With the increasing number of software projects, within‐project defect prediction (WPDP) has already been unable to meet the demand, and cross‐project defect prediction (CPDP) is playing an increasingly significant role in the area of software engineering. The classic CPDP methods mainly concentrated on applying metric features to predict defects. However, these approaches failed to consider the rich semantic information, which usually contains the relationship between software defects and context. Since traditional methods are unable to exploit this characteristic, their performance is often unsatisfactory. In this paper, a transfer long short‐term memory (TLSTM) network model is first proposed. Transfer semantic features are extracted by adding a transfer learning algorithm to the long short‐term memory (LSTM) network. Then, the traditional metric features and semantic features are combined for CPDP. First, the abstract syntax trees (AST) are generated based on the source codes. Second, the AST node contents are converted into integer vectors as inputs to the TLSTM model. Then, the semantic features of the program can be extracted by TLSTM. On the other hand, transferable metric features are extracted by transfer component analysis (TCA). Finally, the semantic features and metric features are combined and input into the logical regression (LR) classifier for training. The presented TLSTM model performs better on the f ‐measure indicator than other machine and deep learning models, according to the outcomes of several open‐source projects of the PROMISE repository. The TLSTM model built with a single feature achieves 0.7% and 2.1% improvement on Log4j‐1.2 and Xalan‐2.7, respectively. When using combined features to train the prediction model, we call this model a transfer long short‐term memory for defect prediction (DPTLSTM). DPTLSTM achieves a 2.9% and 5% improvement on Synapse‐1.2 and Xerces‐1.4.4, respectively. Both prove the superiority of the proposed model on the CPDP task. This is because LSTM capture long‐term dependencies in sequence data and extract features that contain source code structure and context information. It can be concluded that: (1) the TLSTM model has the advantage of preserving information, which can better retain the semantic features related to software defects; (2) compared with the CPDP model trained with traditional metric features, the performance of the model can validly enhance by combining semantic features and metric features.

In this paper, a self-guiding multimodal LSTM (sgLSTM) image captioning model is proposed to handle an uncontrolled imbalanced real-world image-sentence dataset. We collect a FlickrNYC dataset from Flickr as our testbed with 306,165 images and the original text descriptions uploaded by the users are utilized as the ground truth for training. Descriptions in the FlickrNYC dataset vary dramatically ranging from short term-descriptions to long paragraph-descriptions and can describe any visual aspects, or even refer to objects that are not depicted. To deal with the imbalanced and noisy situation and to fully explore the dataset itself, we propose a novel guiding textual feature extracted utilizing a multimodal LSTM (mLSTM) model. Training of mLSTM is based on the portion of data in which the image content and the corresponding descriptions are strongly bonded. Afterward, during the training of sgLSTM on the rest training data, this guiding information serves as additional input to the network along with the image representations and the ground-truth descriptions. By integrating these input components into a multimodal block, we aim to form a training scheme with the textual information tightly coupled with the image content. The experimental results demonstrate that the proposed sgLSTM model outperforms the traditional state-of-the-art multimodal RNN captioning framework in successfully describing the key components of the input images.

The performance of deep neural network (DNN) based monaural speech separation methods is limited in reverberant and noisy room environments. In this paper, we propose a new DNN training target which incorporates geometric information describing the target speaker and microphone to improve the performance in reverberant and noisy room environments. The experiments are based on the IEEE corpus and the NOISEX database and real impulse responses (RIRs). The objective evaluations, short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) confirm the efficiency of the proposed direct path ratio mask (DRM).

Estimation of sea ice motion at a fine scale is essential for climate modeling and naval operations in polar regions. This study proposes an approach for increasing the spatial resolution of the motion estimated from passive microwave satellite images. A hierarchical pattern matching approach, based on normalized cross-correlation and phase correlation, is applied to calculate sea ice drifts between Advanced Microwave Scanning Radiometer 2 (AMSR2) pairs of images captured at different time. Contrary to the widely used approach of embedding oversampling within the pattern matching framework that increases the resolution of the minimum detectable drift, in this study a nearest-neighbor interpolation upscaling is applied directly to the images. This additionally increases the density of the estimated motion vectors. Experiments demonstrate that the proposed method can lead to motion vector field with increased resolution by up to eight times, while outperforming the original images. Image upscaling by four times also outperforms corresponding previous examples with embedded oversampling.

Event detection targets at recognizing and localizing specified spatio-temporal patterns in videos. Most research of human activity recognition in the past decades experimented on relatively clean scenes with limited actors performing explicit actions. Recently, more efforts have been paid to the real-world surveillance videos in which the human activity recognition is more challenging due to large variations caused by factors, such as scaling, resolution, viewpoint, cluttered background, and crowdedness. In this paper, we systematically evaluate seven different types of low-level spatio-temporal features in the context of surveillance event detection (SED) using a uniform experimental setup. Fisher vector is employed to aggregate low-level features as the representation of each video clip. A set of random forests is then learned as the classification models. To bridge the research efforts and real-world applications, we utilize the NIST TRECVID SED as our testbed in which seven events are predefined involving different levels of human activity analysis. Strengths and limitations for each low-level feature type are analyzed and discussed.

Monitoring sea ice activities is particularly critical to safe naval operations in the Arctic Ocean. Accurately tracking sea ice motions is essential to validate or even improve sea ice models for ice hazard forecasts at a fine scale. Fine-scale motions can be tracked from high-resolution radar or optical satellite imagery but with limited coverage. Daily motions over the entire Arctic are retrievable from passive microwave data, but at a much lower spatial resolution. Thus, providing motions at the passive microwave spatial and temporal coverage, but at an enhanced spatial resolution, will be a significant benefit. To break the resolution limitation and to boost tracking accuracy, a sequential super-resolved fine-scale sea ice motion tracking framework is proposed in which a hybrid example-based single image super-resolution algorithm is employed before the tracking procedure. Experiments demonstrate that the proposed framework significantly improves the tracking performance in both accuracy and robustness for a benchmark algorithm and a recently proposed state-of-the-art tracking algorithm.

Depth resolution enhancement aims to recover a high quality depth map from one or multiple low-resolution depth input(s) with missing pixels. While a registered high-resolution intensity image is often utilized to assist, little attention has been paid to the circumstances when there is only one pair of low-resolution depth map and aligned intensity image available. In this paper, we propose a novel resolution enhancement approach that targets at improving the quality of both the input depth map and the low-resolution RGB image. By exploiting the statistical dependency between the input pairs, a label matrix is generated utilizing the support vector machine classifier. Guided by the constructed label matrix and the aligned intensity image, the missing values in the depth map are well predicted in a manner consistent with the embedded structure. After that, the completed depth map and the intensity image are super-resolved through a set of regression models trained via external exemplars. Extensive experiments demonstrate that our framework is effective with satisfying performance.

Image enhancement aims to modify images to achieve a better perception for human visual system or a more suitable representation for further analysis. Based on different attributes of given input images, tasks vary, e.g., noise removal, deblur-ring, resolution enhancement, prediction of missing pixels, etc. The latter two are usually referred to as image super-resolution and image inpainting. There exist complicated circumstances where low-quality input images suffer from insufficient resolution with missing regions. In this paper, we propose a novel uniform framework to accomplish both image super-resolution and inpainting simultaneously. The proposed approach adopts internal exemplar similarities in image level and gradient level where later enhancement results from both levels are fed into a pre-defined cost function to restore the final output. Experimental results demonstrate that our method is capable of generating visually plausible, natural-looking results with clear edges and realistic textures.