An intrusion detection system collects and analyzes information from different areas within a computer or a network to identify possible security threats that include threats from both outside as well as inside of the...An intrusion detection system collects and analyzes information from different areas within a computer or a network to identify possible security threats that include threats from both outside as well as inside of the organization. It deals with large amount of data, which contains various ir-relevant and redundant features and results in increased processing time and low detection rate. Therefore, feature selection should be treated as an indispensable pre-processing step to improve the overall system performance significantly while mining on huge datasets. In this context, in this paper, we focus on a two-step approach of feature selection based on Random Forest. The first step selects the features with higher variable importance score and guides the initialization of search process for the second step whose outputs the final feature subset for classification and in-terpretation. The effectiveness of this algorithm is demonstrated on KDD’99 intrusion detection datasets, which are based on DARPA 98 dataset, provides labeled data for researchers working in the field of intrusion detection. The important deficiency in the KDD’99 data set is the huge number of redundant records as observed earlier. Therefore, we have derived a data set RRE-KDD by eliminating redundant record from KDD’99 train and test dataset, so the classifiers and feature selection method will not be biased towards more frequent records. This RRE-KDD consists of both KDD99Train+ and KDD99Test+ dataset for training and testing purposes, respectively. The experimental results show that the Random Forest based proposed approach can select most im-portant and relevant features useful for classification, which, in turn, reduces not only the number of input features and time but also increases the classification accuracy.展开更多
Background: Despite the recent development of new therapies, multiple myeloma(MM) remains an incurable disease. Thus, new, efective treatments are urgently needed, particularly for relapsed or refractory MM(RRMM). In ...Background: Despite the recent development of new therapies, multiple myeloma(MM) remains an incurable disease. Thus, new, efective treatments are urgently needed, particularly for relapsed or refractory MM(RRMM). In an earlier phase I study, a novel form of recombinant human Apo2L/tumor necrosis factor-related apoptosis-inducing ligand(TRAIL) that is currently in clinical development for the treatment of hematologic malignancies, i.e., circularly permuted TRAIL(CPT), was well tolerated at a dose of 2.5 mg/kg per day and showed promising preliminary activity in patients with RRMM. This phase II, open-label, multicenter study further investigated the eicacy and safety of 2.5-mg/kg per day CPT as single-agent therapy for patients with RRMM.Methods: Patients with RRMM were treated once daily with CPT(2.5 mg/kg, intravenously) for 14 consecutive days for each 21-day cycle. Clinical response and toxicity were assessed after each treatment cycle.Results: Twenty-seven patients received CPT. Using the European Group for Blood and Marrow Transplantation criteria, we calculated the overall response rate of 33.3% with 1 near-complete response(n CR) and 8 partial responses(PRs). The clinical beneit rate(48.1%) included 1 nCR, 8 PRs, and 4 minimal responses. The most common treatmentrelated adverse events(TRAEs) were fever, aspartate aminotransferase elevation, alanine aminotransferase elevation, leucopenia, rash, neutropenia, and thrombocytopenia. We graded toxicity using the Common Toxicity Criteria for Adverse Events, version 3.0, and determined that 37.0% of patients had at least 1 grade 3–4 TRAE.Conclusions: CPT as a single agent can elicit a response in patients with RRMM and is well tolerated. Further clinical investigation is warranted.展开更多
A novel single-channel blind separation algorithm for permuted motion blurred images is proposed by using blind restoration in this paper. Both the motion direction and the length of the point spread function （PSF） ...A novel single-channel blind separation algorithm for permuted motion blurred images is proposed by using blind restoration in this paper. Both the motion direction and the length of the point spread function （PSF） are estimated by Radon transformation and extrema a detection. Using the estimated blur parameters, the permuted image is restored by performing the L-R blind restoration method. The permutation mixing matrices can be accurately estimated by classifying the ringing effect in the restored image, thereby the source images can be separated. Simulation results show a better separation efficiency for the permuted motion blurred image with various permutation operations. The proposed algorithm indicates a better performance on the robustness against Gaussian noise and lossy JPEG compression.展开更多
Simultaneously investigating multiple treatments in a single study achieves considerable efficiency in contrast to the traditional two-arm trials.Balancing treatment allocation for influential covariates has become in...Simultaneously investigating multiple treatments in a single study achieves considerable efficiency in contrast to the traditional two-arm trials.Balancing treatment allocation for influential covariates has become increasingly important in today’s clinical trials.The multi-arm covariate-adaptive randomized clinical trial is one of the most powerful tools to incorporate covariate information and multiple treatments in a single study.Pocock and Simon’s procedure has been extended to the multi-arm case.However,the theoretical properties of multi-arm covariate-adaptive randomization have remained largely elusive for decades.In this paper,we propose a general framework for multi-arm covariate-adaptive designs which also includes the two-arm case,and establish the corresponding theory under widely satisfied conditions.The theoretical results provide new insights into the balance properties of covariate-adaptive randomization procedures and make foundations for most existing statistical inferences under two-arm covariate-adaptive randomization.Furthermore,these open a door to study the theoretical properties of statistical inferences for clinical trials based on multi-arm covariateadaptive randomization procedures.展开更多
文摘An intrusion detection system collects and analyzes information from different areas within a computer or a network to identify possible security threats that include threats from both outside as well as inside of the organization. It deals with large amount of data, which contains various ir-relevant and redundant features and results in increased processing time and low detection rate. Therefore, feature selection should be treated as an indispensable pre-processing step to improve the overall system performance significantly while mining on huge datasets. In this context, in this paper, we focus on a two-step approach of feature selection based on Random Forest. The first step selects the features with higher variable importance score and guides the initialization of search process for the second step whose outputs the final feature subset for classification and in-terpretation. The effectiveness of this algorithm is demonstrated on KDD’99 intrusion detection datasets, which are based on DARPA 98 dataset, provides labeled data for researchers working in the field of intrusion detection. The important deficiency in the KDD’99 data set is the huge number of redundant records as observed earlier. Therefore, we have derived a data set RRE-KDD by eliminating redundant record from KDD’99 train and test dataset, so the classifiers and feature selection method will not be biased towards more frequent records. This RRE-KDD consists of both KDD99Train+ and KDD99Test+ dataset for training and testing purposes, respectively. The experimental results show that the Random Forest based proposed approach can select most im-portant and relevant features useful for classification, which, in turn, reduces not only the number of input features and time but also increases the classification accuracy.
文摘Background: Despite the recent development of new therapies, multiple myeloma(MM) remains an incurable disease. Thus, new, efective treatments are urgently needed, particularly for relapsed or refractory MM(RRMM). In an earlier phase I study, a novel form of recombinant human Apo2L/tumor necrosis factor-related apoptosis-inducing ligand(TRAIL) that is currently in clinical development for the treatment of hematologic malignancies, i.e., circularly permuted TRAIL(CPT), was well tolerated at a dose of 2.5 mg/kg per day and showed promising preliminary activity in patients with RRMM. This phase II, open-label, multicenter study further investigated the eicacy and safety of 2.5-mg/kg per day CPT as single-agent therapy for patients with RRMM.Methods: Patients with RRMM were treated once daily with CPT(2.5 mg/kg, intravenously) for 14 consecutive days for each 21-day cycle. Clinical response and toxicity were assessed after each treatment cycle.Results: Twenty-seven patients received CPT. Using the European Group for Blood and Marrow Transplantation criteria, we calculated the overall response rate of 33.3% with 1 near-complete response(n CR) and 8 partial responses(PRs). The clinical beneit rate(48.1%) included 1 nCR, 8 PRs, and 4 minimal responses. The most common treatmentrelated adverse events(TRAEs) were fever, aspartate aminotransferase elevation, alanine aminotransferase elevation, leucopenia, rash, neutropenia, and thrombocytopenia. We graded toxicity using the Common Toxicity Criteria for Adverse Events, version 3.0, and determined that 37.0% of patients had at least 1 grade 3–4 TRAE.Conclusions: CPT as a single agent can elicit a response in patients with RRMM and is well tolerated. Further clinical investigation is warranted.
基金Project supported by the National Natural Science Foundation of China (Grant No.60872114)the Shanghai Leading Academic Discipline Project (Grant No.S30108)the Graduate Student Innovation Foundation of Shanghai University (Grant No.SHUCX101086)
文摘A novel single-channel blind separation algorithm for permuted motion blurred images is proposed by using blind restoration in this paper. Both the motion direction and the length of the point spread function （PSF） are estimated by Radon transformation and extrema a detection. Using the estimated blur parameters, the permuted image is restored by performing the L-R blind restoration method. The permutation mixing matrices can be accurately estimated by classifying the ringing effect in the restored image, thereby the source images can be separated. Simulation results show a better separation efficiency for the permuted motion blurred image with various permutation operations. The proposed algorithm indicates a better performance on the robustness against Gaussian noise and lossy JPEG compression.
基金supported by the National Key R&D Program of China (Grant No.2018YFC2000302)National Natural Science Foundation of China (Grant Nos.11731012,11731011 and 12031005)+1 种基金Ten Thousands Talents Plan of Zhejiang Province (Grant No.2018R52042)the Fundamental Research Funds for the Central Universities。
文摘Simultaneously investigating multiple treatments in a single study achieves considerable efficiency in contrast to the traditional two-arm trials.Balancing treatment allocation for influential covariates has become increasingly important in today’s clinical trials.The multi-arm covariate-adaptive randomized clinical trial is one of the most powerful tools to incorporate covariate information and multiple treatments in a single study.Pocock and Simon’s procedure has been extended to the multi-arm case.However,the theoretical properties of multi-arm covariate-adaptive randomization have remained largely elusive for decades.In this paper,we propose a general framework for multi-arm covariate-adaptive designs which also includes the two-arm case,and establish the corresponding theory under widely satisfied conditions.The theoretical results provide new insights into the balance properties of covariate-adaptive randomization procedures and make foundations for most existing statistical inferences under two-arm covariate-adaptive randomization.Furthermore,these open a door to study the theoretical properties of statistical inferences for clinical trials based on multi-arm covariateadaptive randomization procedures.
