Focusing on some high critical application,the conceptions of deadline,slack and critica in real-time system are introduced and improved in DSMS according to the characteristics of continuous query in DSMS.According to these new conceptions,a real-time schedule strategy based on priority is proposed.In this scheduling strategy,the earlier the deadline is or the shorter the slack is or the more critical the query is,the higher the priority is.And a structure of priority tree is proposed to realize the unique execution sequence of priority-based query.The experimental results indicate that the strategy raises the hit value ratio(HVR)and the success ratio of continuous query scheduling.
The study proposed in this paper analyses the problems existing in the college course score prediction methodologies.Because of diversity and multiple choices in course selection, the analysis of college students' course score can be quite complex.This paper proposes a student achievement prediction method based on factor analysis (FA) and Back-Propagation (BP) neural network.Our method is based on the improvement of FA algorithm.Firstly, special factors will be added to complement the equation of common factor score.Secondly, the initial equation of common factor score will be improved.Thirdly, a new equation intended to give an estimation of the special factors mentioned in the first point will be proposed.Finally, an improvement on the common factor loading matrix will be made.We use the improved equation of common factor score to calculate the score of each common factor.Then we use these scores as the input vector of the BP neural network.The output of the neural network is brought into the final equation to get the final prediction result.The experimental results show that the prediction accuracy is very high and the prediction model can be used for most of the college courses.The error on the prediction result is reduced by using the prediction model proposed in this paper.Therefore, the model developed in this paper is very effective and has high application value.
There are problems that standard square convolution kernel has insufficient representation ability and recurrent neural network usually ignores the importance of different elements within an input vector in sound event localization and detection. This paper proposes an element-wise attention gate-asymmetric convolutional recurrent neural network (EleAttG-ACRNN), to improve the performance of sound event localization and detection. First, a convolutional neural network with context gating and asymmetric squeeze excitation residual is constructed, where asymmetric convolution enhances the capability of the square convolution kernel; squeeze excitation can improve the interdependence between channels; context gating can weight the important features and suppress the irrelevant features. Next, in order to improve the expressiveness of the model, we integrate the element-wise attention gate into the bidirectional gated recurrent network, which is to highlight the importance of different elements within an input vector, and further learn the temporal context information. Evaluation results using the TAU Spatial Sound Events 2019-Ambisonic dataset show the effectiveness of the proposed method, and it improves SELD performance up to 0.05 in error rate, 1.7% in F-score, 0.7° in DOA error, and 4.5% in Frame recall compared to a CRNN method.
Cognitive radios (CRs) have been considered for use in mobile ad hoc networks (MANETs). The area of security in Cognitive Radio MANETs (CR-MANETs) has yet to receive much attention. However, some distinct characteristics of CRs introduce new, non-trivial security risks to CR-MANETs. In this paper, we study spectrum sensing data falsification (SSDF) attacks to CR-MANETs, in which intruders send false local spectrum sensing results in cooperative spectrum sensing, and SSDF may result in incorrect spectrum sensing decisions by CRs. We present a consensus-based cooperative spectrum sensing scheme to counter SSDF attacks in CR-MANETs. Our scheme is based on recent advances in consensus algorithms that have taken inspiration from self-organizing behavior of animal groups such as fish. Unlike the existing schemes, there is no need for a common receiver to do the data fusion for reaching the final decision to counter SSDF attacks. Simulation results are presented to show the effectiveness of the proposed scheme.
It is proved that some interval-switching-systems are stabilizable under arbitrary switching,and the overshoot problem of the gain matrix for the interval-switching-systems is discussed.Based on the result,a feedback control law for the interval-switching-systems is given,under which the closed system is exponentially stable.
This paper introduces the concepts and analyses the working principle of Ajax, study on the Ajax Web application model (asynchronous) and compares with traditional classic web development model (synchronous), in the end, paper gives some actual applications in E-Business based on Ajax technology.
Cross-project defect prediction (CPDP) refers to recognizing defective software modules in one project (i.e., target) using historical data collected from other projects (i.e., source), which can help developers find defects and prioritize their testing efforts. Unfortunately, there often exists large distribution difference between the source and target data. Most CPDP methods neglect to select the appropriate source data for a given target at the project level. More importantly, existing CPDP models are parametric methods, which usually require intensive parameter selection and tuning to achieve better prediction performance. This would hinder wide applicability of CPDP in practice. Moreover, most CPDP methods do not address the cross-project class imbalance problem. These limitations lead to suboptimal CPDP results. In this paper, we propose a novel data selection and sampling based domain programming predictor (DSSDPP) for CPDP, which addresses the above limitations. DSSDPP is a non-parametric CPDP method, which can perform knowledge transfer across projects without the need for parameter selection and tuning. By exploiting the structures of source and target data, DSSDPP can learn a discriminative transfer classifier for identifying defects of the target project. Extensive experiments on 22 projects from four datasets indicate that DSSDPP achieves better MCC and AUC results against a range of competing methods both in the single-source and multi-source scenarios. Since DSSDPP is easy, effective, extensible, and efficient, we suggest that future work can use it with the well-chosen source data to conduct CPDP especially for the projects with limited computational budget.
A recent report has shown that there are more than 5,000 malicious applications created for Android devices each day. This creates a need for researchers to develop effective and efficient malware classification and detection approaches. To address this need, we introduce DroidClassifier: a systematic framework for classifying network traffic generated by mobile malware. Our approach utilizes network traffic analysis to construct multiple models in an automated fashion using a supervised method over a set of labeled malware network traffic (the training dataset). Each model is built by extracting common identifiers from multiple HTTP header fields. Adaptive thresholds are designed to capture the disparate characteristics of different malware families. Clustering is then used to improve the classification efficiency. Finally, we aggregate the multiple models to construct a holistic model to conduct cluster-level malware classification. We then perform a comprehensive evaluation of DroidClassifier by using 706 malware samples as the training set and 657 malware samples and 5,215 benign apps as the testing set. Collectively, these malicious and benign apps generate 17,949 network flows. The results show that DroidClassifier successfully identifies over 90% of different families of malware with more than 90% accuracy with accessible computational cost. Thus, DroidClassifier can facilitate network management in a large network, and enable unobtrusive detection of mobile malware. By focusing on analyzing network behaviors, we expect DroidClassifier to work with reasonable accuracy for other mobile platforms such as iOS and Windows Mobile as well.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)