An improved arithmetic optimization algorithm with forced switching mechanism for global optimization problems

<abstract> <p>Arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which is inspired by the arithmetic operators in mathematics. However, the AOA has the weaknesses of insufficient exploration capability and is likely to fall into local optima. To improve the searching quality of original AOA, this paper presents an improved AOA (IAOA) integrated with proposed forced switching mechanism (FSM). The enhanced algorithm uses the random math optimizer probability (<italic>RMOP</italic>) to increase the population diversity for better global search. And then the forced switching mechanism is introduced into the AOA to help the search agents jump out of the local optima. When the search agents cannot find better positions within a certain number of iterations, the proposed FSM will make them conduct the exploratory behavior. Thus the cases of being trapped into local optima can be avoided effectively. The proposed IAOA is extensively tested by twenty-three classical benchmark functions and ten CEC2020 test functions and compared with the AOA and other well-known optimization algorithms. The experimental results show that the proposed algorithm is superior to other comparative algorithms on most of the test functions. Furthermore, the test results of two training problems of multi-layer perceptron (MLP) and three classical engineering design problems also indicate that the proposed IAOA is highly effective when dealing with real-world problems.</p> </abstract>

Core point pixel-level localization by fingerprint features in spatial domain

<abstract> <p>Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve of ridges. The summit of this Curve is regarded as the localization result. Furthermore, an approach for removing false Furcation and Confluence based on their correlations is developed to enhance the method robustness. Experimental results show that the proposed method achieves satisfactory core localization accuracy in a large number of samples.</p> </abstract>

TEP2MP: A text-emotion prediction model oriented to multi-participant text-conversation scenario with hybrid attention enhancement

<abstract> <p>With the rapid development of online social networks, text-communication has become an indispensable part of daily life. Mining the emotion hidden behind the conversation-text is of prime significance and application value when it comes to the government public-opinion supervision, enterprise decision-making, etc. Therefore, in this paper, we propose a text emotion prediction model in a multi-participant text-conversation scenario, which aims to effectively predict the emotion of the text to be posted by target speaker in the future. Specifically, first, an <italic>affective space mapping</italic> is constructed, which represents the original conversation-text as an n-dimensional <italic>affective vector</italic> so as to obtain the text representation on different emotion categories. Second, a similar scene search mechanism is adopted to seek several sub-sequences which contain similar tendency on emotion shift to that of the current conversation scene. Finally, the text emotion prediction model is constructed in a two-layer encoder-decoder structure with the emotion fusion and hybrid attention mechanism introduced at the encoder and decoder side respectively. According to the experimental results, our proposed model can achieve an overall best performance on emotion prediction due to the auxiliary features extracted from similar scenes and the adoption of emotion fusion as well as the hybrid attention mechanism. At the same time, the prediction efficiency can still be controlled at an acceptable level.</p> </abstract>

Sliding-mode variable structure control for complex automatic systems: a survey

<abstract> <p>Automatic systems (ASs) can automatically control the work of controlled objects without unattended participation. They have been extensively used in industry, agriculture, automobiles, robots and other fields in recent years. However, the performance of the controller cannot meet the work requirements under complex environmental conditions. Therefore, improving the control performance is one of the difficult problems that automated systems should solve. Sliding-mode variable structure control has the advantages of fast response, insensitivity to uncertainty and interference and easy implementation; thus, it has been extensively used in the field of complex control systems. This article analyses and explains the research status of motors, microgrids, switched systems, aviation guidance, robots, mechanical systems, automobiles and unmanned aerial vehicles (UAVs) and prospects for the application of sliding-mode variable structure control in complex ASs.</p> </abstract>

The number of COVID-19 clusters in healthcare and elderly care facilities averted by vaccination of healthcare workers in Japan, February–June 2021

<abstract> <p>In Japan, a prioritized COVID-19 vaccination program using Pfizer/BioNTech messenger RNA (mRNA) vaccine among healthcare workers commenced on February 17, 2021. As vaccination coverage increases, clusters in healthcare and elderly care facilities including hospitals and nursing homes are expected to be reduced. The present study aimed to explicitly estimate the protective effect of vaccination in reducing cluster incidence in those facilities. A mathematical model was formulated using three pieces of information: (1) the incidence of clusters in facilities from October 26, 2020 to June 27, 2021; (2) the incidence of confirmed COVID-19 cases during the same period; and (3) vaccine doses among healthcare workers from February 17 to June 27, 2021, extracted from the national Vaccination System database. We found that the estimated proportion at risk in healthcare and elderly care facilities declined substantially as the vaccination coverage among healthcare workers increased; the greater risk reduction was observed in healthcare facilities, at 0.10 (95% confidence interval (CI): 0.04–0.16) times that in the pre-vaccination period, while that in elderly care facilities was 0.34 (95% CI: 0.24–0.43) times that in the earlier period. The averted numbers of clusters in healthcare facilities and elderly care facilities were estimated to be 247 (95% CI: 210–301) and 279 (95% CI: 218–354), respectively. Prioritized vaccination among healthcare workers had a marked impact on preventing the incidence of clusters in facilities.</p> </abstract>

Conditional Ulam stability and its application to von Bertalanffy growth model

<abstract><p>The purpose of this paper is to apply conditional Ulam stability, developed by Popa, Rașa, and Viorel in 2018, to the von Bertalanffy growth model $ \frac{dw}{dt} = aw^{\frac{2}{3}}-bw $, where $ w $ denotes mass and $ a &gt; 0 $ and $ b &gt; 0 $ are the coefficients of anabolism and catabolism, respectively. This study finds an Ulam constant and suggests that the constant is biologically meaningful. To explain the results, numerical simulations are performed.</p></abstract>

Analysis of design parameters of round-window stimulating type electromagnetic transducer by a nonlinear lumped parameter model of implanted human ear

<abstract> <p>Round-window stimulating transducer is a new solution to treat mixed hearing loss. To uncover the factors affecting the round-window stimulation's performance, we investigated the influence of four main design parameters of round-window stimulating type electromagnetic transducer. Firstly, we constructed a human ear nonlinear lumped parameter model and confirmed its validity by comparing the stapes responses predicted by the model with the experimental data. Following this, an electromagnetic transducer's mechanical model, which simulates the floating mass transducer, was built and coupled to the human ear model; thereby, we established a nonlinear lumped parameter model of implanted human ear under round-window stimulation and verified its reliability. Finally, based on this model, the influences of the four main design parameters, i.e., the excitation voltage, the electromechanical coupling coefficient, the support stiffness, and the preload force, were analyzed. The results show that the change of excitation voltage does not alter the system's natural frequency. Chaotic motion occurs when the electromechanical coupling coefficient is small. Meanwhile, the stapes displacement appears to increase firstly and then decrease with the increase of the electromechanical coupling coefficient. The increase of the support stiffness enlarges the resonance frequency of the stapes displacement and reduces the stapes displacement near the resonance frequency, deteriorating the transducer's hearing compensation at low frequency. The preload force can improve the transducer's hearing compensation performance in mid-high frequency region.</p> </abstract>

Fuzzy-interval inequalities for generalized preinvex fuzzy interval valued functions

<abstract> <p>In this paper, firstly we define the concept of <italic>h</italic>-preinvex fuzzy-interval-valued functions (<italic>h</italic>-preinvex FIVF). Secondly, some new Hermite-Hadamard type inequalities (<italic>H</italic>-<italic>H</italic> type inequalities) for <italic>h</italic>-preinvex FIVFs via fuzzy integrals are established by means of fuzzy order relation. Finally, we obtain Hermite-Hadamard Fejér type inequalities (<italic>H</italic>-<italic>H</italic> Fejér type inequalities) for <italic>h</italic>-preinvex FIVFs by using above relationship. To strengthen our result, we provide some examples to illustrate the validation of our results, and several new and previously known results are obtained.</p> </abstract>

PBDiff: Neural network based program-wide diffing method for binaries

<abstract><p>Program-wide binary code diffing is widely used in the binary analysis field, such as vulnerability detection. Mature tools, including BinDiff and TurboDiff, make program-wide diffing using rigorous comparison basis that varies across versions, optimization levels and architectures, leading to a relatively inaccurate comparison result. In this paper, we propose a program-wide binary diffing method based on neural network model that can make diffing across versions, optimization levels and architectures. We analyze the target comparison files in four different granularities, and implement the diffing by both top down process and bottom up process according to the granularities. The top down process aims to narrow the comparison scope, selecting the candidate functions that are likely to be similar according to the call relationship. Neural network model is applied in the bottom up process to vectorize the semantic features of candidate functions into matrices, and calculate the similarity score to obtain the corresponding relationship between functions to be compared. The bottom up process improves the comparison accuracy, while the top down process guarantees efficiency. We have implemented a prototype PBDiff and verified its better performance compared with state-of-the-art BinDiff, Asm2vec and TurboDiff. The effectiveness of PBDiff is further illustrated through the case study of diffing and vulnerability detection in real-world firmware files.</p></abstract>

Calibration and prediction for the inexact SIR model

<abstract><p>A Susceptible Infective Recovered (SIR) model is usually unable to mimic the actual epidemiological system exactly. The reasons for this inaccuracy include observation errors and model discrepancies due to assumptions and simplifications made by the SIR model. Hence, this work proposes calibration and prediction methods for the SIR model with a one-time reported number of infected cases. Given that the observation errors of the reported data are assumed to be heteroscedastic, we propose two predictors to predict the actual epidemiological system by modeling the model discrepancy through a Gaussian Process model. One is the calibrated SIR model, and the other one is the discrepancy-corrected predictor, which integrates the calibrated SIR model with the Gaussian Process predictor to solve the model discrepancy. A wild bootstrap method quantifies the two predictors' uncertainty, while two numerical studies assess the performance of the proposed method. The numerical results show that, the proposed predictors outperform the existing ones and the prediction accuracy of the discrepancy-corrected predictor is improved by at least $ 49.95\% $.</p></abstract>