Formal Approach to Workflow Application Fragmentations Over Cloud Deployment Models

Workflow management technologies have been dramatically improving their deployment architectures and systems along with the evolution and proliferation of cloud distributed computing environments. Especially, such cloud computing environments ought to be providing a suitable distributed computing paradigm to deploy very large-scale workflow processes and applications with scalable on-demand services. In this paper, we focus on the distribution paradigm and its deployment formalism for such very large-scale workflow applications being deployed and enacted across the multiple and heterogeneous cloud computing environments. We propose a formal approach to vertically as well as horizontally fragment very large-scale workflow processes and their applications and to deploy the workflow process and application fragments over three types of cloud deployment models and architectures. To concretize the formal approach, we firstly devise a series of operational situations fragmenting into cloud workflow process and application components and deploying onto three different types of cloud deployment models and architectures. These concrete approaches are called the deployment-driven fragmentation mechanism to be applied to such very large-scale workflow process and applications as an implementing component for cloud workflow management systems. Finally, we strongly believe that our approach with the fragmentation formalisms becomes a theoretical basis of designing and implementing very large-scale and maximally distributed workflow processes and applications to be deployed on cloud deployment models and architectural computing environments as well.     

An analysis model of diagnosis and treatment for COVID-19 pandemic based on medical information fusion

Exploring the complicated relationships underlying the clinical information is essential for the diagnosis and treatment of the Coronavirus Disease 2019 (COVID-19). Currently, few approaches are mature enough to show operational impact. Based on electronic medical records (EMRs) of 570 COVID-19 inpatients, we proposed an analysis model of diagnosis and treatment for COVID-19 based on the machine learning algorithms and complex networks. Introducing the medical information fusion, we constructed the heterogeneous information network to discover the complex relationships among the syndromes, symptoms, and medicines. We generated the numerical symptom (medicine) embeddings and divided them into seven communities (syndromes) using the combination of Skip-Gram model and Spectral Clustering (SC) algorithm. After analyzing the symptoms and medicine networks, we identified the key factors using six evaluation metrics of node centrality. The experimental results indicate that the proposed analysis model is capable of discovering the critical symptoms and symptom distribution for diagnosis; the key medicines and medicine combinations for treatment. Based on the latest COVID-19 clinical guidelines, this model could result in the higher accuracy results than the other representative clustering algorithms. Furthermore, the proposed model is able to provide tremendously valuable guidance and help the physicians to combat the COVID-19.     

DEM: Deep Entity Matching Across Heterogeneous Information Networks

Heterogeneous information networks, which consist of multi-typed vertices representing objects and multi-typed edges representing relations between objects, are ubiquitous in the real world. In this paper, we study the problem of entity matching for heterogeneous information networks based on distributed network embedding and multi-layer perceptron with a highway network, and we propose a new method named DEM short for Deep Entity Matching. In contrast to the traditional entity matching methods, DEM utilizes the multi-layer perceptron with a highway network to explore the hidden relations to improve the performance of matching. Importantly, we incorporate DEM with the network embedding methodology, enabling highly efficient computing in a vectorized manner. DEM’s generic modeling of both the network structure and the entity attributes enables it to model various heterogeneous information networks flexibly. To illustrate its functionality, we apply the DEM algorithm to two real-world entity matching applications: user linkage under the social network analysis scenario that predicts the same or matched users in different social platforms and record linkage that predicts the same or matched records in different citation networks. Extensive experiments on real-world datasets demonstrate DEM’s effectiveness and rationality.

     

Temporal information based GoP adaptation for linear video delivery schemes

An original wireless video transmission scheme called SoftCast has been recently proposed to deal with the issues encountered in conventional wireless video broadcasting systems (e.g. cliff effect). In this paper, we evaluate and optimize the performance of the SoftCast scheme according to the transmitted video content. Precisely, an adaptive coding mechanism based on GoP-size adaptation, which takes into account the temporal information fluctuations of the video, is proposed. This extension denoted Adaptive GoP-size mechanism based on Content and Cut detection for SoftCast (AGCC-SoftCast) significantly improves the performance of the SoftCast scheme. It consists in modifying the GoP-size according to the shot changes and the spatio-temporal characteristics of the transmitted video. When hardware capacities, such as buffer or processor performance are limited, an alternative method based only on the shot changes detection (AGCut-SoftCast) is also proposed. Improvements up to 16 dB for the PSNR and up to 0.55 for the SSIM are observed with the proposed solutions at the cut boundaries. In addition, temporal visual quality fluctuations are reduced under 1dB in average, showing the effectiveness of the proposed methods.     

基于条件信息卷积生成对抗网络的图像识别

传统的图像识别方法需要大量有标签样本进行训练，且模型训练难以达到稳定。针对这些问题，结合条件生成网络和信息最大化生成网络的结构优势建立了条件信息卷积生成网络（C-Info-DCGAN）。模型增加图像的类别信息和潜在信息作为输入数据，然后利用Q网络去更好地发挥类别信息和潜在信息对训练的引导作用，并且利用深度卷积网络来加强对图像特征的提取能力。实验结果表明，该方法能够加快模型训练收敛速度，并有效提高图像识别的准确率。     

结合边缘特征先验引导的深度卷积显著性检测

针对当前基于深度学习的显著性检测算法缺少利用先验特征和边缘信息，且在复杂场景中难以检测出鲁棒性强的显著性区域的问题，提出了一种结合边缘特征，利用先验信息引导的全卷积神经网络显著性检测算法。该算法利用三种被经常用到的先验知识结合边缘信息形成先验图，通过注意力机制将提取的先验特征与深度特征有效融合，最终通过提出的循环卷积反馈优化策略迭代地学习改进显著性区域，从而产生更可靠的最终显著图预测。经过实验定性定量分析，对比证明了算法的可靠性。     

水利信息网络与信息资源整合共享的必要性研究

长期以来,我国在水利资源管理上存在整体性不足、各自为战的特点,因而在未来发展的过程中,要想提高水利管理的现代化程度,就要重视网络与信息安全结合的整体性,并不断提高研究的层级。基于此,文章主要分析水利信息网络与资源整合共享的必要性,并探讨系统框架分析建设路径,以提高水利网络与信息安全资源共享的实际效果。     

Energy Filtering of Charge Carriers: Current Trends,Challenges, and Prospects for Thermoelectric Materials

Exhaustive attempts are made in recent decades to improve the performance of thermoelectric materials that are utilized for waste heat‐to‐electricity conversion. Energy filtering of charge carriers is directed toward enhancing the material thermopower. This paper focuses on the theoretical concepts, experimental evidence, and the authors' view of energy filtering in the context of thermoelectric materials. Recent studies suggest that not all materials experience this effect with the same intensity. Although this effect theoretically demonstrates improvement of the thermopower, applying it poses certain constraints, which demands further research. Predicated on data documented in literature, the unusual dependence of the thermopower and conductivity upon charge carrier concentrations can be altered through the energy filtering approach. Upon surmounting the physical constraints discussed in this article, thermoelectric materials research may gain a new direction to enhance the power factor and thermoelectric figure of merit.