近年来,随着网络规模的不断扩大,结构和功能日趋复杂,网络流量快速增长,各种新型网络应用不断出现,对传统网络的配置、运行和管理提出了严峻挑战.软件定义网络(software defined networking,SDN)作为新兴的网络架构,较好地适应了各种网络应用的需求.在SDN网络中进行测量任务能及时了解、监控和掌握网络状态,是优化网络结构、改善网络服务质量、实现网络故障诊断和恢复的重要手段.本文对SDN网络测量技术进行综述,研究网络性能测量中的延迟测量、丢包测量和带宽测量等方面以及网络流量测量等相关技术,从设计理念、测量方式、测量对象、技术特点,以及优缺点等方面做了详尽的描述.文章最后进行了总结并对未来工作进行了展望.
By using efficient and timely medical diagnostic decision making,clinicians can positively impact the quality and cost of medical care.However,the high similarity of clinical manifestations between diseases and the limitation of clinicians’knowledge both bring much difficulty to decision making in diagnosis.Therefore,building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain.In this paper,we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease categories,and compare this method with the traditional medical expert system to verify the performance.To select the best subset of patient features,we propose a feature selection method based on the composition and distribution of symptoms in electronic medical records and compare it with the traditional feature selection methods such as chi-square test.We evaluate the feature selection methods and diagnostic models from two aspects,false negative rate(FNR)and accuracy.Extensive experiments have conducted on a real-world Chinese electronic medical record database.The evaluation results demonstrate that our proposed feature selection method can improve the accuracy and reduce the FNR compare to the traditional feature selection methods,and the multi-label classification framework have better accuracy and lower FNR than the traditional expert system.
Shengqun FangZhiping CaiWencheng SunAnfeng LiuFang LiuZhiyao LiangGuoyan Wang
The extreme imbalanced data problem is the core issue in anomaly detection.The amount of abnormal data is so small that we cannot get adequate information to analyze it.The mainstream methods focus on taking fully advantages of the normal data,of which the discrimination method is that the data not belonging to normal data distribution is the anomaly.From the view of data science,we concentrate on the abnormal data and generate artificial abnormal samples by machine learning method.In this kind of technologies,Synthetic Minority Over-sampling Technique and its improved algorithms are representative milestones,which generate synthetic examples randomly in selected line segments.In our work,we break the limitation of line segment and propose an Imbalanced Triangle Synthetic Data method.In theory,our method covers a wider range.In experiment with real world data,our method performs better than the SMOTE and its meliorations.
While smart devices based on ARM processor bring us a lot of convenience,they also become an attractive target of cyber-attacks.The threat is exaggerated as commodity OSes usually have a large code base and suffer from various software vulnerabilities.Nowadays,adversaries prefer to steal sensitive data by leaking the content of display output by a security-sensitive application.A promising solution is to exploit the hardware visualization extensions provided by modern ARM processors to construct a secure display path between the applications and the display device.In this work,we present a scheme named SecDisplay for trusted display service,it protects sensitive data displayed from being stolen or tampered surreptitiously by a compromised OS.The TCB of SecDisplay mainly consists of a tiny hypervisor and a super light-weight rendering painter,and has only^1400 lines of code.We implemented a prototype of SecDisplay and evaluated its performance overhead.The results show that SecDisplay only incurs an average drop of 3.4%.
Jinhua CuiYuanyuan ZhangZhiping CaiAnfeng LiuYangyang Li
