Stroke segmentation is a fundamental technique for various character analysis and synthesis applications. However, existing methods often face challenges such as over-segmentation, under-segmentation, low segmentation accuracy, and limited generalization capability when segmenting characters of diverse fonts. To address these issues, we propose a novel stroke segmentation method based on contour matching and utilize a similarity-based chain propagation strategy to tackle the challenges posed by fonts with significant structural and stylistic variations. Extensive visual and quantitative experiments on a newly created high-quality dataset demonstrate that our approach outperforms state-of-the-art methods and effectively handles a wide range of fonts.

Cartoon animation video is a popular visual entertainment form worldwide, however many classic animations were produced in a 4:3 aspect ratio that is incompatible with modern widescreen displays. Existing methods like cropping lead to information loss while retargeting causes distortion. Animation companies still rely on manual labor to renovate classic cartoon animations, which is tedious and labor-intensive, but can yield higher-quality videos. Conventional extrapolation or inpainting methods tailored for natural videos struggle with cartoon animations due to the lack of textures in anime, which affects the motion estimation of the objects. In this article, we propose a novel framework designed to automatically outpaint 4:3 anime to 16:9 via region-guided motion inference. Our core concept is to identify the motion correspondences between frames within a sequence in order to reconstruct missing pixels. Initially, we estimate optical flow guided by region information to address challenges posed by exaggerated movements and solid-color regions in cartoon animations. Subsequently, frames are stitched to produce a pre-filled guide frame, offering structural clues for the extension of optical flow maps. Finally, a voting and fusion scheme utilizes learned fusion weights to blend the aligned neighboring reference frames, resulting in the final outpainting frame. Extensive experiments confirm the superiority of our approach over existing methods.

Existing soft hand mostly focus on mechanism and material innovation, as well as biological behavior imitation, which have problems of fewer sensing abilities and unfriendly human-robot interaction (HRI). In this paper, we propose a novel bionic soft hand (BSH) with dexterity operation, multi-modal perception and bidirectional HRI. For dexterity operation, we first designed and fabricated a dual-joint bellows (DJB) soft finger, and then, five fingers were integrated into BSH with inertial measurement units (IMUs), contact pressure sensors, curvature sensors and air pressure sensors distributed appropriately. For multi-modal perception, the control and perception system were established to drive BSH in pneumatic mode and process the multi-modal sensor information to perform robust perception capabilities using support vector machine (SVM), extreme learning machine (ELM) and BP neural network (BPNN). For bidirectional HRI, a data glove with force reproduction was utilized to perform posture mapping and tactile force mapping. The experimental results verify that the proposed BSH has a wide workspace range and dexterous operation ability, and can accurately recognize human posture and object properties. Also, hand posture mapping and tactile force mapping have been demonstratedNote to Practitioners—This work was motivated by the research status of existing soft hands that lack sensing abilities and friendly HRI, and aimed to develop a bionic soft hand (BSH) with dexterity operation, multi-modal perception and tactile force interaction. We increased the degrees of freedom of BSH by adopting a bellows structure to build a two-joint soft finger, enhanced the perception ability of BSH by utilizing multi-modal sensors and multiple machine learning algorithms, performed posture mapping and tactile force mapping for bidirectional HRI by designing a novel data glove with multi-modal sensors and a variable stiffness jamming layer. Experimental results verify that the proposed BSH has a wide workspace range and dexterous operation ability, and can accurately recognize human posture and object properties by multi-modal sensors. Future work will focus on imitating the joint structure of human hand and make BSH be closer to the dexterity and perception performance of human hand. Also introducing humanbrain-machine interface is a meaningful challenge.

Time series forecasting plays a crucial role in various real-world applications, such as finance, energy, traffic, and healthcare, providing valuable insights for decision-making processes. The aggregation of information windows with different resolutions has proven effective in time series forecasting tasks and provides the model diverse contextual information. As a result, the network can better capture and model the heterogeneity present in the data, thereby improving performance. However, most of the current work focuses on extracting multilevel-resolution information without considering the possibility that important information can be supplemented. Meanwhile, these methods also tend to ignore the effect of resolution on frequency. To address these challenges, we introduce the Time-Frequency Domain Multi-Resolution Expansion Network (TFMRN) for long-series forecasting using multi-resolution time-frequency data. The proposed TFMRN aims to expand the data in both the time and frequency domains, enabling the model to capture finer details that may not be evident in the original data. In addition, we also propose an Information Gating Unit (IGU) to enhance the selection and guidance of rich information from the expanded time-frequency multi-resolution data. Experimental results demonstrate that the proposed method yields better performance compared with the state-of-the-art methods in both univariate and multivariate time forecasting tasks. Codes are available athttps://github.com/CurtainKevin/TFMRN-master.

Shading plays an important role in cartoon drawings to present the 3D lighting and depth information in a 2D image to improve the visual information and pleasantness. But it also introduces apparent challenges in analyzing and processing the cartoon drawings for different computer graphics and vision applications, such as segmentation, depth estimation, and relighting. Extensive research has been made in removing or separating the shading information to facilitate these applications. Unfortunately, the existing researches only focused on natural images, which are natively different from cartoons since the shading in natural images is physically correct and can be modeled based on physical priors. However, shading in cartoons is manually created by artists, which may be imprecise, abstract, and stylized. This makes it extremely difficult to model the shading in cartoon drawings. Without modeling the shading prior, in the paper, we propose a learning-based solution to separate the shading from the original colors using a two-branch system consisting of two subnetworks. To the best of our knowledge, our method is the first attempt in separating shading information from cartoon drawings. Our method significantly outperforms the methods tailored for natural images. Extensive evaluations have been performed with convincing results in all cases.

Manga screening is a critical process in manga production, which still requires intensive labor and cost. Existing manga screening methods either generate simple dotted screentones only or rely on color information and manual hints during screentone selection. Due to the large domain gap between line drawings and screened manga, and the difficulties in generating high-quality, properly selected and shaded screentones, even state-of-the-art deep learning methods cannot convert line drawings to screened manga well. Besides, ambiguity exists in the screening process since different artists may screen differently for the same line drawing. In this article, we propose to introduce shaded line drawing as the intermediate counterpart of the screened manga so that the manga screening task can be decomposed into two sub-tasks, generating shading from a line drawing and replacing shading with proper screentones. The reference image is adopted to resolve the ambiguity issue and provides options and controls on the generated screened manga. We proposed a reference-based shading generation network and a reference-based screentone generation module to achieve the two sub-tasks individually. We conduct extensive visual and quantitative experiments to verify the effectiveness of our system. Results and statistics show that our method outperforms existing methods on the manga screening task.

Automatic polyp segmentation from colonoscopy videos is a prerequisite for the development of a computer-assisted colon cancer examination and diagnosis system. However, it remains a very challenging task owing to the large variation of polyps, the low contrast between polyps and background, and the blurring boundaries of polyps. More importantly, real-time performance is a necessity of this task, as it is anticipated that the segmented results can be immediately presented to the doctor during the colonoscopy intervention for his/her prompt decision and action. It is difficult to develop a model with powerful representation capability, yielding satisfactory segmentation results and, simultaneously, maintaining real-time performance. In this article, we present a novel lightweight context-aware network, namely, PolypSeg+, attempting to capture distinguishable features of polyps without increasing network complexity and sacrificing time performance. To achieve this, a set of novel lightweight techniques is developed and integrated into the proposed PolypSeg+, including an adaptive scale context (ASC) module equipped with a lightweight attention mechanism to tackle the large-scale variation of polyps, an efficient global context (EGC) module to promote the fusion of low-level and high-level features by excluding background noise and preserving boundary details, and a lightweight feature pyramid fusion (FPF) module to further refine the features extracted from the ASC and EGC. We extensively evaluate the proposed PolypSeg+ on two famous public available datasets for the polyp segmentation task: 1) Kvasir-SEG and 2) CVC-Endoscenestill. The experimental results demonstrate that our PolypSeg+ consistently outperforms other state-of-the-art networks by achieving better segmentation accuracy in much less running time. The code is available at https://github.com/szu-zzb/polypsegplus.

We present a novel deep network (namely BUSSeg) equipped with both within- and cross-image long-range dependency modeling for automated lesions segmentation from breast ultrasound images, which is a quite daunting task due to (1) the large variation of breast lesions, (2) the ambiguous lesion boundaries, and (3) the existence of speckle noise and artifacts in ultrasound images. Our work is motivated by the fact that most existing methods only focus on modeling the within-image dependencies while neglecting the cross-image dependencies, which are essential for this task under limited training data and noise. We first propose a novel cross-image dependency module (CDM) with a cross-image contextual modeling scheme and a cross-image dependency loss (CDL) to capture more consistent feature expression and alleviate noise interference. Compared with existing cross-image methods, the proposed CDM has two merits. First, we utilize more complete spatial features instead of commonly used discrete pixel vectors to capture the semantic dependencies between images, mitigating the negative effects of speckle noise and making the acquired features more representative. Second, the proposed CDM includes both intra- and inter-class contextual modeling rather than just extracting homogeneous contextual dependencies. Furthermore, we develop a parallel bi-encoder architecture (PBA) to tame a Transformer and a convolutional neural network to enhance BUSSeg's capability in capturing within-image long-range dependencies and hence offer richer features for CDM. We conducted extensive experiments on two representative public breast ultrasound datasets, and the results demonstrate that the proposed BUSSeg consistently outperforms state-of-the-art approaches in most metrics.

Detecting cells in blood smear images is of great significance for automatic diagnosis of blood diseases. However, this task is rather challenging, mainly because there are dense cells that are often overlapping, making some of the occluded boundary parts invisible. In this paper, we propose a generic and effective detection framework that exploits non-overlapping regions (NOR) for providing discriminative and confident information to compensate the intensity deficiency. In particular, we propose a feature masking (FM) to exploit the NOR mask generated from the original annotation information, which can guide the network to extract NOR features as supplementary information. Furthermore, we exploit NOR features to directly predict the NOR bounding boxes (NOR BBoxes). NOR BBoxes are combined with the original BBoxes for generating one-to-one corresponding BBox-pairs that are used for further improving the detection performance. Different from the non-maximum suppression (NMS), our proposed non-overlapping regions NMS (NOR-NMS) uses the NOR BBoxes in the BBox-pairs to calculate intersection over union (IoU) for suppressing redundant BBoxes, and consequently retains the corresponding original BBoxes, circumventing the dilemma of NMS. We conducted extensive experiments on two publicly available datasets, with positive results demonstrating the effectiveness of the proposed method against existing methods.

Most state-of-the-art deep networks proposed for biomedical image segmentation are developed based on U-Net. While remarkable success has been achieved, its inherent limitations hinder it from yielding more precise segmentation. First, its receptive field is limited due to the fixed kernel size, which prevents the network from modeling global context information. Second, when spatial information captured by shallower layer is directly transmitted to higher layers by skip connections, the process inevitably introduces noise and irrelevant information to feature maps and blurs their semantic meanings. In this article, we propose a novel segmentation network equipped with a new context prior guidance (CPG) module to overcome these limitations for biomedical image segmentation, namely context prior guidance network (CPG-Net). Specifically, we first extract a set of context priors under the supervision of a coarse segmentation and then employ these context priors to model the global context information and bridge the spatial-semantic gap between high-level features and low-level features. The CPG module contains two major components: context prior representation (CPR) and semantic complement flow (SCF). CPR is used to extract pixels belonging to the same objects and hence produce more discriminative features to distinguish different objects. We further introduce deep semantic information for each CPR by the SCF mechanism to compensate the semantic information diluted during the decoding. We extensively evaluate the proposed CPG-Net on three famous biomedical image segmentation tasks with diverse imaging modalities and semantic environments. Experimental results demonstrate the effectiveness of our network, consistently outperforming state-of-the-art segmentation networks in all the three tasks. Codes are available at https://github.com/zzw-szu/CPGNet .

The cardiac is one of the essential organs, and the segmentation of the left and right ventricular of cardiac is essential in diagnosing various heart diseases. The most popular method for the segmentation of 3D MRI images is the nnUNet. However, the 3D MRI volume of the ventricular contains other organs which interfere with the segmentation of the ventricular. Hence, we proposed a novel region-aware U-Net segmentation method RegUNet for ventricular segmentation. RegUNet improves the ventricular's segmentation performance by first capturing the region of interest (RoI) of the ventricular and then segmenting the ventricular with the captured RoI features, which reduces the segmentation module's difficulty by keeping the cardiac's features and leaving others such that RegUNet can focus on ventricular segmentation. Besides, since the model segments the ventricular with the captured RoI features, it saves the model's computing resources from identifying the background of the volume. Since 3D cardiac MRI volumes scanned by the different devices have diverse statistical characteristics, which causes the model's performance in processing the multi-source cardiac volumes to be unstable. We stabilize the model's performance with a multi-sources feature normalization strategy, which normalizes the feature from a different source with different parameters. We validated the proposed method on the M&MS dataset, a multi-sources 3D MRI cardiac segmentation dataset. Experiments showed that RegUNet's segmentation ability reached the state-of-the-art.

Abstract Vectorization is a commonly used technique for converting raster images to vector format and has long been a research focus in computer graphics and vision. While a number of attempts have been made to extract the topology of line drawings and further convert them to vector representations, the existing methods commonly focused on resolving junctions composed of thin lines. They usually fail for line drawings composed of thick lines, especially at junctions. In this paper, we propose an automatic line drawing vectorization method that can reconstruct the topology of line drawings of arbitrary thickness. Our key observation is that no matter the lines are thin or thick, the boundaries of the lines always provide reliable hints for reconstructing the topology. For example, the boundaries of two continuous line segments at a junction are usually smoothly connected. By analyzing the continuity of boundaries, we can better analyze the topology at junctions. In particular, we first extract the skeleton of the input line drawing via thinning. Then we analyze the reliability of the skeleton points based on boundaries. Reliable skeleton points are preserved while unreliable skeleton points are reconstructed based on boundaries again. Finally, the skeleton after reconstruction is vectorized as the output. We apply our method on line drawings of various contents and styles. Satisfying results are obtained. Our method significantly outperforms existing methods for line drawings composed of thick lines.

Line drawings are frequently used to illustrate ideas and concepts in digital documents and presentations. To compose a line drawing, it is common for users to retrieve multiple line drawings from the Internet and combine them as one image. However, different line drawings may have different line styles and are visually inconsistent when put together. In order that the line drawings can have consistent looks, in this paper, we make the first attempt to perform style transfer for line drawings. The key of our design lies in the fact that centerline plays a very important role in preserving line topology and extracting style features. With this finding, we propose to formulate the style transfer problem as a centerline stylization problem and solve it via a novel style-guided image-to-image translation network. Results and statistics show that our method significantly outperforms the existing methods both visually and quantitatively.

We present a novel deep learning model equipped with a new region-aware global context modeling technique for automatic nerve segmentation from ultrasound images, which is a challenging task due to (1) the large variation and blurred boundaries of targets, (2) the large amount of speckle noise in ultrasound images, and (3) the inherent real-time requirement of this task. It is essential to efficiently capture long-range dependencies by global context modeling for a segmentation network to overcome these challenges. Traditional global context modeling techniques usually explore pixel-aware correlations to establish long-range dependencies, which are usually computation-intensive and greatly degrade time performance. In addition, in this application, pixel-aware modeling may inevitably introduce much speckle noise in the computation and potentially degrade segmentation performance. In this paper, we propose a novel region-aware modeling technique to establish long-range dependencies based on different regions to improve segmentation accuracy while maintaining real-time performance; we call it region-aware pyramid aggregation (RPA) module. In order to adaptively divide the feature maps into a set of semantic-independent regions, we develop an attention mechanism and integrate it into the spatial pyramid network to evaluate the semantic similarity of different regions. We further develop an adaptive pyramid fusion (APF) module to dynamically fuse the multi-level features generated from the decoder to refining the segmentation results. We conducted extensive experiments on a famous public ultrasound nerve image segmentation dataset. Experimental results demonstrate that our method consistently outperforms our rivals in terms of segmentation accuracy. The code is available at https://github.com/jsonliu-szu/RAGCM.

We propose a novel convolutional neural network (ConvNet) equipped with two new semantic calibration and refinement approaches for automatic polyp segmentation from colonoscopy videos. While ConvNets set state-of-the-are performance for this task, it is still difficult to achieve satisfactory results in a real-time manner, which is a necessity in clinical practice. The main obstacle is the huge semantic gap between high-level features and low-level features, making it difficult to take full advantage of complementary semantic information contained in these hierarchical features. Compared with existing solutions, which either directly aggregate these features without considering the semantic gap or employ sophisticated non-local modeling techniques to refine semantic information by introduce many extra computational costs, the proposed ConvNet is able to more precisely yet efficiently calibrate and refine semantic information for better segmentation performance without increasing model complexity; we call the proposed ConvNet as SCR-Net, which has two key modules. We first propose a semantic calibration module (SCM) to effectively transmit the semantic information from high-level layers to low-level layers by learning the semantic-spatial relations during the training procedure. We then propose a semantic refinement module (SRM) to, based on the features calibrated by SCM, enhance the discrimination capability of the features for targeting objects. Extensive experiments on the Kvasir-SEG dataset demonstrate that the proposed SCR-Net is capable of achieving better segmentation accuracy than state-of-the-art approaches with a faster speed. The proposed techniques are general enough to be applied to similar applications where precise and efficient multi-level feature fusion is critical. The code is available at https://github.com/jiafuz/SCR-Net.

Automatic polyp segmentation from colonoscopy images is an essential step in computer aided diagnosis for colorectal cancer. Most of polyp segmentation methods reported in recent years are based on fully supervised deep learning. However, annotation for polyp images by physicians during the diagnosis is time-consuming and costly. In this paper, we present a novel semi-supervised polyp segmentation via collaborative and adversarial learning of focused and dispersive representations learning model, where focused and dispersive extraction module are used to deal with the diversity of location and shape of polyps. In addition, confidence maps produced by a discriminator in an adversarial training framework shows the effectiveness of leveraging unlabeled data and improving the performance of segmentation network. Consistent regularization is further employed to optimize the segmentation networks to strengthen the representation of the outputs of focused and dispersive extraction module. We also propose an auxiliary adversarial learning method to better leverage unlabeled examples to further improve semantic segmentation accuracy. We conduct extensive experiments on two famous polyp datasets: Kvasir-SEG and CVC-Clinic DB. Experimental results demonstrate the effectiveness of the proposed model, consistently outperforming state-of-the-art semi-supervised segmentation models based on adversarial training and even some advanced fully supervised models.

Symmetry detection is a method to extract the ideal mid-sagittal plane (MSP) from brain magnetic resonance (MR) images, which can significantly improve the diagnostic accuracy of brain diseases. In this article, we propose an automatic symmetry detection method for brain MR images in 2-D slices based on a 2-channel convolutional neural network (CNN). Different from the existing detection methods that mainly rely on the local image features (gradient, edge, etc.) to determine the MSP, we use a CNN-based model to implement the brain symmetry detection, which does not require any local feature detections and feature matchings. By training to learn a wide variety of benchmarks in the brain images, we can further use a 2-channel CNN to evaluate the similarity between the pairs of brain patches, which are randomly extracted from the whole brain slice based on a Poisson sampling. Finally, a scoring and ranking scheme is used to identify the optimal symmetry axis for each input brain MR slice. Our method was evaluated in 2166 artificial synthesized brain images and 3064 collected in vivo MR images, which included both healthy and pathological cases. The experimental results display that our method achieves excellent performance for symmetry detection. Comparisons with the state-of-the-art methods also demonstrate the effectiveness and advantages for our approach in achieving higher accuracy than the previous competitors.

We present a convolutional neural network (CNN) equipped with a novel and efficient adaptive dual attention module (ADAM) for automated skin lesion segmentation from dermoscopic images, which is an essential yet challenging step for the development of a computer-assisted skin disease diagnosis system. The proposed ADAM has three compelling characteristics. First, we integrate two global context modeling mechanisms into the ADAM, one aiming at capturing the boundary continuity of skin lesion by global average pooling while the other dealing with the shape irregularity by pixel-wise correlation. In this regard, our network, thanks to the proposed ADAM, is capable of extracting more comprehensive and discriminative features for recognizing the boundary of skin lesions. Second, the proposed ADAM supports multi-scale resolution fusion, and hence can capture multi-scale features to further improve the segmentation accuracy. Third, as we harness a spatial information weighting method in the proposed network, our method can reduce a lot of redundancies compared with traditional CNNs. The proposed network is implemented based on a dual encoder architecture, which is able to enlarge the receptive field without greatly increasing the network parameters. In addition, we assign different dilation rates to different ADAMs so that it can adaptively capture distinguishing features according to the size of a lesion. We extensively evaluate the proposed method on both ISBI2017 and ISIC2018 datasets and the experimental results demonstrate that, without using network ensemble schemes, our method is capable of achieving better segmentation performance than state-of-the-art deep learning models, particularly those equipped with attention mechanisms.

Texture exemplar has been widely used in synthesizing 3D movie scenes and appearances of virtual objects. Unfortunately, conventional texture synthesis methods usually only emphasized on generating optimal target textures with arbitrary sizes or diverse effects, and put little attention to automatic texture exemplar extraction. Obtaining texture exemplars is still a labor intensive task, which usually requires carefully cropping and post-processing. In this paper, we present an automatic texture exemplar extraction based on Trimmed Texture Convolutional Neural Network (Trimmed T-CNN). Specifically, our Trimmed T-CNN is filter banks for texture exemplar classification and recognition. Our Trimmed T-CNN is learned with a standard ideal exemplar dataset containing thousands of desired texture exemplars, which were collected and cropped by our invited artists. To efficiently identify the exemplar candidates from an input image, we employ a selective search algorithm to extract the potential texture exemplar patches. We then put all candidates into our Trimmed T-CNN for learning ideal texture exemplars based on our filter banks. Finally, optimal texture exemplars are identified with a scoring and ranking scheme. Our method is evaluated with various kinds of textures and user studies. Comparisons with different feature-based methods and different deep CNN architectures (AlexNet, VGG-M, Deep-TEN and FV-CNN) are also conducted to demonstrate its effectiveness.

Texture synthesis is widely used for modeling the appearance of virtual objects. However, traditional texture synthesis techniques emphasize creation of optimal target textures, and pay insufficient attention to choice of suitable input texture exemplars. Currently, obtaining texture exemplars from natural images is a labor intensive task for the artists, requiring careful photography and significant postprocessing. In this paper, we present an automatic texture exemplar extraction method based on global and local textureness measures. To improve the efficiency of dominant texture identification, we first perform Poisson disk sampling to randomly and uniformly crop patches from a natural image. For global textureness assessment, we use a GIST descriptor to distinguish textured patches from non-textured patches, in conjunction with SVM prediction. To identify real texture exemplars consisting solely of the dominant texture, we further measure the local textureness of a patch by extracting and matching the local structure (using binary Gabor pattern (BGP)) and dominant color features (using color histograms) between a patch and its sub-regions. Finally, we obtain optimal texture exemplars by scoring and ranking extracted patches using these global and local textureness measures. We evaluate our method on a variety of images with different kinds of textures. A convincing visual comparison with textures manually selected by an artist and a statistical study demonstrate its effectiveness.

BACKGROUND: The rapid progress of high-throughput DNA sequencing techniques has dramatically reduced the costs of whole genome sequencing, which leads to revolutionary advances in gene industry. The explosively increasing volume of raw data outpaces the decreasing disk cost and the storage of huge sequencing data has become a bottleneck of downstream analyses. Data compression is considered as a solution to reduce the dependency on storage. Efficient sequencing data compression methods are highly demanded. RESULTS: In this article, we present a lossless reference-based compression method namely LW-FQZip 2 targeted at FASTQ files. LW-FQZip 2 is improved from LW-FQZip 1 by introducing more efficient coding scheme and parallelism. Particularly, LW-FQZip 2 is equipped with a light-weight mapping model, bitwise prediction by partial matching model, arithmetic coding, and multi-threading parallelism. LW-FQZip 2 is evaluated on both short-read and long-read data generated from various sequencing platforms. The experimental results show that LW-FQZip 2 is able to obtain promising compression ratios at reasonable time and memory space costs. CONCLUSIONS: The competence enables LW-FQZip 2 to serve as a candidate tool for archival or space-sensitive applications of high-throughput DNA sequencing data. LW-FQZip 2 is freely available at http://csse.szu.edu.cn/staff/zhuzx/LWFQZip2 and https://github.com/Zhuzxlab/LW-FQZip2 .

Summary Recently, social network has been one of the biggest information exchange platforms of the Internet. Moreover, the users in social network used to watch videos through social network application. To provide a proper recommended video list, the video recommendation algorithm for social network is becoming a hot research issue. On one hand, more and more researchers introduce the concept of trust into video recommendation algorithms. However, most of them only select the trust friends based on the similarity and neglect the characteristics of social network. On the other hand, most previous video recommendation algorithms are only based on the number that a video is viewed to evaluate a video's quality. They do not make good use of the social relationship in social network and the video's reputation. This paper mainly focuses on the challenge that the effectiveness and performance of current video recommendation algorithm in social network cannot satisfy the users. In this paper, we propose a novel video recommendation algorithm based on the combination of video content and social network. Our proposed algorithm consists of the trust friends computing model and video's quality evaluation model. The trust friends computing method takes into account similarity between users, interaction between users, and the active degree of a user. In our video's quality evaluation model, we combine the acceptance ratio of a video with a video's reputation. The video can be given an appropriate rating score through this model. We design corresponding trust friends computing algorithm and video recommendation algorithm respectively for two proposed models. Our integral video recommendation algorithm consists of these two algorithms. The experimental results indicate that the performance and effectiveness of our algorithm are better than those of two classical video recommendation algorithms (i.e., user‐based collaborative filtering algorithm and TBR‐d algorithm), in terms of precision, recall and F1‐measure. Copyright © 2016 John Wiley & Sons, Ltd.

In the recent few years, an increasing number of studies have shown that microRNAs (miRNAs) play critical roles in many fundamental and important biological processes. As one of pathogenetic factors, the molecular mechanisms underlying human complex diseases still have not been completely understood from the perspective of miRNA. Predicting potential miRNA-disease associations makes important contributions to understanding the pathogenesis of diseases, developing new drugs, and formulating individualized diagnosis and treatment for diverse human complex diseases. Instead of only depending on expensive and time-consuming biological experiments, computational prediction models are effective by predicting potential miRNA-disease associations, prioritizing candidate miRNAs for the investigated diseases, and selecting those miRNAs with higher association probabilities for further experimental validation. In this study, Path-Based MiRNA-Disease Association (PBMDA) prediction model was proposed by integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. This model constructed a heterogeneous graph consisting of three interlinked sub-graphs and further adopted depth-first search algorithm to infer potential miRNA-disease associations. As a result, PBMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.8341 and 0.9169, respectively) and 5-fold cross validation (average AUC of 0.9172). In the cases studies of three important human diseases, 88% (Esophageal Neoplasms), 88% (Kidney Neoplasms) and 90% (Colon Neoplasms) of top-50 predicted miRNAs have been manually confirmed by previous experimental reports from literatures. Through the comparison performance between PBMDA and other previous models in case studies, the reliable performance also demonstrates that PBMDA could serve as a powerful computational tool to accelerate the identification of disease-miRNA associations.

Automatic plant recognition has become a research focus and received more and more attentions recently. However, existing methods usually only focused on leaf recognition from small databases that usually only contain no more than hundreds of species, and none of them reported a stable performance in either recognition accuracy or recognition speed when compared with a big image database. In this paper, we present a novel method for leaf recognition from a big hierarchical image database. Unlike the existing approaches, our method combines the textural gradient histogram with the shape context to form a more distinctive feature for leaf recognition. To achieve efficient leaf image retrieval, we divided the big database into a set of subsets based on mean-shift clustering on the extracted features and build hierarchical k-dimensional trees (KD-trees) to index each cluster in parallel. Finally, the proposed parallel indexing and searching schemes are implemented with MapReduce architectures. Our method is evaluated with extensive experiments on different databases with different sizes. Comparisons to state-of-the-art techniques were also conducted to validate the proposed method. Both visual results and statistical results are shown to demonstrate its effectiveness.