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The rate distortion optimization (RDO) process aims at achieving optimal coding performance by determining the optimal coding mode according to a certain strategy in the AV1 video coding. However, the high computational complexity and strong data dependency in RDO impede real-time applications. To address these issues, a fast RDO algorithm suitable for hardware implementation is proposed. Firstly, we propose a high-frequency coefficients zero-setting approach to optimize the hardware memory occupation. Then, in the rate-distortion calculation stage, an efficient rate estimation method is proposed based on a statistical feature for the number of quantization coefficients, and the distortion estimation method is proposed by considering intrinsic features in the all-zero block. Finally, a reconstruction approximate model is proposed to solve the low parallelism issue caused by the coupling of pixel reconstruction and prediction data. Experimental results show that the proposed algorithm achieves 68.49% and 50.77% time-saving by 2.73% and 2.95% Bjøntegaard delta rate (BD-Rate) increase on average under all intra (AI) and random access (RA) configurations, respectively. [ABSTRACT FROM AUTHOR]

Financial commodity markets have an impact on company values and cash flows, where price movements within frequent time intervals can be both significant and random. Understanding highly frequent price movements is both important and difficult. In this paper, I measured and forecasted volatility for high-frequency (mostly twelve hours per day) WTI Oil front month price movements from 2012 to 2024 (about 40,500 observations). I created a new stochastic volatility model for extracting latent volatility using the non-linear Kalman filter. Stable and strictly ergodic hourly price series, along with the BIC optimal non-linear (generic) general method of moments model coefficients, enabled this process. The latent volatility seemed to separate into two volatility factors. One factor that was very persistent suggested slow mean reversion, and one choppy strongly mean-reverting factor. The data dependence found in the factor volatility series suggested forecasting ability. I applied classical static forecasts and three machine learning regression techniques to predict one-step-ahead volatility. The two volatility factors and one summarizing exponential factor were reported, along with several fit measures, including the root mean square error and Theil's measure of covariance.

Wireless Sensor Networks (WSNs) play a vital role in various applications, necessitating robust network security to protect sensitive data. Intrusion Detection Systems (IDSs) are crucial for preserving the integrity, availability, and confidentiality of WSNs by detecting and countering potential attacks. Despite significant research efforts, the existing IDS solutions still suffer from challenges related to detection accuracy and false alarms. To address these challenges, in this paper, we propose a Bayesian optimization-based Deep Learning (DL) model. However, the proposed optimized DL model, while showing promising results in enhancing security, encounters challenges such as data dependency, computational complexity, and the potential for overfitting. In the literature, researchers have employed Reinforcement Learning (RL) to address these issues. However, it also introduces its own concerns, including exploration, reward design, and prolonged training times. Consequently, to address these challenges, this paper proposes an Innovative Integrated RL-based Advanced DL Algorithm (IRADA) for attack detection in WSNs. IRADA leverages the convergence of DL and RL models to achieve superior intrusion detection performance. The performance analysis of IRADA reveals impressive results, including accuracy (99.50%), specificity (99.94%), sensitivity (99.48%), F1-Score (98.26%), Kappa statistics (99.42%), and area under the curve (99.38%). Additionally, we analyze IRADA's robustness against adversarial attacks, ensuring its applicability in real-world security scenarios. [ABSTRACT FROM AUTHOR]

Financial commodity markets have an impact on company values and cash flows, where price movements within frequent time intervals can be both significant and random. Understanding highly frequent price movements is both important and difficult. In this paper, I measured and forecasted volatility for high-frequency (mostly twelve hours per day) WTI Oil front month price movements from 2012 to 2024 (about 40,500 observations). I created a new stochastic volatility model for extracting latent volatility using the non-linear Kalman filter. Stable and strictly ergodic hourly price series, along with the BIC optimal non-linear (generic) general method of moments model coefficients, enabled this process. The latent volatility seemed to separate into two volatility factors. One factor that was very persistent suggested slow mean reversion, and one choppy strongly mean-reverting factor. The data dependence found in the factor volatility series suggested forecasting ability. I applied classical static forecasts and three machine learning regression techniques to predict one-step-ahead volatility. The two volatility factors and one summarizing exponential factor were reported, along with several fit measures, including the root mean square error and Theil's measure of covariance. [ABSTRACT FROM AUTHOR]

Picard's three step iteration algorithm was one of the iteration algorithms that was recently shown to be faster than some other iterative algorithms in the existing literature. The purpose of this paper was to study using this iteration algorithm for the solution of nonlinear Volterra integral equations. It was investigated that the sequences obtained from this iteration algorithm converged to the solution of nonlinear Volterra integral equations. Moreover, data dependence was obtained for nonlinear Volterra integral equations. An example was given that confirmed the applicability of the newly proven theorems. [ABSTRACT FROM AUTHOR]

Usurelu et al. (Int J Comput Math 98:1049–1068, 2021) presented stability and data dependence results for a TTP (Thakur–Thakur–Postolache) iteration algorithm associated with quasi-strictly contractive mappings and contraction mappings, respectively, but these results were subject to strong conditions on the parametric control sequences used in the TTP iteration algorithm. This article aims to expand those results conducting a thorough analysis of the convergence of TTP and S iteration algorithms and improve those results by removing the restrictions on the parametric control sequences. Additionally, a data dependence result for the TTP iteration algorithm of quasi-strictly contractive mappings is established and several collage theorems are introduced to offer new insights into the data dependence of fixed points of quasi-strictly contractive mappings and to solve related inverse problems. In order to exhibit the dependability and effectiveness of all the results discussed in this work, a multitude of numerical examples are furnished, encompassing both linear and nonlinear differential equations (DEs) and partial differential equations (PDEs). This work can be viewed as an important refinement and complement to the study by Usurelu et al. (Int J Comput Math 98:1049–1068, 2021). [ABSTRACT FROM AUTHOR]

Picard's three step iteration algorithm was one of the iteration algorithms that was recently shown to be faster than some other iterative algorithms in the existing literature. The purpose of this paper was to study using this iteration algorithm for the solution of nonlinear Volterra integral equations. It was investigated that the sequences obtained from this iteration algorithm converged to the solution of nonlinear Volterra integral equations. Moreover, data dependence was obtained for nonlinear Volterra integral equations. An example was given that confirmed the applicability of the newly proven theorems.

In this article, the solution of the second type of nonhomogeneous linear Fredholm integral equations is investigated using a three-step iteration algorithm. It has been shown that the sequences obtained from this algorithm converge to the solution of the mentioned equations. Morever, data dependency is obtained for the second type of nonhomogeneous linear Fredholm integral equations. This result is supported by an example. [ABSTRACT FROM AUTHOR]

The latest video coding standards such as AVS3 adopts numerous novel coding tools and new partition mechanisms to achieve a better coding performance. But they also massively increase the computation overhead. Integer motion estimation (IME) is one of the most important and complex coding tools. The growing complexity of these video coding standards brings up severe challenges for both software and hardware implemented IME. Different from the software oriented IME algorithms, hardware-friendly IME algorithms often require low data dependency, regular data access and reasonable trade-off among compression performance, latency and resources. The challenges also highly relate to the hardware encoder architecture applying the IME algorithm. We propose a novel hardware-friendly integer motion estimation algorithm for AVS3 targeted for the Largest Coding Unit (LCU) level pipe-lined encoder architectures. The proposed algorithm consists of three parts, namely, a predictive motion vector prediction (PMVP) algorithm to derive predictive motion vectors (PMV) in advance to avoid the data dependency, a multi-resolution hierarchical motion estimation method that generates motion vectors (MVs) for 60% of the divided coding units (CUs) and a motion vector inference (MVI) method to infer MVs for the rest of the CUs. The proposed algorithms reduce the computation complexity by 88.64% for IME and only suffers 0.11% performance degradation for the IPPP configuration. [ABSTRACT FROM AUTHOR]

Given the problem that the code slice used by existing deep learning-based vulnerability sniffer methods could not comprehensively encompass the subtle characteristics between vulnerability classes, and a single deep learning sniffer model had insufficient ability to learn long context-dependent information between cross-file and cross-function code statements, a multi-type source code vulnerability sniffer method was proposed.Firstly, fine-grained two-level slices containing the types of vulnerabilities were extracted based on the control dependency and data dependency information in program dependency graph.Secondly, the two-level slices were transformed into initial feature vector.Finally, a fusion model of deep learning vulnerability sniffer suitable for two-level slices was constructed to achieve accurate vulnerability detection of multi-type source code.The experimental results on multiple synthetic datasets and two real datasets show that the proposed method outperforms the existing multi-type source code vulnerability sniffer methods.

Spectrum-based fault localization (SBFL), a widely recognized technique in automated fault localization, has limited effectiveness due to its disregard for the internal information of the program under test suites. To overcome this limitation, a novel TrustRank-based fault localization (TRFL) technique is introduced. TRFL enhances traditional SBFL by incorporating internal data dependencies of the program under the test suite, thereby providing a more comprehensive analysis. It constructs a node-weighted program execution network and employs the TrustRank algorithm to analyze network centrality and re-rank program entities based on their suspiciousness. Furthermore, a bidirectional TrustRank algorithm (Bi-TRFL) is extended that takes into account the influence relationship between network nodes for more accurate fault localization. When applied to large-scale datasets with real faults, such as Defects4J, TRFL, and Bi-TRFL, it significantly outperforms traditional SBFL methods in fault localization. They demonstrate up to 40% and 13% improvement in Top-1 and Top-5 rankings, respectively, proving their robustness and efficiency with minimal sensitivity to related parameters. [ABSTRACT FROM AUTHOR]

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In this paper we introduce multivalued modified F-contraction on a metric space. This is a multivalued mapping obtained by incorporating the idea of the recently introduced F-contraction which has attracted much attention in contemporary research. We explore the fixed point problem associated with the above contractive mapping. We also investigate the data dependence and stability properties of the fixed point sets associated with these multival- ued contractions. We discuss an illustration of the main result and present an application of the single valued version of the main theorem to a problem of an integral equation of Volterra type. The domain of the study is fixed point theory and set valued analysis. [ABSTRACT FROM AUTHOR]

Outlier detection (outlier and anomaly are used interchangeably in this review) in non-independent and identically distributed (non-IID) data refers to identifying unusual or unexpected observations in datasets that do not follow an independent and identically distributed (IID) assumption. This presents a challenge in real-world datasets where correlations, dependencies, and complex structures are common. In recent literature, several methods have been proposed to address this issue and each method has its own strengths and limitations, and the selection depends on the data characteristics and application requirements. However, there is a lack of a comprehensive categorization of these methods in the literature. This study aims to systematically review outlier detection methods for non-IID data published between 2015 and 2023. This study focuses on three major aspects; data characteristics, methods, and evaluation measures. In data characteristics, we discuss the differentiating properties of non-IID data. Then we review the recent methods proposed for outlier detection in non-IID data, covering their theoretical foundations and algorithmic approaches. Finally, we discuss the evaluation metrics proposed to measure the performance of these methods. Additionally, we present a taxonomy for organizing these methods and highlight the application domain of outlier detection in non-IID categorical data, outlier detection in federated learning, and outlier detection in attribute graphs. We provide a comprehensive overview of datasets used in the selected literature. Moreover, we discuss open challenges in outlier detection for non-IID to shed light on future research directions. By synthesizing the existing literature, this study contributes to advancing the understanding and development of outlier detection techniques in non-IID data settings.

IntroductionHow to measure the trace consistency between process models in workflow net systems have become a crucial part of process mapping, process integration, and difference testing. Workflow net with data (WFD-net) is an effective language to describe a complete model of a workflow net system. Checking their trace consistency is a significant challenge in service-based software engineering. However, most of the existing research on trace consistency measurement has not taken the data into consideration. Therefore, they cannot accurately calculate the trace consistency degree of two WFD-net systems, especially when data operations and guard functions are complex.MethodsWe explore all traces in two WFD-net systems based on guard analysis and data dependencies.ResultsWe point out that the trace consistency degree is uncertain when the values of guard functions are considered.DiscussionWe conduct experiments to evaluate our method and show its effectiveness.

The life cycle of the company is characterized today by increasingly frequent phases of change induced by a continuous search for competitiveness. As a result, and in order to preserve their competitive advantages, companies must be able to react quickly to market movements, changes in the needs of their customers and business transformations of the various interlocutors. In this context, business processes playing a major role in the definition and management of information systems, must be flexible to meet the requirements of users and government regulations and all organizations must engage in the continuous maintenance of their business models. However, these models are monolithic which makes the task of maintenance difficult and very complicated. Partitioning the system into microservices is usually done intuitively, based on the experience of the system designers. But, if the functionalities of a system are strongly interconnected, it is difficult to decompose the system into appropriate microservices. To meet so this challenge, this paper proposes a multi-model based on a set of business processes. This approach combines two different independent dimensions: structural dependency and data dependency. We will be based on three clustering algorithm in order to automatically identify potential microservices. [ABSTRACT FROM AUTHOR]

This paper reports the hyperbolic space version of the iterative scheme F. First, under mild conditions, we obtain the Δ and strong convergence of this scheme in the class of mappings enriched with condition (E). To support these results, and to show the high accuracy of the F iterative scheme in the class of mappings enriched with condition (E), we suggest an appropriate numerical example and show that its F iterative scheme is more effective than some other iterative schemes. After this, we establish strong convergence, of this scheme for the general class of contractive-like operators. An example to support this result is also presented and the efficiency of the F iterative scheme is tested with the other iterative schemes. Finally, the weak w2–stability and data dependency results are established. Our findings are new and improve some recent results of the current literature. [ABSTRACT FROM AUTHOR]

In a recent paper, Yu et al. obtained convergence and stability results of the K* iterative method for quasistrictly contractive mappings [An iteration process for a general class of contractive-like operators: Convergence, stability and polynomiography. AIMS Mathematics 2021; 6 (7): 6699-6714.]. To guarantee these convergence and stability results, the authors imposed some strong conditions on parametric control sequences which are used in the K* iterative method. The aim of the presented work is twofold: (a) to recapture the aforementioned results without any restrictions imposed on the mentioned parametric control sequences (b) to complete the work of Yu et al. by adding a result regarding the data dependency of the fixed points of quasi-strictly contractive mappings. We also furnish some illustrative examples to support our results. Our work can be considered an important refinement and complement of the work of Yu et al. [ABSTRACT FROM AUTHOR]

In this sequel to our previous article (Cordero et al., 2020) on general inference systems for reasoning with if–then dependencies, we study transformations of if–then rules to semantically equivalent collections of if–then rules suitable to solve several problems related to reasoning with data dependencies. We work in a framework of general lattice-based if–then rules whose semantics is parameterized by systems of isotone Galois connections. This framework allows us to obtain theoretical insight as well as algorithms on a general level and observe their special cases by choosing various types of parameterizations. This way, we study methods for automated reasoning with different types of if–then rules in a single framework that covers existing as well as novel types of rules. Our approach supports a large family of if–then rules, including fuzzy if–then rules with various types of semantics. The main results in this article include new observations on the syntactic inference of if–then rules, complete collections of rules, reduced normal forms of collections of rules, and automated reasoning methods. We demonstrate the generality of the framework and the results by examples of their particular cases focusing on fuzzy if–then rules. [ABSTRACT FROM AUTHOR]

Spectrum-based fault localization (SBFL), a widely recognized technique in automated fault localization, has limited effectiveness due to its disregard for the internal information of the program under test suites. To overcome this limitation, a novel TrustRank-based fault localization (TRFL) technique is introduced. TRFL enhances traditional SBFL by incorporating internal data dependencies of the program under the test suite, thereby providing a more comprehensive analysis. It constructs a node-weighted program execution network and employs the TrustRank algorithm to analyze network centrality and re-rank program entities based on their suspiciousness. Furthermore, a bidirectional TrustRank algorithm (Bi-TRFL) is extended that takes into account the influence relationship between network nodes for more accurate fault localization. When applied to large-scale datasets with real faults, such as Defects4J, TRFL, and Bi-TRFL, it significantly outperforms traditional SBFL methods in fault localization. They demonstrate up to 40% and 13% improvement in Top-1 and Top-5 rankings, respectively, proving their robustness and efficiency with minimal sensitivity to related parameters.

A faulty situation occurred in Service Oriented Architecture (SOA) based systems can affect its functionality. It is challenging to guarantee the reliability of service utilization in a distributed, concurrent and dynamic composition of web services. This paper aims to present a fault detection approach of SOA using dependency analysis. Data and control dependencies are identified and a fault diagnosis approach based on these dependencies is proposed in this paper. The presented approach extends discrete state space techniques to simulate the concurrent and distributed behavior through Colored Petri Nets (CPNs) for the SOA-based systems in case of the faulty situation. Concrete translation from SOA based system to a CPN model is presented. Model-based technique is used and some heuristics are presented for dependency analysis and fault detection purpose. The experimental performance analysis shows the feasibility of our proposed approach.

In recent years, Cloud computing has been developed and become the foundation of a wide range of applications. It allows users to access a catalog of standardized services and respond to their business needs flexibly and adaptively, in the event of unforeseen demands, paying solely for the consumption they have made. Task scheduling problem is considered one of the most critical cloud computing challenges. The problem refers to how to reasonably order and allocate the applications tasks provided by the users to be executed on virtual machines. Furthermore, the quality of scheduling performance has a direct effect on customer satisfaction. The task scheduling problem in cloud computing must be more accurately described in order to improve scheduling performance. In this paper, a multi-objective task scheduling algorithm is proposed based on the decision tree in a heterogenous environment. We introduce a new Task Scheduling-Decision Tree (TS-DT) algorithm for allocating and executing an application’s task. To evaluate the performance of the proposed TS-DT algorithm, a comparative study was conducted among the existing algorithms; Heterogeneous Earliest Finish Time (HEFT), Technique for Order of Preference by Similarity to Ideal Solution that incorporates the Entropy Weight Method (TOPSIS-EWM), and combining Q-Learning with the Heterogeneous Earliest Finish Time (QL-HEFT). Our results show that the proposed TS-DT algorithm outperforms the existing HEFT, TOPSIS-EWM, and QL-HEFT algorithms by reducing makespan by 5.21%, 2.54%, and 3.32%, respectively, improving resource utilization by 4.69%, 6.81%, and 8.27%, respectively, and improving load balancing by 33.36%, 19.69%, and 59.06%, respectively in average.

Multilevel modeling (MLM) is widely used for the multilevel data structure in social science. Two MLM limitations involve partitioning variance for a within-level predictor. First, MLM does not automatically partial out the between-level variance for a within-level predictor. Second, MLM assumes a within-level predictor to be measurement-error free. Thus, the error variance cannot be separated out. The study evaluated the impact of improperly partitioning two-level variances and measurement error variances for a within-level predictor in MLM. Our findings suggested (a) whether the research interest lies in a within-level or a between-level predictor, group-mean centering or latent-mean centering must be used to partition two-level variances; (b) for the measurement error variance, the within-level factor loadings should be as high as possible (≥0.80 in our simulation settings). If these two requirements are not met, multilevel structural equation modeling (MSEM) should always be adopted for the analysis. [ABSTRACT FROM AUTHOR]

In view of the data dependence that is the major drawback of the supervised deep neural network ( DNN )-based denoising models, this paper proposed an unsupervised deep image generation ( UDIG) denoising model. Firstly, it utilized a noise level estimation ( NLE) algorithm to measure the noise level of a given noisy image, the estimated noise level and the noisy image fed into the state-of-the-art denoising model ( i. e., fast and flexible denoising convolutional neural network, FFDNet) to obtain a preliminary denoised image as the input to the UDIG model. Secondly, it chose the encoder-decoder architecture as backbone network, while using the sum of the mean square error among the output image of UDIG model ( i. e . generated image), and the preliminary denoised image, and the given noisy image to define mixed loss function. Then, the regular stochastic gradient descent ( SGD) algorithm optimized the loss function, adjusted the hyper-parameters of the UDIG model to generating a series of generated images. Finally, when the standard deviation of the residual image between the noisy image and generated image approximates the noise level measured by the NLE algorithm, the network iterative process was terminated adaptively, ensuring the image quality of the gene-rated image ( treated as the denoised image) . Extensive experiments show that, the proposed UDIG denoising model has a better performance than other state-of-the-art counterparts with regard to denoising effect. [ABSTRACT FROM AUTHOR]

Null pointer dereference (NPD) is a widespread vulnerability that occurs whenever an executing program attempts to dereference a null pointer. NPD vulnerability can be exploited by hackers to maliciously crash a process to cause a denial of service or execute an arbitrary code under specific conditions. This typical taint-style vulnerability requires an accurate data dependency analysis to trace whether a source is propagated to a sensitive sink without proper sanitization. The primary challenge in data dependency analysis is pointer aliasing, which may significantly affect the vulnerability detection accuracy. Although there have been many studies and open-source tools, they still have limitations when detecting a real-world binary. In this paper, we propose a static binary analysis approach to detect an NPD vulnerability. To improve detection accuracy and practicality, we first identify two challenges that affect the accuracy of binary NPD detection: (i) pointer aliasing, and (ii) untrusted source identification. Then we implement a prototype of the proposed approach, NPDHunter, and evaluate it against 318 test cases provided by Juliet Test Suite v1.3. For the Juliet dataset, NPDHunter is accurate in detecting NPDs and generates 0% false negatives; as compared to bap-toolkit and cwe_checker, which have false-negative rates of 70.89% and 89.81%, respectively. We also evaluate NPDHunter for real-world binaries which recently reported NPD vulnerability. We have analyzed XNU kernel (large-scale), Redis, Bitlbee, libredwg, and libvncserver binaries and NPDHunter can detect all NPD cases, which justifies its usefulness for real-world binaries; compiled for x86_64 architecture.

A faulty situation occurred in Service Oriented Architecture (SOA) based systems can affect its functionality. It is challenging to guarantee the reliability of service utilization in a distributed, concurrent and dynamic composition of web services. This paper aims to present a fault detection approach of SOA using dependency analysis. Data and control dependencies are identified and a fault diagnosis approach based on these dependencies is proposed in this paper. The presented approach extends discrete state space techniques to simulate the concurrent and distributed behavior through Colored Petri Nets (CPNs) for the SOA-based systems in case of the faulty situation. Concrete translation from SOA based system to a CPN model is presented. Model-based technique is used and some heuristics are presented for dependency analysis and fault detection purpose. The experimental performance analysis shows the feasibility of our proposed approach. [ABSTRACT FROM AUTHOR]

Cloud computing will continue to play a critical role due to its ability to deliver various types of on-demand information technology (IT) services according to customer needs. Unfortunately, cloud computing has faced many challenges. The task scheduling problem is considered one of the main challenges because the user has to pay for a resource. Therefore, a good mapping of users' tasks on the available resources is required to reduce the total execution time of the users' tasks (i.e., reduce makespan), execution cost, enhance load balancing, increase resource utilization, etc. Many heuristic algorithms have been existed to solve task scheduling problems. Although, the Heterogeneous Earliest Finish Time (HEFT) heuristic algorithm is considered one of the best task scheduling algorithms in a heterogeneous environment, it does not concern load balancing. In this paper, a modification has been introduced to the HEFT algorithm to satisfy load balancing. The modified algorithm is called Load Balancing HEFT (LB-HEFT). A comparative study has been done among the proposed LB-HEFT, the Enhancement Heterogeneous Earliest Finish Time (E-HEFT), and HEFT algorithms to evaluate the performance of the proposed LB-HEFT algorithm. According to the comparative results, it is found that the proposed LB-HEFT algorithm outperforms the existing E-HEFT and HEFT algorithms by improving load balancing by 43.49% and 72.59%, respectively in average, enhancing resource utilization by 2.28% and 5.61%, respectively in average, and reducing makespan by 7.55%, and 3.75% respectively in average. [ABSTRACT FROM AUTHOR]

In business process collaboration (BPC), especially when it comes to message communication and data exchange, there are complex data dependencies among sender process, receiver process and messages. However, each participant of the overall BPC develops its part independently as a service, including its own communication part and data flow. As a result, data flow errors across processes may occur easily. In this article, we propose a method based on BPMN to detect these errors caused by data dependency violations. Our method is inspired by the study of detecting data flow errors within a single process and focuses on a subset of the elements of the BPC model, without having to consider the complete set. In particular, we define a set of data flow error patterns by analyzing and formalizing data dependencies in order to clearly clarify and identify errors. Then we give the corresponding automatic detection algorithm. Finally, through two evaluations, we demonstrate the effectiveness of our proposal.

The convergence of normal S-iterative method to solution of a nonlinear Fredholm integral equation with modified argument is established. The corresponding data dependence result has also been proved. An example in support of the established results is included in our analysis. [ABSTRACT FROM AUTHOR]

We study some qualitative features like convergence, stability and data dependency for Picard-S iteration method of a quasi-strictly contractive operator under weaker conditions imposed on parametric sequences in the mentioned method. We compare the rate of convergence among the Mann, Ishikawa, Noor, normal-S, and Picard-S iteration methods for the quasi-strictly contractive operators. Results reveal that the Picard-S iteration method converges fastest to the fixed point of quasi-strictly contractive operators. Some numerical examples are given to validate the results obtained herein. Our results substantially improve many other results available in the literature.

Scientific computations with a wide range of applications in domains such as developing vaccines, forecasting the weather, predicting natural disasters, simulating aerodynamics of spacecraft, and exploring oil resources, create the main workloads of supercomputers. The key integration of such scientific computations is modeling physical phenomena that are done with the aid of partial differential equations (PDEs). Solving PDEs on supercomputers, even with those equipped with GPUs, consumes a large amount of power and yet is not as fast as desired. The main reason behind such slow processing is data dependency. The key challenge is that software techniques cannot resolve these dependencies, therefore, such applications cannot benefit from the parallelism provided by processors such as GPUs. Our key insight to address this challenge is that although we cannot resolve the dependencies, we can reduce their negative impacts by using hardware/software co-optimization. To this end, we propose breaking down the data-dependent operations into two groups of operations: a majority of parallelizable and the minority of data-dependent operations. We execute these two groups in the desired order: first, we put together all parallelizable operations and execute them all, subsequently; then, we switch to execute the small data-dependent part. As long as the data-dependent part is small, we can accelerate them by using fast hardware mechanisms. Besides, our proposed hardware mechanisms guarantee quickly switching between the two groups of operations. To follow the same order of execution, dictated by our software mechanism, and implemented in hardware, we also propose a new low-overhead compression format – sparsity is another attribute of PDEs that require compression. Furthermore, the core generic architecture of our proposed hardware allows the execution of other applications including sparse matrix-vector multiplication (SpMV) and graph algorithms. The key feature of the proposed hardware is partial reconfigurability, which on one hand, facilitates the execution of data-dependent computations, and on the other hand, allows executing broad application without changing the entire configuration. Our evaluations show that compared to GPUs, we achieve an average speedup of 15.6× for scientific computations while consuming 14× less energy. [ABSTRACT FROM AUTHOR]

In this paper, we present a result of stability, data Dependency and errors estimation for D Iteration Method. We also prove that errors in D iterative process is controllable. Especially stability, data dependence, controllability, error accumulation of such iterative methods are being studied. [ABSTRACT FROM AUTHOR]

Nowadays, cloud computing and other distributed computing systems have been developed to support various types of workflows in applications. Due to the restrictions onthe use ofone cloud provider, the concept of multiple clouds as been proposed.Inmultipleclouds, schedulingworkflowswithlarge amounts ofdata is a well-knownNP-Hard problem. The existing scheduling algorithms have not paid attention to the data dependency issues and their importance in scheduling criteria such as time and cost. In this paper, we propose a communication-based algorithm for workflows with huge volumes of data in a multi-cloud environment. The proposed algorithm changes the definition of the Partial Critical Paths(PCP) to minimize the cost of workflow executionwhile meeting a user defined deadline.

This article investigates the existence and uniqueness of solutions to the second-order Volterra integrodifferential equations with nonlocal and boundary conditions through its equivalent integral equations and fixed point of Banach. Furthermore, utilizing the Picard operator theory, we obtain the dependence of solutions on the initial nonlocal data and on functions involved on the right-hand side of the equations. [ABSTRACT FROM AUTHOR]

The current truth discovery method cannot solve the case where the object is composed of many single-valued attributes and multi-valued attributes. Separate processing of these attributes will destroy the original association between attributes, resulting in inaccurate results . This paper proposed an ATD4MA algorithm. It modeled the observation values of the object using the chromosomes in the genetic algorithm. Then it improved the population initialization algorithm and the basic action of the chromosome according to the problem characteristics. By controlling the evolution behavior of chromosomes, it established the optimization model to minimize the weighted sum of difference between the truth-value chromosomes of each object and the ob served values provided by each data source. Therefore it solved the problem of truth discovery where the object contained multiple attributes. Experiments on two real data sets show the correctness and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we extend the study of an iteration process introduced by Thakur-Thakur-Postolache [J. Inequal. Appl., (2014), 147–155] from the class of nonexpansive operators to the more general class of Suzuki operators. We prove weak and strong convergence theorems regarding the iteration method for mappings satisfying the condition (C) of Suzuki in uniformly convex Banach spaces. We study the stability of this iteration process, and introduce a data dependency result for the subclass of contractive operators. An example illustrates the theoretical outcome. [ABSTRACT FROM AUTHOR]