We propose a new method for combining multialgorithm score-based face recognition systems, which we call the two-step calibration method. Typically, algorithms for face recognition systems produce dependent scores. The two-step method is based on parametric copulas to handle this dependence. Its goal is to minimize discrimination loss. For synthetic and real databases (NIST-face and Face3D) we will show that our method is accurate and reliable using the cost of log likelihood ratio and the information-theoretical empirical cross-entropy (ECE).

Fixed FAR correction factor of score level fusion Susyanto, N.; Veldhuis, R.N.J.; Spreeuwers, L.J.; Klaassen, C.A.J. AbstractIn biometric score level fusion, the scores are often assumed to be independent to simplify the fusion algorithm. In some cases, the "average" performance under this independence assumption is surprisingly successful, even competing with a fusion that incorporates dependence. We present two main contributions in score level fusion: (i) proposing a new method of measuring the performance of a fusion strategy at fixed FAR via Jeffreys credible interval analysis and (ii) subsequently providing a method to improve the fusion strategy under the independence assumption by taking the dependence into account via parametric copulas, which we call fixed FAR fusion. Using synthetic data, we will show that one should take the dependence into account even for scores with a low dependence level. Finally, we test our method on some public databases (FVC2002, NIST-face, and Face3D), compare it to Gaussian mixture model and linear logistic methods, which are also designed to handle dependence, and notice its significance improvement with respect to our evaluation method.

<abstract><p>In this paper, a mathematical model describing the dynamical of the spread of hepatitis C virus (HCV) at a cellular level with a stochastic noise in the transmission rate is developed from the deterministic model. The unique time-global solution for any positive initial value is served. The Ito's Formula, the suitable Lyapunov function, and other stochastic analysis techniques are used to analyze the model dynamics. The numerical simulations are carried out to describe the analytical results. These results highlight the impact of the noise intensity accelerating the extinction of the disease.</p></abstract>

A family of Sigmoidal non-linear models is commonly used to determine the critical period of weed control (CPWC) and acceptable yield loss (AYL) in annual crops. We tried to prove another non-linear model to determine CPWC and AYL in a soybean agroforestry system with kayu putih. The three-year experiment (from 2019–2021) was conducted using a randomised complete block design factorial with five blocks as replications. The treatments comprised weedy and weed-free periods. Non-linear models comprised 45 functions. The results show that the Sigmoidal and Dose-Response Curve (DRC) families were the most suitable for estimating CPWC and AYL. The best fitted non-linear model for weedy and weed-free periods in the dry season used the Sigmoidal family consisting of the Weibull and Richards models, while in the wet season the best fit was obtained using the DRC and Sigmoidal families consisting of the DR-Hill and Richards models, respectively. The CPWC of soybean in the dry season for AYL was 5, 10, and 15%, beginning at 20, 22, and 24 days after emergence (DAE) and ended at 56, 54, and 52 DAE. The AYL in the wet season started at 20, 23, and 26 DAE and ended at 59, 53, and 49 DAE.

This work presents a mathematical model that investigates the impact of smokers on the transmission dynamics of smoking behavior in the Indonesian population. The population is classified into three classes: potential smokers, smokers, and ex-smokers. This model is described by non-linear differential equations using fractional quantities instead of actual populations by scaling the population of each class by the total population. There is also the density-dependent and density-independent death rate in the model to accommodate the difference between the death rate of potential smokers, smokers, and ex-smokers. In this model, two equilibrium points are found. One of them is the smoking-free equilibrium and the other relates to the presence of smoking. Then, the local stability of both equilibrium points is examined. Lastly, numerical simulations are carried out to illustrate the sensitivity of the smoker class to the parameters: the rate of non-smokers become smokers, the rate of smokers become smokers, also the rate of ex-smokers re-adapt smoking habit. The result of this paper can be considered to make a policy to reduce the number of smokers in Indonesia.

We present a mathematical framework for modelling dependence between biometric comparison scores in likelihoodbased fusion by copula models. The pseudo-maximum likelihood estimator (PMLE) for the copula parameters and its asymptotic performance are studied. For a given objective performance measure in a realistic scenario, a resampling method for choosing the best copula pair is proposed. Finally, the proposed method is tested on some public biometric databases from fingerprint, face, speaker, and video-based gait recognitions under some common objective performance measures: maximizing acceptance rate at fixed false acceptance rate, minimizing half total error rate, and minimizing discrimination loss.

Pandemi COVID-19 yang muncul pertama kali pada akhir tahun 2019 saat ini telah menyebar ke seluruh dunia dan mempengaruhi segala sendi kehidupan manusia. Di Indonesia, kasus ini mulai berkembang sejak akhir bulan Februari 2020 dan hingga saat ini masih terus terjadi peningkatan infeksi baru. Beberapa model dan prediksi kasus COVID-19 di Indonesia telah dilakukan oleh para peneliti, namun hasilnya belum sepenuhnya akurat. Hal ini kemungkinan disebabkan adanya pola yang berbeda-beda di setiap daerah, sehingga prediksi yang dilakukan di tingkat nasional perlu mengakomodir perbedaan pola tersebut. Pada artikel ini, akan diperkenalkan model matematika untuk melakukan prediksi awal kasus COVID-19 di wilayah Daerah Istimewa Yogyakarta. Pemodelan dilakukan berbasis model SIR yang parameter-parameternya diestimasi berdasarkan data. Dengan menggunakan model tersebut, akan dikaji dua skenario yang bersifat optimistik dan pesimistik.

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