CV3: Visual Exploration,Assessment, and Comparison of CVs

The Curriculum Vitae (CV, also referred to as “résumé”) is an established representation of a person's academic and professional history. A typical CV is comprised of multiple sections associated with spatio‐temporal, nominal, hierarchical, and ordinal data. The main task of a recruiter is, given a job application with specific requirements, to compare and assess CVs in order to build a short list of promising candidates to interview. Commonly, this is done by viewing CVs in a side‐by‐side fashion. This becomes challenging when comparing more than two CVs, because the reader is required to switch attention between them. Furthermore, there is no guarantee that the CVs are structured similarly, thus making the overview cluttered and significantly slowing down the comparison process. In order to address these challenges, in this paper we propose “CV3”, an interactive exploration environment offering users a new way to explore, assess, and compare multiple CVs, to suggest suitable candidates for specific job requirements. We validate our system by means of domain expert feedback whose results highlight both the efficacy of our approach and its limitations. We learned that CV3 eases the overall burden of recruiters thereby assisting them in the selection process.     

A Generalized Cubemap for Encoding 360° VR Videos using Polynomial Approximation

360^° VR videos provide users with an immersive visual experience. To encode 360^° VR videos, spherical pixels must be mapped onto a two‐dimensional domain to take advantage of the existing video encoding and storage standards. In VR industry, standard cubemap projection is the most widely used projection method for encoding 360^° VR videos. However, it exhibits pixel density variation at different regions due to projection distortion. We present a generalized algorithm to improve the efficiency of cubemap projection using polynomial approximation. In our algorithm, standard cubemap projection can be regarded as a special form with 1st‐order polynomial. Our experiments show that the generalized cubemap projection can significantly reduce the projection distortion using higher order polynomials. As a result, pixel distribution can be well balanced in the resulting 360^° VR videos. We use PSNR, S‐PSNR and CPP‐PSNR to evaluate the visual quality and the experimental results demonstrate promising performance improvement against standard cubemap projection and Google's equi‐angular cubemap.     

转底炉处理赤泥工艺技术

为了综合利用氧化铝冶炼产生的赤泥，探索在转底炉中直接还原赤泥、磨矿磁选获得高品位直接还原铁。通过实验室试验摸索了转底炉还原工艺参数，并在转底炉工业试验线进行了工业试验。实验室结果表明，赤泥还原后的直接还原铁(DRI)金属化率可达88.6%，磁选后的铁品位可达82.1%，磁选后的铁回收率可达88.9%。工业试验中，转底炉还原后，产品金属化率平均为69.2%，将还原后的DRI磁选获得高品位的DRI产品，磁选后DRI的铁品位为72.8%，磁选后铁回收率达到了85.2%,初步打通了在转底炉中还原赤泥、磁选的工艺路径。     

The three Rs of river ecosystem resilience: Resources,recruitment, and refugia

Resilience in river ecosystems requires that organisms must persist in the face of highly dynamic hydrological and geomorphological variations. Disturbance events such as floods and droughts are postulated to shape life history traits that support resilience, but river management and conservation would benefit from greater understanding of the emergent effects in communities of river organisms. We unify current knowledge of taxonomic‐, phylogenetic‐, and trait‐based aspects of river communities that might aid the identification and quantification of resilience mechanisms. Temporal variations in river productivity, physical connectivity, and environmental heterogeneity resulting from floods and droughts are highlighted as key characteristics that promote resilience in these dynamic ecosystems. Three community‐wide mechanisms that underlie resilience are (a) partitioning (competition/facilitation) of dynamically varying resources, (b) dispersal, recolonization, and recruitment promoted by connectivity, and (c) functional redundancy in communities promoted by resource heterogeneity and refugia. Along with taxonomic and phylogenetic identity, biological traits related to feeding specialization, dispersal ability, and habitat specialization mediate organism responses to disturbance. Measures of these factors might also enable assessment of the relative contributions of different mechanisms to community resilience. Interactions between abiotic drivers and biotic aspects of resource use, dispersal, and persistence have clear implications for river conservation and management. To support these management needs, we propose a set of taxonomic, phylogenetic, and life‐history trait metrics that might be used to measure resilience mechanisms. By identifying such indicators, our proposed framework can enable targeted management strategies to adapt river ecosystems to global change.     

基于支持向量机的RSA电磁旁路分析方法

针对在旁路分析过程中由于噪声的影响，不能直接观察出RSA的加密过程这一问题，提出结合支持向量机的旁路分析方法，从分类的角度对RSA二进制模幂运算中的平方、乘法操作进行识别，根据密钥与操作的相关性，推断出RSA二进制密钥序列。基于此，对移动设备PCM-9589F凌动主板进行了RSA电磁旁路分析研究，使用串口通信技术实现了对目标设备CPU的旁路电磁信号的采集，并采用基于边界的单类支持向量机的方法，以分类的正确率为70%为阈值，实现了对RSA二进制模幂运算中平方、乘法操作的识别，提取了OpenSSL加密库中1?024位RSA加密算法的二进制密钥序列。相比于传统的简单分析方法，克服了因旁路信号质量低而无法破解密钥的难题。     

A Dynamic Memory Allocation Optimization Mechanism Based on Spark

Spark is a distributed data processing framework based on memory. Memory allocation is a focus question of Spark research. A good memory allocation scheme can effectively improve the efficiency of task execution and memory resource utilization of the Spark. Aiming at the memory allocation problem in the Spark2.x version, this paper optimizes the memory allocation strategy by analyzing the Spark memory model, the existing cache replacement algorithms and the memory allocation methods, which is on the basis of minimizing the storage area and allocating the execution area according to the demand. It mainly including two parts: cache replacement optimization and memory allocation optimization. Firstly, in the storage area, the cache replacement algorithm is optimized according to the characteristics of RDD Partition, which is combined with PCA dimension. In this section, the four features of RDD Partition are selected. When the RDD cache is replaced, only two most important features are selected by PCA dimension reduction method each time, thereby ensuring the generalization of the cache replacement strategy. Secondly, the memory allocation strategy of the execution area is optimized according to the memory requirement of Task and the memory space of storage area. In this paper, a series of experiments in Spark on Yarn mode are carried out to verify the effectiveness of the optimization algorithm and improve the cluster performance.     

Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network

The container cloud represented by Docker and Kubernetes has the advantages of less additional resource overhead and shorter start-up and destruction time.However there are still resource management issues such as over-supply and under-supply.In order to allow the Kubernetes cluster to respond “in advance” to the resource usage of the applications deployed on it,and then to schedule and allocate resources in a timely,accurate and dynamic manner based on the predicted value,a cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network was proposed,based on historical data to predict future demand for resources.To find the optimal combination of parameters,the parameters were optimized using TPOT thought.Experiments on the CPU and memory of the Google dataset show that the model has better prediction performance than other models.     

Components and Development in Big Data System: A Survey

With the growth of distributed computing systems, the modern Big Data analysis platform products often have diversified characteristics. It is hard for users to make decisions when they are in early contact with Big Data platforms. In this paper, we discussed the design principles and research directions of modern Big Data platforms by presenting research in modern Big Data products. We provided a detailed review and comparison of several state-of-the-art frameworks and concluded into a typical structure with five horizontal and one vertical. According to this structure, this paper presents the components and modern optimization technologies developed for Big Data, which helps to choose the most suitable components and architecture from various Big Data technologies based on requirements.     

Evaluation of the environmental impact assessment (EIA) of Chinese EIA in Myanmar: Myitsone Dam,the Lappadaung Copper Mine and the Sino-Myanmar oil and gas pipelines

The economic relationship between China and Myanmar is regarded as a win–win cooperation. However, Chinese investments, especially in extractive and natural resource sectors, are associated with a number of unwanted environmental consequences. Moreover, the environmental impact assessment (EIA) quality of Chinese enterprises has often been criticized. EIA identifies adverse impacts to the environment through evidence-based decision making. On this basis, this paper provides an evaluation of Chinese EIA performance within the natural resources sector through a structured review of 15 environmental impact statements (EISs). This research also evaluates the EISs of the three largest and most controversial projects, the Myitsone Hydropower Dam, Lappadaung Copper Mine and Sino-Myanmar oil and gas pipelines. The findings reveal several omissions, inadequacies and deficiencies in all the projects with a significant number of EISs falling short of satisfactory quality. Through the analysis, the paper summarizes the factors affecting the EIA performance and proposes feasible recommendations to improve EIA practices in Chinese development projects in Myanmar.     

A thermal-aware VM consolidation mechanism with outage avoidance

Efficient energy and temperature management techniques are essential elements for operators of cloud data centers. Dynamic virtual machine (VM) consolidation using live migration techniques presents a great opportunity for cloud service providers to adaptively reduce energy consumption and optimize their resource utilization. In recent studies, power consumption readings of individual physical hosts were chosen as the main monitoring parameters in their allocation policies, whereas very few have considered host temperature, which has shown to have a negative impact on server reliability, as a migration criterion. In this work, a thermal-aware VM consolidation mechanism is proposed for resource allocation optimization and server reliability assurance. We consider the variability in host temperature as a migration criterion to avoid outage incidents via having better VM consolidations. Extensive simulation results obtained from CloudSim show the promising performance of the proposed mechanism in energy saving while reducing the number of server outage incidents due to fluctuations in host temperature.