Strictly-Black-Box Zero-Knowledge and Efficient Validation of Financial Transactions

This investigation explores the effects of applying fuzzy time series (FTSs) based on neural network models for estimating a variety of spectral functions in integer time series models. The focus is particularly on the skew integer autoregressive of order one (NSINAR(1)) model. To support this estimation, a dataset consisting of NSINAR(1) realizations with a sample size of n = 1000 is created. These input values are then subjected to fuzzification via fuzzy logic. The prowess of artificial neural networks in pinpointing fuzzy relationships is harnessed to improve prediction accuracy by generating output values. The study meticulously analyzes the enhancement in smoothing of spectral function estimators for NSINAR(1) by utilizing both input and output values. The effectiveness of the output value estimates is evaluated by comparing them to input value estimates using a mean-squared error (MSE) analysis, which shows how much better the output value estimates perform.

Establishing parameters of very high voltage power lines for various loadings and atmospheric conditions

Multivalued consensus functions defined from a vector of inputs over the set V of possible input values (and possibly from the previous input and output values) to a single output are investigated. The consensus functions are designed to tolerate t faulty inputs. Two classes of multivalued consensus functions are defined, the exact value and the range value, which require the output to be one of the nonfaulty inputs or in the range of the nonfaulty inputs, respectively. The instability of consensus functions is examined, counting the maximal number of output changes along a geodesic path of input changes, a path in which each input is changed at most once. Lower and upper bounds for the instability of multivalued consensus functions as a function of n, the number of sensors, t, and $|V|$ are presented. A new technique for obtaining such lower bounds, using edgewise simplex subdivision, is presented.

This paper presents a new probabilistie primality test. Upon termination the test outputs "composite" or "prime", along with a short proof of correctness, which can be verified in deterministic polynomial time. The test is different from the tests of Miller [M], Solovay-Strassen [SSI, and Rabin [R] in that its assertions of primality are certain, rather than being correct with high probability or dependent on an unproven assumption.

We present zero-knowledge proofs and arguments for arithmetic circuits over finite prime fields, namely given a circuit, show in zero-knowledge that inputs can be selected leading to a given output. For a field GF(q), where q is an n-bit prime, a<br />circuit of size O(n), and error probability 2^−n, our protocols require communication of O(n^2) bits. This is the same worst-cast complexity as the trivial (non zero-knowledge)<br />interactive proof where the prover just reveals the input values. If the circuit involves n multiplications, the best previously known methods would in general require communication<br />of Omega(n^3 log n) bits.<br />Variations of the technique behind these protocols lead to other interesting applications.<br />We first look at the Boolean Circuit Satisfiability problem and give zero-knowledge proofs and arguments for a circuit of size n and error probability 2^−n in which there is an interactive preprocessing phase requiring communication of O(n^2)<br />bits. In this phase, the statement to be proved later need not be known. Later the prover can non-interactively prove any circuit he wants, i.e. by sending only one message, of size O(n) bits.<br />As a second application, we show that Shamirs (Shens) interactive proof system for the (IP-complete) QBF problem can be transformed to a zero-knowledge proof<br />system with the same asymptotic communication complexity and number of rounds. The security of our protocols can be based on any one-way group homomorphism with a particular set of properties. We give examples of special assumptions sufficient for this, including: the RSA assumption, hardness of discrete log in a prime order group, and polynomial security of Die-Hellman encryption. We note that the constants involved in our asymptotic complexities are small enough for our protocols to be practical with realistic choices of parameters.

Increasing population growth has led to an abundant labor force. The micro and small industries are among the jobs that can absorb this workforce. Therefore, this study analyzes factors affecting labor absorption in the Mirko and small industries. Research samples were taken from six provinces on Java Island from 2017 to 2022. The independent variables are Provincial Minimum Wage, Number of Micro and Small Industry Units, Output Value, and Input Value—analysis using panel data regression by processing using Eviews 12. The research’s findings show that independent variables substantially influence labor absorption in micro and small industries. Partially, the provincial minimum wage negatively impacts the labor absorption of micro and small industries in Java Island during 2017-2022, while the number of Micro and Small Industry units, output value, and input value have a positive effect.

The primary goal of forest modelling is to accurately describe various forest processes and estimate stand- or tree-level indicators through mathematical models. Developing an accurate height-diameter model is a critical step in the creation of forest growth and yield models. The height-diameter curve is a mathematical equation with tree diameter at breast height as an input value and tree height as an output value. The objective of this research was to assess 50 different height-diameter curves to identify the most suitable ones for predicting dominant height using individual tree data from the Estonian Network of Forest Research Plots (ENFRP). Dominant height is defined as the mean height of 100 largest trees per hectare. For the evaluation we used measurement data collected from ENFRP plots between the years 1995 and 2021. The evaluation of height-diameter curves was based on their mathematical properties and the prediction error of dominant height. Among the evaluated equations, a modified version of the Hossfeld IV equation, was identified as the most accurate for predicting dominant height. The same equation, with Artur Nilson’s parameterization, yielded similar results. The results of this study can be used for developing generalized height-diameter models.

A lot of researches have been conducted to estimate the volatility smile effect shown in the option market. This paper proposes a method to approximate an implied volatility function, given noisy real market option data. To construct an implied volatility function, we use Gaussian Processes (GPs). Their output values are implied volatilities while moneyness values (the ratios of strike price to underlying asset price) and time to maturities are as their input values. To show the performances of our proposed method, we conduct experimental simulations with Korean Equity-Linked Warrant (ELW) market data as well as toy data.

Purpose. To increase the efficiency of the process of automation of remote programming of controllers by creating a method of transmitting algorithms for automatic control of technological objects and processes via the Internet or local network using a message that activates certain fragments of the program code. The methods. Graph theory and Boolean algebra were used to set algorithms for automatic control of technological processes. In addition, production rules were used as one of the forms of knowledge representation to describe the control algorithm. Elements of the theory of matrix representation were used to transform the system of production rules into a set of matrices. Also, computer modeling methods were used to confirm the effectiveness of the proposed solution. Findings. A method was created for converting automatic control algorithms, given in the form of a system of production rules, into a set of numerical matrices for remote change of the algorithm for automatic control of the technological process, which is implemented in the controller programmatically. The originality. New regularities of matrix transformation of production rules as a form of knowledge representation about the algorithm of automatic control of technological processes for further transmission over the network in the form of a package to the controller are established. For this purpose, matrices of input values, output values, transition to logical values and execution of logical operations on them are separately created, taking into account the sequence of their execution, which is given by the conditions of the production rules. The filling of the matrices occurs according to specially created rules, which allow to reproduce at the controller program level production rules of any structure with variable input and output values of the control algorithm. The proposed regularities have been further developed provided that a method of remote programming of controllers is created. Practical implementation. The proposed method is a necessary condition for creating complex multi-level automation systems, at the upper level of which the analysis of the collected information takes place and the formation of control influences as a result in the form of certain control tactics, which are given by the production rule systems. This method can also be used in the application of Internet of Things technology and in cyber-physical systems, when “smart” components of complex automation systems exchange not only collected data, but also fragments of the knowledge base with the aim of coordinating actions and performing joint work in an autonomous mode.

Modeling and forecasting financial time series with ordered fuzzy candlesticks

The use of physics-based simulations of manufacturing processes for the prediction of material properties and defects is increasingly widespread in industry. Such simulation tools help answer “what-if” questions, and a materials engineer may have to conduct a number of simulations for decision making. In practice the engineer is often seeking a solution to an “inverse problem”, i.e., prediction of inputs/process parameters for a desired outcome. Such an inverse problem is often solved by formulating it as a constrained-optimization problem. Extensive simulation in the input-parameter space when performing the optimization is avoided by approximate response surfaces iteratively constructed using simulations executed while traversing the design space. In this paper, we present a case study on the application of machine learning techniques to address such inverse problems. Specifically, using data from physics-based simulations, we explore the use of two different kinds of models constructed by machine learning. The first approach constructs a “generative” model (a Bayesian network), from which input values can be obtained directly from output values, without the need of an optimization step. It does however need additional knowledge in the form of conditional (in)-dependences between process parameters, intermediate state variables, and outputs. The second is a purely predictive machine learning model capturing complex non-linearity followed by the use of optimization methods (simulated annealing) for inverse prediction. We present results for modelling of a heat treatment process chain involving carburization, quenching and tempering. Our findings are as follows: For the range of output-values we examined, the predictive model performs better than the generative model. However the generative model has the ability to discover multiple solutions to the inverse problem, unlike in the traditional response-surface-based design of experiments. Thus the generative approach may prove more useful for exploratory industrial practice in the long run.

Performance evolution in machining parameter of Al-Si (LM6) alloy using neural network

Efficiency assessment of blanching and deep-freezing systems through data envelopment analysis

This paper presents the analysis and intelligent prediction for the displacement of stratum and tunnel lining of Qingdao Metro Line 4 by earth pressure balance (EPB) shield tunnel excavation in complex strata. When the tunnel is excavated in different stratum sections, the tunneling parameters of shield machine are systematically analyzed and compared, and the vertical displacement of the tunnel crown and the horizontal convergence deformation on both sides are investigated. When the tunnel body passes through the soft soil stratum and rock stratum, the curves of the vertical displacement of the stratum surface with time are respectively discussed. A machine learning method for predicting stratum surface deformation induced by shield tunnel excavation in complex strata is developed, where extreme learning machine (ELM), particle swarm optimization (PSO) algorithm and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -fold cross-validation method are comprehensively considered. 65 data samples are collected from the field monitoring data of Qingdao Metro Line 4 and each data sample includes seven input values and one output value. The developed PSO-ELM has good prediction performance for stratum surface vertical displacement due to shield tunnel excavation. The case study in this work can provide a practical reference for similar tunneling projects.

The traditional data mining method is featured by no analysis over the data distribution and incomplete derived association rule. As a result, the data mining results have the deficiencies of large redundancy probability, large root-mean-square error of approximation (RMSEA) and long consumption time. To handle these issues, this paper proposes a medical data stream distribution pattern association rule mining algorithm based on density estimation. This paper collects medical data, selects the distance method to detect abnormal orphan data in the data stream, detects the duplicate data in the data stream by the similar field matching degree, and eliminates the abnormal data and the duplicate data. Then, the data stream density is estimated based on the histogram estimation samples. According to the data density estimation results, this paper analyzes the distribution of medical data stream from perspectives of concentration, dispersion and morphological characteristics of data distribution. Afterwards, the data distribution pattern association rule mining model is constructed based on compound neural network, data distribution parameters are entered into model&#x2019;s clustering layer, and in-depth training is conducted over the BP (Back Propagation) neural network at the model&#x2019;s mining layer. Meanwhile, all rules under the combination of hidden layer&#x2019;s neuron activity value and corresponding output value, and all rules under the combination of hidden layer&#x2019;s neuron activity value and corresponding input value are derived, so as to complete association rule mining of medical data stream distribution pattern. The experimental results show that the proposed algorithm has a contour curve closest to the true probability density curve; the dispersion degree of medical data is within a reasonable range, and the medical data has high stability; the data redundancy probability is smaller; the mining result&#x2019;s RMSEA is small; data mining takes less time.