Retrieval-Augmented Generation (RAG) effectively mitigates hallucinations and knowledge gaps in Large Language Models (LLMs) for knowledge-intensive tasks by incorporating external web-based knowledge. However, when integrating diverse yet potentially conflicting web-sourced information, RAG systems are prone to knowledge conflicts that manifest as incorrect or inconsistent model behaviors, ultimately leading to unreliable responses. To address this challenge, this paper proposes Conflict-Aware RAG, a general training framework that leverages the model's inherent conflict-sensing capability to build a more robust RAG system via phased optimization. At the core of this framework lies ConScore, a conflict signal that quantifies the model's awareness of potential knowledge conflicts by comparing generative probabilities across distinct knowledge sources. This signal then guides both the construction of training data and a multi-stage optimization workflow: In the Supervised Fine-Tuning (SFT) stage, conflict features are employed to select representative distracting documents, laying the groundwork for core RAG capabilities; in the Direct Preference Optimization (DPO) stage, high-quality preference pairs are constructed using the conflict signal to boost the model's robustness against distracting knowledge; and in the Reranking stage, conflict confidence and information gain are integrated to synergistically optimize the collaboration mechanism between the retriever and LLM. Experiments on six knowledge-intensive question answering (QA) datasets demonstrate that Conflict-Aware RAG significantly outperforms mainstream baselines. Further ablation studies and quantitative analyses validate the method's stability and generalization, laying the foundation for robust RAG systems.

Sarcasm detection contributes to the understanding of the contrast between the literal meaning of an utterance and the true intention of the speaker, and is considered to be part of the challenge in sentiment analysis. Most models usually focus on prompted inference, ignoring the knowledge hallucination (i.e., the generation of factually incorrect or fabricated information) and inference mistakes generated by large models, which affects the detection accuracy of sarcastic semantics. To solve these complex problems, we propose a novel multidimensional contextual knowledge inference (MCKI) model, which further enhances the contextual inference capability of the model by introducing the multihop chain of thought (CoT) inference technique, combining knowledge enhancement techniques and self-consistency mechanism to better capture the underlying intent of sarcastic text. Specifically, we first utilize multihop context inference techniques to excavate fine-grained contextual and emotional cues in satire through a stepwise inference process. Second, contextual knowledge augmentation is employed to motivate the model to capture deep semantics. Finally, a self-consistency mechanism is adopted to ensure that the generated inference paths are consistent across multiple perspectives, thus improving the accuracy and stability of sarcasm detection. Experimental results illustrate that compared to the state-of-the-art baseline, our model achieves significant improvements on the three baseline datasets, especially on the X dataset by 2.4%, which demonstrates the superior performance of our Multidimensional contextual knowledge Inference model for sarcastic detection.

Aspect-based sentiment analysis (ABSA) constitutes a critical subtask within affective computing, whose central challenge involves the accurate and efficient identification of sentiment polarity associated with specific aspect terms in review sentences. Although syntactic knowledge has demonstrated significant benefits in traditional ABSA models, existing approaches based on large language models (LLMs) have largely overlooked such structural information and often fail to comprehensively model both implicit and explicit sentiment expressions. To bridge this gap, we propose TrustSyn, a novel framework designed to enhance LLMs with trustworthy, constituency-structured dependency knowledge for ABSA. Specifically, the input sentences are first parsed using both dependency and constituency parsers. The resulting syntactic information is then restructured into a unified and reliable representation through a trustworthy syntax integration process. This structured knowledge is formalized and injected into LLMs to augment their comprehension of aspect sentiment associations. To the best of our knowledge, this is the first work to integrate constituency-informed dependency structures into LLMs for ABSA. Finally, experimental results demonstrate that TrustSyn consistently outperforms state-of-the art models across five benchmark datasets. Further ablation studies and analyses confirm its robustness and strong generalization capability.

The ambiguity of name entities is a common problem in information retrieval, which leads to the decline of retrieval quality. This makes name disambiguation particularly important. In academic field, the rapidly increasing large-scale of publications has imposed more challenges to the name disambiguation problem. Existing works mainly focus on leveraging content information to distinguish different name entities. In this paper, we consider jointly utilizing both content information and relational information to disambiguate the same name. Firstly, we construct a Heterogeneous Academic Network based on meta information of publications such as collaborators, institutions and venues. Then, we transform the network into separate homogeneous graphs. After that, we propose Graph Attention Networks to jointly learn content and relational information by optimizing an embedding vector. Finally, a clustering algorithm is presented to gather author names most likely representing the same person. The experiments show that our method is effective and outperforms the state-of-the-art methods in both precision and recall metrics.

A new document retrieval method based on graph was proposed in this paper. Queries and documents are represented by graphs. The paper also proposes the concept of the document semantic unit in consideration of the overhead of graph computing. The size of semantic unit is used as the granularity for graph construction. This new method puts queries and documents in an unequal level instead of regarding them as equivalent entities which conventional IR system does. The paper further proposes the similarity calculating method of graphs based on general maximum common subgraph. The result of the experiment shows this method is able to yield better document retrieval results.

In this paper, we mainly study the problem of computing the k nearest neighbor over the encrypted data. We solve this question from two aspects. Firstly, we focus on the query efficiency. It is necessary to improve the user experience and save computing resources through reducing query time. We propose a k nearest neighbor query algorithm based on hierarchical Clustering. This algorithm uses the pruning rules to exclude most of the non-nearest neighbor data and improve query efficiency. Secondly, considered from data's security, we use the SSED algorithm which can safely compute distance between two encrypted data by using the properties of homomorphic encryption algorithm. Then the SSED algorithm is combined with the hierarchical clustering algorithm to realize the computation of k nearest neighbors over the ciphertext. The experiments show that the algorithm proposed in this paper has high query efficiency.

Uncertain data is inherent in various important applications and Top-[Formula: see text] query on uncertain data is an important query type for many applications. To tackle the performance issue of evaluating Top-[Formula: see text] query on uncertain data, an efficient optimization approach was proposed in this paper. This method can anticipate the tuples most likely to become Top-[Formula: see text] result based on dominant relationship analysis, greatly reducing the amount of data in query processing. When the database is updated, this method could determine whether the change affects the current query result, and help us to avoid unnecessary re-query. The experimental results prove the feasibility and effectiveness of this method.

BACKGROUND: Brain disorders are one of the top causes of human death. Generally, neurologists analyze brain medical images for diagnosis. In the image analysis field, corners are one of the most important features, which makes corner detection and matching studies essential. However, existing corner detection studies do not consider the domain information of brain. This leads to many useless corners and the loss of significant information. Regarding corner matching, the uncertainty and structure of brain are not employed in existing methods. Moreover, most corner matching studies are used for 3D image registration. They are inapplicable for 2D brain image diagnosis because of the different mechanisms. To address these problems, we propose a novel corner-based brain medical image classification method. Specifically, we automatically extract multilayer texture images (MTIs) which embody diagnostic information from neurologists. Moreover, we present a corner matching method utilizing the uncertainty and structure of brain medical images and a bipartite graph model. Finally, we propose a similarity calculation method for diagnosis. RESULTS: Brain CT and MRI image sets are utilized to evaluate the proposed method. First, classifiers are trained in N-fold cross-validation analysis to produce the best θ and K. Then independent brain image sets are tested to evaluate the classifiers. Moreover, the classifiers are also compared with advanced brain image classification studies. For the brain CT image set, the proposed classifier outperforms the comparison methods by at least 8% on accuracy and 2.4% on F1-score. Regarding the brain MRI image set, the proposed classifier is superior to the comparison methods by more than 7.3% on accuracy and 4.9% on F1-score. Results also demonstrate that the proposed method is robust to different intensity ranges of brain medical image. CONCLUSIONS: In this study, we develop a robust corner-based brain medical image classifier. Specifically, we propose a corner detection method utilizing the diagnostic information from neurologists and a corner matching method based on the uncertainty and structure of brain medical images. Additionally, we present a similarity calculation method for brain image classification. Experimental results on two brain image sets show the proposed corner-based brain medical image classifier outperforms the state-of-the-art studies.

Identification of ideal mid-sagittal line (iML) is important for image registration, brain segmentation, pathology detection and particularly for medical image classification. In this paper, iML extraction method based on scale invariant feature transform (SIFT) features is proposed for brain CT images. The method consists of an offline part and an online part. In the offline part, the iML feature points of training set is extracted by an auxiliary tool and an optimized matching template set is obtained by our feature fusion and filtering algorithms. In the online part, a matching point set is generated by matching SIFT features of test images to the offline template. Then the point set is refined by our pruning algorithm and iMLs of test images are fitted by the refined point set. Both real and simulated image data sets are used to verify the accuracy, robustness and execution efficiency of the algorithm. Experimental results show that, our method achieves good accuracy and efficiency in both real and simulation image sets, and performs better tolerance to rotation, noise, fuzzy and asymmetry in comparison with other existing algorithms.

many methods have been developed for corner detection and matching. However, these detection methods do not take the domain knowledge of brain medical images into account. They produce some useless corners and lose essential domain information. Moreover, existing corner matching methods do not consider the uncertainty and structure of brain medical images. And most of them are developed for 3D medical image registration, which are not applicable for 2D image classification. To address these problems, a corner detection method is firstly proposed based on hierarchical textures. Then, based on the uncertainty and structure of brain medical images, a corner matching method is developed to yield an initial and furthermore a maximum matched corner pair sequences. Finally, a similarity function is presented for classification. Experimental results show the proposed corner detection method outperforms the existing method and the classification results based on the maximum matched corner pair sequence are better than the state-of-the-art brain medical image classification methods.

In this work, an interactive visual system MICS is presented for large-scale brain CT image classification. Automatic feature extraction algorithms are added in MICS to improve system efficiency and classification accuracy. In visualization part, we designed an interactive feature extraction interface, enable users to extract and fine-tune image features according to specific requirements. In addition, all image features in database are visualized as dynamic charts in every phase of classification. These allow users to compare the current image with others in some specific feature and re-mark the possible misclassification. Finally, by series experiments and case studies, we verify the performance of the classification algorithm as well as the effectiveness and applicability of the visual design in MICS.

This paper proposed a Hadoop-based iterative sampling approximate aggregation query processing method. According to the user desire precision and the first sample data, we could compute the sample size to meet the user desired precision. In order to avoid the effects of data bias, this paper proposed a “layered sampling” method to ensure that the approximate aggregation result is statistically meaningful.

Medical images are one of the most important tools in doctors' diagnostic decision-making. It has been a research hotspot in medical big data that how to effectively represent medical images and find essential patterns hidden in them to assist doctors to achieve a better diagnosis. Several graph models have been developed to represent medical images. However, the unique structures of domain-specific images are not considered well to lose some essential information. Thus, aiming at brain CT images, we first construct a graph about the Topological Relations between Ventricles and Lesions (TRVL) and present the graph modeling process. Then we propose a method named Frequent Approximate Subgraph Mining based on Graph Edit Distance (FASMGED). This method uses an error-tolerant graph matching strategy that is accordant with ubiquitous noise in practice. Experimental results show that the graph modeling process is computationally scalable and FASMGED can find more significant patterns than current algorithms.

In order to let people be able to get information from the Web easily, search engine comes into being and continues to grow and develop. People begin to explore all kinds of ranking algorithms and try to give user a good result list. However, the expression format of the web information and user queries are very simple, which results in the difficulty of determining the relevance between user queries and web information. The success and popularity of social network systems, such as del.icio.us, Face book, etc., have generated many interesting problems to the research community. This gives us a new viewpoint on how to improve the quality of information retrieval. The contributions of our research are twofold. First, the existing ranking algorithms of search engine are classified. And we extend expression of queries by “keyword and ”, instead of keywords only. Second, a new ranking algorithm based on user feedback and semantic tags is proposed, and it is also compared with Google by several evaluation methods.