A Speculative Trace Reuse Architecture with Reduced Hardware Requirements

Zero Knowledge Proofs (ZKPs) are one of the most striking innovations in theoretical computer science. In practice, the prevalent ZKP methods are, at times, too complicated to be useful for real-life applications. In this paper we present a practically efficient method for ZKPs which has a wide range applications. Specifically, motivated by the need to provide an upon-demand efficient validation of various financial transactions (e.g., the high-volume Internet auctions), we have developed a novel secure and highly efficient method for validating correctness of the output of a transaction while keeping input values secret. The method applies to input values which are publicly committed to by employing generic commitment functions (even input values submitted using tamper-proof hardware solely with input/ output access can be used.) We call these: strictly black box [SBB] commitments. Hence these commitments are typically much faster than public-key ones, and are the only cryptographic/ security tool we give the poly-time players, throughout. The general problem we solve in this work is: Let SLC be a publicly known staight line computation on n input values taken from a finite field and having k output values. The inputs are publicly committed to in a SBB manner. An Evaluator performs the SLC on the inputs and announces the output values. Upon demand the Evaluator, or a Prover acting on his behalf, can present to a Verifier a proof of correctness of the announced output values. This is done in a manner that (1) The input values as well as all intermediate values of the SLC remain information theoretically secret. (2) The probability that the Verifier will accept a false claim of correctness of the output values can be made exponentially small. (3) The Prover can supply any required number of proofs of correctness to multiple Verifiers. (4) The method is highly efficient. The application to financial processes is straight forward. To this end (1) we first use a novel technique for representation of values from a finite field which we call “split representation”, the two coordinates of the split representation are generically committed to; (2) next, the SLC is augmented by the Prover into a ”translation” which is presented to the Verifier as a sequence of generically committed split representations of values; (3) using the translation, the Prover and Verifier conduct a secrecy preserving proof of correctness of the announced SLC output values; (4) in order to exponentially reduce the probability of cheating by the Prover and also to enable multiple proofs, a novel highly efficient method for preparation of any number of committed-to split representations of the n input values is employed. The extreme efficiency of these ZK methods is of decisive importance for large volume applications. Secrecy preserving validation of announced results of Vickrey auctions is our demonstrative example.

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.

Value prediction improves instruction level parallelism in superscalar processors by breaking true data dependencies. Although this technique can significantly improve overall performance, most of the state-of-the-art value prediction approaches require high hardware cost, which is the main obstacle for its wide adoption in current processors. To tackle this issue, we revisit load value prediction as an efficient alternative to the classical approaches that predict all instructions. By speculating only on loads, the pressure over shared resources (e.g., the Physical Register File) and the predictor size can be substantially reduced (e.g., more than 90% reduction compared to recent works). We observe that existing value predictors cannot achieve very high performance when speculating only on load instructions. To solve this problem, we propose a new, accurate and low-cost mechanism for predicting the values of load instructions: the Address-first Value-next Predictor with Value Prefetching (AVPP). The key idea of our predictor is to predict the load address first (which, we find, is much more predictable than the value) and to use a small non-speculative Value Table (VT)—indexed by the predicted address—to predict the value next. To increase the coverage of AVPP, we aim to increase the hit rate of the VT by predicting also the load address of a future instance of the same load instruction and prefetching its value in the VT. We show that AVPP is relatively easy to implement, requiring only 2.5% of the area of a 32KB L1 data cache. We compare our mechanism with five state-of-the-art value prediction techniques, evaluated within the context of load value prediction, in a relatively narrow out-of-order processor. On average, our AVPP predictor achieves 11.2% speedup and 3.7% of energy savings over the baseline processor, outperforming all the state-of-the-art predictors in 16 of the 23 benchmarks we evaluate. We evaluate AVPP implemented together with different prefetching techniques, showing additive performance gains (20% average speedup). In addition, we propose a new taxonomy to classify different value predictor policies regarding predictor update, predictor availability, and in-flight pending updates. We evaluate these policies in detail.

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.

Deep Convolutional Neural Networks (CNNs) perform billions of operations for classifying a single input. To reduce these computations, this paper offers a solution that leverages a combination of runtime information and the algorithmic structure of CNNs. Specifically, in numerous modern CNNs, the outputs of compute-heavy convolution operations are fed to activation units that output zero if their input is negative. By exploiting this unique algorithmic property, we propose a predictive early activation technique, dubbed SnaPEA. This technique cuts the computation of convolution operations short if it determines that the output will be negative. SnaPEA can operate in two distinct modes, exact and predictive. In the exact mode, with no loss in classification accuracy, SnaPEA statically re-orders the weights based on their signs and periodically performs a single-bit sign check on the partial sum. Once the partial sum drops below zero, the rest of computations can simply be ignored, since the output value will be zero in any case. In the predictive mode, which trades the classification accuracy for larger savings, SnaPEA speculatively cuts the computation short even earlier than the exact mode. To control the accuracy, we develop a multi-variable optimization algorithm that thresholds the degree of speculation. As such, the proposed algorithm exposes a knob to gracefully navigate the trade-offs between the classification accuracy and computation reduction. Compared to a state-of-the-art CNN accelerator, SnaPEA in the exact mode, yields, on average, 28% speedup and 16% energy reduction in various modern CNNs without affecting their classification accuracy. With 3% loss in classification accuracy, on average, 67.8% of the convolutional layers can operate in the predictive mode. The average speedup and energy saving of these layers are 2.02x and 1.89x, respectively. The benefits grow to a maximum of 3.59x speedup and 3.14x energy reduction. Compared to static pruning approaches, which are complimentary to the dynamic approach of SnaPEA, our proposed technique offers up to 63% speedup and 49% energy reduction across the convolution layers with no loss in classification accuracy.

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.

Speculative execution and instruction reuse are two important strategies that have been investigated for improving processor performance. Value prediction at the instruction level has been introduced to allow even more aggressive speculation and reuse than previous techniques. This study suggests that using compiler support to extend value reuse to a coarser granularity than a single instruction, such as a basic block, may have substantial performance benefits. We investigate the input and output values of basic blocks and find that these values can be quite regular and predictable. For the SPEC benchmark programs evaluated, 90 percent of the basic blocks have fewer than four register inputs, five live register outputs, four memory inputs, and two memory outputs. About 16 to 41 percent of all the basic blocks are simply repeating earlier calculations when the programs are compiled with the -O2 optimization level in the GCC compiler. Compiler optimizations, such as loop-unrolling and function inlining, affect the sizes of basic blocks, but have no significant or consistent impact on their value locality, nor the resulting performance. Based on these results, we evaluate the potential benefit of basic block reuse using a novel mechanism called the block history buffer. This mechanism records input and live output values of basic blocks to provide value reuse at the basic block level. Simulation results show that using a reasonably sized block history buffer to provide basic block reuse in a 4-way issue superscalar processor can improve execution time for the tested SPEC programs by 1 to 14 percent, with an overall average of 9 percent when using reasonable hardware assumptions.

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