The sparsity of user–item interactions remains a fundamental obstacle in collaborative filtering, limiting the ability of Graph Neural Network (GNN)-based recommender systems to capture high-order user relationships without incurring over-smoothing and computational overhead. Existing social recommendation approaches mitigate this by incorporating social networks, yet most rely on explicit ties and fail to construct informative links in their absence. Meanwhile, contrastive learning (CL) has shown promise in improving representation quality, but current view generation strategies, augmentation-based for robustness and nonaugmentation-based for semantic fidelity, are seldom combined, leaving their complementary potential underexplored. We propose Social Generating with Multiview-guided Tuning (SGMT), a unified framework that addresses both challenges. First, an interest-aware social generation mechanism constructs synthetic user–user links from shared interaction patterns, theoretically shown to compress collaborative paths and uncover latent high-order relations. Second, we present two complementary CL modules, Noise-augmented View and Semantic-explored View, which we theoretically prove to preferentially enhance uniformity and alignment, respectively, two fundamental objectives in CL. Experiments on three real-world datasets show that SGMT outperforms state-of-the-art baselines, validating both the theoretical analysis and the practical efficacy of our model.

Beyond user-item modeling, item-to-item relationships are increasingly used to enhance recommendation. However, common methods largely rely on co-occurrence, making them prone to item popularity bias and user attributes, which degrades embedding quality and performance. Meanwhile, although diversity is acknowledged as a key aspect of recommendation quality, existing research offers limited attention to it, with a notable lack of causal perspectives and theoretical grounding. To address these challenges, we propose Cadence: Diversity Recommendation via Causal Deconfounding of Co-purchase Relations and Counterfactual Exposure—a plug-and-play framework built upon LightGCN as the backbone, primarily designed to enhance recommendation diversity while preserving accuracy. First, we compute the Unbiased Asymmetric Co-purchase Relationship (UACR) between items—excluding item popularity and user attributes—to construct a deconfounded directed item graph, with an aggregation mechanism to refine embeddings. Second, we leverage UACR to identify diverse categories of items that exhibit strong causal relevance to a user's interacted items but have not yet been engaged with. We then simulate their behavior under high-exposure scenarios, thereby significantly enhancing recommendation diversity while preserving relevance. Extensive experiments on real-world datasets demonstrate that our method consistently outperforms state-of-the-art diversity models in both diversity and accuracy, and further validates its effectiveness, transferability, and efficiency over baselines.

Sequential recommendation aims to predict users’ next interactions by analyzing historical behavioral data. Traditional methods typically focus on learning fine-grained feature representations or extracting high-level user preferences to enhance recommendation accuracy. However, they often overlook the dynamic nature of user demand, which can shift over short periods and may resemble random noise. In our previous work, we introduced CoDeR, a framework that captures demand shifts and mitigates confounding biases through backdoor adjustment. Despite its effectiveness, CoDeR has limitations in its causal relation modeling, particularly in neglecting the role of user interest as a confounder. In this work, we propose CoDeR+, an enhanced framework that refines key components of CoDeR. First, we extend the original User Demand Extraction module into Interest-aware User Demand Modeling, introducing two submodules that explicitly model user interest and integrate it into demand representations. Second, we introduce a new Robust Counterfactual Demand Reasoning module, where user interest is treated as an additional confounder alongside demand drift, improving the causal correction process. Additionally, we provide a rigorous theoretical analysis of the updated backdoor adjustment and propose a simplified probability estimation method that reduces computational complexity. Extensive experiments on four real-world datasets demonstrate the effectiveness of CoDeR+. The source code for both CoDeR and CoDeR+ is publicly available at https://github.com/hellolst23/CoDeR .

Graph neural networks (GNNs) have demonstrated significant success in solving real-world problems using both static and dynamic graph data. While static graphs remain constant, dynamic graphs evolve over time, presenting unique challenges that necessitate integrating GNN computations with sequential models. Despite advancements, existing research has primarily focused on static graphs, with dynamic graphs receiving comparatively less attention. This study extends the investigation of over-squashing—a phenomenon where excessive information compression leads to the loss of distant node information—from static to dynamic graphs. Over-squashing is exacerbated in dynamic graphs due to the combined compression of spatial and temporal information into narrow time windows. To address this issue, we propose the spatial and temporal compensation model for dynamic graphs, which is theoretically validated and incorporates two key modules: the structural similarity-based spatial compensation (SSSC) module and the representation and trend similarity-based temporal compensation (RTSTC) module. The former module mitigates spatial information loss by leveraging structural similarities among nodes, while the latter module addresses temporal information loss by integrating historical data and trends. The extensive experiments on real-world dynamic graph datasets demonstrate that our approach achieves state-of-the-art performance. The datasets and source codes are released at:https://github.com/wuchaokai/STCDG/

Sequential recommendation systems aim to predict the next item based on users' historical interactions. While traditional methods focus on learning feature representations or user preferences, they often struggle with detecting subtle demand shifts in short sequences, especially when these shifts are obscured by noise or biases. To address these issues, we propose CoDeR (Counterfactual Demand Reasoning), a novel framework designed to handle demand shifts in sequential recommendations with greater precision. CoDeR features two key modules: (1) the User Demand Extraction module, which utilizes self-attention mechanisms and demand graphs to identify and model demand shifts from minimal user interactions; and (2) the Counterfactual Demand Reasoning module, which employs causal effect analysis and backdoor adjustment techniques to distinguish true demand shifts from noisy or biased signals. Our approach represents the first application of counterfactual reasoning to sequential recommendation systems. Comprehensive experiments on three real-world datasets demonstrate that CoDeR significantly outperforms existing baselines.

Multimodal Large Language Models have advanced AI in applications like text-to-video generation and visual question answering. These models rely on visual encoders to convert non-text data into vectors, but current encoders either lack semantic alignment or overlook non-salient objects. We propose the Guiding Visual Encoder to Perceive Overlooked Information (GiVE) approach. GiVE enhances visual representation with an Attention-Guided Adapter (AG-Adapter) module and an Object-focused Visual Semantic Learning module. These incorporate three novel loss terms: Object-focused Image-Text Contrast (OITC) loss, Object-focused Image-Image Contrast (OIIC) loss, and Object-focused Image Discrimination (OID) loss, improving object consideration, retrieval accuracy, and comprehensiveness. Our contributions include dynamic visual focus adjustment, novel loss functions to enhance object retrieval, and the Multi-Object Instruction (MOInst) dataset. Experiments show our approach achieves state-of-the-art performance.

The explosive growth of the video game industry has created an urgent need for recommendation systems that can scale with expanding catalogs and maintain user engagement. While prior work has explored accuracy and diversity in recommendations, existing models underutilize playtime, a rich behavioral signal unique to gaming platforms, and overlook the potential of multimodal information to enhance diversity. In this paper, we propose DP 2 Rec, a novel Dual-Phase Playtime-guided Recommendation model designed to jointly optimize accuracy and diversity. First, we introduce a playtime-guided interest intensity exploration module that separates strong and weak preferences via dual-beta modeling, enabling fine-grained user profiling and more accurate recommendations. Second, we present a playtime-guided multimodal random walks module that simulates player exploration using transitions guided by both playtime-derived interest similarity and multimodal semantic similarity. This mechanism preserves core preferences while promoting cross-category discovery through latent semantic associations and adaptive category balancing. Extensive experiments on a real-world game dataset show that DP 2 Rec outperforms existing methods in both recommendation accuracy and diversity. The dataset and source code are released at https://github.com/zqxwcevrtyui/DP2Rec

Vision-language models have been explored for radiology report generation with promising results. Yet, uncertainty elaborated in findings and the reasoning process for reaching clinical impressions are seldom explicitly modeled, reducing the clinical accuracy and trustworthiness of the generated reports. We present CURV, a novel framework that alleviates the limitations through integrated awareness of uncertainty and explicit reasoning capabilities. Our approach consists of three key components: (1) an uncertainty modeling mechanism that teaches the model to recognize and express appropriate levels of diagnostic confidence, (2) a structured reasoning framework that generates intermediate explanatory steps connecting visual findings to clinical impressions, and (3) a reasoning coherence reward that ensures logical consistency among findings, reasoning, and impressions. We implement CURV through a three-stage training pipeline that combines uncertainty-aware fine-tuning, reasoning initialization, and reinforcement learning. In particular, we adopt a comprehensive reward function addresses multiple aspects of report quality, incorporating medical term matching, uncertainty expression evaluation, and semantic coherence evaluation. Experimental results demonstrate that CURV generates clinically relevant reports with appropriate uncertainty expressions and transparent reasoning traces, significantly outperforming previous methods. CURV represents a substantial advancement toward interpretable and trustworthy AI-generated radiology reports, with broader implications for the deployment of vision-language models in high-stakes clinical environments where uncertainty awareness and reasoning transparency are essential.

Community detection has gained significant research interest within the data mining field. It involves identifying subsets of nodes with dense internal connections and sparse external connections. Most studies on community detection focus solely on identifying non-overlapping communities in a static graph. However, in practice, communities often overlap, and the structure of the graphs is dynamically evolving. This dynamic nature leads to community changes and poses a significant challenge in detecting overlapping communities on temporal graphs (T-OCD). While graph neural networks have shown great performance in generating node representations for community detection, learning representations that capture temporal graph structures and support overlapping community detection remain an open question. To address these challenges, we present T-OCDIB , a novel approach for T emporal O verlapping C ommunity D etection guided by I nformation B ottleneck. Specifically, we first propose an overlapping community detection approach for static graphs, under the guidance of a community-oriented information bottleneck. This approach allows us to learn discriminative node representations specific to each community, facilitating the detection of overlapping communities. Following this, we extend this method to temporal graphs by presenting a temporal convolution module. This module uses adaptive weight matrices based on evolving graph structures to capture temporal dependencies for community detection. Additionally, to promote smooth transitions between consecutive communities, we introduce a temporal smoothing module to further constrain changes in community structure. We evaluate the proposed approach on both real-world and synthetic temporal networks. Experimental results illustrate the superiority of T-OCDIB over other community detection methods.

Community detection, aiming to group nodes in a graph with strong inner-connection, has attracted increasing research attention. However, existing methods for community detection assume the communities are non-overlapping, which are rare in real-world scenarios. For the overlapping community detection (OCD) task, each community might share overlapping nodes, and thus is difficult to train discriminative node representations to simultaneously distinguish different communities. To address this issue, we propose a novel approach for Overlapping Community Detection under the guidance of Information Bottleneck (OCDIB). Specifically, we separately model the distribution of communities at different latent spaces, from which we can sample the community-specific representation. In this way, we can better characterize the feature of each community. Then, to obtain salient representations, we propose a community-oriented information bottleneck. It aims to reduce noise in representations, while preserving the key information to express the target community. Last, we present a modularity-based loss function to guide the OCDIB to focus on the community-related structure and obtain high-quality community detection results. We have performed comprehensive experiments on both synthetic and real-world datasets, and the promising experimental results further demonstrate the effectiveness of our proposed approach w.r.t. a number of evaluation criteria.

Abstractive text summarization is to generate concise summaries that well preserve both salient information and the overall semantic meanings of the given documents. However, real-world documents, e.g., financial reports, generally contain rich data such as charts and tabular data which invalidates most existing text summarization approaches. This paper is thus motivated to propose this novel approach to simultaneously summarize both textual and tabular data. Particularly, we first manually construct a “table+text → summary” dataset. Then, the tabular data is respectively embedded in a row-wise and column-wise manner, and the textual data is encoded at the sentence-level via an employed pre-trained model. We propose a salient detector gate respectively performed between each pair of row/column and sentence embeddings. The highly correlated content is considered as salient information that must be summarized. Extensive experiments have been performed on our constructed dataset and the promising results demonstrate the effectiveness of the proposed approach w.r.t. a number of both automatic and human evaluation criteria.

Protein is the main material basis of living organisms and plays crucial role in life activities. Understanding the function of protein is of great significance for new drug discovery, disease treatment and vaccine development. In recent years, with the widespread application of deep learning in bioinformatics, researchers have proposed many deep learning models to predict protein functions. However, the existing deep learning methods usually only consider protein sequences, and thus cannot effectively integrate multi-source data to annotate protein functions. In this article, we propose the Prot2GO model, which can integrate protein sequence and PPI network data to predict protein functions. We utilize an improved biased random walk algorithm to extract the features of PPI network. For sequence data, we use a convolutional neural network to obtain the local features of the sequence and a recurrent neural network to capture the long-range associations between amino acid residues in protein sequence. Moreover, Prot2GO adopts the attention mechanism to identify protein motifs and structural domains. Experiments show that Prot2GO model achieves the state-of-the-art performance on multiple metrics.

Heterogeneous graphs, which contain nodes and edges of multiple types, are prevalent in various domains, including bibliographic networks, social media, and knowledge graphs. As a fundamental task in analyzing heterogeneous graphs, relevance measure aims to calculate the relevance between two objects of different types, which has been used in many applications such as web search, recommendation, and community detection. Most of existing relevance measures focus on homogeneous networks where objects are of the same type, and a few measures are developed for heterogeneous graphs, but they often need the pre-defined meta-path. Defining meaningful meta-paths requires much domain knowledge, which largely limits their applications, especially on schema-rich heterogeneous graphs like knowledge graphs. Recently, the Graph Neural Network (GNN) has been widely applied in many graph mining tasks, but it has not been applied for measuring relevance yet. To address the aforementioned problems, we propose a novel GNN-based relevance measure, namely GSim. Specifically, we first theoretically analyze and show that GNN is effective for measuring the relevance of nodes in the graph. We then propose a context path-based graph neural network (CP-GNN) to automatically leverage the semantics in heterogeneous graphs. Moreover, we exploit CP-GNN to support relevance measures between two objects of any type. Extensive experiments demonstrate that GSim outperforms existing measures. (Coda and data is available at this linkhttps://github.com/RManLuo/GSim).

Community detection has long been an important yet challenging task to analyze complex networks with a focus on detecting topological structures of graph data. Essentially, real-world graph data is generally heterogeneous which dynamically varies over time, and this invalidates most existing community detection approaches. To cope with these issues, this paper proposes the temporal-heterogeneous graph convolutional networks (THGCN) to detect communities using the learnt feature representations of a set of temporal heterogeneous graphs. Particularly, we first design a heterogeneous GCN component to represent features of heterogeneous graph at each time step. Then, a residual compressed aggregation component is proposed to learn temporal feature representations extracted from two consecutive heterogeneous graphs. These temporal features are considered to contain evolutionary patterns of underlying communities. To the best of our knowledge, this is the first attempt to detect communities from temporal heterogeneous graphs. To evaluate the model performance, extensive experiments are performed on two real-world datasets, i.e., DBLP and IMDB. The promising results have demonstrated that the proposed THGCN is superior to both benchmark and the state-of-the-art approaches, e.g., GCN, GAT, GNN, LGNN, HAN and STAR, with respect to a number of evaluation criteria.

Session-based recommendation has recently attracted more and more research efforts. Most existing approaches are intuitively proposed to discover users' potential preferences or interests from the anonymous session data. This apparently ignores the fact that these sequential behavior data usually reflect session user's potential demand, i.e., a semantic level factor, and therefore how to estimate underlying demands from a session has become a challenging task. To tackle the aforementioned issue, this paper proposes a novel demand-aware graph neural network model. Particularly, a demand modeling component is designed to extract the underlying multiple demands of each session. Then, the demand-aware graph neural network is designed to first construct session demand graphs and then learn the demand-aware item embeddings to make the recommendation. The mutual information loss is further designed to enhance the quality of the learnt embeddings. Extensive experiments have been performed on two real-world datasets and the proposed model achieves the SOTA model performance.

Being a fundamental graph operator, reachability query has been widely studied by the data mining community in the past decades. In a directed acyclic graph (DAG), one vertex is reachable by another if there exists a chain of directed edges connecting the two vertexes. The state-of-the-art (SOTA) reachability query methods mostly first index all the vertexes in the underlying DAG and assign them with different labels, and then use these indexes and/or labels to efficiently filter out as many unreachable queries as possible. Thus, because a large portion of unreachable queries can be identified without evoking any tedious path-finding process, the overall time taken by a huge number of queries is much shortened with a tolerable compensation on the additional index and/or label preprocessing time and space. In this paper, we propose the Extreme Labeling Filter (ELF), which is a novel generic filter that can be applied to existing reachability query methods to additionally identify a large number of unreachable queries. Based on the analysis of the given DAG in a systematic and autonomous manner, ELF first determines whether to use predecessors or successors to label the vertexes. Based on such self-determined labels, ELF is then able to identify a large number of unreachable queries with a low time complexity of O(1). To evaluate the performance of ELF, we apply it on 4 reachability query methods (1 conventional and 3 SOTA, all designated for reachability query in DAGs) and conduct experiments on 17 datasets of different sizes. The experimental results show that by applying ELF, all methods significantly shorten the query time.

Community detection, aiming to group the graph nodes into clusters with dense inner-connection, is a fundamental graph mining task. Recently, it has been studied on the heterogeneous graph, which contains multiple types of nodes and edges, posing great challenges for modeling the high-order relationship between nodes. With the surge of graph embedding mechanism, it has also been adopted to community detection. A remarkable group of works use the meta-path to capture the high-order relationship between nodes and embed them into nodes' embedding to facilitate community detection. However, defining meaningful meta-paths requires much domain knowledge, which largely limits their applications, especially on schema-rich heterogeneous graphs like knowledge graphs. To alleviate this issue, in this paper, we propose to exploit the context path to capture the high-order relationship between nodes, and build a Context Path-based Graph Neural Network (CP-GNN) model. It recursively embeds the high-order relationship between nodes into the node embedding with attention mechanisms to discriminate the importance of different relationships. By maximizing the expectation of the co-occurrence of nodes connected by context paths, the model can learn the nodes' embeddings that both well preserve the high-order relationship between nodes and are helpful for community detection. Extensive experimental results on four real-world datasets show that CP-GNN outperforms the state-of-the-art community detection methods1.

Generating long text conditionally depending on the short input text has recently attracted more and more research efforts.Most existing approaches focus more on introducing extra knowledge to supplement the short input text, but ignore the coherence issue of the generated texts.To address aforementioned research issue, this paper proposes a novel twostage approach to generate coherent long text.Particularly, we first build a document-level path for each output text with each sentence embedding as its node, and a revised selforganising map (SOM) is proposed to cluster similar nodes of a family of document-level paths to construct the directed semantic graph.Then, three subgraph alignment methods are proposed to extract the maximum matching paths or subgraphs.These directed subgraphs are considered to well preserve extra but relevant content to the short input text, and then they are decoded by the employed pre-trained model to generate coherent long text.Extensive experiments have been performed on three real-world datasets, and the promising results demonstrate that the proposed approach is superior to the state-of-the-art approaches w.r.t. a number of evaluation criteria.

Recent works on adversarial examples for image classification focus on directly modifying pixels with minor perturbations. The small perturbation requirement is imposed to ensure the generated adversarial examples being natural and realistic to humans, which, however, puts a curb on the attack space thus limiting the attack ability and transferability especially for systems protected by a defense mechanism. In this paper, we propose the novel concepts of structure patterns and structure-aware perturbations that relax the small perturbation constraint while still keeping images natural. The key idea of our approach is to allow perceptible deviation in adversarial examples while keeping structure patterns that are central to a human classifier. Built upon these concepts, we propose a \emph{structure-preserving attack (SPA)} for generating natural adversarial examples with extremely high transferability. Empirical results on the MNIST and the CIFAR10 datasets show that SPA exhibits strong attack ability in both the white-box and black-box setting even defenses are applied. Moreover, with the integration of PGD or CW attack, its attack ability escalates sharply under the white-box setting, without losing the outstanding transferability inherited from SPA.

Skin disease is one of the most common types of human diseases, which may happen to everyone regardless of age, gender or race. Due to the high visual diversity, human diagnosis highly relies on personal experience; and there is a serious shortage of experienced dermatologists in many countries. To alleviate this problem, computer-aided diagnosis with state-of-the-art (SOTA) machine learning techniques would be a promising solution. In this paper, we aim at understanding the performance of convolutional neural network (CNN) based approaches. We first build two versions of skin disease datasets from Internet images: (a) Skin -10, which contains 10 common classes of skin disease with a total of 10,218 images; (b) Skin -100, which is a larger dataset that consists of 19,807 images of 100 skin disease classes. Based on these datasets, we benchmark several SOTA CNN models and show that the accuracy of skin -100 is much lower than the accuracy of skin -10. We then implement an ensemble method based on several CNN models and achieve the best accuracy of 79.01% for Skin -10 and 53.54% for Skin -100. We also present an object detection based approach by introducing bounding boxes into the Skin -10 dataset. Our results show that object detection can help improve the accuracy of some skin disease classes.

Road extraction from aerial images is a hot research topic. With the advancement of convolutional neural network (CNN), several CNN-based road detection methods have been developed. However, most of them do not make full use of the hierarchical features from the original aerial images. In this paper, we propose a novel residual dense U-Net (RDUN), a semantic segmentation network which combines the strengths of residual learning, DenseNet, and U-Net, to overcome the drawback. Our proposed RDUN can fully exploit the hierarchical features from all the convolutional layers, which utilizes the residual dense blocks (RDB) to build up a U-Net architecture. The benefits of our model are two-fold. First, by using the RDB abundant local features can be extracted and fused effectively. Second, based the local features, hierarchical features are constructed by shortcut connections between layers in RDB. Extensive experiments are carried out on a real-world road detection dataset and the results demonstrate the proposed RDUN outperforms state-of-the-art competitors.

Road information extraction based on aerial images is a critical task for many applications, and it has attracted considerable attention from researchers in the field of remote sensing. The problem is mainly composed of two subtasks, namely, road detection and centerline extraction. Most of the previous studies rely on multistage-based learning methods to solve the problem. However, these approaches may suffer from the well-known problem of propagation errors. In this paper, we propose a novel deep learning model, recurrent convolution neural network U-Net (RCNN-UNet), to tackle the aforementioned problem. Our proposed RCNN-UNet has three distinct advantages. First, the end-to-end deep learning scheme eliminates the propagation errors. Second, a carefully designed RCNN unit is leveraged to build our deep learning architecture, which can better exploit the spatial context and the rich low-level visual features. Thereby, it alleviates the detection problems caused by noises, occlusions, and complex backgrounds of roads. Third, as the tasks of road detection and centerline extraction are strongly correlated, a multitask learning scheme is designed so that two predictors can be simultaneously trained to improve both effectiveness and efficiency. Extensive experiments were carried out based on two publicly available benchmark data sets, and nine state-of-the-art baselines were used in a comparative evaluation. Our experimental results demonstrate the superiority of the proposed RCNN-UNet model for both the road detection and the centerline extraction tasks.

Deep learning has achieved great successes in conventional computer vision tasks. In this paper, we exploit deep learning techniques to address the hyperspectral image classification problem. In contrast to conventional computer vision tasks that only examine the spatial context, our proposed method can exploit both spatial context and spectral correlation to enhance hyperspectral image classification. In particular, we advocate four new deep learning models, namely, 2-D convolutional neural network (2-D-CNN), 3-D-CNN, recurrent 2-D CNN (R-2-D-CNN), and recurrent 3-D-CNN (R-3-D-CNN) for hyperspectral image classification. We conducted rigorous experiments based on six publicly available data sets. Through a comparative evaluation with other state-of-the-art methods, our experimental results confirm the superiority of the proposed deep learning models, especially the R-3-D-CNN and the R-2-D-CNN deep learning models.

Learning object detection models from weakly labeled data is an important topic in computer vision. Among various types of weak annotations, image-level object labeling is a natural one that tells the existence, but not the precise locations, of object instances in images. Learning object detectors from image-level labels can be naturally cast as a multiple instance learning (MIL) problem. Existing MIL approaches for object detection still suffer from high false positive rates due to the lack of advanced instances selection techniques. In this study, a subspace-based generative model is proposed to select positive instances by minimizing rank of the coefficient matrix associated with the subspace models. An incoherence term between the subspace model and some “hard” negative instances in then modeled by an ε-insensitive loss function. To further improve the discriminative ability, an ensemble strategy is proposed by employing multiple subspace models. Rigorous experiments are performed on several datasets, and the promising experimental results demonstrate that the proposed approach is superior to the state-of-the-art weakly supervised learning algorithms in terms of precision, recall, and F-score.

Country disaster rescue is becoming more and more important and it requires a rapid response for disaster rescue. The key component for disaster rescue is to plan the optimal rescue path. Traditionally, the optimal rescue path seriously relies on the recognition on images of the damaged areas and the corresponding recognition algorithms are proposed for analyzing satellite images. However, due to its low updating frequency satellite images are not suitable for disaster rescue. Therefore, unmanned aerial vehicle is a good alternative approach to acquire real time images on damaged areas. Techniques are then needed to recognize the UAV images. To cope with this situation, we first extract UAV videos to images, segments these images into fixed size pieces, and manually labeled these data. We then study whether the conventional methods such as mathematical morphology, Hough transform and P-value segmentation approaches can be used to extract roads from UAV images. At last, we propose to adopt SVM and combine it with GA to improve the performance of this approach. Empirical studies are performed on data sets both collected by us and collected from the Internet. Experimental results demonstrate that our approach works well on these data sets when compared with conventional approaches. This indicates that UAV could be able to facilitate the country disaster rescue.

Automatically assigning functions for unknown proteins is a key task in computational biology. Proteins in nature have multiple classes according to the functions they perform. Many efforts have been made to cast the protein function prediction into a multi-label learning problem. This paper proposes a novel Cluster Tree based Multi-label Learning algorithm (CTML) for protein function prediction. The main idea is to compute a set of predictive labels associated at each node for multi-label prediction by using the k-means clustering techniques and the predictive functions via the learning data at the nodes. With the propagation of the predictive labels from the root node to the leaf node, the correlations between labels can be preserved. Experimental results on benchmark data (genbase and yeast datasets) show that the proposed CTML algorithm is effective in predicting protein functions. Moreover, the classification performance of the CTML algorithm is competitive against the other baseline multi-label learning algorithms.

In web mining, there are situations in which only few data is labeled which imposes difficulties on traditional web page classification algorithms. Active learning scheme is then proposed to sample the most representative unlabeled data, which are then annotated by external oracles. Most present active methods are based on series-mode query strategy, which deduces the process of active learning inefficient and unstable. In this paper, we propose a novel text oriented active semi-supervised classification model, which is so-called active SSC. Comparing with other active approaches, our model has the characteristic of comprehensibility, and thus it is easy to design a batch-mode query strategy. Experimental results on public text data showed our method is an effect and stable active approach.

Principal Component Analysis (PCA) and other SVD related approaches are commonly used in dimension reduction and reconstruction of images. However, as linear methods they may not be appropriate for some non-linear cases. Recently a new approach named as Exponential Family Principle Component Analysis (E-PCA) is proposed for non-linear compression and has been successfully used to solve the belief states' dimension reduction of Partially observable Markov Decision Process (POMDP). In this paper, we attempted to adapt E-PCA to image compression and reconstruction due to the reason that it can guarantee nonnegative reconstruction and is fit for some nonlinearly distributed data. The original E-PCA formulations are also simplified in this paper to accelerate the parameters learning process. Experiments are performed on some standard image data sets to verify the effectiveness of E-PCA on image compression. From the experimental results, we can conclude that the new adaption of E-PCA on image compression is particularly effective when the image data follows some kinds of distribution.

Mining distributed data for global knowledge is getting more attention recently. The problem is especially challenging when data sharing is prohibited due to local constraints like limited bandwidth and data privacy. In this paper, we investigate how to derive the embedded manifold (as a 2-D map) for a horizontally partitioned data set, where data cannot be shared among the partitions directly. We propose a model-based approach which computes hierarchical local data abstractions, aggregates the abstractions, and finally learns a global generative model - generative topographic mapping (GTM) based on the aggregated data abstraction. We applied the proposed method to two benchmarking data sets and demonstrated that the accuracy of the derived manifold can effectively be controlled by adjusting the data granularity level of the adopted local abstraction.

Distributed data mining has been a topic getting more important nowadays as there are many cases where physically sharing of data is probibited, e.g., due to huge data volume or data privacy. In this paper, we are interested in learning a global cluster model by exploring data in distributed sources. A methodology based on periodic model exchange and merge is proposed and applied to hyperlinked Web pages analysis. In addition, we have tested a number of variations of the basic idea, including putting more emphasis on the privacy concern and testing the effect of having different numbers of distributed sources. Experimental results show that the proposed distributed learning scheme is effective with accuracy close to the case with all the data physically shared for the learning.