Concurrency and Computation-Practice & Experience最新文献

In sedimentology, the integration of intelligent engineering decision-making with edge computing environments aims to furnish engineers and decision-makers with precise, real-time insights into sediment-related issues. This approach markedly reduces data transfer time and response latency by harnessing the computational power of edge computing, thereby bolstering the decision-making process. Concurrently, the establishment of a sediment knowledge graph serves as a pivotal conduit for disseminating sediment-related knowledge in the realm of intelligent engineering decision-making. Moreover, it facilitates a comprehensive exploration of the intricate evolutionary and transformative processes inherent in sediment materials. By unveiling the evolutionary trajectory of life on Earth, the sediment knowledge graph catalyzes a deeper understanding of our planet's history and dynamics. Relationship extraction, as a key step in knowledge graph construction, implements automatic extraction and establishment of associations between entities from a large amount of sedimentary literature data. However, sedimentological literature presents multi-source heterogeneous features, which leads to a weak representation of hidden relationships, thus decreasing the accuracy of relationship extraction. In this article, we propose an attribute-extended relation extraction approach (AERE), which is specifically designed for sedimentary relation extraction scenarios. First, context statements containing sediment entities are obtained from the literature. Then, a cohesive hierarchical clustering algorithm is used to extend the relationship attributes between sediments. Finally, mine the relationships between entities based on AERE. The experimental results show that the proposed model can effectively extract the hidden relations and exhibits strong robustness in dealing with redundant noise before and after sentences, which in turn improves the completeness of the relations between deposits. After the relationship extraction, a proprietary sediment knowledge graph is constructed with the extracted triads.

The Internet of Things (IoT) is an ever-expanding network technology that connects diverse objects and devices, generating vast amounts of heterogeneous data at the network edge. These vast volumes of data present significant challenges in data management, transmission, and storage. In fog-cloud-enabled IoT, where data are processed at the edge (fog) and in the cloud, efficient data reduction strategies become imperative. One such method is clustering, which groups similar data points together to reduce redundancy and facilitate more efficient data management. In this paper, we introduce ClusFC-IoT, a novel two-phase clustering-based approach designed to optimize the management of IoT-generated data. In the first phase, which is performed in the fog layer, we used the K-means clustering algorithm to group the received data from the IoT layer based on similarity. This initial clustering creates distinct clusters, with a central data point representing each cluster. Incoming data from the IoT side is assigned to these existing clusters if they have similar characteristics, which reduces data redundancy and transfers to the cloud layer. In a second phase performed in the cloud layer, we performed additional K-means clustering on the data obtained from the fog layer. In this secondary clustering phase, we stabilized the similarities between the clusters created in the fog layer further optimized the data display, and reduced the redundancy. To verify the effectiveness of ClusFC-IoT, we implemented it using four different IoT data sets in Python 3. The implementation results show a reduction in data transmission compared to other methods, which makes ClusFC-IoT very suitable for resource-constrained IoT environments.

Software vulnerability detection is an important problem in software security. In recent years, deep learning offers a novel approach for source code vulnerability detection. Due to the similarities between programming languages and natural languages, many natural language processing techniques have been applied to vulnerability detection tasks. However, specific problems within vulnerability detection tasks, such as buffer overflow, involve numerical reasoning. For these problems, the model needs to not only consider long dependencies and multiple relationships between statements of code but also capture the magnitude property of numerical literals in the program through high-quality number embeddings. Therefore, we propose VDTransformer, a Transformer-based method that improves source code embedding by integrating word and number embeddings. Furthermore, we employ Transformer encoders to construct a hierarchical neural network that extracts semantic features from the code and enables line-level vulnerability detection. To evaluate the effectiveness of the method, we construct a dataset named OverflowGen based on templates for buffer overflow. Experimental comparisons on OverflowGen with a well-known static vulnerability detector and two state-of-the-art deep learning-based methods confirm the effectiveness of VDTransformer and the importance of high-quality number embeddings in vulnerability detection tasks involving numerical features.

Fraud keeps increasing in our society and security applications become crucial and needed in our daily life. Biometric technology attempts to stop fraud and falsification in different opportunities such as bank services, access to controlled areas or crossing frontiers, by recognizing the identity of a person using his physiological (fingerprint, iris, face) or behavioral modalities (gait, signature). In this article, we focus on an emerging biometric modality called the finger knuckle print (FKP). In fact, this modality has several advantages such as the easy distinction between different persons, stability over time and user acceptance. So, an FKP identification approach is proposed using scale invariant feature transform descriptors based sparse representation method. The classification step, between training and testing FKP samples, is made using the support vector machines method. Experiments applied on two public FKP databases: The Hong Kong Polytechnic University (PolyU) Contactless Finger Knuckle Images Database and the Indian Institute of Technology Delhi (IITD) Finger Knuckle Database, containing respectively 2500 and 790 images, demonstrate high correct identification rates by reaching 98.58% and 99.15% for these two databases.

Multivariate time series classification holds significant importance in fields such as healthcare, energy management, and industrial manufacturing. Existing research focuses on capturing temporal changes or calculating time similarities to accomplish classification tasks. However, as the state of the system changes, capturing spatial-temporal consistency within multivariate time series is key to the ability of the model to classify accurately. This paper proposes the MSTformer model, specifically designed for multivariate time series classification tasks. Based on the Transformer architecture, this model uniquely focuses on multiscale information across both time and feature dimensions. The encoder, through a designed learnable multiscale attention mechanism, divides data into sequences of varying temporal scales to learn multiscale temporal features. The decoder, which receives the spatial view of the data, utilizes a dynamic scale attention mechanism to learn spatial-temporal consistency in a one-dimensional space. In addition, this paper proposes an adaptive aggregation mechanism to synchronize and combine the outputs of the encoder and decoder. It also introduces a multiscale 2D separable convolution designed to learn spatial-temporal consistency in two-dimensional space, enhancing the ability of the model to learn spatial-temporal consistency representation. Extensive experiments were conducted on 30 datasets, where the MSTformer outperformed other models with an average accuracy rate of 85.6%. Ablation studies further demonstrate the reliability and stability of MSTformer.

In the context of electronic governance, traditional monolithic architectures often struggle with the efficient exchange of information and analytics due to their centralized nature. Emerging architectural paradigms such as Service-Oriented Architecture, Microservices Architecture (MSA), Distributed/Decentralized Technology, and Blockchain Technology offer potential solutions to these challenges. This white paper conducts a literature review to identify the factors influencing the decision to migrate from monolithic systems to these new architectures. By applying a multi-attribute fuzzy-based technique for order preference by similarity to ideal solution (TOPSIS), the study evaluates and ranks these architectures based on their ability to meet the requirements of modern electronic governance applications. The results are compared with other ranking multi-criteria decision-making techniques like fuzzy analytical hierarchical process and intuitionistic fuzzy TOPSIS (IFTOPSIS). The findings indicate that MSA ranks highest among the available options. Each architecture offers distinct advantages that can address the limitations of traditional systems but also come with challenges. This paper also considers these factors along with a well-defined strategy and risk management plan essential for a successful migration.

The precise diagnosis and effective treatment of patients rely heavily on healthcare data. However, sharing healthcare information can be challenging due to the potential risks of unauthorized tampering and data leakage. To address these concerns and facilitate secure and efficient data access for stakeholders within and outside the healthcare system, this study introduces a patient-enabled blockchain system (PEBS). Patient-enabled blockchain system uses the Model View Controller (MVC) approach where the model manages the off-chain and on-chain data, the view is the user-accessible module, and the controller acts as an interface between a user interface and storage layer. It enables patients to control their data by determining specific access permissions and executes various smart contracts for stakeholders' registration, authorization, data storage, query, and update operations. Patient-enabled blockchain system incorporates Modified Proof-of-Authority (MPoA), which has been compared against various consensus algorithms such as Proof-of-Work (PoW), Proof-of-Authority (PoA), and Istanbul Byzantine Fault Tolerance (IBFT). Furthermore, the suggested system incorporates the utilization of the Interplanetary File System (IPFS) to address concerns related to performance and storage. We conducted an in-depth analysis and comparison of the system's performance using key parameters such as transaction latency and throughput. Experiments are carried out using network sizes of 10 and 30, with transaction counts from 5 to 500. The experiments show that the highest latency for the proposed system is 58,105 ms, almost 4.8 times less than PoW, which is 283,575 and provides 2.7 times higher throughput (101 transactions per second) than PoW (38 transactions per second).

When selecting services from a cloud center to build applications, the quality of service (QoS) is an important nonfunctional attribute to be considered. However, in actual application scenarios, the QoS details for many services may not be available. This has led to a situation where prediction of the missing QoS records for services has become a key problem for service selection. This article presents a selective ensemble learning (SEL) framework for prefilling-based matrix factorization (PFMF) predictors. In each PFMF predictor, the improved collaborative filtering is defined by examining the stability of the QoS records when measuring the similarity of users (or services), and then used to prefill empty records in the initial QoS matrix. To ensure the diversity of the basic PFMF predictors, various prefilled QoS matrices are constructed for the matrix factorization. In this process, different reference weights are assigned to the original and the prefilled QoS records. Finally, particle swarm optimization is used to set the ensemble weights for the basic PFMF predictors. The proposed SEL on PFMF (SEL-PFMF) algorithm is validated on a public dataset, where its prediction performance outperforms the state-of-the-art algorithms, and also shows good stability.