Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

In order to deal with the problem that the traditional stage costume artistry analysis method cannot correct the results of big data clustering, which leads to deviations in the extraction of costume artistry features, this paper proposes a clothing artistic modeling method based on big data clustering algorithm. The proposed method provides a database for big data clustering by constructing the attribute set of the big data feature sequence training set and, at the same time, constructing a second-order cone programming model to correct the big data. Aiming at the problem that traditional stage costume art analysis methods cannot correct the clustering results of big data. On this basis, the costume elements of the opera stage are segmented, initialized, and transformed into a binary function. Finally, using the convolutional neural network, combining the element segmentation results and the large data clustering space state vector, a feature extraction model of stage costume art is constructed. Experimental results show that the model has good convergence, short time-consuming, high accuracy, and ideal feature recognition capabilities.

Pitfalls in quantifying exploration in reward-based motor learning and how to avoid them

When learning a movement based on binary success information, one is more variable following failure than following success. Theoretically, the additional variability post-failure might reflect exploration of possibilities to obtain success. When average behavior is changing (as in learning), variability can be estimated from differences between subsequent movements. Can one estimate exploration reliably from such trial-to-trial changes when studying reward-based motor learning? To answer this question, we tried to reconstruct the exploration underlying learning as described by four existing reward-based motor learning models. We simulated learning for various learner and task characteristics. If we simply determined the additional change post-failure, estimates of exploration were sensitive to learner and task characteristics. We identified two pitfalls in quantifying exploration based on trial-to-trial changes. Firstly, performance-dependent feedback can cause correlated samples of motor noise and exploration on successful trials, which biases exploration estimates. Secondly, the trial relative to which trial-to-trial change is calculated may also contain exploration, which causes underestimation. As a solution, we developed the additional trial-to-trial change (ATTC) method. By moving the reference trial one trial back and subtracting trial-to-trial changes following specific sequences of trial outcomes, exploration can be estimated reliably for the three models that explore based on the outcome of only the previous trial. Since ATTC estimates are based on a selection of trial sequences, this method requires many trials. In conclusion, if exploration is a binary function of previous trial outcome, the ATTC method allows for a model-free quantification of exploration.

Optimization of Assembly Production Based on Imitation of Petri Nets

The paper describes the approach to solving the problem of optimal planning of the production process. A discrete production system represented by the operations of machining, welding and painting was chosen as the object of research. The study states the problem of optimization of assembly production, which contains a typical criterion of optimality. A mechanism for meeting the criterion using a simulation model based on a Petri net is determined. The rules for developing feedback on the state of the network model and a method for controlling the simulation of the Petri net based on the analysis of its states are given. A binary function is used to analyze the states of the model. The developed approach to process optimization develops the theory of Petri nets, makes it more suitable for modeling complex systems with a branched structure and a large number of interconnections, which is a typical situation for production systems. The most universal approaches of control theory, e.g. feedback principle, are used, which implies a significant degree of universality and replicability of the approach. On the basis of the developed theoretical provisions, a test example is presented that characterizes the effect of their application. The presence of assembly production at most mechanical-engineering enterprises determines the high practical significance of the developed approach

The terms of a language with one constant, one binary function, and one 4-ary function have an odd number of symbols

We show using induction on complexity that all terms of a language with one constant, one binary function, and one 4-ary function have an odd number of symbols.

Quantifying SARS-CoV-2 infection risk within the Google/Apple exposure notification framework to inform quarantine recommendations

Background: Most Bluetooth-based exposure notification apps use three binary classifications to recommend quarantine following SARS-CoV-2 exposure: a window of infectiousness in the transmitter, ≥15 minutes duration, and Bluetooth attenuation below a threshold. However, Bluetooth attenuation is not a reliable measure of distance, and infection risk is not a binary function of distance, nor duration, nor timing. Methods: We model uncertainty in the shape and orientation of an exhaled virus-containing plume and in inhalation parameters, and measure uncertainty in distance as a function of Bluetooth attenuation. We calculate expected dose by combining this with estimated infectiousness based on timing relative to symptom onset. We calibrate an exponential dose-response curve on the basis of the infection probabilities of household contacts. The conditional probability of current or future infectiousness, conditioned on how long post-exposure an exposed individual has been free of symptoms, decreases during quarantine, with shape determined by the distribution of incubation periods, proportion of asymptomatic cases, and distribution of asymptomatic shedding durations. It can be adjusted for negative test results using Bayes Theorem. Findings: We capture a 10-fold range of risk using 6 infectiousness values, 11-fold range using 3 Bluetooth attenuation bins, ~6-fold range from exposure duration given the 30 minute duration cap imposed by the Google/Apple v1.1, and ~11-fold between the beginning and end of 14 day quarantine. Imposing a consistent risk threshold for the probability of infection can recommend quarantine with weaker Bluetooth signal, even when not recommended for the entirety of the infectious period. Interpretation: The Covid-Watch app is currently programmed either to use a threshold on initial infection risk to determine 14-day quarantine onset, or on the conditional probability of current and future infectiousness conditions to determine both quarantine and duration. Either threshold can be set by public health authorities.

Order Matters: Semantic-Aware Neural Networks for Binary Code Similarity Detection

Binary code similarity detection, whose goal is to detect similar binary functions without having access to the source code, is an essential task in computer security. Traditional methods usually use graph matching algorithms, which are slow and inaccurate. Recently, neural network-based approaches have made great achievements. A binary function is first represented as an control-flow graph (CFG) with manually selected block features, and then graph neural network (GNN) is adopted to compute the graph embedding. While these methods are effective and efficient, they could not capture enough semantic information of the binary code. In this paper we propose semantic-aware neural networks to extract the semantic information of the binary code. Specially, we use BERT to pre-train the binary code on one token-level task, one block-level task, and two graph-level tasks. Moreover, we find that the order of the CFG's nodes is important for graph similarity detection, so we adopt convolutional neural network (CNN) on adjacency matrices to extract the order information. We conduct experiments on two tasks with four datasets. The results demonstrate that our method outperforms the state-of-art models.