A code for square permutations and convex permutominoes

To overcome the shortcomings of the current popular conflict-free generation algorithm for pseudo-random sequences, a new conflict-free and efficient random permutation generation algorithm is presented. At the same time, it also introduces two other classical pseudo-random sequences generation algorithms, Hash algorithm and GUID algorithm. Compare the performance of the three algorithms by calculating time complexity and recording time consumption. The results show that the random permutation generation algorithm has high time efficiency and is more suitable for generating large amounts of conflict-free pseudo-random sequence.

As random algorithm can assure fairness, moreover, it can solve some of the problems which the definite algorithm can't solve; the random algorithm has wide spread use. In this paper, it designed an algorithm with time complexity of Ø(NlgN) to solve the probability weight oriented permutation problem, further, the algorithm need no extra space. The probability weight oriented random permutation algorithm does not only guarantee that the permutation is random, but it also guarantees that the element with higher weight has higher probability of ranking in the front of the permutation, and vice versa. Basically, there are two types of algorithms for random permutation, one for the same possibilities and the other for different possibilities. Compare the result of the test and the original probability weight, It shows that the permutation is random and the permutation embodies the probability weight of each element.

The analysis of many processes modeled by directed graphs requires the selection of a subset of vertices which cut all the cycles in the graph. Reducing the size of such a cutset usually leads to a simpler and more efficient analysis, but the problem of finding minimum cutsets in general directed graphs is known to be $NP$-complete. In this paper we show that in reducible graphs (and thus in almost all the “practical” flowcharts of programs), minimum cutsets can be found in linear time. We further show that the linear algorithm can check its own applicability to a given graph, thus eliminating the need of prechecking (in nonlinear time) whether it is reducible or not. An immediate application of this result is in program verification systems based on Floyd’s inductive assertions method.

Hybridization of cognitive computing for food services

Biased random test generation is one of the most important methods for the verification of modern complex processors. As the complexity of processors grows, the bottleneck remains in generating suitable test programs that meet coverage metrics automatically. Many technologies have been proposed to implement the automatic feedback loop. In this paper, we introduce our coverage directed test generation scheme which combines traditional biased random test generation and genetic algorithms to feed back process. It is the first time we use our scheme in our real industrial processor verification independently and successfully without human intervention. The efficiency of our approach has been demonstrated by the practical results.

Randomized approximation algorithms for set multicover problems with applications to reverse engineering of protein and gene networks

Radar Altimeters (RA) are widely used as a landing aid for aerospace vehicles because of its better measurement accuracy particularly at low altitude ranges. This paper describes the linear frequency modulated signal generation and the signal processing algorithm developed for an FM-CW altimeter. The altimeter uses two modulation rates to satisfy the measurement accuracy criteria for different applications. For generating the linear frequency modulated signal for both modulation rates, a Direct Digital Synthesis (DDS) based methodology is adopted. The altitude of the aerospace vehicle is measured based on the instantaneous frequency difference (beat signal frequency) between the transmitted and received signals. The beat signal is processed by Fast Fourier Transform (FFT) techniques which provide an additional gain so that the frequency of the required signal can be measured even if the signal to noise ratio is near zero dB. The proposed Radar Altimeter signal processing algorithm decides the modulation rate based on the measured altitude and the strength of the received signal. Higher resolution is ensured at lower altitudes by using a higher modulation rate.

Modern requirements present many challenging applications in the field of artificial intelligence (AI). Machine learning (ML) is concerned with how to construct a computer program that can increase its presentation on an assignment through understanding the input. An assignment can be a task to be learned. The functions responsibility is to produce output based on the input parameters. In this paper, we discuss on predicting the cost of pre-owned cars by using linear regression algorithms. The linear regression and random forest algorithms are applied on the dataset for future predictions. These methods are compared using R square and root mean square error methods. The results exhibited that random forest outperforms in the prediction of price of pre-owned cars comparing with linear regression.

We consider the problem of waking up n processors in a completely broadcast system. We analyze this problem in both globally and locally synchronous models, with or without n being known to the processors and with or without labeling of the processors. The main question we answer is: how fast we can wake up all the processors with probability 1 - ? in each of these eight models? In [12] a logarithmic waking algorithm for the strongest set of assumptions is described, while for weaker models only linear and quadratic algorithms were obtained. We prove that in the weakest model (local synchronization, no knowledge of n or labeling) the best waking time is O(n/log n). We also show logarithmic or polylogarithmic probabilistic waking algorithms for all stronger models, which in some cases gives an exponential improvement over previous results.

Planarity, bipartiteness and (directed) acyclicity are basic graph properties with classic linear time recognition algorithms. However, the problems of testing whether a given (di)graph has k vertices whose deletion makes it planar, bipartite or a directed acyclic graph (DAG) are all fundamental NP-complete problems when k is part of the input. As a result, a significant amount of research has been devoted to understanding whether, for every fixed k, these problems admit a polynomial time algorithm (where the exponent in the polynomial is independent of k) and in particular, whether they admit linear time algorithms. While we now know that for any fixed k, we can test in linear time whether a graph is k vertices away from being planar [FOCS 2009, SODA 2014] or bipartite [SODA 2014, SICOMP 2016], the best known algorithms in the case of directed acyclicity are the algorithm of Garey and Tarjan [IPL 78] which runs in time O(nk–1m) and the algorithm of Chen, Liu, Lu, O'Sullivan and Razgon [JACM 2008] which runs in time O(k!4kk4nm). In other words, it has remained open whether it is possible to recognize in linear time, a graph which is 2 vertices away from being acyclic! In this paper, we settle this question by giving an algorithm that decides whether a given graph is k vertices away from being acyclic, in time O(k!4kk5(n + m)). That is, for every fixed k, our algorithm runs in time O(m + n), thus mirroring the case for planarity and bipartiteness. Our algorithm is designed via a general methodology that shaves off a factor of n from some algorithms that use the powerful technique of iterative compression. The two main features of our methodology are: (i) This is the first generic technique for designing linear time algorithms for directed cut-problems and (ii) it can be used in combination with future improvements in algorithms for the compression version of other well-studied cut-problems such as Multicut and Directed Subset Feedback Vertex Set.

Traditionally, exactness, numerical stability and speed are the three main criteria for evaluating algorithms for random variate generation. However, it is sometimes required that the algorithms provide correlation between generated variates for the purpose of inducing dependence among the output of simulation runs. The inverse transformation, which produces optimal correlation induction, often performs poorly in terms of the first three criteria. Algorithms based on composition, rejection, and special properties which often excel in terms of the first three criteria, tend to scramble the use of random numbers, causing many attempts at common random numbers, antithetic variates and external control variates to fail. The concept of obtaining correlation via algorithms other than the inverse transformation is examined here. To demonstrate feasibility, previously developed algorithms for Poisson and binomial random variate generation are modified to obtain both positive and negative correlation between runs....

A new 3D mesoscale computational approach to simulate the mechanical behavior of soil–rock mixtures (SRMs) with the consideration of the grain-crushing process is proposed in this study. The proposed approach adopts a random SRM mesostructure generation algorithm to create a random SRM structure. Based on the generated mesostructure, the whole simulation area is divided into discrete cubic numbers, and the mesostructure is transformed into a material distribution matrix as an input for the computational approach. The computational approach is achieved by the coupling calculation of Matlab and COMSOL. Theimulations are presented alongside experimental data to validate the efficiency of the proposed approach. The simulation results indicate that the proposed computational approach can accurately capture the mechanical behavior of SRMs under loadings. This method helps to predict the physical properties of SRMs and has promising applications in engineering.

Research on software protection based on virtual machine

In industry, it is becoming common to detect and recognize industrial workpieces using deep learning methods. In this field, the lack of datasets is a big problem, and collecting and annotating datasets in this field is very labor intensive. The researchers need to perform dataset annotation if a dataset is generated by themselves. It is also one of the restrictive factors that the current method based on deep learning cannot expand well. At present, there are very few workpiece datasets for industrial fields, and the existing datasets are generated from ideal workpiece computer aided design (CAD) models, for which few actual workpiece images were collected and utilized. We propose an automatic industrial workpiece dataset generation method and an automatic ground truth annotation method. Included in our methods are three algorithms that we proposed: a point cloud based spatial plane segmentation algorithm to segment the workpieces in the real scene and to obtain the annotation information of the workpieces in the images captured in the real scene; a random multiple workpiece generation algorithm to generate abundant composition datasets with random rotation workpiece angles and positions; and a tangent vector based contour tracking and completion algorithm to get improved contour images. With our procedures, annotation information can be obtained using the algorithms proposed in this paper. Upon completion of the annotation process, a json format file is generated. Faster R-CNN (Faster R-convolutional neural network), SSD (single shot multibox detector) and YOLO (you only look once: unified, real-time object detection) are trained using the datasets proposed in this paper. The experimental results show the effectiveness and integrity of this dataset generation and annotation method.

The standard approach to solving the interpolation problem for a trace-driven simulation involving a continuous random variable is to construct a piecewise-linear cdf that fills in the gaps between the data values. Some probabilistic properties of this estimator are derived, and three extensions to the standard approach (matching moments, weighted values, and right-censored data) are presented, along with associated random variate generation algorithms. The algorithm is a nonparametric blackbox variate generator requiring only observed data from the user.