报告包括电子商务方面的重要观点、政策与环境、网购平台、行业应用、电商物流、电商金融等研究。

E-books are books available in a digital format, which allows readers to access content anywhere and at any time using their handheld devices. The Global E-book market is the fastest growing segment in the Global Book Publishing industry.

FEA, a software used in engineering, aims to evaluate the functionality of a product design before prototypes are produced. FEA software is used in manufacturing industries for the estimation of structural strength and behavior, modeling, simulation, and design optimization.

Safety and security are critical to any country's prosperity. Public safety is all about getting the right information to the right person at the right time to keep the surroundings safe. This can be achieved by using mobile broadband, which is a transformational technology. Mobil broadband is the introduction of next generation wireless data technology in field applications, equipping people with the best real-time data, video, voice or other forms of communication, and rich media services - all of which can be especially important in mission-critical situations. Ambulance crews can send vision of an injured person to doctors at a hospital so that an early diagnosis can be made and treatment started as quickly as possible. Police officers responding to an incident can have access to information, images, and video of the scene they are about to walk into. Fire crews can use public safety mobile broadband to obtain weather updates, look at maps of the local terrain, and review complex modeling of how a fire is advancing.

随着开源软件数量的增多,从开源软件社区中有效检索到所需的开源软件是具有挑战性的工作.现有方法通常是:首先,人工给每个软件赋予多个描述其功能、用途的标注;然后,通过关键词匹配寻找用户所需的软件.由于其简单、方便,基于标注进行软件检索得到了广泛的应用.然而,用户通常不愿意主动为其上载的开源软件提供标注,这使得根据用户上载软件的文字描述信息,从众多备选软件标注中为其自动选择能够表征其功能、用途的标注,成为了有效检索该软件的关键.把开源软件自动标注形式化为一个代价敏感多标记学习问题,并提出了一种新型代价敏感多标记学习方法 ML-CKNN.该方法通过在多标记学习中引入代价信息,有效缓解了对每一个标注而言具有该标注的示例与不具有该标注的示例分布非均衡性给多标记学习造成的影响.在3个开源软件社区上的实验结果表明:所提出的ML-CKNN方法能够为新上载的开源软件提供高质量的标注,其标注性能显著优于现有方法.

在多标记学习框架中,每个对象由一个示例(属性向量)描述,却同时具有多个类别标记.在已有的多标记学习算法中,一种常用的策略是将相同的属性集合应用于所有类别标记的预测中.然而,该策略并不一定是最优选择,原因在于每个标记可能具有其自身独有的特征.基于这个假设,目前已经出现了基于标记的类属属性进行建模的多标记学习算法LIFT.LIFT包含两个步骤:属属性构建与分类模型训练.LIFT首先通过在标记的正类与负类示例上进行聚类分析,构建该标记的类属属性;然后,使用每个标记的类属属性训练对应的二类分类模型.在保留LIFT分类模型训练方法的同时,考察了另外3种多标记类属属性构造机制,从而实现LIFT算法的3种变体——LIFTMDDM,LIFT-INSDIF以及LIFT-MLF.在12个数据集上进行了两组实验,验证了类属属性对多标记学习系统性能的影响以及LIFT采用的类属属性构造方法的有效性. 

In parallel with the implementation of information and communications systems, health care organizations are beginning to amass large-scale repositories of clinical and administrative data. Many nations seek to leverage so-called Big Data repositories to support improvements in health outcomes, drug safety, health surveillance, and care delivery processes. An unsupported assumption is that electronic health care data are of sufficient quality to enable the varied use cases envisioned by health ministries. The reality is that many electronic health data sources are of suboptimal quality and unfit for particular uses. To more systematically define, characterize and improve electronic health data quality, we propose a novel framework for health data stewardship. The framework is adapted from prior data quality research outside of health, but it has been reshaped to apply a systems approach to data quality with an emphasis on health outcomes. The proposed framework is a beginning, not an end. We invite the biomedical informatics community to use and adapt the framework to improve health data quality and outcomes for populations in nations around the world.