations (M > 3 M⊙) the shape of the core-mass function is notably different from the initial mass function, showing an overpopulation of high-mass cores. We suggest that the formation and mass distribution of the dense cores within G305.137+0.069 can be explained as the result of hierarchical or turbulent fragmentation in a gravitationally collapsing clump.
Study on Cloud Storage Model of Map/Reduce-based Index Data

The main problems of current search engine system applied on intelligent terminals are limited storage capacity with massive data,high-concurrency access of users and search delay of system.Aiming to tackling these problems,this paper proposes a cloud storage model of index classification and adopts a new index storage algorithm based on Map/Reduce programming model.The algorithm calculates the secondary weight of index term in the process of index classification in order to lower the fuzzy granularity of the classification.Based on the experimental results,the proposed storage model can not only improve the mass data processing efficiency,but also to some extent reduce query delay and ameliorate the search efficiency.
Dual‐band metasurface‐based CP low‐profile patch antenna with parasitic elements

A single-feed dual-band circularly polarised (CP) patch antenna is proposed. The antenna consists of a corner-truncated square patch antenna and a superstrate composed of a lattice of 2 × 2 metallic patches surrounded by parasitic elements. The driven patch generates one resonant mode. The metasurface with parasitic elements excites two additional resonant modes (TM 10 and TM 20 ). The combined resonances result in minimum axial ratio points, and a good dual-band CP operation is achieved. Simulations show that the antenna has a CP operating bandwidth of 40 MHz centred at 3.72 GHz (1.07%), and a CP operating bandwidth from 5.37 to 6.05 GHz (11.9%). The simulated gain is ∼7.05–7.1 dBic for the low-frequency band and 4.17–6.07 dBic for the high-frequency band, respectively. Measurements and simulations are in good agreement.
Statistical Image Analysis for Image Evolution

This thesis is focused on using genetic programming to evolve images based on lightweight features extracted from a given target image. The main motivation of this thesis is research by Lombardi et al. in which an image retrieval system is developed based on lightweight statistical features of images for comparing and classifying them in painting style categories; primarily based on color matching. In this thesis, automatic fitness scoring of variations of up to 17 lightweight image features using many-objective fitness evaluation was used to evolve textures. Evolved results were shown to have similar color characteristics to target images. Although a human survey was conducted to confirm those results, it was inconclusive. Acknowledgements I would like to thank Dr. Brian J. Ross for guiding me in this path patiently and then I would like to thank my parents and my sister, my friends, and faculty of department of computer science for their great support.
The Effect of Image Compression on Face Recognition Algorithms

Abstract: Face recognition becomes an important field via the revolution in technology and computer vision. This paper concentrated on recognition rate of face recognition algorithms. The There are many researches related to face recognition algorithms examined are: Principal Component Analysis, Two Dimensional Principal Component Analysis in Column Direction, Two Dimensional Principal Component Analysis in Row Direction and Two Dimensional Two Directional Principal Component Analysis. All these algorithms are implemented into two environments: training environment and recognition environment. Then a comparison between these four algorithms with respect to recognition rate is implemented. The proposed algorithm is implemented via Discrete Wavelet Transform (DWT) that minimizes the images size. A complexity reduction is achieved by optimizing the number of operations needed. This optimization does not increase the recognition rate only, but also reduce the execution time. A recognition rate improvement of 4% to 5% is achieved by introducing DWT through PCA algorithms.
The Application of Code Switching in English Vocabulary Teaching

Agreat deal of attention has been givento code switching by manylinguists and studyfields.However,there is little application of the technique to classroomteaching of secondlanguage.This article tryto establish aconnectionbetween code switching and English vocabulary teaching,involving the use of an L1 word in an L2utterance,to help learners’acquisition of newvocabulary.The study indicates that code switching can be aneffective and efficient methodin newvocabulary acquisition while employed properly.
Online Banking Fraud Detection Based on Local and Global Behavior

This paper presents a fraud detection system proposed for online banking that is based on local and global observations of users' behavior. Differential analysis is used to obtain local evidence of fraud where a significant deviation from normal behavior indicates a potential fraud. This evidence is strengthened or weakened by the user's global behavior. In this case, the evidence of fraud is based on the number of accesses performed by the user and by a probability value that varies over time. The Dempster's rule of combination is applied to these evidences for final suspicion score of fraud. Our main contribution is a fraud detection method based on effective identification of devices used to access the accounts and assessing the likelihood of being a fraud by tracking the number of different accounts accessed by each device.
Sparsity Measure and the Detection of Significant Data

The paper provides a formal description of the sparsity of a representation via the detection thresholds. The formalism proposed derives from theoretical results about the detection of significant coefficients when data are observed in presence of additive white Gaussian noise. The detection thresholds depend on two parameters describing the sparsity degree for the representation of a signal. The standard universal and minimax thresholds correspond to detection thresholds associated with different sparsity degrees.
Study of charmless four-body decays of neutral b-baryons with the LHCb spectrometer

Le travail presente dans ce document est dedie a l’etude des desintegrations faibles de deux baryons beaux, Λ0b (|udb>) et Ξ0b (|usb>), et utilise des echantillons de donnees enregistres par l’experience LHCb en 2011 et en 2012. La production abondante des baryons Λ0b et Ξ0b dans les collisions proton-proton ainsi que la grande probabilite pour un quark b d’hadroniser en un baryon beau au Large Hadron Collider (LHC), donne a l’experience LHCb l’opportunite d’etudier les desintegrations multicorps sans charme de baryons beaux. Une part significative de cette etude est dediee aux mesures des rapports d’embranchements des desintegrations sans charme a quatre corps des baryons Λ0b et Ξ0b . Dans le Modele Standard, ces desintegrations procedent simultanement via les transitions de quarks b→u (a l’arbre) et b→d,s (transitions a courant neutre de type pingouin). La difference de phase faible induite par cette interference est a priori une opportunite de rechercher la violation CP dans ces desintegrations de baryons beaux. De plus, ces desintegrations sans charme a quatre corps contiennent des structures resonantes, simultanement dans la region non-baryonique de basse masse a deux corps (i.e. les masses invariantes de π+π- , K± π∓ , K+ K- ) ainsi que dans la region baryonique de basse masse (i.e. les masses invariantes de pK, pπ). Par consequent, la difference de phases fortes induite par l’interference de ces amplitudes peut augmenter l’effet d’une asymetrie CP . Des mesures d’asymetries CP (∆ACP ) dans les modes de desintegrations discutes plus haut sont presentees dans ce document. Certaines sont produites dans des regions de l’espace des phases afin de favoriser la presence de structures resonantes. Ainsi, sept desintegrations inclusives de Λ0b et Ξ0b sont simultanement etudiees: Λ0b→pπππ, Λ0b→pKππ, Λ0b→pKKπ , Λ0b → pKKK, Ξ0b→ pKππ, Ξ0b→pKπK et Ξ0b→ pKKK ainsi que des desintegrations specifiques qui incluent plusieurs resonances intermediaires comme, par exemple, Λ0b → pa1 , Λ0b→∆++ K- π- ou Λ0b → Λ∗ (1520)0 φ0 . Six modes de desintegrations sont observes, parmi lesquels quatre sont etablis pour la premiere fois. Six mesures de rapports d’embranchements sont realisees, et des limites a 90% et 95% degre de confiance sont placees sur le rapport d’embranchement du mode Ξ0b→ pKKK pour lequel seul un indice de son existence est obtenu. Pour les mesures de ∆ACP realisees, aucun signe significatif de violation de CP n’est observe.
Modeling of HIV and AIDS in Indonesia Using Bivariate Negative Binomial Regression

The problem of HIV and AIDS in Indonesia is a frightening health problem with a number of cases that tend to increase each year. The aim of this research is to model the number of HIV and AIDS cases in Indonesia using bivariate negative binomial regression approach. Bivariate negative binomial regression is a regression method for modeling a pair of response variables in the form of count data with negative binomial distribution and correlating to each other. This research uses secondary data from the ministry of health in 2017 about the number of HIV and AIDS cases in Indonesia. From the results of this research, we obtained the deviance value of 38.9197 which was used to describe the goodness of fit test.
Improved Algorithm of Text Feature Weighting Based on Information Gain

The idf function of traditional tf.idf algorithm can only evaluate the ability of features to discriminate different documents in a macroscopically way,which can not reflect the differences of distribution proportion for features in each document and each class of the whole training set,it reduces the accuracy of text representation.To solve the above problem,this paper proposes an improved feature weighting method called tf.igt. igC.This method begins from analyzing the characteristics of feature distribution,through introducing the concept of information gain in the information theory,realizes the comprehensive consideration of the two specific dimensions of feature distributions,and overcomes the shortcomings of the traditional formula.Experimental results on the two open source corpus show that compared to other two feature weighting methods,tf.igt. igC is more effective in terms of calculating the feature weighting.
A Lightweight and Discriminative Model for Remote Sensing Scene Classification With Multidilation Pooling Module

With the growing spatial resolution of satellite images, high spatial resolution (HSR) remote sensing imagery scene classification has become a challenging task due to the highly complex geometrical structures and spatial patterns in HSR imagery. The key issue in scene classification is how to understand the semantic content of the images effectively, and researchers have been looking for ways to improve the process. Convolutional neural networks (CNNs), which have achieved amazing results in natural image classification, were introduced for remote sensing image scene classification. Most of the researches to date have improved the final classification accuracy by merging the features of CNNs. However, the entire models become relatively complex and cannot extract more effective features. To solve this problem, in this paper, we propose a lightweight and effective CNN which is capable of maintaining high accuracy. We use MobileNet V2 as a base network and introduce the dilated convolution and channel attention to extract discriminative features. To improve the performance of the CNN further, we also propose a multidilation pooling module to extract multiscale features. Experiments are performed on six datasets, and the results verify that our method can achieve higher accuracy compared to the current state-of-the-art methods.
Towards Effective and Efficient NFS Querying on XML Document

This paper discusses the issue of meaningful result determination for non-fully structured query (NFS) and proposes a scalable PE model to determine meaningful results based on the concept of pattern and entity. Within the framework of PE model, the paper proposes the method of identifying equivalent patterns and equivalent terms, puts forward the PE index and the improved inverted index I2P, and develops the effective and efficient NFS query algorithm for meaningful result evaluation, and the path query and keyword-based query. Experimental results indicate that the authors’ approach outperforms XSEarch system and Timber system both on the querying quality and the efficiency of query processing significantly.
Algorithms for analyzing and mining real-world graphs

This thesis is about algorithms for analyzing large real-world graphs (or networks). Examples include (online) social networks, webgraphs, information networks, biological networks and scientific collaboration and citation networks. Although these graphs differ in terms of what kind of information the objects and relationships represent, it turns out that the structure of each these networks is surprisingly similar. For computer scientists, there is an obvious challenge to design efficient algorithms that allow large graphs to be processed and analyzed in a practical setting, facing the challenges of processing millions of nodes and billions of edges. Specifically, there is an opportunity to exploit the non-random structure of real-world graphs to efficiently compute or approximate various properties and measures that would be too hard to compute using traditional graph algorithms. Examples include computation of node-to-node distances and extreme distance measures such as the exact diameter and radius of a graph.
Structure Learning of Bayesian Network Based on Adaptive Thresholding

Direct dependencies and conditional dependencies in restricted Bayesian network classifiers (BNCs) are two basic kinds of dependencies. Traditional approaches, such as filter and wrapper, have proved to be beneficial to identify non-significant dependencies one by one, whereas the high computational overheads make them inefficient especially for those BNCs with high structural complexity. Study of the distributions of information-theoretic measures provides a feasible approach to identifying non-significant dependencies in batch that may help increase the structure reliability and avoid overfitting. In this paper, we investigate two extensions to the k-dependence Bayesian classifier, MI-based feature selection, and CMI-based dependence selection. These two techniques apply a novel adaptive thresholding method to filter out redundancy and can work jointly. Experimental results on 30 datasets from the UCI machine learning repository demonstrate that adaptive thresholds can help distinguish between dependencies and independencies and the proposed algorithm achieves competitive classification performance compared to several state-of-the-art BNCs in terms of 0–1 loss, root mean squared error, bias, and variance.
Multiperiod-Ahead Wind Speed Forecasting Using Deep Neural Architecture and Ensemble Learning

Accurate forecasting of wind speed plays a fundamental role in enabling reliable operation and planning for large-scale integration of wind turbines. It is difficult to obtain the accurate wind speed forecasting (WSF) due to the intermittent and random nature of wind energy. In this paper, a multiperiod-ahead WSF model based on the analysis of variance, stacked denoising autoencoder (SDAE), and ensemble learning is proposed. The analysis of variance classifies the training samples into different categories. The stacked denoising autoencoder as a deep learning architecture is later built for unsupervised feature learning in each category. The ensemble of extreme learning machine (ELM) is applied to fine-tune the SDAE for multiperiod-ahead wind speed forecasting. Experimental results are made to demonstrate that the proposed model has the best performance compared with the classic WSF methods including the single SDAE-ELM, ELMAN, and adaptive neuron-fuzzy inference system (ANFIS).
Research of Semantic Image Classification Based on Support Vector Machine

With the continually development of multimedia database,traditional image retrival method which is based on key words can not satisfy most of the requirement of image retrieval.In recent years,more and more researchers have transferred their focus of scientific researches on content-based image retrieval.Extract the color and texture feature of the image,integrate color feature and texture feature as the feature vectors.Use support vector machine correlating image low-level feature with high-level semantic.Experimental result shows that,the retrieval of image with multiple features is better than that of image with single feature.The introduction of gray scale texture feature into HSV color feature can improve the efficiency of image retrieval.Furthermore,support vector machine can be successfully used in image semantic retrieval.
A mini-review of electroanalytical methods for pesticides quantification

This review paper shows the progress made in the last five years in electrochemical methods applied for the purpose of detection and quantification of pesticides nowadays the most serious pollutants of the environment. Most pesticides can be successfully analyzed by chromatographic techniques, but because of high prices and immobility of the apparatus, the complexity of the method and the necessary special preparation of the samples, electrochemical methods have been recognized as an excellent alternative solution due to their advantages in speed, economy, simplicity and no exhaustive sample preparation. As the main limitation for use of this methods is electrochemical inactivity of many pesticides, this paper gives the essence of all elctroanalytical methods for pesticide quantification applied in last period, with an overview of the electrode materials and modifiers applied in a purpose to enhance analytical procedures application and characteristics of electrochemical sensors.
Performance Evaluation of Line Simplification Algorithms Based on Hierarchical Information Content

Simplification has always been a commonly-used generalization operator.It is natural to further pay more attention to the distortions caused by generalization.The study takes line feature as an example and develops a new evaluation indicator(i.e.information content)at three levels(i.e.element,neighborhood and holistic levels)from the view of information transmission.The process of performance evaluation mainly involves the calculation and the comparison of the information content before and after line simplification.For the former,the quantitative computational methods of information content at the three levels are presented.For the latter,the difference of information content before and after line simplification is computed and utilized to measure the performance of line simplification algorithms.The difference of information content at element level reflects the ability of line simplification algorithms to select key points;the difference of information content at neighborhood level reflects the ability of line simplification algorithms to maintain bends;the difference of information content at holistic level reflects the ability of line simplification algorithm to maintain the trend of line features.Finally,a river network dataset is used to test the performance evaluations of four common-used line simplification algorithms according to the proposed indicator of information content.It is proven that this new indicator is very rational to evaluate the performance of these four line simplification algorithms.At the meantime,a comparative test is made to show the advantages of the new evaluation indicator.
The Complete Tolkien Companion

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Automatic Recognition of Chinese Place Names

Aiming at solving recognition of Chinese place names,this paper constructs the base of recognition-rule and quantifies the rules to denote the differences of reliability in detecting place names.The leveled strategy is adopted and different recognition methods are utilized by the each level.Evaluation on the open test corpus shows that the recall is 92.23% and the precision is 83.88%.