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Existing software intelligent defect classification approaches don’t consider radar characters and prior statistics information. Thus when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. To solve this problem, a new intelligent defect classification approach based on the latent Dirichlet allocation (LDA) topic model is proposed for radar software in this paper. The proposed approach includes the defect text segmentation algorithm based on the dictionary of radar domain, the modified LDA model combining radar software requirement, the top acquisition and classification approach of radar software defect based on the modified LDA model. The proposed approach is applied on the typical radar software defects to validate the effectiveness and applicability. The application results illustrate that the prediction precison rate and recall rate of the poposed approach are improved up to 15%~20% compared with the other defect classification approaches. Thus, the proposed approach can be applied in the segmentation and classification of radar software defecs effectively to improve the identifying adequacy of the defects in radar software.

In this paper, the secrecy performance in internet of things (IoTs) networks with cooperative eavesdroppers has been investigated, where simultaneous wireless information and power transfer (SWIPT) nodes perform as relay nodes. The secrecy energy efficiency (SEE) maximization problem for single relay node has been introduced to extend the life-time for IoT nodes without power supply. The optimization problem is firstly transformed into two sub-problems, and then each sub-problem is solved by bisection method. Furthermore, the model has been extended into multiple relay nodes and a novel relay selection algorithm for SEE maximization has been proposed. Finally, the advantage of the proposed algorithm has been verified from simulations.

Adaptive testing (AT) is a software testing approach that uses a feedback mechanism to enhance test effectiveness. Its testing strategy can be adjusted online by using the testing data collected during the software testing process. However, it requires complex parameter estimation which results in excessive computational overhead that may hinder the applicability of AT. In this paper, we propose an approach called AT based on moment estimation (AT-ME) to address this problem. The proposed approach uses moment estimation to serve as the algorithm of parameter estimation, which reduces the complexity of AT-ME. In addition, a dynamic length for testing action is set to limit the number of decisions without influencing the test effectiveness. The proposed approach has been validated on the Siemens test suite, which includes seven real programs. The experiments show that AT-ME can reduce the computational overhead of AT without compromising overall testing efficiency. Results demonstrate that AT-ME is a feasible and effective AT strategy.

ObjectiveIntravenous tissue plasminogen activator (tPA) is the standard therapy for patients with acute ischaemic stroke (AIS) within 4.5 hours of onset. Recent trials have expanded the endovascular treatment window to 24 hours. We investigated the efficacy and safety of using multimodal MRI to guide intravenous tPA treatment for patients with AIS of unknown time of onset (UTO).MethodsData on patients with AIS with UTO and within 4.5 hours of onset were reviewed. Data elements collected and analysed included: demographics, National Institutes of Health Stroke Scale (NIHSS) score at baseline and 2 hours, 24 hours, 7 days after thrombolysis and before discharge, the modified Rankin Scale (mRS) score at 3 months after discharge, imaging findings and any adverse event.ResultsForty-two patients with UTO and 62 in control group treated within 4.5 hours of onset were treated with intravenous tPA. The NIHSS scores after thrombolysis and/or before discharge in UTO group were significantly improved compared with the baseline (p0.05). Utilising the non-inferiority test, to compare mRS scores (0–2) at 3 months between the two groups, the difference was 5.2% (92% CI, OR 0.196). Patients in the UTO group had mRS scores of 0-2, which were non-inferior to the control group. Their incidence of adverse events was similar.ConclusionsUtilising multimodal MRI to guide intravenous only thrombolysis for patients with AIS with UTO was safe and effective. In those patients with AIS between 6 and 24 hours of time of onset but without large arterial occlusion, intravenous thrombolysis could be considered an option.

With the rise of enterprise-government joint ventures, how to carry out the construction of corporate culture to promote the development of enterprise-government joint ventures has gradually become the focus of scholars’ research. However, at present, the articles on the cultural construction of enterprise-government joint venture companies are still insufficient, and the academic circles have not discussed it too deeply. Taking Changning Company as an example, this paper deeply analyzes the problems existing in promoting the construction of corporate culture in Changning Company as an enterprise-government joint venture company, and probes into the deeper causes of the corresponding problems, and then puts forward some countermeasures. Through this study, it fills up the deficiency in the research on the cultural construction of the enterprise-government joint venture company, and provides some reference for later researchers.

As an important software maintenance measure, the key of software refactoring is to improve the unreasonable part of code module, namely code smell. There are many refactoring points with different priorities and severity in the reconstruction activities of large-scale software system. How to determine a suitable reconstruction scheme is an important problem to be solved. In view of the above problems, a multi-objective software reconstruction scheme model is established, which aims at eliminating the number of code smell, the severity of code smell and its importance. Meanwhile, the number of reading code and the difficulty degree are used as the auxiliary decision indicators. NSGA-II algorithm is used to solve the optimal solution set, and the software system JHotDraw is used to verify. Two kinds of scheme sets with higher robustness are obtained considering the fluctuation of severity and importance. Two trade-offs are obtained from the two schemes set. Then, the reconstruction activities are sorted according to the reconstruction cost, which can further improve the purpose, robustness and effectiveness of the code reconstruction activities.

CUDA is a parallel computing platform and programming model for the graphics processing unit (GPU) of NVIDIA. With CUDA programming, general purpose computing on GPU (GPGPU) is possible. However, the correctness of CUDA programs relies on the correctness of CUDA compilers, which is difficult to test due to its complexity. In this work, we propose CUDAsmith, a fuzzing framework for CUDA compilers. Our tool can randomly generate deterministic and valid CUDA kernel code with several different strategies. Moreover, it adopts random differential testing and EMI testing techniques to solve the test oracle problems of CUDA compiler testing. In particular, we lift live code injection to CUDA compiler testing to help generate EMI variants. Our fuzzing experiments with both the NVCC compiler and the Clang compiler for CUDA have detected thousands of failures, some of which have been confirmed by compiler developers. Finally, the cost-effectiveness of CUDAsmith is also thoroughly evaluated in our fuzzing experiment.

As an important maintenance measure, software reconfiguration is the key to detect the unreasonable part of the code module, namely code smell. Traditional detection methods rely on the experience of engineers, and the location efficiency of reconfiguration points is low. The existing automatic detection tools identify code smell with limited accuracy. Aiming at the problem that the number of reconstructed points in software system is huge and various, and the automation of reconstructed activities is low and difficult to optimize, the research framework of software smell prediction based on machine learning is studied and designed. Taking four common code smells as the research object, the classification algorithm and detection model of the best code smell are established, and the dimension reduction method of feature extraction is further improved. The highest accuracy rate is 89.8%, which can improve the automation level of software smell detection.

With the rising trends in interconnectivity, ensuring cyber security is of paramount importance. Cyber security experimentation platform can help researchers simulate real-world cyber topologies, analyze various attacks and experiment defense strategies. While traditional platforms does not scale well with the evolving cyber topologies and does not have much flexibility in-terms of functionalities. This paper presents a new architecture for experimentation platform of cyber security, based on Docker. It possesses the scalability factor necessary for large cyber simulations and also provides a flexible extension mechanism. This feature enables users to customize cyber node's topologies, software environment, and also support the customization of important experiment indicators. Kubernetes enables virtual nodes to be deployed on distributed servers readily, and the important experiment indicators can be relayed back in real-time which reduces the overall cost and helps speedup the experimental analysis.

There are a large number of studies on geospatial object detection. However, many existing methods only focus on either accuracy or speed. Methods with both fast speed and high accuracy are of great importance in some scenes, like search and rescue, and military information acquisition. In remote sensing images, there are some targets that are small and have few textures and low contrast compared with the background, which impose challenges on object detection. In this paper, we propose an accurate and fast single shot detector (AF-SSD) for high spatial remote sensing imagery to solve these problems. Firstly, we design a lightweight backbone to reduce the number of trainable parameters of the network. In this lightweight backbone, we also use some wide and deep convolutional blocks to extract more semantic information and keep the high detection precision. Secondly, a novel encoding&ndash, decoding module is employed to detect small targets accurately. With up-sampling and summation operations, the encoding&ndash, decoding module can add strong high-level semantic information to low-level features. Thirdly, we design a cascade structure with spatial and channel attention modules for targets with low contrast (named low-contrast targets) and few textures (named few-texture targets). The spatial attention module can extract long-range features for few-texture targets. By weighting each channel of a feature map, the channel attention module can guide the network to concentrate on easily identifiable features for low-contrast and few-texture targets. The experimental results on the NWPU VHR-10 dataset show that our proposed AF-SSD achieves superior detection performance: parameters 5.7 M, mAP 88.7%, and 0.035 s per image on average on an NVIDIA GTX-1080Ti GPU.

Controllability of complex networks has been a hot topic in recent years. Real networks regarded as interdependent networks are always coupled together by multiple networks. The cascading process of interdependent networks including interdependent failure and overload failure will destroy the robustness of controllability for the whole network. Therefore, the optimization of the robustness of interdependent network controllability is of great importance in the research area of complex networks. In this paper, based on the model of interdependent networks constructed first, we determine the cascading process under different proportions of node attacks. Then, the structural controllability of interdependent networks is measured by the minimum driver nodes. Furthermore, we propose a parameter which can be obtained by the structure and minimum driver set of interdependent networks under different proportions of node attacks and analyze the robustness for interdependent network controllability. Finally, we optimize the robustness of interdependent network controllability by redundant design including node backup and redundancy edge backup and improve the redundant design by proposing different strategies according to their cost. Comparative strategies of redundant design are conducted to find the best strategy. Results shows that node backup and redundancy edge backup can indeed decrease those nodes suffering from failure and improve the robustness of controllability. Considering the cost of redundant design, we should choose BBS (betweenness-based strategy) or DBS (degree based strategy) for node backup and HDF(high degree first) for redundancy edge backup. Above all, our proposed strategies are feasible and effective at improving the robustness of interdependent network controllability.

High reliability and performance are essential attributes of software systems designed for critical real-time applications. To improve the reliability and performance of software, many systems incorporate some form of fault recovery mechanism. However, contemporary models of software reliability and performance rarely consider these fault recovery mechanisms. Another notable shortcoming of many software models is that they make the simplifying assumption that component failures are statistically independent, which disagrees with several experimental studies that have shown that the failures of software components can exhibit correlation. This paper presents an architecture-based model of software reliability and performance that explicitly considers a two-stage fault recovery mechanism implementing component restarts and application-level retries. The application architecture is characterized by a Discrete Time Markov Chain (DTMC) to represent the dynamic branching behavior of control between the components of the application. Correlations between the component failures are computed with an efficient numerical algorithm for a multivariate Bernoulli (MVB) distribution. We illustrate the utility of the model through a case study of an embedded software application. The results suggest that the model can be used to quantify the impact of software fault recovery and correlated component failures on application reliability and performance.

Iron overload plays a key role in brain injury after intracerebral hemorrhage (ICH). We explored the roles of ferric iron chelator-deferiprone (DFP)-and ferrous iron chelator-clioquinol (CQ)-in ICH rats through the outcomes, iron deposits, reactive oxygen species (ROS), brain water content, and related iron transporters. One hundred eight Sprague-Dawley rats received intra-striatal infusions of 0.5 U of type IV collagenase to establish ICH models. The rats were randomly assigned to the sham, vehicle, DFP, and CQ groups. We evaluated the outcomes, iron levels, brain water content, and ROS; meanwhile, immunohistochemistry and real-time quantitative polymerase chain reaction (RT-qPCR) were utilized to determine ferritin, transferrin, transferrin receptor, divalent metal transport 1 (DMT1), and ferroportin at 48 and 72 h, 7 and 14 days after surgery. Our results showed ICH induced iron overload, brain edema, ROS formation, and neurological deficits. Both iron chelators decreased iron levels; CQ improved the neurological outcome, attenuated brain edema, and ROS production. DFP reduced iron contents but not brain water content and ROS generation. DFP failed to improve the outcome. ICH initiated endogenous iron chelators and transporters, both exogenous iron chelators enhanced expression of transferrin and transferrin receptor. CQ enhanced expression of ferroportin but not DMT1, while DFP enhanced expression of DMT1 but not ferroportin. Ferrous iron contributed to brain injury, and binding ferrous iron can modestly improve outcome after ICH in rats. The exogenous ferrous iron chelator possibly functioned via endogenous ferrous iron transporters on ICH. Therefore, ferrous iron may be a promising target for ICH in future.

With the rapid development of technology, several infrastructure networks are often coupled together and thus need to be modeled as interdependent networks. Because of potential overloads, interdependent networks are more fragile when suffering from attacks. Existing studies have primarily concentrated on the cascading failure process of interdependent networks without load, or the robustness of isolated network with traffic. Only limited research have been done on the cascading failure process caused by overload in interdependent networks. Redundancy is a primary design approach that can enhance the reliability and robustness of the system. In this paper, we propose a method that adds redundant design for the influential nodes in interdependent networks with traffic to suppress overloading. First, a detailed model with a redundant design and redundant level is introduced. Three strategies (Random, Degree and Betweenness) based on the potential knowledge of network centrality can be used to identify the influential nodes of networks. Then, two indicators are presented to evaluate the robustness of interdependent networks. Lastly, we analyze the common effects between the coupling strength and the redundancy on the invulnerability of interdependent networks. Experiments on two interdependent networks demonstrate that with a suitable coupling attributes (strength and preference) and a higher degree of redundancy, an interdependent network is more robust under random attacks. The results suggest that such a redundant design can permit construction of highly robust interactive networked systems.

Modern infrastructure networks are often coupled together and thus could be modeled as interdependent networks. Overload and interdependent effect make interdependent networks more fragile when suffering from attacks. Existing research has primarily concentrated on the cascading failure process of interdependent networks without load, or the robustness of isolated network with load. Only limited research has been done on the cascading failure process caused by overload in interdependent networks. Redundant design is a primary approach to enhance the reliability and robustness of the system. In this paper, we propose two redundant methods, node back-up and dependency redundancy, and the experiment results indicate that two measures are effective and costless. Two detailed models about redundant design are introduced based on the non-linear load-capacity model. Based on the attributes and historical failure distribution of nodes, we introduce three static selecting strategies-Random-based, Degree-based, Initial load-based and a dynamic strategy-HFD (historical failure distribution) to identify which nodes could have a back-up with priority. In addition, we consider the cost and efficiency of different redundant proportions to determine the best proportion with maximal enhancement and minimal cost. Experiments on interdependent networks demonstrate that the combination of HFD and dependency redundancy is an effective and preferred measure to implement redundant design on interdependent networks. The results suggest that the redundant design proposed in this paper can permit construction of highly robust interactive networked systems.

The reliability of real-time embedded software directly determines the reliability of the whole real-time embedded system, and the effective software testing is an important way to ensure software quality and reliability. Based on the analysis of the characteristics of real-time embedded software, the formal method is introduced into the real-time embedded software testing field and the real-time extended finite state machine (RT-EFSM) model is studied firstly. Then, the time zone division method of real-time embedded system is presented and the definition and description methods of time-constrained transition equivalence class (timeCTEC) are presented. Furthermore, the approaches of the testing sequence and test case generation are put forward. Finally, the proposed method is applied to a typical avionics realtime embedded software testing practice and the examples of the timeCTEC, testing sequences and test cases are given. With the analysis of the testing result, the application verification shows that the proposed method can effectively describe the real-time embedded software state transition characteristics and real-time requirements and play the advantages of the formal methods in accuracy, effectiveness and the automation supporting. Combined with the testing platform, the real-time, closed loop and automated simulation testing for real-time embedded software can be realized effectively.

In this paper, formal methods were introduced into the real-time embedded software testing field and a real-time extended finite state machine, called rt_EFSM, was studied firstly. And then, the process of the integrated application based on real-time extended Unified Modeling Language (UML) and improved formal method in real-time embedded software testing are put forward. Furthermore, the extension scheme of the real-time UML, including the improvement and extension of state transitions and time constraints described mechanism, and the conversion method from real-time UML to rt_EFSM ware proposed. Finally, combined with the testing process of flight control software of an unmanned aerial vehicle (UAV), a method to generate the time-extended UIO sequence (ex_UIO) was put forward. Finally, the method to generate test cases automatically according to ex_UIO sequence was brought forward. The method proposed in this paper can fully make use of the advantages of tool resources of the UML which has been as the industry standard, but also the advantages of formal methods in accuracy, effectiveness and automation support.

At present, real-time embedded software testing is a hot topic, and formal methods has become an important means in real-time embedded software modeling and verification field. The real-time embedded software verification technique based on formal methods can eliminate ambiguity, enhance the accuracy and consistency of verification and improve the automation level and efficiency. In this paper, we surveyed the existing formal testing techniques for real-time embedded software, pointed out the shortcomings of current research and gave the trends of the techniques.

As an important part of airborne avionics system, aircraft safety critical software (ASCS) plays an essential role to the safety of the aircraft, and to ensure its quality and reliability is one of the key problems we are facing. Formal methods have become important means for modeling and verifying safety critical software. In this paper, formal method is introduced into the ASCS verification field and the real-time extended finite state machine model (RT-EFSM) is studied, which includes the detailed real-time extension schemes and its validation methods. The verification process of ASCS based on RT-EFSM is also proposed. Furthermore, combined with the verification of an aircraft inertial/satellite navigation systems, a timed unique input/output sequence (t_UIO) is presented and the automatic generation algorithm of sub-transfer sequences based on t_UIO is given. Finally, the test adequacy criteria are discussed and a time condition coverage criterion is proposed. The actual engineering project application shows that the method proposed in this paper is of great value for the ASCS, which can be generally and effectively used in engineering.

Software testing techniques based on formal methods can reduce the uncertainty, improve accuracy, validity and degree of automation. In this paper, formal methods were introduced into the real-time embedded software testing field and a time-extended finite state machine, called t_EFSM, was proposed firstly. And then, based on the t_EFSM and combined with testing process of flight control software of an unmanned aerial vehicle (UAV), a method to generate the time-extended UIO sequence (t_UIO) was put forward. Finally, the method to generate test cases automatically according to t_UIO sequence was brought forward.