同构计算环境中一种快速有效的静态任务调度算法

快速有效的调度任务是多处理器计算环境中的一个关键问题．目前任务调度算法中刻画任务依赖关系最流行的模型是DAG,在以前的文献中,提出了一种新的更实际、更普遍的TTIG模型及其相应的MATE算法(基于同构计算环境)．延伸了TTIG模型,并提出基于同构系统的新的算法及两种启发式方法(GBHA1和GBHA2)．GBHA以组的形式尽量消除图中回路,因而能获得任务图的全局信息,具有更好的调度性能．在模拟实验中,将此算法与MATE和其他同构环境中基于DAG的有效调度算法,在不同测试条件下进行了比较,结果显示GBHA在性能上明显优于MATE,与基于DAG模型的调度算法比较而言,在性能方面各有千秋,但在算法时间复杂度方面具有显著的优势．     

同构环境中基于通信竞争的任务调度算法

基于DAG的静态任务调度算法已有深入的研究及应用.目前的调度算法大多假定处理器之间可以并行接收数据,而没有考虑实际应用中通信链路的竞争及延迟,进而导致调度算法在具体应用中效率较低.侧重研究同构计算环境下具有依赖关系任务的边调度问题,结合传统任务调度问题中的有效策略,提出基于优化插入的调度算法(OISA).OISA根据实际问题的具体特征,采用改进的路由算法选择负载较少的数据链路,并通过形式化的证明以优化通信数据在链路的开始传输时间,以达到降低调度长度的目的.通过试验测试表明,OISA在性能上明显优于目前已有的相关算法.     

Online short text clustering using infinite extensions of discrete mixture models

Short text clustering is one of the fundamental tasks in natural language processing. Different from traditional documents, short texts are ambiguous and sparse due to their short form and the lack of recurrence in word usage from one text to another, making it very challenging to apply conventional machine learning algorithms directly. In this article, we propose two novel approaches for short texts clustering: collapsed Gibbs sampling infinite generalized Dirichlet multinomial mixture model infinite GSGDMM) and collapsed Gibbs sampling infinite Beta-Liouville multinomial mixture model (infinite GSBLMM). We adopt two flexible and practical priors to the multinomial distribution where in the first one the generalized Dirichlet distribution is integrated, while the second one is based on the Beta-Liouville distribution. We evaluate the proposed approaches on two famous benchmark datasets, namely, Google News and Tweet. The experimental results demonstrate the effectiveness of our models compared to basic approaches that use Dirichlet priors. We further propose to improve the performance of our methods with an online clustering procedure. We also evaluate the performance of our methods for the outlier detection task, in which we achieve accurate results.     

Bounded multivariate generalized Gaussian mixture model using ICA and IVA

Pattern Analysis and Applications - A bounded multivariate generalized Gaussian mixture model with a full covariance matrix is proposed for modeling data in a bounded support region. For model...     

Educational UTAUT-based virtual reality acceptance scale: a validity and reliability study

Virtual Reality - This study aims to fill a gap in current research on virtual reality (VR) by developing a valid and reliable educational VR acceptance scale based on the unified theory of...     

Development of educational virtual reality attitude scale: A validity and reliability study

Virtual Reality - The aim of the study is to address a gap in the literature by developing an educational virtual reality (edVR) attitude measurement instrument, which determines college...     

Preparation and cyclisation reactions of 3-Carboxy-4-naphthylbut-3-enoic Acids

The condensation of naphthalene-1- or 2-carbaldehyde with dimethyl 2,2-dimethyl-succinate (Stobbe conditions) gives predominantly the ( Z )-hemiesters 3 and 11a . The hemiester 3 contains a small amount of the ( E )-isomer. Similar condensation of naphthalene-2-carbaldehyde with dimethyl-2-methylsuccinate gave the hemiester ( E )- 12a . Their configuration and the ratios of the ( Z:E )-hemiesters, their derived acids, anhydrides, indenones and phenanthrene derivatives are inferred by high resolution ¹H-n.m.r. spectroscopy.     

Optimizing the distributed generators integration in electrical distribution networks: efficient modified forensic-based investigation

Neural Computing and Applications - As a result of various loads, including critical installations (industries, nuclear facilities, etc.), electrical distribution networks (EDNs) must operate...     

Plasmonic Superparamagnetic SPION@Ag@chitosan Core-shell: Uptake and Nitric Oxide Inhibition by Colorectal Cell Lines

Using Fusarium oxysporum species (F. oxysporum), a green synthesis of super-paramagnet iron oxide @silver@ Chitosan (SPION@Ag@Cs) was achieved. The physico-chemical characteristics of the SPION@Ag@Cs nanocomposite revealed the development of superparamagnetic phases of iron oxide (Fe₂O₃), as well as fluorescence, Raman absorption, and biocompatibility. Drug and gene delivery, and diagnosis, are all possibilities for the nanocomposite. The uptake of nitric oxide by HT-29 colorectal cell lines is examined in this article, for up to 72 h, the cytotoxicity of the HCT116 cell line was investigated. These characteristics were compared to Streptomyces griseus fungal species (S. griseus) which develops Fe₃O₄ under the same preparation conditions.

     

Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

Cloud Computing (CC) is the most promising and advanced technology to store data and offer online services in an effective manner. When such fast evolving technologies are used in the protection of computer-based systems from cyberattacks, it brings several advantages compared to conventional data protection methods. Some of the computer-based systems that effectively protect the data include Cyber-Physical Systems (CPS), Internet of Things (IoT), mobile devices, desktop and laptop computer, and critical systems. Malicious software (malware) is nothing but a type of software that targets the computer-based systems so as to launch cyber-attacks and threaten the integrity, secrecy, and accessibility of the information. The current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification (OBDDBN-CMC) model. The presented OBDDBN-CMC model intends to recognize and classify the malware that exists in IoT-based cloud platform. To attain this, Z-score data normalization is utilized to scale the data into a uniform format. In addition, BDDBN model is also exploited for recognition and categorization of malware. To effectually fine-tune the hyperparameters related to BDDBN model, Grasshopper Optimization Algorithm (GOA) is applied. This scenario enhances the classification results and also shows the novelty of current study. The experimental analysis was conducted upon OBDDBN-CMC model for validation and the results confirmed the enhanced performance of OBDDBN-CMC model over recent approaches.