Time Complexity Of Algorithm Research Articles

Case report: Quantitative recognition of virtual human technology acceptance based on efficient deep neural network algorithm.

With the advancement of artificial intelligence, robotics education has been a significant way to enhance students' digital competency. In turn, the willingness of teachers to embrace robotics education is related to the effectiveness of robotics education implementation and the sustainability of robotics education. Two hundred and sixty-nine teachers who participated in the "virtual human education in primary and secondary schools in Guangdong and Henan" and the questionnaire were used as the subjects of study. UTAUT model and its corresponding scale were modified by deep learning algorithms to investigate and analyze teachers' acceptance of robotics education in four dimensions: performance expectations, effort expectations, community influence and enabling conditions. Findings show that 53.68% of the teachers were progressively exposed to robotics education in the last three years, which is related to the context of the rise of robotics education in schooling in recent years, where contributing conditions have a direct and significant impact on teachers' acceptance of robotics education. The correlation coefficients between teacher performance expectations, effort expectations, community influence, and enabling conditions and acceptance were 0.290 (p = 0.000<0.001), -0.144 (p = 0.048<0.05), 0.396 (p = 0.000<0.001), and 0.422 (p = 0.000<0.001) respectively, indicating that these four core dimensions both had a significant effect on acceptance. Optimization comparison results of deep learning models show that mDAE and AmDAE provide a substantial reduction in training time compared to existing noise-reducing autoencoder models. It is shown that time-complexity of the deep neural network algorithm is positively related to the number of layers of the model.

Perancangan Sistem Otomatis Transaksi Pembayaran Pada Marketplace UMKM Menggunakan Metode Crawling Horspool

UMKM MAPAN is one of the UMKM communities consisting of 300 UMKM entrepreneurs who are currently developing and located in the Depok area. These UMKM provide a wide variety of products to be marketed. In marketing these products, these UMKM group still uses the conventional method, is still by face-to-face with the buyer, so they have to rent a place first and are still limited in marketing online. This method has many weaknesses, such as the high cost of renting a place and the difficulty of finding a strategic location to market the product. Therefore, in this study, an e-commerce marketplace web system was created that could accommodate 300 UMKM MAPAN entrepreneurs in marketing their products. In addition, an automatic system for payment transactions on the UMKM marketplace was also created using the Horspool crawling method so that it could make it easier for UMKM entrepreneurs to print payment transaction reports. Based on the research results, the success rate of report printing is 100%. In this study, the complexity of the Horspool algorithm is O(n) with n is length of pattern while the time complexity of the Horspool algorithm is O(m+σ) with m is length of search string.

A faster algorithm for Cops and Robbers

We present an algorithm of time complexity O ( k n k + 2 ) deciding whether a graph G on n vertices is k -copwin. The fastest algorithm thus far had time complexity O ( n 2 k + 2 ) .

Acceleration of software execution time for operations involving sequences or matrices

The article describes three methods for lowering program runtime that are solutions to computer science Olympiad problems involving sequences or matrices. The first method relies on the representation of some sequences as matrices, after which the program for calculating the sequence's members will have asymptotics equivalent to the exponentiation algorithm's time complexity and be O(log (n)). The second strategy is to improve the existing code in order to significantly shorten program runtime. For scientists who create code for scientific inquiries and deal with matrix multiplication operations, understanding this approach is crucial. The author's challenge is presented and solved using the third strategy, which is based on minimizing temporal complexity by looking for regularities.

Tent Chaotic Map and Population Classification Evolution Strategy-Based Dragonfly Algorithm for Global Optimization

Dragonfly algorithm (DA) is a recently proposed optimization algorithm based on swarm intelligence, which has been successfully applied in function optimization, feature selection, parameter adjustment, etc. However, it fails to take individual optimal position into consideration but only relies on population optimal position and 5 behaviours to update individual position, leading to low accuracy, slow convergence, and local optima. To overcome these drawbacks, Tent Chaotic Map and Population Classification Evolution Strategy-Based Dragonfly Algorithm (TPDA) is proposed. Tent chaotic map is used to initialize the population, making individuals distributed more uniformly in search space to improve population diversity and search efficiency. Population is classified according to individual fitness value, and different position update methods are adopted for different types of individuals to guide the search process and improve the ability of TPDA to jump out of local optima, thus realizing a balance between exploration and exploitation. The efficiency of TPDA has been validated by tests on 18 basic unconstrained benchmark functions. A comparative performance analysis between TPDA, Particle Swarm Optimization (PSO), DA, and Adaptive Learning Factor and Differential Evolution-Based Dragonfly Algorithm (ADDA) has been carried out. Experimental and statistical results demonstrate that TPDA gives significantly better performances compared with PSO, DA, and ADDA on the average and standard deviation in all 18 functions. The global optimization capability of TPDA on high-dimensional functions and the comparison of the time complexity of TPDA and other swarm intelligence algorithms is also verified in the paper. The results indicate that TPDA is able to perform better on optimizing functions without consuming more computational time.

A Parallel DNA Algorithm for Solving the Quota Traveling Salesman Problem Based on Biocomputing Model.

The quota traveling salesman problem (QTSP) is a variant of the traveling salesman problem (TSP), which is a classical optimization problem. In the QTSP, the salesman visits some of the n cities to meet a given sales quota Q while having minimized travel costs. In this paper, we develop a DNA algorithm based on Adleman-Lipton model to solve the quota traveling salesman problem. Its time complexity is O(n2+Q), which is a significant improvement over previous algorithms with exponential complexity. A coding scheme of element information is pointed out, and a reasonable biological algorithm is raised by using limited conditions, whose feasibility is verified by simulation experiments. The innovation of this study is to propose a polynomial time complexity algorithm to solve the QTSP. This advantage will become more obvious as the problem scale increases compared with the algorithm of exponential computational complexity. The proposed DNA algorithm also has the significant advantages of having a large storage capacity and consuming less energy during the operation. With the maturity of DNA manipulation technology, DNA computing, as one of the parallel biological computing methods, has the potential to solve more complex NP-hard problems.

A ship emission diffusion model based on translation calculation and its application on Huangpu River in Shanghai

Under global warming, how to accurately measure and reduce ship emissions has become an important issue concerned by the transportation sector and the whole society. The diffusion concentration of continuous ship emissions is calculated by the emission concentrations at the time of observation. With the increase of observation time window, diffusion distance, and calculation accuracy, the calculation time of traditional algorithms and the requirements for computing resources increase exponentially. Therefore, this paper proposes a fast algorithm based on data translation (matrix translation and point translation algorithms), which can calculate the emission diffusion concentration of multiple ships, long distances and long duration while ensuring the accuracy. The time complexity of the traditional algorithm and the fast algorithm are O(kT2) and O(kT) respectively, and the fast algorithm can greatly improve the computing efficiency and save the computing time. The matrix translation algorithm is suitable for small-scale calculations with a fixed observation window, the amount of data is relatively small, and the calculation speed is the fastest. The point translation algorithm is suitable for large-scale calculations with a moving observation window and can adjust the calculation accuracy flexibly, but the data size is large and difficult to predict. Finally, in the numerical simulation on the Huangpu River, millions of emission concentrations data, based on trajectory information in 12 h over eight vessels, were calculated only in several minutes, which proves the high efficiency of the fast algorithm.

Creation of the method of multipath routing using known paths in software-defined networks

The object of the research is the creation of a multipath routing algorithm for software-defined networks (SDN) including known paths, subject of the research is network parameters, designed according to a certain topology and using the developed algorithm. One of the most problematic aspects of contemporary computer networks (including applied SDN networks) is overloading. This results in harder control and limiting traffic and amount of users. Most routing algorithms that are used today have a rather large time complexity. In the course of the study, the following methods were used: study of known routing solutions for SDN networks and results of their application, method of path metric computation on the given topology by the amount of «hops» (transitions between network nodes), optimization of procedure for finding the path using SDN technology capabilities. These methods were united and integrated into the development of the overall routing algorithm, which is proposed in this article. The proposed multipath routing algorithm allows for the improvement of the process of traffic construction in the SDN network. This was achieved by decreasing the time complexity of the routing algorithm through the usage of previously known paths in the topology without the need to construct new ones. Involvement in the modification of the algorithm of forming distance vectors facilitated timely network reconfiguration in case its state changed. Using a centralized SDN controller made it possible to increase the stability of the network and save all configuration data in one place. The above factors make it possible to deploy an SDN network on an Edge architecture. Obtained results of the application of the multipath routing algorithm allow to consider it effective when compared with previously proposed algorithms, based on obtained results from a practical network model, where the proposed multipath routing algorithm is used. This is because the research task was formed correctly, and the solution for this task gave correct results. Results of using the described algorithm are demonstrated, and an analysis of the obtained results is conducted, which makes it possible to confirm the accuracy of scientific research.

Shortest path with acceleration constraints: complexity and approximation algorithms

We introduce a variant of the Shortest Path Problem (SPP), in which we impose additional constraints on the acceleration over the arcs, and call it Bounded Acceleration SPP (BASP). This variant is inspired by an industrial application: a vehicle needs to travel from its current position to a target one in minimum-time, following pre-defined geometric paths connecting positions within a facility, while satisfying some speed and acceleration constraints depending on the vehicle position along the currently traveled path. We characterize the complexity of BASP, proving its NP-hardness. We also show that, under additional hypotheses on problem data, the problem admits a pseudo-polynomial time-complexity algorithm. Moreover, we present an approximation algorithm with polynomial time-complexity with respect to the data of the original problem and the inverse of the approximation factor epsilon. Finally, we present some computational experiments to evaluate the performance of the proposed approximation algorithm.

Time complexity in rejang language stemming

Stemming is the process of separating the root word from an affixed word in a sentence by separating the base word and affixes which can consist of prefixes (prefixes), insertions (infixes), and suffixes (suffixes). Between one language and another, there are differences in the algorithm, especially the stemming process, in morphology. The time complexity of the Rejang algorithm is determined based on the affix group. To find out the time complexity of the stemming algorithm in the Rejang language using the method of making a digital word dictionary of the Rejang language, studying and analyzing the morphology of the Rejang language, making the Rejang language stemming algorithm based on the results of the Rejang language morphology analysis, analyzing the algorithm's performance and calculating the time complexity of the stemming results. The result of this research is to produce an efficient and effective Rejang Language stemming algorithm, where efficiency is indicated by the algorithm's time complexity of O(log n), and the effectiveness is shown from the results of accuracy of 99% against the test of 9000 affixed words. This accuracy value indicates that the over stemming and under stemming processes are 1%. Test results on 15 text documents with an average stemming failure rate of 1%.

A Topological Characterization to Arbitrary Resilient Asynchronous Complexity

In this work, we extend the topology-based framework and method for the quantification and classification of general resilient asynchronous complexity. We present the arbitrary resilient asynchronous complexity theorem, applied to decision tasks in an iterated delayed model which is based on a series of communicating objects, each of which mainly consists of the delayed algorithm. In order to do this, we first introduce two topological structures, delayed complex and reduced delayed complex, and build the topological computability model, and then investigate some properties of those structures and the computing power of that model. Our theorem states that the time complexity of any arbitrary resilient asynchronous algorithm is proportional to the level of a reduced delayed complex necessary to allow a simplicial map from a task’s input complex to its output complex. As an application, we use it to derive the bounds on time complexity to approximate agreement with n+1 processes.

GP3: Gaussian Process Path Planning for Reliable Shortest Path in Transportation Networks

This paper investigates the reliable shortest path (RSP) problem in Gaussian process (GP) regulated transportation networks. Specifically, the RSP problem that we are targeting at is to minimize the (weighted) linear combination of mean and standard deviation of the path’s travel time. With the reasonable assumption that the travel times of the underlying transportation network follow a multi-variate Gaussian distribution, we propose a Gaussian process path planning (GP3) algorithm to calculate the a priori optimal path as the RSP solution. With a series of equivalent RSP problem transformations, we are able to reach a polynomial time complexity algorithm with guaranteed solution accuracy. Extensive experimental results over various sizes of realistic transportation networks demonstrate the superior performance of GP3 over the state-of-the-art algorithms.

Component Order Connectivity in Directed Graphs

A directed graph D is semicomplete if for every pair x, y of vertices of D, there is at least one arc between x and y. Thus, a tournament is a semicomplete digraph. In the Directed Component Order Connectivity (DCOC) problem, given a digraph D=(V,A) and a pair of natural numbers k and ell , we are to decide whether there is a subset X of V of size k such that the largest strongly connected component in D-X has at most ell vertices. Note that DCOC reduces to the Directed Feedback Vertex Set problem for ell =1. We study the parameterized complexity of DCOC for general and semicomplete digraphs with the following parameters: k, ell ,ell +k and n-ell . In particular, we prove that DCOC with parameter k on semicomplete digraphs can be solved in time O^*(2^{16k}) but not in time O^*(2^{o(k)}) unless the Exponential Time Hypothesis (ETH) fails. The upper bound O^*(2^{16k}) implies the upper bound O^*(2^{16(n-ell )}) for the parameter n-ell . We complement the latter by showing that there is no algorithm of time complexity O^*(2^{o({n-ell })}) unless ETH fails. Finally, we improve (in dependency on ell ) the upper bound of Göke, Marx and Mnich (2019) for the time complexity of DCOC with parameter ell +k on general digraphs from O^*(2^{O(kell log (kell ))}) to O^*(2^{O(klog (kell ))}). Note that Drange, Dregi and van ’t Hof (2016) proved that even for the undirected version of DCOC on split graphs there is no algorithm of running time O^*(2^{o(klog ell )}) unless ETH fails and it is a long-standing problem to decide whether Directed Feedback Vertex Set admits an algorithm of time complexity O^*(2^{o(klog k)}).

Considering Multisensor Data Fusion and Data-Driven Special Effects Animation Simulation

The data-driven simulation algorithm uses the method of surface matching to establish the corresponding relationship between the surfaces of the two dynamic models at the same time. It also calculates the appropriate deformation field to align the animated character surface and then uses the interpolation operation to further expand the new animated character motion. With the promotion of industrial application requirements such as games, film and television, and education and entertainment, computer animation special effects simulation technology has a wide range of application prospects. Based on a multisensor and data-driven approach, this paper conducts a targeted study on the realistic simulation of typical special effects animations. Aiming at the high time complexity and time-consuming problems of simulation algorithms, data-driven simulation methods are studied and scenes or objects that simulate the real environment can be created. They are given different properties and parameters, and the desired results are obtained through computer calculation and rendering. Finally, it can be output into a smooth animation. Based on the animation simulation data and multisensor data fusion method proposed in this paper, the on-site calculation process can be accelerated and efficient animation simulation driven by data can be realized. In 3ds Max 2013, there is also a small function that can sort the bone list in ascending or descending order in alphabetical and numerical order. This helps manage bones attached to the skin modifier more efficiently. This saves a lot of time when picking bones. About 44.3% of the people during the study were proficient in the use of software. After 1200 frames, the number of foam particles increases sharply, and the two-way coupling algorithm takes time. As the number of foam particles decreases, the time required for bidirectional coupling decreases. The article gives a brief description of the data-driven fusion model. The fluid simulation algorithm generates two different sequences by utilizing two different sets of setup parameters. It is then transformed into a spatiotemporal surface as input data for the fusion model. This research contributes to the development of special effects animation simulations.

Financial supply chain analysis with borrower identification in smart lending platform

The popularity of the online peer-to-peer (P2P) lending platform in the financial supply chain (FSC) has grown tremendously in the past few years. However, it is pretty challenging to develop an efficient financial supply chain for Micro, Small, and Medium-Sized Enterprises (MSMEs) by providing credits and services. In this research, an FSC model analyzes by emphasizing the retailer and supplier relationship. The retailer predicts the market demand to maximize profit, and accordingly, orders to the suppliers and suppliers utilize the production capabilities in limited monetary value. In such a case, the supplier also needs to apply for loans to meet the production commitment. With an intelligent lending platform, applicants can get loans quickly and more conveniently from the lenders. Still, identifying defaulter borrowers is a difficult task on the peer-to-peer lending platform. The main focus of the lenders is to maximize profit and minimize risk by giving the loan to non-defaulter suppliers. The present study describes the importance of the financial parameters (λ) of the retailers and suppliers (MSMEs) and their impact on the related supply chain. We provide the proof of dependency of the financial parameter on the cost of debt and the ratio of the debt-to-equity by developing two propositions. We propose an innovative k-Random Boosting Classifiers (k-RBC) algorithm for identifying potential good and bad borrowers to capture this case. The time complexity of the k-RBC algorithm is O(n2log(n)). The results obtained from the study show a significant improvement in comparison to the outputs from existing approaches on the same datasets. Our algorithm gives 90% accuracy to identify potential good borrowers, whereas existing algorithms achieve up to 87% accuracy. Furthermore, the importance of the borrowers’ features and its impact are analyzed of the lending platform in FSC.

Efficient multi-user for task offloading and server allocation in mobile edge computing systems

Mobile edge computing has emerged as a new paradigm to enhance computing capabilities by offloading complicated tasks to nearby cloud server. To conserve energy as well as maintain quality of service, low time complexity algorithm is proposed to complete task offloading and server allocation. In this paper, a multi-user with multiple tasks and single server scenario is considered for small network, taking full account of factors including data size, bandwidth, channel state information. Furthermore, we consider a multi-server scenario for bigger network, where the influence of task priority is taken into consideration. To jointly minimize delay and energy cost, we propose a distributed unsupervised learning-based offloading framework for task offloading and server allocation. We exploit a memory pool to store input data and corresponding decisions as key-value pairs for model to learn to solve optimization problems. To further reduce time cost and achieve near-optimal performance, we use convolutional neural networks to process mass data based on fully connected networks. Numerical results show that the proposed algorithm performs better than other offloading schemes, which can generate near-optimal offloading decision timely.

A modified convolutional neural network architecture for diabetic retinopathy screening using SVDD

Automatic diabetic retinopathy diagnostic methods are proposed to facilitate the examination process and act as the physician’s helper. Most of the traditional convolution neural network (CNN) algorithms use only spatial features for image category recognition. This approach may not be optimal for the screening diabetic retinopathy because the retinal images have generally the same feature maps with minor differences in spatial domain. We propose a new high level image understanding using a modified CNN architecture mixed with modified support vector domain description (SVDD) as a classifier. This new innovative architecture uses two pathways extracting features of the retinal images in both spatial and spectral domains. The standard pre-trained AlexNet is chosen for modification to avoid the time complexity of the training algorithms. In spite the advantages of the modified AlexNet with two pathways configuration and standard SVDD classification, the different SVDD kernel functions affect the performance of the proposed algorithm. By using the appropriate transformed data into two or three dimensional feature spaces, the proposed SVDD can obtain more flexible and more accurate image descriptions. Also, we compared the performance of our approach with that of the commonly used as classification methods such as K-Means, subtractive and FCM clustering. Our proposed architecture achieves more than 98% precision and sensitivity for two class classification.

Design, Implementation, and Performance Evaluation of a Web-Based Multiple Robot Control System

Heterogeneous multiple robots are currently being used in smart homes and industries for different purposes. The authors have developed the Web interface to control and interact with multiple robots with autonomous robot registration. The autonomous robot registration engine (RRE) was developed to register all robots with relevant ROS topics. The ROS topic identification algorithm was developed to identify the relevant ROS topics for the publication and the subscription. The Gazebo simulator spawns all robots to interact with a user. The initial experiments were conducted with simple instructions and then changed to manage multiple instructions using a state transition diagram. The number of robots was increased to evaluate the system’s performance by measuring the robots’ start and stop response time. The authors have conducted experiments to work with the semantic interpretation from the user instruction. The mathematical equations for the delay in response time have been derived by considering each experiment’s input given and system characteristics. The Big O representation is used to analyze the running time complexity of algorithms developed. The experiment result indicated that the autonomous robot registration was successful, and the communication performance through the Web decreased gradually with the number of robots registered.

A Multi-Strategy Improved Arithmetic Optimization Algorithm

To improve the performance of the arithmetic optimization algorithm (AOA) and solve problems in the AOA, a novel improved AOA using a multi-strategy approach is proposed. Firstly, circle chaotic mapping is used to increase the diversity of the population. Secondly, a math optimizer accelerated (MOA) function optimized by means of a composite cycloid is proposed to improve the convergence speed of the algorithm. Meanwhile, the symmetry of the composite cycloid is used to balance the global search ability in the early and late iterations. Thirdly, an optimal mutation strategy combining the sparrow elite mutation approach and Cauchy disturbances is used to increase the ability of individuals to jump out of the local optimal. The Rastrigin function is selected as the reference test function to analyze the effectiveness of the improved strategy. Twenty benchmark test functions, algorithm time complexity, the Wilcoxon rank-sum test, and the CEC2019 test set are selected to test the overall performance of the improved algorithm, and the results are then compared with those of other algorithms. The test results show that the improved algorithm has obvious advantages in terms of both its global search ability and convergence speed. Finally, the improved algorithm is applied to an engineering example to further verify its practicability.

GPU-accelerated N-1 static security analysis based on fine-grained parallelism HELM

With the increment of scale of power system, the accuracy and efficiency requirements of N-1 static security analysis (SSA) keep increasing. In order to satisfy these requirements, this paper proposed a GPU-accelerated N-1 SSA method to reduce computation burden and ensure the accuracy of alternating current power flow (ACPF) and connectivity test simultaneously. First, in the algorithm, ACPF is performed by holomorphic embedding load flow method (HELM). The construction and solution of sparse linear system both adopts fine-grained parallelism. According to the mathematical lemma of combining linear equations, this paper performs fine-grained parallelism Padé approximation of HELM. Second, considering the high time complexity of the original search-based algorithm and adjacency matrix method which is not suitable for sparse graph, this paper proposes a GPU-accelerated Union Set algorithm. Its main advantage is lower time complexity compared to other graph theory methods. And most importantly, it is very suitable for parallelization. Shared memory can be used in this algorithm well. Therefore, it is not complicated to perform a block-level parallelism in GPU-accelerated Union Set. Finally, the overall framework of GPU-accelerated N-1 SSA method is shown in this paper. It has been tested on large-scale power systems of up to 2869 buses. The computation results are compared with MATPOWER and the execution times are compared with other mainstream method to show the improvement in performance.