Recent advancements in food image recognition have underscored its importance in dietary monitoring, which promotes a healthy lifestyle and aids in the prevention of diseases such as diabetes and obesity. While mainstream food recognition methods excel in scenarios with large-scale annotated datasets, they falter in few-shot regimes where data is limited. This paper addresses this challenge by introducing a variational generative method, the Multivariate Knowledge-guided Variational AutoEncoder (MK-VAE), for few-shot food recognition. MK-VAE leverages handcrafted features and semantic embeddings as multivariate prior knowledge to strengthen feature learning and feature generation in different phases. Specifically, we design a lightweight and flexible feature distillation module that distills handcrafted features to enhance the feature learning network for capturing the salient visual information in few-shot samples. During the feature generation phase, we utilize a variational autoencoder to learn the difference distribution of food data and explicitly boost the latent representation with category-level semantic embeddings to pull homogeneous features closer together while pushing inhomogeneous features apart. Experimental results demonstrate that our proposed MK-VAE significantly outperforms state-of-the-art few-shot food recognition methods in both 5-way 1-shot and 5-way 5-shot settings on three widely-used benchmark datasets: Food-101, VIREO Food-172, and UECFood-256.

Excessive non-actionable warnings generated by static program analysis tools can hinder developers from utilizing these tools effectively. Leveraging learning-based approaches for actionable warning identification has demonstrated promise in boosting developer productivity, minimizing the risk of bugs, and reducing code smells. However, the small sizes of existing datasets have limited the model choices for machine learning researchers, and the lack of aligned fix commits limits the scope of the dataset for research. In this paper, we present AW4C, an actionable warning C dataset that contains 38,134 actionable warnings mined from more than 500 repositories on GitHub. These warnings are generated via Cppcheck, and most importantly, each warning is precisely mapped to the commit where the corrective action occurred. To the best of our knowledge, this is the largest publicly available actionable warning dataset for C programming language to date. The dataset is suited for use in machine/deep learning models and can support a wide range of tasks, such as actionable warning identification and vulnerability detection. Furthermore, we have released our dataset1 and a general framework for collecting actionable warnings on GitHub2 to facilitate other researchers to replicate our work and validate their innovative ideas.

Vehicle identification number (VIN) is a unique serial number used to identify individual vehicles across various applications. The first crucial step in automatically collecting VINs is to accurately localize the VIN area. In this article, we present a novel VIN detection approach called Mirrored EAST (MEAST) based on an efficient and accurate scene text (EAST) Detection framework. MEAST learns to exploit the spatial consistency between an image and its mirrored version to improve localization performance, and employs a lighter but more discriminative backbone network to improve its applicability in mobile scenarios. To evaluate the VIN detection performance, we constructed a large-scale VIN image dataset named CQU-VD20 K, consisting of 20 000 VIN images in real scenarios. Based on this dataset, we have conducted a comprehensive empirical study of VIN detection. The results demonstrate the superiority of MEAST over other methods in VIN detection. Additionally, we also conducted extended experiments on a license plate dataset named CCPD-Rotate, which confirms the effectiveness of our approach in other industrial inspection tasks.

Data augmentation is a popular way for few-shot learning (FSL). It generates more samples as supplements and then transforms the FSL task into a common supervised learning problem for a solution. However, most data-augmentation-based FSL approaches only consider the prior visual knowledge for feature generation, thereby leading to low diversity and poor quality of generated data. In this study, we attempt to address this issue by incorporating both prior visual and prior semantic knowledge to condition the feature generation process. Inspired by some genetic characteristics of semi-identical twins, a novel multimodal generative FSL approach was developed named semi-identical twins variational autoencoder (STVAE) to better exploit the complementarity of these modality information by considering the multimodal conditional feature generation process as a process that semi-identical twins are born and collaborate to simulate their father. STVAE conducts feature synthesis by pairing two conditional variational autoencoders (CVAEs) with the same seed but different modality conditions. Subsequently, the generated features of two CVAEs are considered as semi-identical twins and adaptively combined to yield the final feature, which is considered as their fake father. STVAE requires that the final feature can be converted back into its paired conditions while ensuring these conditions remain consistent with the original in both representation and function. Moreover, STVAE is able to work in the partial modality-absence case due to the adaptive linear feature combination strategy. STVAE essentially provides a novel idea to exploit the complementarity of different modality prior information inspired by genetics in FSL. Extensive experimental results demonstrate that our work achieves promising performances in comparison to the recent state-of-the-art approaches, as well as validate its effectiveness on FSL under various modality settings.

Adaptive random testing (ART) is a family of algorithms to enhance random testing (RT) by generating test cases extensively and evenly. For this purpose, many ART algorithms have been proposed, the most well-known and the first approach is the Fixed-Size-Candidate-Set ART (FSCS-ART). In recent years, researchers have also proposed many ART methods to continuously improve the performance of FSCS-ART, but the focus has been more on reducing the time overhead of FSCSART while retaining its failure detection effectiveness as much as possible due to the boundary effect. To alleviate the boundary effect and improve the effectiveness of FSCS-ART, this paper proposes an algorithm AP-FSCS-ART, an Adaptive Partition-based method on top of FSCS-ART. First, AP-FSCS-ART divides the entire input domain into external and internal sub-domains. Then, two different algorithms are adaptively applied to the two sub-domains to find the next test case from the randomly generated candidate test cases. During the selecting process, APFSCS-ART takes into account not only the most recently executed test case of a candidate test case but also its position relative to the input domain. Experiments using the 12 most common real programs and comparisons with other algorithms in this paper show that the AP-FSCS-ART algorithm has significantly better failure detection capability, with improvements from 8.8% to 11.4% compared to three state-of-the-art ART algorithms, including the FSCS-ART, FSCS-ctsr, and NNDC-ART.

Automatic medical image segmentation plays a crucial role in clinical diagnosis and treatment. However, it is still a challenging task due to the complex interior characteristics ( e.g. , inconsistent intensity, low contrast, texture heterogeneity) and ambiguous external boundary structures. In this paper, we introduce a novel geometric structure learning mechanism (GSLM) to overcome the limitations of existing segmentation models that lack learning ”focus, path, and difficulty.” The geometric structure in this mechanism is jointly characterized by the skeleton-like structure extracted by the mask distance transform (MDT) and the boundary structure extracted by the mask distance inverse transform (MDIT). Among them, the skeleton-like and boundary pay attention to the trend of interior characteristics consistency and external structure continuity, respectively. With this idea, we design GSAL, a novel end-to-end geometric structure adversarial learning for robust medical image segmentation. GSAL has four components: a geometric structure generator, which yields the geometric structure to learn the most discriminative features that preserve interior characteristics consistency and external boundary structure continuity, skeleton-like and boundary structure discriminators , which enhance and correct the characterization of internal and external geometry to mutually promote the capture of global contextual dependencies, and a geometric structure fusion sub-network, which fuses the two complementary and refined skeleton-like and boundary structures to generate the high-quality segmentation results. The proposed approach has been successfully applied to three different challenging medical image segmentation tasks , including polyp segmentation , COVID-19 lung infection segmentation, and lung nodule segmentation. Extensive experimental results demonstrate that the proposed GSAL achieves favorably against most state-of-the-art methods under different evaluation metrics . The code is available at: https://github.com/DLWK/GSAL .

Automatic image-based pavement distress detection and recognition are vital for pavement maintenance and management. However, existing deep learning-based methods largely omit the specific characteristics of pavement images, such as high image resolution and low distress area ratio, and are not end-to-end trainable. In this paper, we present a series of simple yet effective end-to-end deep learning approaches named Weakly Supervised Patch Label Inference Networks (WSPLIN) for efficiently addressing these tasks under various application settings. WSPLIN transforms the fully supervised pavement image classification problem into a weakly supervised pavement patch classification problem for solutions. Specifically, WSPLIN first divides the pavement image under different scales into patches with different collection strategies and then employs a Patch Label Inference Network (PLIN) to infer the labels of these patches to fully exploit the resolution and scale information. Notably, we design a patch label sparsity constraint based on the prior knowledge of distress distribution and leverage the Comprehensive Decision Network (CDN) to guide the training of PLIN in a weakly supervised way. Therefore, the patch labels produced by PLIN provide interpretable intermediate information, such as the rough location and the type of distress. We evaluate our method on a large-scale bituminous pavement distress dataset named CQU-BPDD and the augmented Crack500 (Crack500-PDD) dataset, which is a newly constructed pavement distress detection dataset augmented from the Crack500. Extensive results demonstrate the superiority of our method over baselines in both performance and efficiency. The source codes of WSPLIN are released onhttps://github.com/DearCaat/wsplin.

Software architecture is undergoing a transition from monolithic architectures to microservices to achieve resilience, agility, and scalability in the software life circle. However, microservice architecture is not perfect and suffers from intermittent faults, leading to economic and user losses. Therefore, it is essential to detect anomalies in microservice systems accurately. The key limitation of current approaches lies in a lack of ability to detect multitype anomalies, excessive resource overhead, and requirements of expert knowledge. In this paper, we present a Deep Attentive anomaly detection approach with Multimodal data named DAM. With multimodal fusion, attentive LSTM, and a dynamic threshold selecting algorithm, DAM could detect anomalies accurately and efficiently in an unsupervised manner. We evaluate our approach by injecting six types of anomalies on a widely used microservice system, Train-Ticket. The result shows that DAM could detect multitype anomalies well, with 80.46% F-measure, achieving 16.76% and 29.52% improvement over two state-of-the-art baselines (Donut and DAGMM), respectively.

Logs are widely used for system behavior diagnosis by automatic log mining. Log parsing is an important data preprocessing step that converts semi-structured log messages into structured data as the feature input for log mining. Currently, many studies are devoted to proposing new log parsers. However, to the best of our knowledge, no previous study comprehensively investigates the effectiveness of log parsers in industrial practice. To investigate the effectiveness of the log parsers in industrial practice, in this paper, we conduct an empirical study on the effectiveness of six state-of-the-art log parsers on 10 microservice applications of Ant Group. Our empirical results highlight two challenges for log parsing in practice: 1) various separators. There are various separators in a log message, and the separators in different event templates or different applications are also various. Current log parsers cannot perform well because they do not consider various separators. 2) Various lengths due to nested objects. The log messages belonging to the same event template may also have various lengths due to nested objects. The log messages of 6 out of 10 microservice applications at Ant Group with various lengths due to nested objects. 4 out of 6 state-of-the-art log parsers cannot deal with various lengths due to nested objects. In this paper, we propose an improved log parser named Drain+ based on a state-of-the-art log parser Drain. Drain+ includes two innovative components to address the above two challenges: a statistical-based separators generation component, which generates separators automatically for log message splitting, and a candidate event template merging component, which merges the candidate event templates by a template similarity method. We evaluate the effectiveness of Drain+ on 10 microservice applications of Ant Group and 16 public datasets. The results show that Drain+ outperforms the six state-of-the-art log parsers on industrial applications and public datasets. Finally, we conclude the observations in the road ahead for log parsing to inspire other researchers and practitioners.

Abstract It is a critical quality assurance activity to effectively detect the root cause of faults causing the software crashes (i.e. crashing faults). Previous studies extracted features to characterise crash instances and built models to identify whether the residences of crashing faults locate inside the stack traces. These models all belong to supervised learning methods which require labelled crash data to be involved. In this study, the introduction of an unsupervised model, called T ransfer S pectral C lustering ( TSC ), for the task of crashing fault residence identification under the unlabelled data scenario is proposed. Unlike traditional unsupervised methods which are applied to individual project data, TSC transfers the knowledge of auxiliary unlabelled data from the source project to assist the clustering task on the unlabelled data from the target project. TSC is an unsupervised transfer learning method, and simultaneously considers the data manifold information of the individual project and feature manifold information across projects to facilitate the clustering effect. Extensive experiments are conducted on a benchmark dataset containing seven software projects. Five indicators were chosen for performance evaluation. The results show that TSC achieves better performance than four clustering based unsupervised methods, and competitive performance compared with eight supervised cross‐project methods.

Real-world images are often compositions of multiple objects with different categories, scales, poses and locations. Adding nonexistent objects to an image (composing) or removing existent objects from an image (decomposing) leads to higher discrepancy in appearance, which reveals an important but long-neglected compositional nature of multi-label images. In light of this observation, we propose a novel end-to-end compositional learning framework named Category Prototype Compositional Learning (CPCL) to model such compositional nature for multi-label image classification. In CPCL, each image is represented by a collection of category-related features used to eliminate the negative effects from location information. Then, a compositional learning module is introduced to compose and decompose the category-related features with their corresponding category prototypes, which are derived from the semantic representations of categories. If the image has the given object, the output after composing should be closer to the original input than the output after decomposing. Contrarily, if the image does not have the given object, the output after decomposing should be closer to the original input than the output after composing. We introduce the Transformed Appearance Distance (TAD) to measure the appearance change between the composed and decomposed features relative to the category-related features with respect to each category. Finally, multi-label image classification is accomplished by performing a TAD-based metric learning. Experimental results on three multi-label image classification benchmarks,i.e., NUS-WIDE, MS-COCO and VOC 2007, validate the effectiveness and superiority of our work in comparison with the state-of-the-arts. The source codes of our model have been released onhttps://github.com/ZFT-CQU/CPCL.

The high-precision QoS (quality of service) prediction is based on the comprehensive perception of state information of users and services. However, the current QoS prediction approaches have limited accuracy, for most state information of users and services (i.e., network speed, latency, network type, and more) are hidden due to privacy protection. Therefore, this article proposes a hidden-state-aware network (HSA-Net) that includes three steps called hidden state initialization, hidden state perception, and QoS prediction. A hidden state initialization approach is developed first based on the latent dirichlet allocation (LDA). After that, a hidden-state perception approach is proposed to abstract the initialized hidden state by fusing the known information (e.g., service ID and user location). The perception approach consists of four hidden-state perception (HSP) modes (i.e., known mode, object mode, hybrid mode and overall mode) implemented to generate explainable and fused features through four adaptive convolutional kernels. Finally, the relationship between the fused features and the QoS is discovered through a fully connected network to complete the high-precision QoS prediction process. The proposed HSA-Net is evaluated on two real-world datasets. According to the results, the HSA-Net's mean absolute error (MAE) index reduced by 3.67% and 28.84%, whereas the root mean squared error (RMSE) index decreased by 3.07% and 7.14% compared with ten baselines on average in the two datasets.

Automatic pavement disease recognition is vital for pavement maintenance and management. In this paper, we present an end-to-end deep learning approach named Weakly Super-vised Patch Label Inference Network with Image Pyramid (WSPLIN-IP) for recognizing various types of pavement diseases that are not just limited to the specific ones, such as crack and pothole. WSPLIN-IP first divides the pavement image into patches with an image pyramid for fully exploiting the resolution and scale information. Then, a Patch Label Inference Network (PLIN) is employed for inferring the labels of these patches constrained with a patch label sparsity loss. Finally, the patch labels are fed into a Comprehensive Decision Network (CDN) for disease recognition. Since only the image label is available during whole training, the training of PLIN is conducted in a weakly supervised way under the guidance of CDN and the trained PLIN can provide the interpretable intermediate information. We evaluate our method on a large-scale Bituminous Pavement Disease Dataset named CQU-BPDD whose samples are acquired in the real world. Extensive results demonstrate the superiority of our method over baselines.

Pulmonary nodule detection and segmentation are the necessary successively steps in lung cancer screening with low-dose computed tomography (CT) scans. However, the state-of-the-art models focus on solving tasks separately, thereby ignore the correlation between each task. Besides, most nodule detectors adopt anchor-based method falling to achieve good performance in low FPs per scan. To overcome those barriers, we present a novel multi-task 3D convolutional network (DeepNodule) for simultaneous nodule detection and segmentation in a shared-and-fined manner. Meanwhile, we utilize the center-point of the predicted segmentation masks to refine the bounding box coordinate and get a more precise nodule location. Furthermore, we design a 3D Gated Channel Transformation convolutional attention block for learning nodule features better. Experiments conducted on LUNA16 dataset demonstrates that DeepNodule obtains competitive performance, with the sensitivity of nodule candidate detection achieving 92.0%, and the accuracy of nodule segmentation reaching 80.04%.

Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely imbalanced.Considering the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API categories.We collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs.

Many automated commit message generation (CMG) approaches have been proposed for facilitating the understanding of software changes. They are shown to be promising and can generate commit messages that are semantically relevant to the reference messages for a number of commits. However, a large proportion (over 50%) of semantically irrelevant commit messages are also generated simultaneously. Such messages may mislead developers, require additional efforts of developers to confirm and filter out, and hinder the application of existing CMG approaches in practice. For tackling this problem, prior work mainly focuses on proposing new methods to improve the generation accuracy. However, another promising way for bridging the gap between CMG approaches and the practice has not been well investigated, which is: can we automatically assure the semantic relevance of the generated messages?To that end, in this work, we propose an automated Quality A ssurance framework for commit message generation (QAcom). QAcom can assure the quality of generated commit messages by automatically filtering out the semantically-irrelevant generated messages and preserving the semantically-relevant ones as many as possible. In particular, QAcom consists of a Collaborative-Filtering-based (CF) component and a Retrieval-based (RE) component. Given a commit message generated by a CMG approach, QAcom estimates whether this generated message is semantically relevant to its ground truth, which is unknown when estimating, based on both the collaborative filtering algorithm and the similarity between this commit and historical commits. We evaluate the effectiveness of QAcom by "plugging" it in three state-of-the-art CMG approaches. Experimental results on three public datasets show that QAcom can effectively filter out semantically-irrelevant generated messages and preserve semantically-relevant ones.

The important cues for a realistic lung nodule synthesis include the diversity in shape and background, controllability of semantic feature levels, and overall CT image quality. To incorporate these cues as the multiple learning targets, we introduce the Multi-Target Co-Guided Adversarial Mechanism, which utilizes the foreground and background mask to guide nodule shape and lung tissues, takes advantage of the CT lung and mediastinal window as the guidance of spiculation and texture control, respectively. Further, we propose a Multi-Target Co-Guided Synthesizing Network with a joint loss function to realize the co-guidance of image generation and semantic feature learning. The proposed network contains a Mask-Guided Generative Adversarial Sub-Network (MGGAN) and a Window-Guided Semantic Learning Sub-Network (WGSLN). The MGGAN generates the initial synthesis using the mask combined with the foreground and background masks, guiding the generation of nodule shape and background tissues. Meanwhile, the WGSLN controls the semantic features and refines the synthesis quality by transforming the initial synthesis into the CT lung and mediastinal window, and performing the spiculation and texture learning simultaneously. We validated our method using the quantitative analysis of authenticity under the Fréchet Inception Score, and the results show its state-of-the-art performance. We also evaluated our method as a data augmentation method to predict malignancy level on the LIDC-IDRI database, and the results show that the accuracy of VGG-16 is improved by 5.6%. The experimental results confirm the effectiveness of the proposed method.

Accurate segmentation for lung nodules in lung computed tomography (CT) scans plays a key role in the early diagnosis of lung cancer. Many existing methods, especially U-Net, have made significant progress in lung nodule segmentation. However, due to the complex shapes of lung nodules and the similarity of visual characteristics between nodules and lung tissues, an accurate segmentation with low false positives of lung nodules is still a challenging problem. Considering the fact that both boundary and texture information of lung nodules are important for obtaining an accurate segmentation result, we propose a novel Mask and Texture-driven Generative Adversarial Network (MTGAN) with a joint multi-scale L1 loss for lung nodule segmentation, which takes full advantages of U-Net and adversarial training. The proposed MTGAN leverages adversarial learning strategy guided by the boundary and texture information of lung nodules to generate more accurate segmentation results with lesser false positives. We validate our model with the LIDC-IDRI dataset, and experimental results show that our method achieves excellent segmentation results for a variety of lung nodules, especially for juxtapleural nodules and low-dense nodules. Without any bells and whistles, the proposed MTGAN achieves significant segmentation performance with the Dice similarity coefficient (DSC) of 85.24% on the LIDC-IDRI dataset.

Computer-Aided Diagnosis (CAD) systems for lung nodule diagnosis based on deep learning have attracted much attention in recent years. However, most existing methods ignore the relationships between the segmentation and classification tasks, which leads to unstable performances. To address this problem, we propose a novel multi-task framework, which can provide lung nodule segmentation mask, malignancy prediction, and medical features for interpretable diagnosis at the same time. Our framework mainly contains two sub-network: (1) Multi-Channel Segmentation Sub-network (MSN) for lung nodule segmentation, and (2) Joint Classification Sub-network (JCN) for interpretable lung nodule diagnosis. In the proposed framework, we use U-Net down-sampling processes for extracting low-level deep learning features, which are shared by two sub-networks. The JCN forces the down-sampling processes to learn better low-level deep features, which lead to a better construct of segmentation masks. Meanwhile, two additional channels constructed by OTSU and super-pixel (SLIC) methods, are utilized as the guideline of the feature extraction. The proposed framework takes advantages of deep learning methods and classical methods, which can significantly improve the performances of all tasks. We evaluate the proposed framework on public dataset LIDC-IDRI. Our framework achieves a promising Dice score of 86.43% in segmentation, 87.07% in malignancy level prediction, and convincing results in interpretable medical feature predictions.

Logs are universally available in software systems for troubleshooting. They record system run-time states and messages of system activities. Log analysis is an effective way to diagnosis system exceptions, but it will take a long time for engineers to locate anomalies accurately through logs. Many automatic approaches have been proposed for log-based anomaly detection. However, most of the prior approaches did not consider the corresponding system component of a log message. Such component records the log location, which can help detect the location-sequence-related anomalies. In this paper, we propose LogC, a new Log -based anomaly detection approach with Component-aware analysis. LogC contains two phases: (i) turning log messages into log template sequences and component sequences, (ii) feeding such two sequences to train a combined LSTM model for detecting anomalous logs. LogC only needs normal log sequences to train the combined model. We evaluate LogC on two open-source log datasets: HDFS and ThunderBird. Experimental results show that LogC overall outperforms three baselines (i.e., PCA, IM, and DeepLog) in terms of three metrics (precision, recall, and F-measure).

Weakly supervised temporal action localization (WSTAL) is a practical but challenging issue in video understanding. However, most existing methods have to activate background snippets or deactivate action snippets in cases of no boundary annotations, which inevitably affects the localization of action instances. In this letter, we propose a spatial enhancement and temporal constraint (SETC) model to address this problem from three aspects. Specifically, we first propose a spatial enhancement module to enhance the discrimination of the extracted features. Then we leverage the instance sparse constraint to restrain the drastic fluctuation class activation sequence (CAS). Finally, we use the confidence connectivity enhancement to connect the snippets that are broken up by mistake. Experiments on THUMOS'14 and ActivityNet datasets validate the efficacy of SETC against existing state-of-the-art WSTAL algorithms.

Context: Previous studies have shown that a transfer learning model, TCA+ proposed by Nam et al., can significantly improve the performance of cross-project defect prediction (CPDP). TCA+ achieves the improvement by reducing data distribution difference between source (training data) and target (testing data) projects. However, TCA+ is unstable, i.e., its performance varies largely when using different source projects to build prediction models. In practice, it is hard to choose a suitable source project to build the prediction model. Objective: To address the limitation of TCA+, we propose a two-phase transfer learning model (TPTL) for CPDP. Method: In the first phase, we propose a source project estimator (SPE) to automatically choose two source projects with the highest distribution similarity to a target project from candidates. Next, two source projects that are estimated to achieve the highest values of F1-score and cost-effectiveness are selected. In the second phase, we leverage TCA+ to build two prediction models based on the two selected projects and combine their prediction results to further improve the prediction performance. Results: We evaluate TPTL on 42 defect datasets from PROMISE repository, and compare it with two versions of TCA+ (TCA+_Rnd, randomly selecting one source project; TCA+_All, using all alternative source projects), a related source project selection model TDS proposed by Herbold, a state-of-the-art CPDP model leveraging a log transformation (LT) method, and a transfer learning model Dycom with better form of TCA. Experiment results show that, on average across 42 datasets, TPTL respectively improves these baseline models by 19%, 5%, 36%, 27%, and 11% in terms of F1-score; by 64%, 92%, 71%, 11%, and 66% in terms of cost-effectiveness. Conclusion: The proposed TPTL model can solve the instability problem of TCA+, showing substantial improvements over the state-of-the-art and related CPDP models.

Software change-proneness prediction (whether or not class files in a project will be changed in the next release) can help software developers to focus on preventive actions to reduce maintenance costs, and managers to allocate resources more effectively. Prior studies found that change-proneness prediction works well if there is sufficient amount of training data to build a model. However, it is not feasible for projects with limited historical data especially for new projects. To address this issue, cross-project change-proneness prediction, which builds a prediction model by using data in another project (i.e., source project), and predicts the change-proneness in a target project, is proposed. Considering there are a large number of source projects, one challenge for cross-project change-proneness prediction is that given a target project, how to automatically select a source project which could show good prediction accuracy on it. In this paper, we propose a selective cross-project (SCP) model for change-proneness prediction. SCP automatically finds the source project which has the similar data distribution with the target project by measuring distribution similarity between source and target projects. We evaluate SCP by conducting an empirical study on 14 open source projects. We compare it with 2 most related change-proneness models, including RCP (Random Cross-Project prediction) proposed by Malhotra and Bansal, and CLAMI+ developed by Yan et al. Experiment results show that SCP improves RCP and CLAMI+ by 25.34% and 4.30% in terms of AUC respectively; and by 171.42% and 172.31% in terms of cost-effectiveness, respectively.

Residuals and inception are two commonly used module that makes the network deeper and wider to achieve better performance. And the combination of these two modules which is usually referred to as inception-resnet can get a better result. In this paper, we propose a new type of combination to give full play to the role of residuals and inception, making network learning more abundant features. The new proposed module is called Residual Inception (RI) which enjoys the same width as the inception module in GoogLeNet. In RI, each parallel cascade structure is replaced by a densely block or a modified residual block for gaining a better performance and a lower computational cost. Finally, we evaluate our proposed network on three highly competitive datasets and the results demonstrate its superiority in comparison with the state-of-the-art.

In this paper, to exploit more discriminative information of the global-body and body-parts features, we present a novel deep multi-metric learning (DMML) network for person re-identification under the triplet framework. The main novelty of our learning framework lies in two aspects: 1) Unlike most existing metric learning-based approaches, which learn only one distance metric for comparison, our DMM-L method aims to learn different metrics for the global-body and body-parts features respectively by using convolutional neural network (CNN); 2) A new multi-metric loss function is proposed to train the DMML network, under which the distance of each negative pair is greater than that of each positive pair by a predefined margin, and the correlations of different metrics are maximized. Compared with the previous person re-identification methods that have shown state-of-the-art performances, our DMML approach can achieve competitive results on the challenging CUHK03, CUHKOl, VIPeR and iLIDS datasets.

As a commonly used data representation technique, Nonnegative Matrix Factorization (NMF) has received extensive attentions in the pattern recognition and machine learning communities over decades, since its working mechanism is in accordance with the way how the human brain recognizes objects. Inspired by the remarkable successes of manifold learning, more and more researchers attempt to incorporate the manifold learning into NMF for finding a compact representation ,which uncovers the hidden semantics and respects the intrinsic geometric structure simultaneously. Graph regularized Nonnegative Matrix Factorization (GNMF) is one of the representative approaches in this category. The core of such approach is the graph, since a good graph can accurately reveal the relations of samples which benefits the data geometric structure depiction. In this paper, we leverage the sparse representation to construct a sparse hypergraph for better capturing the manifold structure of data, and then impose the sparse hypergraph as a regularization to the NMF framework to present a novel GNMF algorithm called Sparse Hypergraph regularized Nonnegative Matrix Factorization (SHNMF). Since the sparse hypergraph inherits the merits of both the sparse representation and the hypergraph model, SHNMF enjoys more robustness and can better exploit the high-order discriminant manifold information for data representation . We apply our work to address the image clustering issue for evaluation. The experimental results on five popular image databases show the promising performances of the proposed approach in comparison with the state-of-the-art NMF algorithms.

The single-feature-based background model often fails in complex scenes, since a pixel is better described by several features, which highlight different characteristics of it. Therefore, the multi-feature-based background model has drawn much attention recently. In this paper, we propose a novel multi-feature-based background model, named stability of adaptive feature (SoAF) model, which utilizes the stabilities of different features in a pixel to adaptively weigh the contributions of these features for foreground detection. We do this mainly due to the fact that the features of pixels in the background are often more stable. In SoAF, a pixel is described by several features and each of these features is depicted by a unimodal model that offers an initial label of the target pixel. Then, we measure the stability of each feature by its histogram statistics over a time sequence and use them as weights to assemble the aforementioned unimodal models to yield the final label. The experiments on some standard benchmarks, which contain the complex scenes, demonstrate that the proposed approach achieves promising performance in comparison with some state-of-the-art approaches.

We present a novel approach for improving the shape statistics of medical image objects by generating correspondence of skeletal points. Each object's interior is modeled by an s-rep, i.e., by a sampled, folded, two-sided skeletal sheet with spoke vectors proceeding from the skeletal sheet to the boundary. The skeleton is divided into three parts: the up side, the down side, and the fold curve. The spokes on each part are treated separately and, using spoke interpolation, are shifted along that skeleton in each training sample so as to tighten the probability distribution on those spokes' geometric properties while sampling the object interior regularly. As with the surface/boundary-based correspondence method of Cates et al., entropy is used to measure both the probability distribution tightness and the sampling regularity, here of the spokes' geometric properties. Evaluation on synthetic and real world lateral ventricle and hippocampus data sets demonstrate improvement in the performance of statistics using the resulting probability distributions. This improvement is greater than that achieved by an entropy-based correspondence method on the boundary points.

In this paper, we present a novel face alignment method using a two-level cascaded auto-encoder networks (2-LCAN). In our framework, the first level auto-encoder networks generate rough facial landmarks locations by taking detected face images with low-resolution as inputs. The second level autoencoder networks are constructed by cascading several sub stacked auto-encoder networks (SSAN) in a coarse-to-fine manner. Each SSAN extracts SIFT features and local pixels features around current landmark positions, then fuses them together to further refine landmarks of different facial components with higher image resolutions. Finally, experimental results on LFPW and HELEN datasets demonstrate that our proposed method is significantly superior to the compared approaches both in accuracy and robustness.

Software Quality model is a well-accepted way for assessing high-level quality characteristics (e.g., maintainability) by aggregation from low-level metrics. Aggregation method in a software quality model denotes how to aggregate low-level metrics to high-level quality characteristics. Most of the existing quality models adopt the weighted linear aggregation method. The main drawback of weighted linear method is that it suffers from a lack of consensus in how to decide the correct weights. To address this issue, we present an automated aggregation method which adopts a kind of probabilistic weight instead of the subjective weight in previous aggregation methods. In particular, we leverage a topic modeling technique to estimate the probabilistic weight by learning from a software benchmark.In this manner, our approach can enable automated quality assessment by using the learned probabilistic relationship without manual effort. To evaluate the effectiveness of proposed aggregation approach, we conduct an empirical study on assessing one typical high-level quality characteristic (i.e., maintainability) which is regarded as an important characteristic defined in ISO 9126. The achieved results on 10 open source projects with totally 269 versions show that our method can reveal maintainability well and it outperforms a weighted linear aggregation method baseline in most of the projects.

The traditional duplicate bug reports detection approaches are usually based on vector space model. However, the experimental result is rarely satisfying since this method cannot distinguish semantic correlation among bug reports which written by natural languages. Topic model, as a method to model underlying topics of texts, can solve the problem of document similarity calculation methods used in the information retrieving. It can find the semantic topics among the texts through massive training data, and obtain semantic relatedness among documents. Therefore, this paper proposes a novel duplication detection method based on topic model. Through selecting bug reports with execution information and combing with classified information of bugs, not only does this new method overcome the problem of high dimension, sparse data and loud noise, but also avoid the problem of synonymy and ambiguity in the natural languages. Comparing to the traditional SVM method, the recall rate and precision rate of our proposed approach have obviously increased, which indicates the effectiveness of this new method.

Collaborative representation (CR), known as an effective way to address the signal representation (regression) problem, has achieved remarkable success in visual classification. According to our theoretical analysis, the subspace learning issue can also be deemed as a signal representation problem. Therefore, we extend the graph embedding (GE) framework as a CR model to improve the discriminating power of the subspace learning algorithm. The new GE framework, which is named collaborative GE (CGE) framework, enjoys many desirable properties of CR. From theoretical analysis, CGE is robust to the noise and has the same computational complexity as GE. From experimental analysis, CGE can generally enhance the subspace learning algorithms and a reasonable regularization parameter can be inferred from its intrinsic graph. Several state-of-the-art subspace learning algorithms are plugged into our framework to produce their collaborative versions. Meanwhile, by exploring the intrinsic relation among GE methods, we present a new collaborative method named collaborative class-scattering locality preserving projections (CCSLPPs). The results of extensive experiments on ORL, AR, Scene15, Caltech256, LFW-A, and OU-ISIR-A databases demonstrate that the collaborative versions consistently outperform their original algorithms with a remarkable improvement and CCSLPP gets the best performance compared with all used methods.

We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is casted as a regularized hypergraph cut problem, in which a collection of attribute projections is jointly learnt from the feature space to a hypergraph embedding space aligned with the attributes. The learned projections directly act as attribute classifiers (linear and kernelized). This formulation leads to a very efficient approach. By considering our model as a multi-graph cut task, our framework can flexibly incorporate other available information, in particular class label. We apply our approach to attribute prediction, Zero-shot and N-shot learning tasks. The results on AWA, USAA and CUB databases demonstrate the value of our methods in comparison with the state-of-the-art approaches.

Programming question and answer (Q&A) websites, such as Stack Overflow, gathered knowledge and expertise of developers from all over the world, this knowledge reflects some insight into the development activities. To comprehend the actual thoughts and needs of the developers, we analyzed the non-functional requirements (NFRs) on Stack Overflow. In this paper, we acquired the textual content of Stack Overflow discussions, utilized the topic model, latent Dirichlet allocation (LDA), to discover the main topics of Stack Overflow discussions, and we used the wordlists to find the relationship between the discussions and NFRs. We focus on the hot and unresolved NFRs, the evolutions and trends of the NFRs in their discussions. We found that the most frequent topics the developers discuss are about usability and reliability while they concern few about maintainability and efficiency. The most unresolved problems also occurred in usability and reliability. Moreover, from the visualization of the NFR evolutions over time, we can find the trend for each NFR.

A novel active appearance model (AAM) search algorithm based on partial least squares (PLS) regression is proposed. PLS models the relationship between independent (texture residuals) and dependent (error in the model parameters) variables in the training phase by extracting from independent and dependent variables a set of orthogonal factors called latent variables respectively which have the maximum covariance. During search, the parameter updates with the best predictive power are extracted from the texture residuals. On the other hand, PLS is well suited for the low observation-to-variable ratio context, where the sample covariance matrix is likely to be singular, which is very common in AAM. Experiments show that the proposed method has better performance than the original AAM and comparable performance to AAM search based on Canonical correlation analysis (CCA-AAM) in terms of convergence speed, whilst affording superior computational efficiency.

Scale-space behavior of corners is important for developing an efficient corner detection algorithm. In this paper, we analyze the scale-space behavior with the Laplacian of Gaussian (LoG) operator on a planar curve which constructs Laplacian Scale Space (LSS). The analytical expression of a Laplacian Scale-Space map (LSS map) is obtained, demonstrating the Laplacian Scale-Space behavior of the planar curve corners, based on a newly defined unified corner model. With this formula, some Laplacian Scale-Space behavior is summarized. Although LSS demonstrates some similarities to Curvature Scale Space (CSS), there are still some differences. First, no new extreme points are generated in the LSS. Second, the behavior of different cases of a corner model is consistent and simple. This makes it easy to trace the corner in a scale space. At last, the behavior of LSS is verified in an experiment on a digital curve.

We present a scheme that propagates a reference skeletal model (s-rep) into a particular case of an object, thereby propagating the initial shape-related layout of the skeleton-to-boundary vectors, called spokes. The scheme represents the surfaces of the template as well as the target objects by spherical harmonics and computes a warp between these via a thin plate spline. To form the propagated s-rep, it applies the warp to the spokes of the template s-rep and then statistically refines. This automatic approach promises to make s-rep fitting robust for complicated objects, which allows s-rep based statistics to be available to all. The improvement in fitting and statistics is significant compared with the previous methods and in statistics compared with a state-of-the-art boundary based method.

We present a novel manifold-based approach for cross-speed gait recognition. In our approach, the walking action is considered as residing on a manifold, in the feature space, that is homomorphic to a unit circle. We employ thin plate spline (TPS) kernel-based radial basis function (RBF) interpolation to fit such manifold. TPS kernel-based RBF interpolation separates the learned coefficients into an affine component and a nonaffine component, which, respectively, encodes the dynamic and static characteristics of the gait manifold. We introduce the use of the nonaffine component as a cross-speed gait representation, and denote it speed invariant gait template (SIGT). We also propose an enhanced locality preserving projections (LPP) algorithm named globality LPP (GLPP) for reducing the dimension of SIGT. In GLPP, the graph Laplacians of intrasubject part and intersubjects part are separately constructed, and then to combine as a new graph Laplacian. Finally, a manifold learning-based classifier named normalized hypergraph classifier is employed for classification. Experimental results on two gait databases demonstrate the effectiveness of our proposed approach in comparison with the state-of-the-art gait recognition methods.

As a considerable technique in image processing and computer vision, Nonnegative Matrix Factorization (NMF) generates its bases by iteratively multiplicative update with two initial random nonnegative matrices W and H, that leads to the randomness of the bases selection. For this reason, the potentials of NMF algorithms are not completely exploited. To address this issue, we present a novel framework which uses the feature selection techniques to evaluate and rank the bases of the NMF algorithms to enhance the NMF algorithms. We adopted the well known Fisher criterion and Least Reconstruction Error criterion, which is proposed by us, as two instances to show how that works successfully under our framework. Moreover, in order to avoid the hard combinatorial optimization issue in ranking procedure, a de-correlation constraint can be optionally imposed to the NMF algorithms for giving a better approximation to the global optimum of the NMF projections. We evaluate our works in face recognition, object recognition and image reconstruction on ORL and ETH-80 databases and the results demonstrate the enhancement of the state-of-the-art NMF under our framework.

We present a novel approach for cross-speed gait recognition. In our approach, the cyclic walking action is considered as residing on a manifold which is homeomorphic to a unit circle in the gait space. Thin Plate Spline (TPS) kernel-based Radial Basis Function (RBF) interpolation is used to fit the walking manifold for each gait sequence. The sub-ject related kernel mapping coefficients are learned for representing the gait. According to the property of TPS, the coefficients can be naturally separated as an affine component and a non-affine component. The affine component is the style factor corresponding to the deformation of the homeomorphic manifold caused by the walking action, while the non-affine component is the shape factor, invariant to the walking speed. We denote this non-affine component as Speed Invariant Gait Template (SIGT) and use it as cross-speed gait feature. To address the curse of dimensionality issue and speed up the recognition, we use Globality Locality Preserving Projections (GLPP) to reduce the dimensions of SIGTs. Two walking speeds related gait databases are employed for evaluating our pro-posed method. The experimental results demonstrate the superiority of our method over the state-of-the-art. 1

In this paper, we investigate the problem of estimating human ages from full body images. To our best knowledge, this problem has not been formally addressed before possibly due to the great challenges and lacking of such publicly available datasets. However, estimating human ages at a distance has a number of potential applications, especially for visual surveillance in such places as supermarkets, airports, building entrances, and shopping malls. In this paper, we propose a new human age estimation approach from full body images with frontal or back views. Our contributions are three-fold. First, we collect a human body image dataset containing 1500 public figures or celebrities searched from the internet, as well as the age label information of each image. Second, we explore several widely used human local appearance feature descriptors with a regression model to estimate human ages from these body images. Lastly, we apply a multiview canonical correlation analysis (MCCA) method by making use of multiple feature descriptors to exploit complementary information to further improve the age estimation performance. Experimental results have clearly demonstrated the feasibility of using fully body images to estimate human age and the efficacy of our proposed approach.

The emerging technologies such as the Internet-of-Things, sensors, communication networks, have been or will be introduced to conventional domotics to provide a wide variety of smart home services to facilitate the household appliances or home cares and improve the lifestyles of people. Currently, smart home system are integrated with different features from product line and equipped with various sensors and actuators to meet the requirements of house occupants by specifying their customised user policies. However, the introduction of features and policies may result in undesired behaviours, and this effect is known as feature interactions. In this study, the authors proposed a Semantic Web-based policy interaction detection method with rules to model smart home services and policies with the aids of ontological analysis in the smart home domain, so as to construct a semantic context for inferring the interaction of policies. The authors focus their work on user policies interaction, which are detected by using the Semantic Web rule language in semantic context. The approach is successfully applied to the smart home system and is able to detect 90 interactions among 32 user policies by automated reasoning with tools support as Protégé and Jess.

This paper is concerned with the problem of keyframe detection in appearance-based visual SLAM. Appearance SLAM models a robot's environment topologically by a graph whose nodes represent strategically interesting places that have been visited by the robot and whose arcs represent spatial connectivity between these places. Specifically, we discuss and compare various methods for identifying the next location that is sufficiently different visually from the previously visited location or node in the map graph in order to decide whether a new node should be created. We survey existing techniques of keyframe detection in image retrieval and video analysis. Using experimental results obtained from visual SLAM datasets, we conclude that the feature matching method offers the best performance among five representative methods in terms of accurately measuring the amount of appearance change between robot's views and thus can serve as a simple and effective metric for detecting keyframes. This study fills an important but missing step in the current appearance SLAM research.

In this work, we propose a novel saliency-based objective quality assessment metric, for assessing the perceptual quality of decoded video sequences affected by packet loses. The proposed method weights the error at each pixel by the visual saliency of the pixel. Different weighting methods are explored and compared. Our test results show that the predicted scores by the proposed metrics correlate very well with mean subjective scores, significantly better than the mean square error (MSE), mean absolute difference (MAD) or structure similarity (SSIM).