23243364
EBM Metadata Based on Dublin Core Better Presenting Validity of Clinical Trials
To help clinicians find better evidence, a metadata schema for Evidence Based Medicine (EBM) is developed. Dublin Core metadata standard (DC) was adopted to help build a metadata schema. An experimental system was developed to test the validity of the metadata and full text papers of clinical therapy on stomach ulcer extracted using PubMed. An EBM metadata schema was developed. Citations were created from original papers using the metadata schema. Three clinicians evaluated papers by utilizing metadata and full texts respectively. Agreement of evaluation was analyzed, and the result on weighted kappa was 0.55 (95%CI, 0.42–0.67). It reveals that there is moderate agreement between evaluation of metadata citations and full texts. It is possible to use the metadata to select papers before reading the full texts. A further study should be made to prove the applicability of the metadata in the real world setting.
26455783
Neuroscience output of China: A MEDLINE-based bibliometric study
Neuroscience is one of the most active research fields in many countries including China, an economically and scientifically emerging country, where a rapid development has been occurred since the 1970s. In this study, a MEDLINE-based bibliometric analysis of Chinese international output in neuroscience was conducted for the period from 1984 through 2001. An attempt was made to identify the pattern of the growth and to obtain some quantitative indicators over the literature studied in order to review at the developing steps of neuroscience in China during the period.
34452017
On using flexure-hinge five-bar linkages to develop novel walking mechanisms and small-scale grippers for microrobots
7498053
Multi-labelled classification using maximum entropy method
Many classification problems require classifiers to assign each single document into more than one category, which is called multi-labelled classification. The categories in such problems usually are neither conditionally independent from each other nor mutually exclusive, therefore it is not trivial to directly employ state-of-the-art classification algorithms without losing information of relation among categories. In this paper, we explore correlations among categories with maximum entropy method and derive a classification algorithm for multi-labelled documents. Our experiments show that this method significantly outperforms the combination of single label approach.
850350
Online System Problem Detection by Mining Patterns of Console Logs
We describe a novel application of using data mining and statistical learning methods to automatically monitor and detect abnormal execution traces from console logs in an online setting. Different from existing solutions, we use a two stage detection system. The first stage uses frequent pattern mining and distribution estimation techniques to capture the dominant patterns (both frequent sequences and time duration). The second stage use principal component analysis based anomaly detection technique to identify actual problems. Using real system data from a 203-node Hadoop [1] cluster, we show that we can not only achieve highly accurate and fast problem detection, but also help operators better understand execution patterns in their system.
7346557
Predictive Control for Dynamic Resource Allocation in Enterprise Data Centers
It is challenging to reduce resource over-provisioning for enterprise applications while maintaining service level objectives (SLOs) due to their time-varying and stochastic workloads. In this paper, we study the effect of prediction on dynamic resource allocation to virtualized servers running enterprise applications. We present predictive controllers using three different prediction algorithms based on a standard auto-regressive (AR) model, a combined ANOVA-AR model, as well as a multi-pulse (MP) model. We compare the properties of the predictive controllers with an adaptive integral (I) controller designed in our earlier work on controlling relative utilization of resource containers. The controllers are evaluated in a hypothetical virtual server environment where we use the CPU utilization traces collected on 36 servers in an enterprise data center. Since these traces were collected in an open-loop environment, we use a simple queuing algorithm to simulate the closed-loop CPU usage under dynamic control of CPU allocation. We also study the controllers by emulating the utilization traces on a test bed where a Web server was hosted inside a Xen virtual machine. We compare the results of these controllers from all the servers and find that the MP-based predictive controller performed slightly better statistically than the other two predictive controllers. The ANOVA-AR-based approach is highly sensitive to the existence of periodic patterns in the trace, while the other three methods are not. In addition, all the three predictive schemes performed significantly better when the prediction error was accounted for using a feedback mechanism. The MP-based method also demonstrated an interesting self-learning behavior
156293
An efficient and backwards-compatible transformation to ensure memory safety of C programs
Memory-related errors, such as buffer overflows and dangling pointers, remain one of the principal reasons for failures of C programs. As a result, a number of recent research efforts have focused on the problem of dynamic detection of memory errors in C programs. However, existing approaches suffer from one or more of the following problems: inability to detect all memory errors (e.g., Purify), requiring non-trivial modifications to existing C programs (e.g., Cyclone), changing the memory management model of C to use garbage collection (e.g., CCured), and excessive performance overheads. In this paper, we present a new approach that addresses these problems. Our approach operates via source code transformation and combines efficient data-structures with simple, localized optimizations to obtain good performance.