Miracle-dz/UniFAR-FTUs huggingface dataset:
Local binary patterns-based facial expression recognition using supervised laplacianfaces

Nowadays,many approaches based on manifold can't reflect accurately the underlying structure of expressional manifold,so their recognition accuracy is not high.Aimed at this problem,this paper proposed a method based on supervised Laplacianfaces(SLAP)and local binary patterns(LBP). LSLAP is short for the proposed method.The advantages of SLAP are preserving locality and considering the class information into account,which make the different sample classes well separated in the subspace of SLAP.LBP have the advantages of discriminative ability,illumination resistance and simple computation while LBP descripe face image in three different levels of locality.By projecting LBP features onto SLAP subspace,LSLAP extracts the final features and then uses the nearest-neighbor classifier.Experiements on JAFFE and Cohn-Kanade facial expression databases demonstrate that SLAP can reflect accurately the expressional manifold structure and LSLAP is superior to Eigenfaces,Fisherfaces,Laplacianfaces and their respective application to LBP,i.e.,Eigenfaces+LBP,Fisherfaces+LBP,Laplacianfaces+LBP,in terms of recognition accuracy.
Electroencephalograph analysis based on complex networks

This paper investigated one dimension EEG by converting it into complex networks via phase space reconstruction.To construct a complex networks,regarded each vector point as a node,and determined the edges by the phase space distance of each pair of vector points.A selective threshold value,which made the complex networks satisfy connectivity,could transform the distance matrix into a binary matrix.The binary matrix viewed as the adjacent matrix of complex networks was used to draw network topology and to analyze network characteristics.Applied the constructing networks method to distinguish EEG during eye-open and eye-closed resting conditions.The results indicate that recurrence plot,network topology,degree distribution and motifs distribution of the two networks show a distinct difference.Therefore complex networks as a data representation framework provide a new way for analyzing and distinguishing electroencephalograph.
Translating B machines into UML diagrams

This report describes transformations of B abstract machines into UML class and state-transition diagrams. The basic goal of this work is to produce translation rules for deriving UML class diagrams and state machines from sets, variables and operations in a B model. Our translation is interactive and does not necessarily produce a unique UML model, but it takes into account the use of the elements in the B model as a whole to constrain the translation into UML. We also consider how the refinement relationship of B machines maps to relations between the resulting UML models.
Novel approach to design scale-free networks

A novel approach is proposed that leads to the design and evolution of a scale-free network that satisfies any given power-law distribution.The approach makes use of another important feature of the scale-free network: the final formation of a scale-free network is controlled by the geometric mean of its degrees.Therefore,an algorithm is provided: starting from a rational small-world network,continuously deleting or adding an edge from or to the network by the "rich get richer" principle,meanwhile keeping the total number of nodes,the total number of edges and the connectivity of the network unchanged,until the geometric mean of the degrees of the network reaches a certain value,a prerequisite scale-free network will be formed.Numerical simulations show that the above algorithm can always lead to the evolution of a scale-free network that satisfies any given power-law distribution.
Image matching algorithm with high precision

A novel algorithm for image matching with high accuracy is proposed to achieve the requirement of spectrum restoration based on optical flow computation.This matching algorithm overcomes the defects of traditional image matching algorithm based on gray matching,which are affected by interpolation accuracy,and breaks the conflict between operation speed and accuracy.Experiments on real hyper-spectral image sequences show that our method is not only effective both in efficiency and accuracy,but also more suitable for the hardware realization compared with MAD block matching algorithm and normalized correlation coefficient block matching algorithm.
Lifetime Estimation of High-Power White LEDs

In this paper,the current accelerated aging experiment was used to investigate the luminous attenuation and lifetime of the high-power white LEDs (1W). Two kinds of luminous attenuation process were found:the slow attenuation at the early stage and the fast attenuation at the late stage. Moreover,the LED lifetimes with different operating currents were calculated by the exponential fitting of the slow luminous attenuation. The result implied that the Eying model was applicable for the relation between the lifetime and the operating current. Thus,the lifetime with standard current (350 mA) was calculated as 1924 h.
Effective and secure identity-based hybrid signcryption scheme

The existing identity-based hybrid signcryption schemes have either known security flaws or higher computational costs.With the vBNN-IBS signature algorithm,a new identity-based hybrid signcryption scheme was proposed.The new scheme,based on the Discrete Logarithm Problem(DLP) and the Computational Diffie-Hellman(CDH) problem on elliptic curve,did not use bilinear operation.So,the total computational costs of the new scheme were effectively reduced,and had only 7 scalar multiplication operations on elliptic curve,which were much lower than the other silimar schemes.In the random oracle model,the new scheme was verified safe enough to achieve the unforgeability and confidentiality.In addition,the paper also pointed out that the Sun et al's scheme(Sun Y X,Li H.Efficient certificateless hybrid signcryption.Journal of Software,2011,22(7):1690-1698) did not satisfy the unforgeability.
Graphene-based 2 μm Tm 3+ -doped fiber passively Q-switched laser

The output characteristics of graphene-based passively Q-switched pulses at 2 μm wavelengths were reported. On the basis of linear resonant cavity, Tm3+-doped fiber and graphene saturable absorber mirror served as gain medium and passively Q-switched device respectively. Tm3+-doped fiber was end pumped by 792 nm semiconductor laser. Light in the cavity was focused onto graphene membrane through a set of collimation and focusing lens. Passively Q-switched pulses centered at 1 958 nm wavelength were acquired successfully. The minimum pulse width of 1.02 μs was obtained when pump power rise to 3.0 W, and corresponding average output power of 26 mW, repetition rate of 116 kHz, single pulse energy of 224 nJ were obtained respectively. Furthermore, average output power and pulse width presented approximate linear relationship with incident pump power. The experimental results indicate that excellent saturated absorption characteristic of graphene is effective for passively Q-switched pulse operation at 2 μm wavelengths.
Improved adding weight first order local prediction method for chaotic time series

Adding weight first order local prediction method is the most usually used method to predict chaotic time series.Based on phase space delay-coordinate reconstruction of a chaotic dynamics system,an improved adding weight first order local prediction method is proposed to predict chaotic time series in this paper.Simulation results show that the improved method can effectively make multi-step and one-step prediction of chaotic time series and the multi-step prediction performance and one-step prediction accuracy of the improved method are superior to those of the adding weight first order local prediction method.
A Parametric Design Method of Reduced-order State Observer in Linear Systems

On the case of observability of linear systems,the design problem of reduced-order state observers is studied.By using some non-singular linear transformation to the linear system,the corresponding algebraic equivalent system is obtained and a part of states needed to be observered are isolated.Based on the parametric solutions of a Sylvester matrix equation,a parametric expression for the gain matrix of reduced-order state observers are presented,and a parametric design method of reduced-order state observers in linear systems is proposed.Finally,a numerical example shows the simplicity and validity of the proposed parametric design method for reduced-order state observers in linear systems.
Simulation of Phase Transition with Lattice Boltzmann Method

A lattice Boltzmann model is established to describe liquidvapor phase transitions using a model proposed by Shan instead of the R-K model.Evaporation from a higher density phase to a lower density phase is simulated.The model improves calculation efficiency and obtains good results.The model is also used to simulate phase transitions in porous media with a pore scale to demonstrate feasibility of the model for complicate phase transition problems.
Deep Learning Methods for Streaming Image Reconstruction in Fixed-camera Settings

A streaming video reconstruction system is described and implemented as a convolutional neural network. The system performs combined 2x super-resolution and H.264 artefacts removal with a processing speed of about 6 frames per second at 1920×1080 output resolution on current workstation-grade hardware. In 4x super-resolution mode, the system can output 3840×2160 video at a similar rate. The base system provides quality improvements of 0.010–0.025 SSIM over Lanczos filtering. Scene-specific training, in which the system automatically adapts to the current scene viewed by the camera, is shown to achieve up to 0.030 SSIM additional improvement in some scenarios. It is further shown that scene-specific training can provide some improvement even when reconstructing an unfamiliar scene, as long as the camera and capture settings remain the same. (Less)
Assessment of Power Grid Vulnerability Based on Small-World Topological Model

A new method to assess vulnerability of large-scale power grid based on small-world topological model is proposed to explore the spread mechanism of cascading failure in large-scale power grid.Firstly,the weighted topological modeling and calculation of basic characteristic parameter of power network are conducted;then under the presupposition of random attack and malicious attack,by means of calculating global efficiency indices the vulnerability of power grid is analyzed;finally,by use of percolation theory and maximum power supply area theory the critical levels of the grid under both attacks are computed.The research on a certain regional power grid in China shows that the dependence of small-world power grid on key nodes is much greater than that on common nodes,and external factors cannot improve inherent structural vulnerability of power grid by the root,thus rationally arrangement of grid structure contributes to improve reliability and stability level of power grid.
Secured Information Retrieval from Cloud Involving OTP and Human Voice

Cloud computing is an emerging technological paradigm that provides a flexible, scalable and reliable infrastructure and services for organizations. Cloud data are stored and accessed in a remote server with the help of services provided by cloud service providers. There are enormous numbers of security problems associated with cloud computing but that is not resolved now. These security issues can be faced by cloud providers or by their customers. The cloud provider should make sure that whatever services they are offering to their customers are secure and their customer’s data is safe while the customer should check that cloud providers are using security measures to keep their data safe. Cloud computing security is categorized from sub-domain of Information security In cloud security, dissemination of private data can be possible that requires stringent security policies with latest technology. Web Services are the essential feature of cloud computing. As web services helps in providing services easily to end users and manage cloud services, So, now-a-days all cloud providers support a large number of web services. But , some novice developers are making data unprotected that can be exploited by the attackers while deploying it on cloud service To combat dissemination of private data ,we are proposing secure information retrieval tactics that provides two level of authentication using OTP and voice recognition of the authorized person.
JOINING RADIO WITH X-RAYS: A REVISED MODEL FOR SN 1993J

A joint analysis is done of the radio and X-ray observations of SN 1993J. It is argued that neither synchrotron cooling behind the forward shock nor thermal cooling behind the reverse shock is supported by observations. In order for adiabatic models to be consistent, a reinterpretation of the radius of the spatially resolved very long baseline interferometry-source (VLBI) is needed during the first few hundred days. Instead of reflecting the position of the forward shock, it is then associated with the expansion of the Rayleigh–Taylor unstable region emanating from the contact discontinuity. Although observations imply a constant ratio between the energy densities in magnetic fields and relativistic electrons, they do not appear to scale individually with the thermal energy density behind the forward shock; rather, in adiabatic models, the evolution of the magnetic field strength is best understood as scaling inversely with the supernova radius.
A Novel Diversity Measure of Multiple Classifier Systems Based on Distance of Evidence

Multiple classifier systems can effectively improve the classification performance in many applications,which is why they have attracted a great deal of interest.Diversity among member classifiers is a necessary condition for improvement in classifier ensemble performance.In this paper,a novel diversity measure of multiple classifier systems is proposed based on the distance of evidence and a new approach to multiple classifier system design is presented.By using jointly the proposed diversity measure,the traditional diversity measure and the classification performance on training samples,an effective multiple classifier system can be implemented.It is experimentally shown that the proposed diversity measure and the proposed approach to multiple classifier system design are rational and effective.
Monitoring clinical trials based on predictive probability of significance.

At a given point in a clinical trial, investigators may ask the question: "What is the likelihood of a significant result if the trial were continued? " One possible answer to this question is to examine a predictive probability of the significant difference with further patient accrual. [See, for example, Choi, Smith, and Becker (1985, Controlled Clinical Trials 6, 280-288). ] This paper proposes and investigates the approach in trials for comparing the means of two normal populations. Two methods for calculating the predictive probability are examined. The results indicate that the predictive probability can be a useful conservative measure in monitoring trials.
Greedy algorithm for solving energy hole problem in wireless sensor networks

In Wireless Sensor Networks(WSNs),energy hole problem is a key factor of affecting the lifetime of networks.In a circular multi-hop sensor network(modeled as concentric coronas),the optimal transmission ranges of all coronas can effectively improve system lifetime.We propose an approximate greedy algorithm for searching trees with maximal lifetime,ASGT(Algorithm for Searching Good Trees). In ASGT the paper use graph scheme to analyzes the problem,and transform it to a problem of generating optimal tree from original graph.ASGT can not only reduce the searching complexity but also get results approximated to the optimal solution.Furthermore,the simulation results indicate that the lifetime got from ASGT approximates to the lifetime from optimal list.Compared with algorithm in other literatures,ASGT can prolong more than 2 times of system lifetime.
Mark-recapture models with parameters constant in time.

: The Jolly-Seber method, which allows for both death and immigration, is easy to apply but often requires a larger number of parameters to be estimated tha would otherwise be necessary. If (i) survival rate, phi, or (ii) probability of capture, p, or (iii) both phi and p can be assumed constant over the experimental period, models with a reduced number of parameters are desirable. In the present paper, maximum likelihood (ML) solutions for these three situations are derived from the general ML equations of Jolly [1979, in Sampling Biological Populations, R. M. Cormack, G. P. Patil and D. S. Robson (eds), 277-282]. A test is proposed for heterogeneity arising from a breakdown of assumptions in the general Jolly-Seber model. Tests for constancy of phi and p are provided. An example is given, in which these models are fitted to data from a local butterfly population.
Effects of simple knee sleeves on pain and knee adduction moment in early unilateral knee osteoarthritis

Knee sleeves are often prescribed to alleviate pain in people with early knee osteoarthritis. However, the biomechanics underlying their pain-relieving effect are still not well understood. This pre–post study aims at evaluating and comparing the effects of two different types of knee sleeves on knee adduction moment. Patients with clinically diagnosed knee osteoarthritis were recruited from the University of Malaya Medical Centre and were randomly assigned to two test groups using (1) a simple knee sleeve and (2) a simple sleeve with patella cutout. Knee adduction moment was collected using the Vicon motion capture system with two Kistler force plates. Pain, stiffness and physical functions were recorded using the Western Ontario and McMaster Universities Osteoarthritis Index. All measurements were taken before, immediately after and at the completion of 6 weeks of application (primary time point). In total, 17 participants with early unilateral knee osteoarthritis (47.7 (9.7) years) completed the study. Overall results show significant reduction in pain, early stance and late stance knee adduction moment and increased walking speed after 6 weeks of both knee sleeves application. This study results suggest that knee sleeves can reduce knee adduction moments in early unilateral knee osteoarthritis by 14.0% and 12.1% using the simple sleeve and the sleeve with patella cutout, respectively, and can potentially delay disease progression. In addition, knee sleeve with patella cutout does not provide additional benefits when compared to the simple knee sleeve.
DIVERSITY-BASED INTERESTINGNESS MEASURES FOR ASSOCIATION RULE MINING

Association rule interestingness measures are used to help select and rank association rule patterns. Diversity-based measures have been used to determine the relative interestingness of summaries. However, little work has been done that investigates diversity measures with association rule mining. Besides support, confidence, and lift, there are other interestingness measures, which include generality (also known as coverage), reliability, peculiarity, novelty, surprisingness, utility, and applicability. This paper investigates the application of diversity-based measures to association rule mining.
Platelets and Intravascular Immunity: Guardians of the Vascular Space During Bloodstream Infections and Sepsis

Despite their humble origins as anuclear fragments of megakaryocytes, platelets have emerged as versatile mediators of thrombosis and immunity. The diverse spectrum of platelet functions are on full display during the host response to severe infection and sepsis, with platelets taking center-stage in the intravascular immune response to blood-borne pathogens. Platelets are endowed with a comprehensive armamentarium of pathogen detection systems that enable them to function as sentinels in the bloodstream for rapid identification of microbial invasion. Through both autonomous anti-microbial effector functions and collaborations with other innate immune cells, platelets orchestrate a complex intravascular immune defense system that protects against bacterial dissemination. As with any powerful immune defense system, dysregulation of platelet-mediated intravascular immunity can lead to profound collateral damage to host cells and tissues, resulting in sepsis-associated organ dysfunction. In this article, the cellular and molecular contributions of platelets to intravascular immune defenses in sepsis will be reviewed, including the roles of platelets in surveillance of the microcirculation and elicitation of protective anti-bacterial responses. Mechanisms of platelet-mediated thromboinflammatory organ dysfunction will be explored, with linkages to clinical biomarkers of platelet homeostasis that aid in the diagnosis and prognostication of human sepsis. Lastly, we discuss novel therapeutic opportunities that take advantage of our evolving understanding of platelets and intravascular immunity in severe infection.
The CONSORT statement and its impact on quality of reporting of trials

A well-designed and adequately powered randomized controlled trial (RCT) is considered the gold standard for evaluating the efficacy of new treatments. However, in addition to good science and methodology, proper reporting of RCTs is essential to allow readers to appraise the validity of trial findings. Reporting of RCTs is facilitated by guidelines such as the Consolidated Statement of Reporting Trials (CONSORT). [1] The CONSORT checklist for reporting trials serves as a template for authors, reviewers, and editors to ensure uniformity and enhance the quality of reporting of trials.
Outcomes of Crowding in Emergency Departments; a Systematic Review

Introduction: Emergency Department (ED) crowding is a global public health phenomenon affecting access and quality of care. In this study, we seek to conduct a systematic review concerning the challenges and outcomes of ED crowding. Methods: This systematic review utilized original research articles published from 1st January 2007, to 1st January 2019. Relevant articles from the PubMed (MEDLINE), EMBASE, and Google scholar databases were extracted using predesigned keywords. Following the PRISMA guidelines, two reviewers independently evaluated the quality of the studies using Critical Appraisal Skills Programme for cohort studies and qualitative studies, and Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument for studies. Results: Out of the total of 73 articles in the final record, we excluded 15 of them because of poor quality. This systematic review synthesized the reports of 58 original articles. The outcomes of multiple individual patients and healthcare-related challenges are comprehensively assessed. Conclusions: ED crowding affects individual patients, healthcare systems and communities at large. The negative influences of crowding on healthcare service delivery result in delayed service delivery, poor quality care, and inefficiency; all negatively affecting the emergency patients' healthcare outcomes, in turn.