Remote sensing imagery plays a crucial role in monitoring Earth's surface changes. With the advancement of computational power, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been increasingly applied in change detection tasks. This paper proposes a novel approach, the dual-path 3D Convolutional Recurrent Neural Network (3DCNN-LSTM), which integrates 3D CNNs and Long Short-Term Memory to fuse bi-temporal Zhuhai-1 Orbita hyperspectral and multi-season Sentinel-2 remote sensing imagery for semantic change detection. The proposed method extracts joint spectral-spatial features using 3D convolution layers. Subsequently, bi-temporal features and multi-season features are spliced as one time-sequence and fed to one LSTM modules (referred to as Str1). In another strategy (Str2), multi-source features are fed separately to two LSTM modules and fused in fully connected layers. Experiments demonstrate that Str1 and Str2 achieves better performance than Siamese convolutional multiple-layers RNN (SiamCRNN). Str1 obtained the overall accuracy of 99.12%, kappa coefficient of 98.77%, and F1 score of 98.91%, achieving improvements of 4.81%, 6.7%, and 6.51%, respectively, compared to SiamCRNN.

Multi-agent collaboration in the agent oriented Collective Adaptive Systems could offset the failure of goal achievement by an independent agent. Commitments among agents to meld collaborative patterns typically defined in design time, which may cannot service the request of ACSs in response to changes in the system itself or in its environment. To address this problem, we propose a Goal-Capability-Commitment (GCC) based multi-agent collaboration mediation for ACSs at run-time. There are two issues with this state of the art approach: (1) the ability to represent the capability of agent under different contexts and diagnose the invalids of goals and agents leaded by the changing environments, and (2) the ability of mediation to select an appropriate capability or generate a new commitment in order to satisfy adaptive goal. We illustrate our approach through two experiments over the simulated scenario of automated hospital cart transportation system and discuss the results of experiments.

Automatic protocol mining is a promising approach for inferring accurate and complete API protocols. However, just as with any data-mining technique, this approach requires sufficient training data (object usage scenarios). Existing approaches resolve the problem by analyzing more programs, which may cause significant runtime overhead. In this paper, we propose an inheritance-based oversampling approach for object usage scenarios (OUSs). Our technique is based on the inheritance relationship in object-oriented programs. Given an object-oriented program p , generally, the OUSs that can be collected from a run of p are not more than the objects used during the run. With our technique, a maximum of n times more OUSs can be achieved, where n is the average number of super-classes of all general OUSs. To investigate the effect of our technique, we implement it in our previous prototype tool, ISpecMiner, and use the tool to mine protocols from several real-world programs. Experimental results show that our technique can collect 1.95 times more OUSs than general approaches. Additionally, accurate and complete API protocols are more likely to be achieved. Furthermore, our technique can mine API protocols for classes never even used in programs, which are valuable for validating software architectures, program documentation, and understanding. Although our technique will introduce some runtime overhead, it is trivial and acceptable.

The Microsoft Kinect sensor has been widely used in many applications, but it suffers from the drawback of low depth accuracy. In this paper, we present a unified depth modification model to improve the Kinect depth accuracy by registering depth and color images in an iterative manner. Specifically, in each iteration, we first establish a coarse correspondence based on the feature descriptor of the canny edge. Then, we estimate the fine correspondence using a robust estimator called the L2E with the nonparametric model. Finally, we correct the depth data according to the correspondence results. In order to evaluate the effectiveness of our approach, we have performed extensive experiments and then analyzed the experimental results from the following respects: the accuracy of depth data, the accuracy of correspondence between color and depth images as well as the measurement error in the 3D reconstruction by our method. The experimental results show that our approach greatly improves the depth accuracy.

In this paper, a novel closed-form transformation estimation method based on feature guided moving least squares together with manifold regularization is proposed for nonrigid image/surface deformation. The method takes the user-controlled point-offset-vectors and the feature points of the image/surface as input, and estimates the spatial transformation between the two control point sets for each pixel/voxel. To achieve a detail-preserving and realistic deformation, the transformation estimation is formulated as a vector-field interpolation problem using a feature guided moving least squares method, where a manifold regularization is imposed as a prior on the transformation to capture the underlying intrinsic geometry of the input image/surface. The non-rigid transformation is specified in a reproducing kernel Hilbert space. We derive a closed-form solution of the transformation and adopt a sparse approximation to achieve a fast implementation, which largely reduces the computation complexity without performance sacrifice. In addition, the proposed method can give a wonderful user experience, fast and convenient manipulating. Extensive experiments on both 2D and 3D data demonstrate that the proposed method can produce more natural deformations compared with other state-of-the-art methods.

Recently, sparse representation based classifiers (SRC) and collaborative representation based classifiers (CRC) have been shown to give very good performance under controlled scenarios. However, in practical applications, face recognition often encounters variations in illumination, expression, noise and occlusion, which cause severe performance degradation (due to the outliers in testing). In this paper, we present a novel robust face recognition algorithm based on class-wise low-rank constrained collaborative representations. We impose a low-rank constraint on the representation coefficient matrix to discriminate against outliers. The resulting low-rank constrained collaborative representation based classifier (LCRC) jointly minimizes the class-wise reconstruction error and rank of coefficient matrix. Experiments show that LCRC outperforms popular classifiers such as SRC, CRC, SVM, PROCRC on the AR, CMU PIE and LFW databases.

Dynamic Protocol Mining (DPM) techniques are a promising approach to infer useful API protocols automatically.However, their results are biased to input test cases and the instrumentation overhead discounts their usability in industrial practice.In this paper, we propose a distributed dynamic protocol mining framework NSpecMiner.Our framework is based on a client-server architecture, where the client tracer gathers Program Execution Traces (PETs) and sends them to the server for mining.Mined protocols are saved on the server to provide various kinds of remote services, such as API protocol retrieval and program verification, etc.Compared with local miners, NSpecMiner has many advantages: 1) A large number of diverse PETs are likely to be collected from multiple clients, which is essential for mining accurate and complete API protocols.2) Instrumentation overhead can be balanced among multiple clients.3) Via integrating the client tracer into widely used software, we can mine API protocols transparently and automatically without any human effort.To evaluate our technique, we performed a comparison test with a local miner ISpecMiner and NSpecMiner.Preliminary results show that our approach is effective to mine useful API protocols as local miners.While our method is able to gather PETs concurrently from multiple clients and other merits of the distributed technology will further benefit DPM significantly.

Static analysis is an efficient approach for software assurance. It is indicated that its most effective usage is to perform analysis in an interactive way through the software development process, which has a high performance requirement. This paper concentrates on rule-based static analysis tools and proposes an optimized rule-checking algorithm. Our technique improves the performance of static analysis tools by filtering vulnerability rules in terms of characteristic objects before checking source files. Since a source file always contains vulnerabilities of a small part of rules rather than all, our approach may achieve better performance. To investigate our technique’s feasibility and effectiveness, we implemented it in an open source static analysis tool called PMD and used it to conduct experiments. Experimental results show that our approach can obtain an average performance promotion of 28.7% compared with the original PMD. While our approach is effective and precise in detecting vulnerabilities, there is no side effect.

Combinatorial interaction testing is a well-studied testing strategy that aims at constructing an effective interaction test suite (ITS) of a specific generation strength to identify interaction faults caused by the interactions among factors. Due to limited testing resources in practice, for example in combinatorial interaction regression testing, interaction test suite prioritization (ITSP) has been proposed to improve the efficiency of testing. An intuitive ITSP strategy that has been widely used in practice is fixed-strength interaction coverage based prioritization (FICBP). FICBP makes use of a property of the ITS: interaction coverage at a fixed prioritization strength. However, a challenge facing FICBP is that, when the ITS is large, the prioritization cost can be very high. In this paper, we propose a new FICBP method that, by repeatedly using base choice coverage (i.e., one-wise coverage) during the prioritization process, improves testing efficiency while maintaining testing effectiveness. The empirical studies show that our method has fault detection capability comparable to current FICBP methods, but obtains more stable results in many cases. Additionally, our method requires considerably less prioritization time than other FICBP methods at different prioritization strengths.

Class temporal specification is a kind of important program specifications, which specifies that methods of a class should be called in a particular sequence.Dynamic specification mining is a promising approach to achieve this kind of specifications automatically.However, they always infer partial specifications, that is, the mined specifications are biased to input programs or program execution traces.In this paper, we propose to mine class temporal specifications based on a probabilistic model in an online mode.Since our method can evolve mined specifications persistently, universal specifications can be achieved.To investigate our technique's feasibility and effectiveness, we implemented it in a prototype tool ISpecMiner and used the tool to perform experiments.Experimental results show that our method is promising to infer universal specifications if sufficient traces are provided for mining.

Automatic protocol mining is a promising approach to infer precise and complete API protocols.However, the effect of the approach largely depends upon the quality of input object usage scenarios, in terms of noise and diversity.This paper aims to extract as many object usage scenarios as possible from object-oriented programs for automatic protocol mining.A large corpus of object usage scenarios can help with eliminating noise accurately and is likely to be diverse.Therefore, precise and complete protocols may be achieved.Given an object-oriented program p, generally, object usage scenarios that can be collected from a run of p is not more than the number of instances used in p. Relying on the inheritance relationship among classes, our technique can extract a maximum of n times more object usage scenarios from p, where n is the average inheritance depth of all object usage scenarios in p.In order to investigate the effect of our technique on mining protocols, we implement it in our previous prototype tool ISpecMiner and use the tool to mine protocols from several real-world applications.The experimental results show that our technique is promising to achieve complete and precise API protocols.In addition, protocols of classes that have not been used in programs can be also achieved, which is helpful for program documentation and understanding.

With the continuous evolution of software systems, test suites often grow very large.Rerunning all test cases may be impractical in regression testing under limited resources.Coverage-based test case prioritization techniques have been proposed to improve the effectiveness of regression testing.The original test suite often contains some test cases which are designed for exercising production features or exceptional behaviors, rather than for code coverage.Therefore, coverage-based prioritization techniques do not always generate satisfactory results.In this context, we propose a global similarity-based regression test case prioritization approach.The approach reschedules the execution order of test cases based on the distances between pair-wise test cases.We designed and conducted empirical studies on four C programs to validate the effectiveness of our proposed approach.Moreover, we also empirically compared the effects of six similarity measures on the global similarity-based test case prioritization approach.Experimental results illustrate that the global similarity-based regression test case prioritization approach using Euclidean distance is the most effective.This study aims at providing practical guidelines for picking the appropriate similarity measures.

Class temporal specification is a kind of important program specifications especially for object-oriented programs, which specifies that interface methods of a class should be called in a particular sequence. Currently, most existing approaches mine this kind of specifications based on finite state automaton. Observed that finite state automaton is a kind of deterministic models with inability to tolerate noise. In this paper, we propose to mine class temporal specifications relying on a probabilistic model extending from Markov chain. To the best of our knowledge, this is the first work of learning specifications from object-oriented programs dynamically based on probabilistic models. Different from similar works, our technique does not require annotating programs. Additionally, it learns specifications in an online mode, which can refine existing models continuously. Above all, we talk about problems regarding noise and connectivity of mined models and a strategy of computing thresholds is proposed to resolve them. To investigate our technique's feasibility and effectiveness, we implemented our technique in a prototype tool ISpecMiner and used it to conduct several experiments. Results of the experiments show that our technique can deal with noise effectively and useful specifications can be learned. Furthermore, our method of computing thresholds provides a strong assurance for mined models to be connected.

Although fingerprint based localization is promising for indoor applications, its accuracy still remains a huge challenge. Most of existing approaches rely on the Radio Signal Strength (RSS) to generate fingerprints. However, merely using RSS is unable to accurately localize objects since such an one-dimensional fingerprint will be seriously influenced by the interference and multi-path effect in the indoor environment. In this paper, we propose a new localization approach based on multidimensional Wi-Fi fingerprint. Instead of only using RSS to construct fingerprint, we employ RSS, transmitted power, and channel information to construct an integrated fingerprint. The extended fingerprint enables fine-grained localization and tracking services. We also deign a cosine similarity based matching algorithm and enhanced particle filter mechanism to achieve accurate localization and tracking. Extensive experiment and implementation results show that the new fingerprint and proposed algorithms can achieve an accuracy within two meters in 90% of testing points, while demonstrating a good adaptability to complex indoor environments.

Automatic image annotation is an important task for multimedia retrieval. By allocating relevant words to un-annotated images, these images can be retrieved in response to textual queries. There are many researches on the problem of image annotation and most of them construct models based on joint probability or posterior probabilities of words. In this paper we estimate the probabilities that words generate the images, and propose a two-phase generation model for the generation procedure. Each word first generates its related words, then these words generate an un-annotated image, and the relation between the words and the un-annotated image is obtained by the probability of the two-phase generation. The textual words usually contain more semantic information than visual content of images, thus the probabilities that words generate images is more reliable than the probability that images generate words. As a result, our model estimates the more reliable probability than other probabilistic methods for image annotation. The other advantage of our model is the relation of words is taken into consideration. The experimental results on Corel 5K and MIR Flickr demonstrate that our model performs better than other previous methods. And two-phase generation which considering word's relation for annotation is better than one-phase generation which only consider the relation between words and images. Moreover, the methods which estimate the generative probability obtain better performance than SVM which estimates the posterior probability.