Automated Inference System for End-To-End Diagnosis of Network Performance Issues in Client-Terminal Devices

Traditional network diagnosis methods of Client-Terminal Device (CTD) problems tend to be laborintensive, time consuming, and contribute to increased customer dissatisfaction. In this paper, we propose an automated solution for rapidly diagnose the root causes of network performance issues in CTD. Based on a new intelligent inference technique, we create the Intelligent Automated Client Diagnostic (IACD) system, which only relies on collection of Transmission Control Protocol (TCP) packet traces. Using soft-margin Support Vector Machine (SVM) classifiers, the system (i) distinguishes link problems from client problems and (ii) identifies characteristics unique to the specific fault to report the root cause. The modular design of the system enables support for new access link and fault types. Experimental evaluation demonstrated the capability of the IACD system to distinguish between faulty and healthy links and to diagnose the client faults with 98% accuracy. The system can perform fault diagnosis independent of the user's specific TCP implementation, enabling diagnosis of diverse range of client devicesComment: arXiv admin note: substantial text overlap with arXiv:1207.356

Thresholds of logging intensity to maintain tropical forest biodiversity

Primary tropical forests are lost at an alarming rate, and much of the remaining forest is being degraded by selective logging. Yet, the impacts of logging on biodiversity remain poorly understood, in part due to the seemingly conflicting findings of case studies: about as many studies have reported increases in biodiversity after selective logging as have reported decreases. Consequently, meta-analytical studies that treat selective logging as a uniform land use tend to conclude that logging has negligible effects on biodiversity. However, selectively logged forests might not all be the same. Through a pantropical meta-analysis and using an information-theoretic approach, we compared and tested alternative hypotheses for key predictors of the richness of tropical forest fauna in logged forest. We found that the species richness of invertebrates, amphibians, and mammals decreases as logging intensity increases and that this effect varies with taxonomic group and continental location. In particular, mammals and amphibians would suffer a halving of species richness at logging intensities of 38 m(3) ha(-1) and 63 m(3) ha(-1), respectively. Birds exhibit an opposing trend as their total species richness increases with logging intensity. An analysis of forest bird species, however, suggests that this pattern is largely due to an influx of habitat generalists into heavily logged areas while forest specialist species decline. Our study provides a quantitative analysis of the nuanced responses of species along a gradient of logging intensity, which could help inform evidence-based sustainable logging practices from the perspective of biodiversity conservation.Zuzana Burivalova, Çağan Hakkı Şekercioğlu, Lian Pin Ko