A survey on deploying mobile deep learning applications: A systemic and technical perspective

With the rapid development of mobile devices and deep learning, mobile smart applications using deep learning technology have sprung up. It satisfies multiple needs of users, network operators and service providers, and rapidly becomes a main research focus. In recent years, deep learning has achieved tremendous success in image processing, natural language processing, language analysis and other research fields. Despite the task performance has been greatly improved, the resources required to run these models have increased significantly. This poses a major challenge for deploying such applications on resource-restricted mobile devices. Mobile intelligence needs faster mobile processors, more storage space, smaller but more accurate models, and even the assistance of other network nodes. To help the readers establish a global concept of the entire research direction concisely, we classify the latest works in this field into two categories, which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks. We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications. Finally, we conjecture what the future may hold for deploying deep learning applications on mobile devices research, which may help to stimulate new ideas.     

Research advances on blockchain-as-a-service: architectures,applications and challenges

Due to the complexity of blockchain technology, it usually costs too much effort to build, maintain and monitor a blockchain system that supports a targeted application. To this end, the emerging “Blockchain as a Service” (BaaS) makes the blockchain and distributed ledgers more accessible, particularly for businesses, by reducing costs and overheads. BaaS combines the high computing power of cloud computing, the pervasiveness of IoT and the decentralization of blockchain, allowing people to build their own applications while ensuring the transparency and openness of the system. This paper surveys the research outputs of both academia and industry. First, it introduces the representative architectures of BaaS systems and then summarizes the research contributions of BaaS from the technologies for service provision, roles, container and virtualization, interfaces, customization and evaluation. The typical applications of BaaS in both academic and practical domains are also introduced. At present, the research on the blockchain is abundant, but research on BaaS is still in its infancy. Six challenges of BaaS are concluded in this paper for further study directions.     

Evaluation Model of Click Rate of Electronic Commerce Advertising Based on Fuzzy Genetic Algorithm

Mobile Networks and Applications - In order to improve the ability of quantitative evaluation of e-commerce advertising click rate, a model of e-commerce advertising click rate evaluation based on...     

n型单晶硅片的电阻率和扩散方阻对IBC太阳电池电性能的影响

叉指背接触式(IBC)太阳电池因正面没有金属栅线遮挡,具有较高的短路电流,且组件外观更加美观。但由于IBC太阳电池正负电极在背面交叉式分布,在制备过程中需要采用光刻掩模技术进行隔离,难以实现大规模生产。采用Quokka软件仿真模拟了电阻率和扩散方阻对n型IBC太阳电池效率的影响,并对不同电阻率和扩散方阻的电池片进行了实验验证,从n型单晶硅片电阻率的选择和扩散工艺优化方面为IBC太阳电池的规模化生产提供了理论基础。实验结果表明,电阻率为3~5Ω·cm、扩散方阻为70Ω/时,小批量生产的IBC太阳电池平均光电转换效率可达23.73%,开路电压为693 mV,短路电流密度为42.44 mA/cm²,填充因子为80.69%。     

Service placement strategy for joint network selection and resource scheduling in edge computing

The Journal of Supercomputing - In the edge computing, service placement refers to the process of installing service platforms, databases, and configuration files corresponding to computing tasks...     

A Survey on Long-Tailed Visual Recognition

The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the performance of deep learning models is often dominated by the head classes while the learning of the tail classes is severely underdeveloped. In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. In this survey, we focus on the problems caused by long-tailed data distribution, sort out the representative long-tailed visual recognition datasets and summarize some mainstream long-tailed studies. Specifically, we summarize these studies into ten categories from the perspective of representation learning, and outline the highlights and limitations of each category. Besides, we have studied four quantitative metrics for evaluating the imbalance, and suggest using the Gini coefficient to evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade, and find that the long-tailed phenomenon is widespread and has not been fully studied. Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers.

     

Occluded Video Instance Segmentation: A Benchmark

International Journal of Computer Vision - Can our video understanding systems perceive objects when a heavy occlusion exists in a scene? To answer this question, we collect a large-scale dataset...     

世界主要国家工程科技重大计划与前沿问题综述

本文梳理了近年来世界各主要国家工程科技领域的重大战略计划和措施,简要分析了当前工程科技主要领域发展状况、水平,对主要国家工程科技领域计划的目标、路径与内容进行了比较分析,描绘了工程科技计划关系图、聚焦点和前沿问题。     

Ultrathin Co3O4 nanosheets highly dispersed on reduced graphene oxide: An efficient bi-functional catalyst for Li-O2 battery

Ultrathin Co₃O₄ nanosheets grown on the reduced graphene oxide (Co₃O₄/rGO) was synthesized by a simple hydrothermal method and was investigated as a cathode in a Li-O₂ battery. Benefited from the synergistic effect between Co₃O₄ and rGO, the hybrid exhibits a high initial capacity of 10,528 mAh g^?1 along with a high coulombic efficiency (84.4%) at 100 mA g^?1. In addition, the batteries show an enhanced cycling stability and after 113 cycles, the cut-off discharge voltage remains above 2.5 V. The outstanding performance is intimately related to the high surface area of rGO, which not only provide carbon skeleton for the uniform distribution of Co₃O₄ nanosheets but also facilitate the reversible formation and decomposition of insoluble Li₂O₂. The results of electrochemical tests confirm that the Co₃O₄/rGO hybrid is a promising candidate for the Li-O₂ batteries.