Concurrency and Computation-Practice & Experience最新文献

The rapid advancement of financial technology (FinTech) has led to the integration of advanced technologies like data science, blockchain, cloud computing, and artificial intelligence. However, trust evaluation remains a critical challenge in dynamic landscape. Existing trust evaluation methods often neglect key aspects of timeliness, reliability, and non-invasiveness, leading to imprecise trust assessments and insufficient detection of malicious user behavior. This paper introduces a robust four-layer architectural framework with the blockchain layer, edge computing service layer, cloud computing service layer, and terminal user application layer leveraging blockchain technology for authentication and trust evaluation. Blockchain technology transforms FinTech data into linked data, ensuring data security and decentralization during information transfers. A novel hybrid consensus protocol combining Proof of Elapsed Time (PoET) and Proof of Stake (PoS) is introduced to enhance the efficiency and security of the blockchain. Extensive simulation experiments have demonstrated significant improvements in data security, reliability, and accuracy of trust assessments compared to existing methods. This paper presents a comprehensive solution for enhancing trust evaluation in FinTech, emphasizing timeliness, reliability, and non-invasiveness of assessments.

This article proposes a solution to the factorization problem in cryptographic systems by leveraging the steps of the Toom-Cook algorithm for large-number multiplication. This approach can factor a 200-bit number, with performance varying depending on memory and processing power. Experiments demonstrate that the factorization problem in cryptography can be solved more efficiently by employing algorithms designed for fast and straightforward multiplication of large numbers. Examples include the Schönhage–Strassen algorithm, which is based on polynomials and Fourier transforms, the Fürer algorithm, the second Schönhage–Strassen algorithm using modular arithmetic, and Karatsuba's algorithm. This advancement significantly impacts modern computing and cryptography, enhancing both security and reliability. The proposed technique was extensively tested through simulations using the MATLAB simulator. Experimental results indicate improvements of 91% in efficiency and 95% in accuracy compared to state-of-the-art techniques.

This study proposes strategies to reduce the financial cost of using cloud clusters, through Amazon Web Services (AWS) ParallelCluster, to run the weather forecast model Brazilian developments on the Regional Atmospheric Modeling System (BRAMS). We developed an instance selection algorithm that obtains and compares the costs of various instance types in different regions and markets, recommending those with the lowest costs. If the suggested instance is a Spot instance and is revoked by the cloud provider, the proposed strategy resumes the application execution from a pre-recorded checkpoint by rescheduling it on On-Demand instances. This study also presents a detailed analysis of BRAMS execution across various instance architectures and proposes a novel three-queue architecture for managing BRAMS execution on On-Demand and Spot instances within AWS ParallelCluster. The results obtained from small and large spatial domains executed in AWS ParallelCluster using the proposed strategies show that adopting a cloud cluster is a promising alternative for this type of High-Performance Computing application, compared with execution on a supercomputer.

Antibiotic resistance presents a significant challenge to public health, as bacteria can develop resistance to antibiotics through random mutations during their life cycles, making the drugs ineffective. Understanding how these mutations contribute to drug resistance at the molecular level is crucial for designing new treatment approaches. Recent advancements in molecular biology tools have made it possible to conduct comprehensive analyses of protein mutations. Computational methods for assessing molecular fitness, such as binding energies, are not as precise as experimental techniques like deep mutational scanning. Although full atomistic alchemical free energy calculations offer the necessary precision, they are seldom used to assess high throughput data as they require significantly more computational resources. We generated a computational library using deep mutational scanning for dihydrofolate reductase (DHFR), a protein commonly studied in antibiotic resistance research. Due to resource limitations, we analyzed 33 out of 159 positions, identifying 16 single amino acid replacements. Calculations were conducted for DHFR in its drug-free state and in the presence of two different inhibitors. We demonstrate the feasibility of such calculations, made possible due to the enhancements in computational resources and their optimized use.

For systems with an accelerator component, it is important from an operational and planning perspective to understand how and to what extent the accelerators are being used. Having a framework for tracking the utilization of accelerator resources is important both for judging how efficiently used a system is and for capacity and configuration planning of future systems. In addition to tracking total utilization and accelerator efficiency numbers, some attention should also be paid to the types of research and workflows that are being executed on the system. In the past, the demand for accelerator resources was largely driven by more traditional simulation codes, such as molecular dynamics. But with the growing popularity of deep learning and artificial intelligence workflows, accelerators have become even more highly sought after and are being used in new ways. Provisioning resources to researchers via an allocation system allows sites to track a project's usage and workflow as well as the scientific impact of the project. With such tools and data in hand, characterizing the GPU utilization of deep learning frameworks versus more traditional GPU-enabled applications becomes possible. In this paper we present a survey of GPU monitoring tools used in sites and a framework for tracking the utilization of NVIDIA GPUs on Slurm-scheduled HPC systems used at Indiana University. We also present an analysis of accelerator utilization on multiple systems, including an HPE Apollo system targeting AI workflows and a Cray EX system.

Aiming to address issues such as non-uniform rain density and misjudgment caused by noise in image de-raining, we propose a single-image de-raining method based on a generative adversarial network with contextual information aggregation and multi-scale feature fusion. First, we design a generator composed of encoding, context information aggregation, and decoding stages. Features are extracted using convolution, while expansion convolution effectively aggregates context information. Transposition convolution is then used to restore the image, enhancing the model's ability to perceive image details and achieve accurate image information judgment and content reconstruction. Second, we design a multi-scale feature fusion discriminator structure to capture different image details using convolution kernels of different scales and connect feature maps from different scales. This improves the model's ability to understand image details and differentiate between authentic and fake images. Finally, we propose a new refinement loss function to reduce grid artifact generation and add Lipschitz constraints to further minimize the imaging gap. In this paper, peak signal-to-noise ratio and structural similarity are used as evaluation criteria, and experiments conducted on real and synthesized rain maps demonstrate the superior rain removal performance of the proposed method.

In scenarios where network resources are restricted, IPv6 addresses are frequently allocated to devices via SLAAC-based EUI-64 addressing to ensure their uniqueness. The consistent interface identifier (IID) resulting from this approach across networks poses a vulnerability to diverse reconnaissance attacks, encompassing activities like tracking locations, correlating network actions, scanning addresses, and targeting specific devices and so on. In response to this concern, our study introduces an innovative addressing strategy by utilizing the elegant pairing function. This technique ensures the creation of IPv6 addresses that are both nonpredictable and unique and effectively counteracts various reconnaissance attacks. Through empirical assessment, our proposed method achieves a 100% address success rate (ASR), effectively mitigating reconnaissance attacks without introducing additional communication overhead or heightened energy consumption.

In today's data-intensive client-server systems, traditional caching methods often fail to meet the demands of modern applications, especially in mobile environments with unstable network conditions. This research addresses the challenge of improving data delivery by proposing an advanced prefetching framework that utilizes various embedding techniques. We explore how to model user navigation using graph-based, autoencoder-based, and sequence-to-sequence-based embedding methods and assess their impact on prefetching accuracy and efficiency. Our study shows that utilizing these embedding techniques with supervised learning models improves prefetching performance. We also present a software architecture that blends supervised and unsupervised learning approaches, along with user-specific and collective learning models, to create a robust prefetching mechanism. The contributions of this study include developing a scalable prefetching solution using machine learning/deep learning algorithms and providing an open-source prototype of the proposed architecture. This paper offers a significant improvement over previous research and provides valuable insights for enhancing the performance of data-intensive applications.

The Density Peaks Clustering (DPC) algorithm is well-known for its simplicity and efficiency in clustering data of arbitrary shapes. However, it faces challenges such as inconsistent local density definitions and sample assignment errors. This paper introduces the Shared Neighbors and Natural Neighbors Density Peaks Clustering (SN-DPC) algorithm to address these issues. SN-DPC redefines local density by incorporating weighted shared neighbors, which enhances the density contribution from distant samples and provides a better representation of the data distribution. It also establishes a new similarity measure between samples using shared and natural neighbors, which increases intra-cluster similarity and reduces assignment errors, thereby improving clustering performance. Compared with DPC-CE, IDPC-FA, DPCSA, FNDPC, and traditional DPC, SN-DPC demonstrated superior effectiveness on both synthetic and real datasets. When applied to the analysis of electricity consumption patterns, it more accurately identified load consumption patterns and usage habits.