Open Shop Scheduling Problem is one of the most important scheduling problems in the field of engineering and industry. This kind of problem includes m machines and n jobs, each job contains a certain number of operations, and each operation has a predetermined processing time on its corresponding machine. The order of processing of these operations affects the completion times of all jobs. Therefore, the purpose of OSSP is to achieve a proper order of processing of jobs using specified machines, (...) so that the completion time of all jobs is minimized. In this paper, the strengths and limitations of three methods are evaluated by comparing the results of solving the OSSP in large-scale and small-scale benchmarks. In this case, the minimized completion time and total tardiness are considered the objective functions of the adapted methods. To solve small-scale problems, we adapt a mathematical model called Multiobjective Mixed Linear Programming. To solve large-scale problems, two metaheuristic algorithms including Multiobjective Parallel Genetic Algorithm and Multiobjective Parallel Simulated Annealing are adapted. In experimental results, we randomly generated small-scale problems to compare MOMILP with the Genetic Algorithm and Simulate Annealing. To compare MOPSA and MOPGA with the state of the art and show their strengths and limitations, we use a standard large-scale benchmark. The simulation results of the proposed algorithms show that although the MOPSA algorithm is faster, the MOPGA algorithm is more efficient in achieving optimal solutions for large-scale problems compared with other methods. (shrink)

Parallel attribute reduction is one of the most important topics in current research on rough set theory. Although some parallel algorithms were well documented, most of them are still faced with some challenges for effectively dealing with the complex heterogeneous data including categorical and numerical attributes. Aiming at this problem, a novel attribute reduction algorithm based on neighborhood multigranulation rough sets was developed to process the massive heterogeneous data in the parallel way. The MapReduce-based parallelization method (...) for attribute reduction was proposed in the framework of neighborhood multigranulation rough sets. To improve the reduction efficiency, the hashing Map/Reduce functions were designed to speed up the positive region calculation. Thereafter, a quick parallel attribute reduction algorithm using MapReduce was developed. The effectiveness and superiority of this parallel algorithm were demonstrated by theoretical analysis and comparison experiments. (shrink)

Frequent pattern mining is an effective approach for spatiotemporal association analysis of mobile trajectory big data in data-driven intelligent transportation systems. While existing parallel algorithms have been successfully applied to frequent pattern mining of large-scale trajectory data, two major challenges are how to overcome the inherent defects of Hadoop to cope with taxi trajectory big data including massive small files and how to discover the implicitly spatiotemporal frequent patterns with MapReduce. To conquer these challenges, this paper presents a (...) MapReduce-based Parallel Frequent Pattern growth algorithm to analyze the spatiotemporal characteristics of taxi operating using large-scale taxi trajectories with massive small file processing strategies on a Hadoop platform. More specifically, we first implement three methods, that is, Hadoop Archives, CombineFileInputFormat, and Sequence Files, to overcome the existing defects of Hadoop and then propose two strategies based on their performance evaluations. Next, we incorporate SF into Frequent Pattern growth algorithm and then implement the optimized FP-growth algorithm on a MapReduce framework. Finally, we analyze the characteristics of taxi operating in both spatial and temporal dimensions by MR-PFP in parallel. The results demonstrate that MR-PFP is superior to existing Parallel FP-growth algorithm in efficiency and scalability. (shrink)

The N-Queens problem plays an important role in academic research and practical application. Heuristic algorithm is often used to solve variant 2 of the N-Queens problem. In the process of solving, evaluation of the candidate solution, namely, fitness function, often occupies the vast majority of running time and becomes the key to improve speed. In this paper, three parallel schemes based on CPU and four parallel schemes based on GPU are proposed, and a serial scheme is implemented at (...) the baseline. The experimental results show that, for a large-scale N-Queens problem, the coarse-grained GPU scheme achieved a maximum 307-fold speedup over a single-threaded CPU counterpart in evaluating a candidate solution. When the coarse-grained GPU scheme is applied to simulated annealing in solving N-Queens problem variant 2 with a problem size no more than 3000, the speedup is up to 9.3. (shrink)

In this work, we present a new approach to solve the location management problem by using the reporting cells strategy. Location management is a very important and complex problem in mobile computing which aims to minimize the costs involved. In the reporting cells location management scheme, some cells in the network are designated as reporting cells . The choice of these cells is not trivial because they affect directly to the cost of the mobile network. This article is focused on (...) the use of high performance computing to execute a parallel heuristic that places optimally the RCs in a mobile network, minimizing its total cost. The main goal of this work is to demonstrate that the collaborative work of different evolutionary algorithms can obtain very good results. For this reason, we have implemented a parallel heuristic and six evolutionary algorithms that works in a parallel way on a cluster to solve the RCs problem. (shrink)

We describe the constructive content of proofs in a fragment of propositional Infinitary Linear Logic in terms of strategies for a suitable class of games. Such strategies interpret linear proofs as parallel algorithms as long as the asymmetry of the connectives ? and ! allows it.

A fixed-parameter is an algorithm that provides an optimal solution to a combinatorial problem. This research-level text is an application-oriented introduction to the growing and highly topical area of the development and analysis of efficient fixed-parameter algorithms for hard problems. The book is divided into three parts: a broad introduction that provides the general philosophy and motivation; followed by coverage of algorithmic methods developed over the years in fixed-parameter algorithmics forming the core of the book; and a discussion of (...) the essential from parameterized hardness theory with a focus on W [1]-hardness, which parallels NP-hardness, then stating some relations to polynomial-time approximation algorithms, and finishing up with a list of selected case studies to show the wide range of applicability of the presented methodology. Aimed at graduate and research mathematicians, programmers, algorithm designers and computer scientists, the book introduces the basic techniques and results and provides a fresh view on this highly innovative field of algorithmic research. (shrink)

Parallel processing as a method to improve computer performance has become a development trend. Based on rough set theory and divide-and-conquer idea of knowledge reduction, this paper proposes a classification method that supports parallel attribute reduction processing, the method makes the relative positive domain which needs to be calculated repeatedly independent, and the independent relative positive domain calculation could be processed in parallel; thus, attribute reduction could be handled in parallel based on this classification method. Finally, (...) the proposed algorithm and the traditional algorithm are analyzed and compared by experiments, and the results show that the proposed method in this paper has more advantages in time efficiency, which proves that the method could improve the processing efficiency of attribute reduction and makes it more suitable for massive data sets. (shrink)

The efficient processing of large-scale data has very important practical value. In this study, a data mining platform based on Hadoop distributed file system was designed, and then K-means algorithm was improved with the idea of max-min distance. On Hadoop distributed file system platform, the parallelization was realized by MapReduce. Finally, the data processing effect of the algorithm was analyzed with Iris data set. The results showed that the parallel algorithm divided more correct samples than the traditional algorithm; in (...) the single-machine environment, the parallel algorithm ran longer; in the face of large data sets, the traditional algorithm had insufficient memory, but the parallel algorithm completed the calculation task; the acceleration ratio of the parallel algorithm was raised with the expansion of cluster size and data set size, showing a good parallel effect. The experimental results verifies the reliability of parallel algorithm in big data processing, which makes some contributions to further improve the efficiency of data mining. (shrink)

The cerebral cortex is a rich and diverse structure that is the basis of intelligent behavior. One of the deepest mysteries of the function of cortex is that neural processing times are only about one hundred times as fast as the fastest response times for complex behavior. At the very least, this would seem to indicate that the cortex does massive amounts of parallel computation.This paper explores the hypothesis that an important part of the cortex can be modeled as (...) a connectionist computer that is especially suited for parallel problem solving. The connectionist computer uses a special representation, termed value unit encoding, that represents small subsets of parameters in a way that allows parallel access to many different parameter values. This computer can be thought of as computing hierarchies of sensorimotor invariants. The neural substrate can be interpreted as a commitment to data structures and algorithms that compute invariants fast enough to explain the behavioral response times. A detailed consideration of this model has several implications for the underlying anatomy and physiology. (shrink)

Recently, a multinomial process tree model was developed to measure an agent’s consequence sensitivity, norm sensitivity, and generalized inaction/action preferences when making moral decisions (CNI model). However, the CNI model presupposed that an agent considersconsequences—norms—generalizedinaction/actionpreferences sequentially, which is untenable based on recent evidence. Besides, the CNI model generates parameters at the group level based on binary categorical data. Hence, theC/N/Iparameters cannot be used for correlation analyses or other conventional research designs. To solve these limitations, we developed the CAN algorithm to (...) computenormandconsequencesensitivities and overallaction/inactionpreferences algebraically in a parallel manner. We re-analyzed the raw data of the original CNI model to test the methodological predictions. Our results demonstrate that: (1) theCparameter is approximately equal between the CNI model and CAN algorithm; (2) theNparameter under the CNI model approximately equalsN/(1 −C) under the CAN algorithm; (3) theIparameter andAparameter are reversed around 0.5 – the larger theIparameter, the more the generalized inactionversusaction preference and the larger theAparameter, the more overall actionversusinaction preference; (4) tests of differences in parameters between groups with the CNI model and CAN algorithm led to almost the same statistical conclusion; (5) parameters from the CAN algorithm can be used for correlational analyses and multiple comparisons, and this is an advantage over the parameters from the CNI model. The theoretical and methodological implications of our study were also discussed. (shrink)

Modern society makes extensive use of automated algorithmic decisions, fueled by advances in artificial intelligence. However, since these systems are not perfect, questions about fairness are increasingly investigated in the literature. In particular, many authors take a Rawlsian approach to algorithmic fairness. This article aims to identify some complications with this approach: Under which circumstances can Rawls’s original position reasonably be applied to algorithmic fairness decisions? First, it is argued that there are important differences between Rawls’s original position and a (...) parallel algorithmic fairness original position with respect to risk attitudes. Second, it is argued that the application of Rawls’s original position to algorithmic fairness faces a boundary problem in defining relevant stakeholders. Third, it is observed that the definition of the least advantaged, necessary for applying the difference principle, requires some attention in the context of algorithmic fairness. Finally, it is argued that appropriate deliberation in algorithmic fairness contexts often require more knowledge about probabilities than the Rawlsian original position allows. Provided that these complications are duly considered, the thought-experiment of the Rawlsian original position can be useful in algorithmic fairness decisions. (shrink)

We consider the problem of quadrilaterizing an orthogonal polygon P, that is to decompose P into nonoverlapping convex quadrangles without adding new vertices. In this paper we present a CREW-algorithm for this problem which runs in O time using Θ processors if the rectangle decomposition of P is given, and Θ processors if not. Furthermore we will show that the latter result is optimal if the polygon is allowed to contain holes.

Distributed programming paradigms such as MapReduce and Spark have alleviated sequential bottleneck while mining of massive transaction databases. Of significant importance is mining High Utility Itemset that incorporates the revenue of the items purchased in a transaction. Although a few algorithms to mine HUIs in the distributed environment exist, workload skew and data transfer overhead due to shuffling operations remain major issues. In the current study, Parallel Utility Computation algorithm has been proposed with novel grouping and load balancing (...) strategies for an efficient mining of HUIs in a distributed environment. To group the items, Transaction Weighted Utility values as a degree of transaction similarity is employed. Subsequently, these groups are assigned to the nodes across the cluster by taking into account the mining load due to the items in the group. Experimental evaluation on real and synthetic datasets demonstrate that PUC with TWU grouping in conjunction with load balancing converges mining faster. Due to reduced data transfer, and load balancing-based assignment strategy, PUC outperforms different grouping strategies and random assignment of groups across the cluster. Also, PUC is shown to be faster than PHUI-Growth algorithm with a promising speedup. (shrink)

A growing literature is taking an institutionalist and governance perspective on how algorithms shape society based on unprecedented capacities for managing social complexity. Algorithmic governance altogether emerges as a novel and distinctive kind of societal steering. It appears to transcend established categories and modes of governance—and thus seems to call for new ways of thinking about how social relations can be regulated and ordered. However, as this paper argues, despite its novel way of realizing outcomes of collective steering and (...) coordination, it can nevertheless be grasped with an old and fundamental figure in political philosophy: that of Thomas Hobbes’ Leviathan. Comparing algorithmic governance with this figure serves to highlight their similarities as socio-political arrangements, and specifically to clarify how algorithmic governance parallels the apolitical traits of the Leviathan—it eliminates the political as it requires compliance and forgoing contestation to best fulfill its role and to produce satisfying outcomes. (shrink)

A wave of recent scholarship has warned about the potential for discriminatory harms of algorithmic systems, spurring an interest in algorithmic accountability and regulation. Meanwhile, parallel concerns about surveillance practices have already led to multiple successful regulatory efforts of surveillance technologies—many of which have algorithmic components. Here, we examine municipal surveillance regulation as offering lessons for algorithmic oversight. Taking the 2017 Seattle Surveillance Ordinance as our primary case study and surveying efforts across five other cities, we describe the features (...) of existing surveillance regulation; including procedures for describing surveillance technologies in detail, requirements for public engagement, and processes for establishing acceptable uses. Although the Seattle Surveillance Ordinance was not intended to address algorithmic accountability, we find these considerations to be relevant to the law’s aim of surfacing disparate impacts of systems in use. We also find that in notable cases government employees did not identify regulated algorithmic surveillance technologies as reliant on algorithmic or machine learning systems, highlighting definitional gaps that could hinder future efforts toward algorithmic regulation. We argue that finer-grained distinctions between types of information systems in the language of law and policy, and risk assessment tools integrated into their implementation would strengthen future regulatory efforts by rendering underlying algorithmic components more legible and accountable to political and community stakeholders. (shrink)

This paper begins with the thought that there is something out of place about offloading inquiry into one's own mind to AI. The paper's primary goal is to articulate the unease felt when considering cases of doing so. It draws a parallel between the use of algorithms in the criminal law: in both cases one feels entitled to be treated as an exception to a verdict made on the basis of a certain kind of evidence. Then it identifies (...) an account of first‐person authority that can make good on this: agentialism. Thus, the paper constitutes an argument in favor of an agentialist treatment of self‐knowledge and first‐person authority. (shrink)

The LNCS series reports state-of-the-art results in computer science research, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community, with numerous individuals, as well as with prestigious Organizations and societies, LNCS has grown into the most comprehensive computer science research forum available. The scope of LNCS, including its subseries LNAI and LNBI, spans the whole range of computer science and information technology including interdisciplinary topics in a variety of (...) application fields. In Parallel to the printed book, each new volume is published electronically in LNCS Online. (shrink)

The results of evolutionary algorithms depend on population diversity that normally decreases by increasing the selection pressure from generation to generation. Usually, this can lead the evolution process to get stuck in local optima. This study is focused on mechanisms to avoid this undesired phenomenon by introducing parallel self-adapted differential evolution that decomposes a monolithic population into more variable-sized sub-populations and combining this with the characteristics of evolutionary multi-agent systems into a hybrid algorithm. The proposed hybrid algorithm operates (...) with individuals having some characteristics of agents, e.g. they act autonomously by selecting actions, with which they affect the state of the environment. Additionally, this algorithm incorporates two additional mechanisms: ageing and adaptive population growth, which help the individuals by decision-making. The proposed parallel differential evolution was applied to the CEC’18 benchmark function suite, while the produced results were compared with some traditional stochastic nature-inspired population-based and state-of-the-art algorithms. (shrink)

This article presents the design and development of a genetic algorithm to generate long-range transmit codes with low autocorrelation side lobes for radar to minimize target profile estimation error. The GA described in this work has a parallel processing design and has been used to generate codes with multiple constellations for various code lengths with low estimated error of a radar target profile.

Uninhabited combat air vehicle path planning is a complicated, high-dimension optimization problem. To solve this problem, we present in this article an improved glowworm swarm optimization algorithm based on the particle swarm optimization algorithm, which we call the PGSO algorithm. In PGSO, the mechanism of a glowworm individual was modified via the individual generation mechanism of PSO. Meanwhile, to improve the presented algorithm’s convergence rate and computational accuracy, we reference the idea of parallel hybrid mutation and local search near (...) the global optimal location. To prove the performance of the proposed algorithm, PGSO was compared with 10 other population-based optimization methods. The experiment results show that the proposed approach is more effective in UCAV path planning than most of the other meta-heuristic algorithms. (shrink)

Search engines are important contemporary sources of information and contribute to shaping our beliefs about the world. Each time they are consulted, various algorithms filter and order content to show us relevant results for the inputted search query. Because these search engines are frequently and widely consulted, it is necessary to have a clear understanding of the distinctively epistemic role that these algorithms play in the background of our online experiences. To aid in such understanding, this paper argues (...) that search engine algorithms are providers of “bent testimony”—that, within certain contexts of interactions, users act as if these algorithms provide us with testimony—and acquire or alter beliefs on that basis. Specifically, we treat search engine algorithms as if they were asserting as true the content ordered at the top of a search results page—which has interesting parallels with how we might treat an ordinary testifier. As such, existing discussions in the philosophy of testimony can help us better understand and, in turn, improve our interactions with search engines. By explicating the mechanisms by which we come to accept this “bent testimony,” our paper discusses methods to help us control our epistemic reliance on search engine algorithms and clarifies the normative expectations one ought to place on the search engines that deploy these algorithms. (shrink)

Load is the main external disturbance of a parallel robot manipulator. This disturbance will cause dynamic coupling among different degrees of freedom and make heaps of model-based control methods difficult to apply. In order to compensate this disturbance, it is crucial to obtain an accurate dynamic model of load. However, in practice, the load is always uncertain and its dynamic parameters are arduous to know a priori. To cope with this problem, this paper proposes a novel and simple approach (...) to identify the dynamic parameters of load. Firstly, the dynamic model of the parallel robot manipulator with uncertain load is established and the dynamic coupling caused by load is also analyzed. Then, according to the dynamic model, the excitation signal is designed and a weak nonlinear dynamic model is derived. Furthermore, the identification model is presented and the identification algorithm based on the extended Kalman filter is designed. Lastly, numerical simulation results, obtained using a six-degree-of-freedom Gough–Stewart parallel manipulator, demonstrate the good estimation performance of the proposed method. (shrink)

Whale optimization algorithm, known as a novel nature-inspired swarm optimization algorithm, demonstrates superiority in handling global continuous optimization problems. However, its performance deteriorates when applied to large-scale complex problems due to rapidly increasing execution time required for huge computational tasks. Based on interactions within the population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. In this paper, field programmable gate array is used as an accelerator, of which the high-level synthesis utilizes (...) open computing language as a general programming paradigm for heterogeneous System-on-Chip. With above platform, a novel parallel framework of WOA named PWOA is presented. The proposed framework comprises two feasible parallel models called partial parallel and all-FPGA parallel, respectively. Experiments are conducted by performing WOA on CPU and PWOA on OpenCL-based FPGA heterogeneous platform, to solve ten well-known benchmark functions. Meanwhile, other two classic algorithms including particle swarm optimization and competitive swarm optimizer are adopted for comparison. Numerical results show that the proposed approach achieves a promising computational performance coupled with efficient optimization on relatively large-scale complex problems. (shrink)

While the academic separation of policing and regulation is still largely operative, points of convergence are more significant than ever in the digital age, starting with concomitant debates about algorithms as a new figure of power. From the policing of illegal activities to the regulation of legal ones, the algorithmization of such critical social ordering practices has been the subject of growing attention. These burgeoning discussions are focused on one common element: big data surveillance. In accordance with such similarities (...) and paralleled developments in policing and regulation, the article aims to further bridge the gap between literatures to respond to the calls for studying big data surveillance across institutional domains and social fields. To do so, it is focused on one case study that articulates algorithmic policing and regulation domains, in-between security and economic fields. This is the fight against illicit finance, i.e. ‘the global action against the financial flows that fuel crime and terrorism’. To what extent does big data surveillance make a difference in the main global policy of crime-fighting and financial regulation? Drawing on a fieldwork in a large North American bank, the present article takes stock of the algorithmic overlap between policing and regulation. It argues that the final result is policing and regulation of neither too much nor too little, which gives rise to automated and everyday mass surveillance while remaining as far removed from regulatory and crime-fighting ambitions as it is from dystopian visions of big data and algorithmic drama. (shrink)

The original target tracking algorithm based on a single model has long been unable to meet the complex and changeable characteristics of the target, and then there are problems such as poor tracking accuracy, target loss, and model mismatch. The interactive multimodel algorithm uses multiple motion models to track the target, obtains the degree of adaptation between the actual motion state of the target and each model according to the calculated likelihood function, and then combines the updated weight values of (...) each filter to obtain a weighted sum. Therefore, the interactive multimodel algorithm can achieve better performance. This paper proposes an improved interactive multimodel algorithm that can achieve player tracking and trajectory feature matching. First, this paper proposes an improved Kalman filtering algorithm. This method is developed from the unbiased conversion measurement Kalman filter, which can obtain more accurate target state and covariance estimation. Secondly, using the parallel processing mode of the IMM algorithm to efficiently solve the data association between multiple filters, the IMM-IKF model is proposed. Finally, in order to solve the problem of low computational efficiency and high mismatch rate in image feature point matching, a method of introducing a minimum spanning tree in two-view matching is proposed. Experimental results show that the improved IMM-IKF algorithm can quickly respond to changes in the target state and can find the matching path with the lowest matching cost. In the case of ensuring the matching accuracy, the real-time performance of image matching is ensured. (shrink)

Introduction of deep neural networks to the machine translation research ameliorated conventional machine translation systems in multiple ways, specifically in terms of translation quality. The ability of deep neural networks to learn a sensible representation of words is one of the major reasons for this improvement. Despite machine translation using deep neural architecture is showing state-of-the-art results in translating European languages, we cannot directly apply these algorithms in Indian languages mainly because of two reasons: unavailability of the good corpus (...) and Indian languages are morphologically rich. In this paper, we propose a neural machine translation (NMT) system for four language pairs: English–Malayalam, English–Hindi, English–Tamil, and English–Punjabi. We also collected sentences from different sources and cleaned them to make four parallel corpora for each of the language pairs, and then used them to model the translation system. The encoder network in the NMT architecture was designed with long short-term memory (LSTM) networks and bi-directional recurrent neural networks (Bi-RNN). Evaluation of the obtained models was performed both automatically and manually. For automatic evaluation, the bilingual evaluation understudy (BLEU) score was used, and for manual evaluation, three metrics such as adequacy, fluency, and overall ranking were used. Analysis of the results showed the presence of lengthy sentences in English–Malayalam, and the English–Hindi corpus affected the translation. Attention mechanism was employed with a view to addressing the problem of translating lengthy sentences (sentences contain more than 50 words), and the system was able to perceive long-term contexts in the sentences. (shrink)

Battery energy storage is the pivotal project of renewable energy systems reform and an effective regulator of energy flow. Parallel battery packs can effectively increase the capacity of battery modules. However, the power loss caused by the uncertainty of parallel battery branch current poses severe challenge to the economy and safety of electric vehicles. Accuracy of battery branch current prediction is needed to improve the parallel connection. This paper proposes a radial basis function neural network model based (...) on the pigeon-inspired optimization method and successfully applies the algorithm to predict the parallel branch current of the battery pack. Numerical results demonstrate the high accuracy of the proposed pigeon-inspired optimized RBF model for parallel battery branch forecasting and provide a useful tool for the prediction of parallel branch currents of battery packs. (shrink)

In this paper, a lightweight and modular design of a quadruped robot with two-degree-of-freedom parallel legs is presented. To reduce the weight and enhance the transmission accuracy, the horizontal layout of the driving end is adopted for designing the legs of the quadruped robot. The rotation angle of each actuator for the quadruped robot is analyzed by the inverse kinematics algorithm. Moreover, the trajectory of the foot-end, including support and swing phases, is planned to reduce the impact between the (...) foot-end and the ground. Furthermore, the gait of the four legs of the quadruped robot is designed by considering the conditions of trot, standing, take-off, and walking. Finally, the effectiveness of the foot-end trajectory and the stable gait is verified by conducting experiments on a prototype platform. (shrink)

This paper provides an in-depth analysis and discussion of the key issues of quality management, evaluation, and detection contained in big data for automotive application scenarios. A generalized big data quality management model and programming framework are proposed, and a series of data quality detection and repair interfaces are built to express the processing semantics of various data quality issues. Through this data quality management model and detection and repair interfaces, users can quickly build custom data quality detection and repair (...) tasks for different data quality requirements. To improve the operational efficiency of complex data quality management algorithms in large-scale data scenarios, corresponding parallelization algorithms are studied and implemented for detection and repair algorithms with long computation time, including priority-based multiconditional function-dependent detection and repair algorithms, entity detection, and extraction algorithms based on semantic information and chunking techniques, and plain Bayesian-based missing value filling algorithms, and this paper proposes a data validity evaluation algorithm and enhances the validity of the original data in practical applications by adding temporal weights, and finally it passed the experimental validation. Through the comprehensive detection process of data importance, network busyness, duration of transmission process, and failure situation, the efficiency has been increased by 20%, and an adaptive data integrity detection method based on random algorithm and encryption algorithm is designed. After experimental verification, this method can effectively detect the integrity of the data transmission process and improve the application of data value, and the final effect is increased by 30.5%. (shrink)

This paper shows how an efficient and parallel algorithm for inference in Bayesian Networks (BNs) can be built and implemented combining sparse matrix factorization methods with variable elimination algorithms for BNs. This entails a complete separation between a first symbolic phase, and a second numerical phase.

In today’s era of rapid development in science and technology, the development of digital technology has increasingly higher requirements for data processing functions. The matrix signal commonly used in engineering applications also puts forward higher requirements for processing speed. The eigenvalues of the matrix represent many characteristics of the matrix. Its mathematical meaning represents the expansion of the inherent vector, and its physical meaning represents the spectrum of vibration. The eigenvalue of a matrix is the focus of matrix theory. The (...) problem of matrix eigenvalues is widely used in many research fields such as physics, chemistry, and biology. A neural network is a neuron model constructed by imitating biological neural networks. Since it was proposed, the application research of its typical models, such as recurrent neural networks and cellular neural networks, has become a new hot spot. With the emergence of deep neural network theory, scholars continue to combine deep neural networks to calculate matrix eigenvalues. This article aims to study the estimation and application of matrix eigenvalues based on deep neural networks. This article introduces the related methods of matrix eigenvalue estimation based on deep neural networks, and also designs experiments to compare the time of matrix eigenvalue estimation methods based on deep neural networks and traditional algorithms. It was found that under the serial algorithm, the algorithm based on the deep neural network reduced the calculation time by about 7% compared with the traditional algorithm, and under the parallel algorithm, the calculation time was reduced by about 17%. Experiments are also designed to calculate matrix eigenvalues with Obj and recurrent neural networks (RNNS) models, which proves that the Oja algorithm is only suitable for calculating the maximum eigenvalues of non-negative matrices, while RNNS is commonly used in general models. (shrink)

In this paper we deal with the logical description of complexity classes arising in the real number model of computation introduced by Blum, Shub, and Smale [4]. We adapt the approach of descriptive complexity theory for this model developped in [14] and extend it to capture some further complexity classes over the reals by logical means. Among the latter we find NC R , PAR R , EXP R and some others more.

Algorithmic software for mathematical modeling of structural stability is considered, which is reduced to solving a partial generalized eigenvalues problem of sparse matrices, with automatic parallelization of calculations on modern parallel computers with graphics processors. Peculiarities of realization of parallel algorithms for different structures of sparse matrices are presented. The times of solving the problem of stability of composite materialsusing a three-dimensional model of "finite size fibers" on computers of different architectures are given. In mathematical modeling of (...) physical and technical processes in many cases there is a need to solve problems of algebraic problem of eigenvalues with sparse matrices of large volumes. In particular, such problems arise in the analysis of the strength of structures in civil and industrial construction, aircraft construction, electric welding, etc. The solving to these problems is to determine the eigenvalues and eigenvectors of sparse matrices of different structure. The efficiency of solving these problems largely depends on the effectiveness of mathematical modeling of the problem as a whole. Continuous growth of task parameters, calculation of more complete models of objects and processes on computers require an increase in computer productivity. High-performance computing requirements are far ahead of traditional parallel computing, even with multicore processors. High-performance computing requirements are far ahead of traditional parallel computing, even with multicore processors. Today, this problem is solved by using powerful supercomputers of hybrid architecture, such as computers with multicore processors and graphics processors, which combine MIMD and SIMD architectures. But the potential of high-performance computers can be used to the fullest only with algorithmic software that takes into account both the properties of the task and the features of the hybrid architecture. Complicating the architecture of modern high-performance supercomputers of hybrid architecture, which are actively used for mathematical modeling means a significant complication of efficient use of these resources in creating parallel algorithms and programs. here are problems with the creation of algorithmic software with automatic execution of stages of work, which are associated with the efficient use of computing resources, ways to store and process sparse matrices, analysis of the reliability of computer results. This makes it possible to significantly increase the efficiency of mathematical modeling of practical problems on modern high-performance computers, as well as free users from the problems of parallelization of complex problems. he developed algorithmic software automatically implements all stages of parallel computing and processing of sparse matrices on a hybrid computer. It was used at the Institute of Mechanics named after S.P. Tymoshenko NAS of Ukraine in modeling the strength problems of composite material. A significant improvement in the time characteristics of mathematical modeling was obtained. Problems of mathematical modeling of the properties of composite materials has an important role in designing the processes of deformation and destruction of products in various subject areas. Algorithmic software for mathematical modeling of structural stability is considered, which is reduced to solving a partial generalized problem of eigen values of sparse matrices of different structure of large orders, with automatic parallelization of calculations on modern parallel computers with graphics processors. The main methodological principles and features of implementation of parallel algorithms for different structures of sparse matrices are presented, which ensure effective implementation of multilevel parallelism of a hybrid system and reduce data exchange time during the computational process. As an example of these approaches, a hybrid algorithm of the iteration method in subspace for tape and block-diagonal matrices with a frame for computers of hybrid architecture is given. Peculiarities of data decomposition for matrices of profile structure at realization of parallel algorithms are considered. The proposed approach provides automatic determination of the required topology of the hybrid computer and the optimal amount of resources for the organization of an efficient computational process. The results of testing the developed algorithmic software for problems from the collection of the University of Florida, as well as the times of solving the problem of stability of composite materials using a three-dimensional model of "finite size fibers" on computers of different architectures. The results show a significant improvement in the time characteristics of solving problems. (shrink)

In representing classification rules by decision trees, simplicity of tree structure is as important as predictive accuracy especially in consideration of the comprehensibility to a human, the memory capacity and the time required to classify. Trees tend to be complex when they get high accuracy. This paper proposes a novel method for generating accurate and simple decision trees based on symbiotic evolution. It is distinctive of symbiotic evolution that two different populations are evolved in parallel through genetic algorithms. (...) In our method one's individuals are partial trees of height 1, and the other's individuals are whole trees represented by the combinations of the former individuals. Generally, overfitting to training examples prevents getting high predictive accuracy. In order to circumvent this difficulty, individuals are evaluated with not only the accuracy in training examples but also the correct answer biased rate indicating the dispersion of the correct answers in the terminal nodes. Based on our method we developed a system called SESAT for generating decision trees. Our experimental results show that SESAT compares favorably with other systems on several datasets in the UCI repository. SESAT has the ability to generate more simple trees than C5.0 without sacrificing predictive accuracy. (shrink)

Evolutionary computation has been applied to numerous design tasks, including design of electric circuits, neural networks, and genetic circuits. Though it is a very effective solution for optimizing network structures, genetic algorithm faces many difficulties, often referred to as the permutation problems, when both topologies and the weights of the network are the target of optimization. We propose a new crossover method used in conjunction with a genotype with information tags. The information tags allow GA to recognize and preserve the (...) common structure of parent chromosomes during genetic crossover. The method is implemented along with subpopulating strategies to make the parallel evolution of network topology and weights feasible and efficient. The proposed method is evaluated on a few typical and practical problems, and shows improvement from conventional methodologies and genotypes. (shrink)

"The Emperor's New Mind" by Roger Penrose has received a great deal of both praise and criticism. This review discusses philosophical aspects of the book that form an attack on the "strong" AI thesis. Eight different versions of this thesis are distinguished, and sources of ambiguity diagnosed, including different requirements for relationships between program and behaviour. Excessively strong versions attacked by Penrose (and Searle) are not worth defending or attacking, whereas weaker versions remain problematic. Penrose (like Searle) regards the notion (...) of an algorithm as central to AI, whereas it is argued here that for the purpose of explaining mental capabilities the architecture of an intelligent system is more important than the concept of an algorithm, using the premise that what makes something intelligent is not what it does but how it does it. What needs to be explained is also unclear: Penrose thinks we all know what consciousness is and claims that the ability to judge Go "del's formula to be true depends on it. He also suggests that quantum phenomena underly consciousness. This is rebutted by arguing that our existing concept of "consciousness" is too vague and muddled to be of use in science. This and related concepts will gradually be replaced by a more powerful theory-based taxonomy of types of mental states and processes. The central argument offered by Penrose against the strong AI thesis depends on a tempting but unjustified interpretation of Goedel's incompleteness theorem. Some critics are shown to have missed the point of his argument. A stronger criticism is mounted, and the relevance of mathematical Platonism analysed. Architectural requirements for intelligence are discussed and differences between serial and parallel implementations analysed. (shrink)

For each vertex of a simple polygon P an integer valued weight is given. We consider the path p1, p2, ..., pk in P which is created according to the following strategy: p1 is a designated start vertex s and pi+1 is obtained by choosing the vertex with smallest weight among all vertices visible from pi and different from p1, p2, ..., pi. If there is no such vertex the path is finished. This path is called geometric lexicographic dead end (...) path. We shall prove the problem of determining whether a distinguished vertex t of P is on the geometric lexicographic dead end path or not to be P-complete. (shrink)