Concurrency and Computation-Practice & Experience最新文献

The advancement of teaching quality is an indispensable section of the reform and growth of universities, and ideological and political education has critical impact on ideological education. The quality of classroom education can provide data support for efficient development, and has crucial influence on achieving scientific, reasonable, and accurate evaluation of ideological and political teaching performance. Thus, a performance assessment system for ideological and political education in universities with genetic algorithm optimized neural network algorithm is put forward. First, based on existing teaching evaluation indicators and combined with actual situations, a targeted teaching quality evaluation system is proposed. Then, based on BP, an adaptive genetic algorithm is proposed for improvement, and the output results are improved using entropy method. The results indicated that the proposed model could reach its optimal state after 81 iterations in this study. In the fitting test, it reached 0.971. In actual testing, the average error was only 2.68, which was much bigger than the other three algorithms. Its accuracy was 2%–3.2% higher than that of the best existing algorithms. These results indicated that the method put forward in this study had better practical significance, lower error, more accurate evaluation results, and offered scientific data support for the education reform work of universities, which can better accelerate the development and construction of universities.

In label distribution learning (LDL), an instance is involved with many labels in different importance degrees, and the feature space of instances is accompanied with thousands of redundant and/or irrelevant features. Therefore, the main characteristic of feature selection in LDL is to evaluate the ability of each feature. Motivated by neighborhood rough set (NRS), which can be used to measure the dependency degree of feature via constructing neighborhood relations on feature space and label space, respectively, this article proposes a novel label distribution feature selection method. In this article, the neighborhood class of instance in label distribution space is defined, which is beneficial to recognize the logical class of target instance. Then, a new NRS model for LDL is proposed. Specially, the dependency degree of feature combining label weight is defined. Finally, a label distribution feature selection based on NRS is presented. Extensive experiments on 12 data sets show the effectiveness of the proposed algorithm.

The Cray HPE Slingshot 11 network is used on the new exascale systems arriving at the U.S. Department of Energy (DoE) laboratories (e.g., Frontier, Aurora, Perlmutter). As such, the support of this network is an important capability to meet the needs of exascale applications. This article highlights recent work to develop supporting infrastructure to enable Open MPI to efficiently support these new platforms. A key component of this effort involves development of a new Open Fabrics Interface (OFI) provider, LinkX. We discuss the design and development of enhancements that take advantage of the new Slingshot 11 network and AMD GPUs. We include performance data from tests on the Frontier supercomputer using synthetic communication benchmarks, and the vendor provided MPI as a baseline for comparison. The tests demonstrate full functionality of Open MPI on the system and initial results show favorable performance when compared to the highly tuned vendor implementation.

Distributed cache systems are utilized to enhance I/O performance between computing applications and storage systems. However, the traditional file access predictors employed in these cache systems are only suitable for workloads with simple file access patterns, rendering them inadequate for the complex access patterns found in big data computing scenarios. In this article, we propose a file access predictor (DFAP) based on WaveNet, which has exhibited promising results in file access tasks when compared to other baseline models. Cache systems are often constrained by limited cache space due to cost, cluster size, and other factors. In big data scenarios, cached data and prefetched data often compete for limited space. To address this issue, we introduce a cache prefetching algorithm (CBAP) for cache systems, which is based on cost-benefit analysis to improve cache utilization. Furthermore, we implement a novel file prefetching framework on Alluxio, which accelerates computing jobs by up to 18%.

The foundation of urban landscape design optimization is the precise evaluation of the effectiveness. To address the issues of strong subjectivity, low efficiency, and poor accuracy in urban landscape design evaluation methods, an intelligent evaluation method combining improved genetic algorithm and error backpropagation neural network is proposed. First, based on Maslow's demand theory and questionnaire survey results, it selects indicators to construct an evaluation index system for urban landscape design. Second, in response to the performance defects of the error backpropagation neural network model, the moth flame algorithm is used to optimize it. Then, in response to the defect that the optimization effect of the moth flame algorithm is not ideal enough, a multiple strategy including improved genetic algorithm is adopted to optimize it. Finally, an urban landscape design evaluation model is constructed based on improved error backpropagation neural network. The experimental results show that the fitting coefficient of the model is 0.9523, with a minimum deviation of less than 1%. The above results indicate that the proposed model can effectively improve the accuracy and efficiency of urban landscape design evaluation, providing data support for urban landscape design optimization. The research on the intelligent development of urban landscape design is of reference significance and has to some extent promoted the development of urban landscape design.

Network traffic classification task has become increasingly challenging. The objective behind this classification is to effectively handle bandwidth, prioritize certain types of traffic, enhance application performance, and more. In recent times, there has been a surge in exploring deep learning approaches for network traffic categorization. However, these models demand substantial volumes of training data. Additionally, many classification methods necessitate manual feature extraction, a process that is not only time-consuming but also laborious. Addressing the challenge of identifying optimal features to enhance classification accuracy, this work introduces a deep learning model designed for effective classification of network traffic. The model comprises the following key stages: (a) The dataset involves TCP flows captured from running different network stress and web crawling tools, (b) Pre-processing for removal of anomalies and noises using Label Encoder and OneHotEncoder, (c) The utilization of K-BERT for feature extraction aims to retrieve local spatial–temporal features, (d) feature selection using linear regression model (LASSO) and finally, and (e) The classification of network traffic involves neural network. The model serves to enhance the precision and efficiency of the classification mission. Through comprehensive experimental analysis, it was observed that the Masked Language-based Regression model surpassed other referenced models, achieving an exceptional accuracy of 0.97.

The growing sophistication of cyber attacks, vulnerabilities in high computing systems and increasing dependency on cryptography to protect our digital data, make it more important to keep secret keys safe and secure. A few major issues of secret keys, like incorrect use of keys, inappropriate storage of keys, inadequate protection of keys, insecure movement of keys, lack of audit logging, insider threats and nondestruction of keys can compromise the whole security system severely. In this work, we propose a field programmable gate array (FPGA)-based trusted platform module (TPM) framework for operating system companies and OS users, utilizing blockchain to address NIST-recommended secret key management issues. The security processor used in OS user machines is partitioned into three areas such that processor area, confidential area, and crypto area. The isolated secret key memory in confidential area, along with a private blockchain (BC) can log the life cycle of secret keys of TPM standard. We have also implemented a special custom bus interconnect, which receives custom crypto instructions from Processing Element (PE). During the execution of crypto instructions, the architecture ensures that secret keys are present in confidential area and crypto area but never in the processor area. The movements of secret keys between confidential area, and crypto area are recorded cryptographically after the proper authentication process controlled by the proposed hardware-based private BC framework. To the best of our knowledge, this work is the first attempt to implement a blockchain-based framework between OS company and OS users to address NIST recommended secret key management issues of TPM standard hardware environment. The additional cost of resource usage and timing complexity we spent to implement the proposed idea is nominal. The proposed architecture is implemented with Xilinx $��V�i�v�a�d�o�$ EDA tool using $��A�r�t�i�x��7�$ FPGA board.

Artificial bee colony (ABC) is a popular intelligent algorithm that is widely applied to many optimization problems. However, it is challenging for ABC to solve many-objective optimization problems (MaOPs). To tackle this issue, this article proposes a many-objective ABC based on multi-population (called MMaOABC) for MaOPs. In MMaOABC, the population is divided into multiple sub-populations, and each sub-population optimizes one objective. Three search strategies are constructed based on multiple sub-populations to improve convergence and diversity. In the employed bee stage, some excellent solutions in multiple sub-populations are used to guide the convergence. In the onlooker bee stage, new selection probabilities based on diversity metrics are designed to enhance the diversity. Dimensional learning is introduced in the scout bee stage to avoid falling into local minimum. In addition, environmental selection and external archives are utilized for communications among sub-populations. To validate the performance of MMaOABC, two benchmark sets (DTLZ and MaF) with 3, 5, 8, and 15 objectives are tested. Computational results show that MMaOABC is competitive when compared with seven other many-objective evolutionary algorithms (MaOEAs). Finally, MMaOABC is applied to many-objective cascade reservoir scheduling. Simulation results show that MMaOABC still obtains promising performance.

Object detection in crowded scenes involves various difficulties, such as small objects, occluded objects, and insufficient features. Existing models for crowded object detection often focus on only one detection difficulty, and they are too large to be applied in practice. To address the diverse challenges of object detection in crowded scenes, we construct a lightweight crowded object detector called MBB-YOLO, which contains several modules for comprehensive improvement. To improve the network's ability to extract fine-grained features, we use SPD-Conv and the proposed MS-Conv to replace the strided convolution in the network. An bi-branch multi-scale convolution attention (BMCA) module is proposed to aggregate multi-scale contextual information. We also propose boundary-NMS to better identify proposal boxes from different objects, which reduces suppression errors caused by object occlusion. MBB-YOLO achieves 87.6% AP and an inference speed of 78.8 FPS on the CrowdHuman dataset, which surpasses other mainstream lightweight object detectors.

In this research, we present an innovative cryptographic key generation method utilizing a Generative Adversarial Network (GAN), enhanced by Merkel tree verification, marking a significant advancement in cryptographic security. Our approach successfully generates a large 6272-bit key, rigorously tested for randomness and reliability using the Dieharder and NIST test suites. This groundbreaking method harmoniously blends cutting-edge machine learning techniques with traditional cryptographic verification, setting a new standard in data encryption and security. Our findings not only demonstrate the efficacy of GANs in producing highly secure cryptographic keys but also highlight the effectiveness of Merkel tree verification in ensuring the integrity of these keys. The integration of merkel tree in our method provides a means to efficiently verify the authenticity of the large generated key sets. This research has broad implications for the future of secure communications, providing a robust solution in a world increasingly reliant on digital security. The integration of machine learning and cryptographic principles opens up new avenues for research and development, promising to bolster security measures in an era where digital threats are constantly evolving. This work contributes significantly to the field of cryptography, offering a novel perspective and robust solutions to the challenges of digital data protection.