Towards a self-evolving software defect detection process

Software defect detection research typically focuses on individual inspection and testing techniques. However, to be effective in applying defect detection techniques, it is important to recognize when to use inspection techniques and when to use testing techniques. In addition, it is important to know when to deliver a product and use maintenance activities, such as trouble shooting and bug fixing, to address the remaining defects in the software.To be more effective detecting software defects, not only should defect detection techniques be studied and compared, but the entire software defect detection process should be studied to give us a better idea of how it can be conducted, controlled, evaluated and improved.This thesis presents a self-evolving software defect detection process (SEDD) that provides a systematic approach to software defect detection and guides us as to when inspection, testing or maintenance activities are best performed. The approach is self-evolving in that it is continuously improved by assessing the outcome of the defect detection techniques in comparison with historical data.A software architecture and prototype implementation of the approach is also presented along with a case study that was conducted to validate the approach. Initial results of using the self-evolving defect detection approach are promising

Security Enhancement Mechanism Based on Contextual Authentication and Role Analysis for 2G-RFID Systems

The traditional Radio Frequency Identification (RFID) system, in which the information maintained in tags is passive and static, has no intelligent decision-making ability to suit application and environment dynamics. The Second-Generation RFID (2G-RFID) system, referred as 2G-RFID-sys, is an evolution of the traditional RFID system to ensure better quality of service in future networks. Due to the openness of the active mobile codes in the 2G-RFID system, the realization of conveying intelligence brings a critical issue: how can we make sure the backend system will interpret and execute mobile codes in the right way without misuse so as to avoid malicious attacks? To address this issue, this paper expands the concept of Role-Based Access Control (RBAC) by introducing context-aware computing, and then designs a secure middleware for backend systems, named Two-Level Security Enhancement Mechanism or 2L-SEM, in order to ensure the usability and validity of the mobile code through contextual authentication and role analysis. According to the given contextual restrictions, 2L-SEM can filtrate the illegal and invalid mobile codes contained in tags. Finally, a reference architecture and its typical application are given to illustrate the implementation of 2L-SEM in a 2G-RFID system, along with the simulation results to evaluate how the proposed mechanism can guarantee secure execution of mobile codes for the system

Analysis of Hot Points on Data Mining Research of Medical in Foreign Countries

To promote the current development of medical data mining research, a quantitative statistics and qualitative analysis of the papers in the field of medical data mining technologies were made with the methodology of bibliometric and knowledge mapping, which were enlisted in the database of Web of Science analyzing the general situation of the papers about data mining from several aspects: period sequences, subject funds, countries and regions, core authors and research institutions, the hotspots and research frontiers. Our analysis exposed that the research of data mining in medical showed a multi-disciplinary integration of the development trend, but high-yield leading author group has not yet formed. It is important to note that scholars should raise awareness of clinical medical data mining as well as explore new research directions for further studying

Effects of Rh-endostar in Combination with Radiotherapy on Rats with Lung Cancer

Background and objective Radiation sensitivity is closely related to tissue oxygen, and rh-endostatin can induce the high level of oxygen content in tumor by "normalizing" tumor angiogenesis which is associated with radiotherapy sensitivity. The aim of this study is to observe the effect of combination of radiotherapy with rh-endostatin in the rats with lung cancer. Methods Immediate lewis cancerous ascetic injection method was used to make rats tumors bearing model, then the rats was divided into four groups randomly: group A was treated with saline; group B was treated with rh-endostatin; group C was treated with irradiation and group D was treated with rh-endostatin and irradiation. After all rats were treated, inhibition rates and the tumor growth curve were calculated. Immunohistochemisty was adopted to check the expressions of vascular endothelial growth factor (VEGF) and microvessel density (MVD). Results Compared with group A, the growth rates of the tumors in the other group were obviously slower, and the tumor weights were significantly different form group A (P<0.05). Compared with the other groups, the tumor weights of group D were obviously reduced (P<0.05). Compared with group A, VEGF and MVD of other three groups were reduced (P<0.05), and group D were significantly cut down. Conclusion Combination with radiotherapy and rh-endostatin could inhibit the lung cancer significantly in rats. The possible mechanisms are to decrease the expression of VEGF and inhibit the production of angiogenesis

Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification.

Jointly using spatial and spectral information has been widely applied to hyperspectral image (HSI) classification. Especially, convolutional neural networks (CNN) have gained attention in recent years due to their detailed representation of features. However, most of CNN-based HSI classification methods mainly use patches as input classifier. This limits the range of use for spatial neighbor information and reduces processing efficiency in training and testing. To overcome this problem, we propose an image-based classification framework that is efficient and straight forward. Based on this framework, we propose a multiscale spatial-spectral CNN for HSIs (HyMSCN) to integrate both multiple receptive fields fused features and multiscale spatial features at different levels. The fused features are exploited using a lightweight block called the multiple receptive field feature block (MRFF), which contains various types of dilation convolution. By fusing multiple receptive field features and multiscale spatial features, the HyMSCN has comprehensive feature representation for classification. Experimental results from three real hyperspectral images prove the efficiency of the proposed framework. The proposed method also achieves superior performance for HSI classification

A NUMERICAL SIMULATION OF POOL BOILING USING CAS MODEL

ABSTRACT This paper presents a new numerical model, called the CAS model, for boiling heat transfer. The CAS model is based on the cellular automata technique that is integrated into the popular-SIMPLER algorithm for CFD problems. In the model, the cellular automata technique deals with the microscopic nonlinear dynamic interactions of bubbles while the traditional CFD algorithm is used to determine macroscopic system parameters such as pressure and temperature. The popular SIMPLER algorithm is employed for the CFD treatment. The model is then employed to simulate a pool boiling process. The computational results show that the CAS model can reproduce most of the basic features of boiling and capture the fundamental characteristics of boiling phenomena. The heat transfer coefficient predicted by the CAS model is in excellent agreement with the experimental data and existing empirical correlations

Predicting 1-, 3-, 5-, and 8-year all-cause mortality in a community-dwelling older adult cohort: relevance for predictive, preventive, and personalized medicine

Background: Population aging is a global public health issue involving increased prevalence of age-related diseases, and concomitant burden on medical resources and the economy. Ninety-two diseases have been identified as age-related, accounting for 51.3% of the global adult disease burden. The economic cost per capita for older people over 60 years is 10 times that of the younger population. From the aspects of predictive, preventive, and personalized medicine (PPPM), developing a risk-prediction model can help identify individuals at high risk for all-cause mortality and provide an opportunity for targeted prevention through personalized intervention at an early stage. However, there is still a lack of predictive models to help community-dwelling older adults do well in healthcare. Objectives: This study aims to develop an accurate 1-, 3-, 5-, and 8-year all-cause mortality risk-prediction model by using clinical multidimensional variables, and investigate risk factors for 1-, 3-, 5-, and 8-year all-cause mortality in community-dwelling older adults to guide primary prevention. Methods: This is a two-center cohort study. Inclusion criteria: (1) community-dwelling adult, (2) resided in the districts of Chaonan or Haojiang for more than 6 months in the past 12 months, and (3) completed a health examination. Exclusion criteria: (1) age less than 60 years, (2) more than 30 incomplete variables, (3) no signed informed consent. The primary outcome of the study was all-cause mortality obtained from face-to-face interviews, telephone interviews, and the medical death database from 2012 to 2021. Finally, we enrolled 5085 community-dwelling adults, 60 years and older, who underwent routine health screening in the Chaonan and Haojiang districts, southern China, from 2012 to 2021. Of them, 3091 participants from Chaonan were recruited as the primary training and internal validation study cohort, while 1994 participants from Haojiang were recruited as the external validation cohort. A total of 95 clinical multidimensional variables, including demographics, lifestyle behaviors, symptoms, medical history, family history, physical examination, laboratory tests, and electrocardiogram (ECG) data were collected to identify candidate risk factors and characteristics. Risk factors were identified using least absolute shrinkage and selection operator (LASSO) models and multivariable Cox proportional hazards regression analysis. A nomogram predictive model for 1-, 3-, 5- and 8-year all-cause mortality was constructed. The accuracy and calibration of the nomogram prediction model were assessed using the concordance index (C-index), integrated Brier score (IBS), receiver operating characteristic (ROC), and calibration curves. The clinical validity of the model was assessed using decision curve analysis (DCA). Results: Nine independent risk factors for 1-, 3-, 5-, and 8-year all-cause mortality were identified, including increased age, male, alcohol status, higher daily liquor consumption, history of cancer, elevated fasting glucose, lower hemoglobin, higher heart rate, and the occurrence of heart block. The acquisition of risk factor criteria is low cost, easily obtained, convenient for clinical application, and provides new insights and targets for the development of personalized prevention and interventions for high-risk individuals. The areas under the curve (AUC) of the nomogram model were 0.767, 0.776, and 0.806, and the C-indexes were 0.765, 0.775, and 0.797, in the training, internal validation, and external validation sets, respectively. The IBS was less than 0.25, which indicates good calibration. Calibration and decision curves showed that the predicted probabilities were in good agreement with the actual probabilities and had good clinical predictive value for PPPM. Conclusion: The personalized risk prediction model can identify individuals at high risk of all-cause mortality, help offer primary care to prevent all-cause mortality, and provide personalized medical treatment for these high-risk individuals from the PPPM perspective. Strict control of daily liquor consumption, lowering fasting glucose, raising hemoglobin, controlling heart rate, and treatment of heart block could be beneficial for improving survival in elderly populations