Multimodal data holds great potential in enhancing the accuracy of sentiment analysis. However, the multimodal sentiment analysis (MSA) task faces two practical challenges: i) multimodal heterogeneity significantly increases the difficulty of integrating multimodal information, resulting in insufficient multimodal learning; and ii) multimodal incompleteness easily triggers cross-modal inference biases, seriously hindering multimodal understanding. In this paper, we propose a novel multimodal framework namedcross-modal explicit invariancecoordinateddualconstraintreconstruction (Clive-Dcr) to mitigate MSA for heterogeneous incomplete multimodal data. Specifically, we present a language-oriented cross-modal invariance reasoning (LCIR) approach that can alleviate the inter-modal representation gaps under the supervision of fine-grained sentiment information. More importantly, LCIR can explicitly quantify inter-modal consistency and break the vagueness of heterogeneous modal information, thereby fully exploring the complementary advantages between invariant features and specific features. In the prediction head, we develop a multimodal purification learning mechanism to minimize the intervention of sentiment-irrelevant information on MSA. To enhance the robustness under incomplete modalities, we design a dual constraint reconstruction (DCR) strategy. During the reconstruction of missing features, DCR not only performs high-level feature distribution alignment but also extends the cross-modal invariance reasoning from the complete to the incomplete multimodal scenarios. Comprehensive experiments on three multimodal datasets verify that our Clive-Dcr achieves remarkable performance under complete and incomplete multimodal patterns.

With the continuous rise in the prevalence of skin diseases, accurate classification has become an urgent demand in clinical diagnosis and treatment. In recent years, deep learningbased methods have demonstrated excellent performance in skin lesion classification tasks. To further improve diagnostic accuracy, multimodal fusion approaches integrating dermoscopic images, clinical photographs, and metadata have been explored. However, many existing methods still fail to effectively capture the deep semantic associations among these modalities. This oversight leads to semantic fragmentation across modalities, loss of critical diagnostic cues, and ultimately hinders classification accuracy. To address this challenge, we propose a Metadata-Guided Multimodal Consistency Constraints (MG-MCC) framework, which leverages metadata as a semantic anchor and enforces collaborative constraints through three specially designed loss functions: Metadata-Aware Alignment (MA), Lesion Feature Purification (LFP), and Semantic Consistency Preservation (SCP). Specifically, the MA loss enforces precise cross-modal alignment between image features and metadata in a unified space; the LFP loss strengthens semantic associations between core lesion features and metadata to accurately separate background features; and the SCP loss avoids the loss of diagnostic ROI information caused by Instance Normalization. Extensive experiments conducted on two public skin lesion datasets demonstrate that the proposed MG-MCC framework significantly outperforms state-of-the-art methods in skin lesion classification tasks.

Blockchain technology has transformed financial services sectors by providing security, transparency, and immutability through decentralized ledger systems. However, while blockchain data can support a range of applications-such as user quality analysis, illegal activity detection, and transaction pattern identification-existing systems are restricted to basic queries on blocks and transactions due to their sequential data storage. To support queries more generally, we propose VGQ, the first verifiable graph query (VGQ) framework that enables efficient graph queries on blockchain systems without altering blockchain storage structures. VGQ integrates a query layer with an external graph database system and represents blockchain data as a directed transaction graph to improve the efficiency of graph query execution. To ensure reliable results, VGQ includes result verification with three key performance enhancing optimizations: (i) computing connected components to exclude irrelevant vertices and edges during verification; (ii) merging information from edges to accelerate completeness verification; and (iii) employing a dual pointer algorithm for efficient soundness verification. Experiments offer evidence that VGQ can improve on the state-of-the-art framework in terms of query efficiency by up to one order of magnitude and in terms of verification efficiency by up to two orders of magnitude.

More accurate construction of brain effective conncetivity networks remains a great challenge to achieve accurate auxiliary diagnosis of brain diseases and in-depth exploration of brain function. However, existing methods only consider higher-order or non-stationary assumptions, rather than simultaneously constructing higher-order and non-stationary networks. Among many existing methods, Bayesian network methods demonstrate superior network structure learning ability. In this work, the forward-backward search (FBS) method is optimized by using brain active information, which is improved to a higher-order network structure learning method, called TSTAI. Firstly, in the process of non-stationary network structure learning, two-stage idea is used to search the change points. Then, in the process of learning higher-order network structure, FBS method is combined with two kinds of brain active information to improve the condition set filtering process and scoring function, respectively. Finally, the pruning strategy is used to reduce the search space. Extensive experiments on simulated and real data demonstrate the effectiveness of TSTAI. Through experiments, the TSTAI is compared with state-of-the-art higher-order network construction methods, and the proposed method achieves an improvement of 3.6% and 17.4% respectively in the network construction accuracy.

Integrating multimodal learning (ML) with polysomnography (PSG) has emerged as a research hotspot for reliable sleep staging. However, the complexity of these signals and the discomfort associated with wearing multi-lead devices somewhat limit the feasibility of daily and ubiquitous sleep monitoring. Unfortunately, most existing ML paradigms are constrained by consistent and fixed input patterns. When the number of modalities is less than required by the ML framework, it is easy to cause inference bias, resulting in a significant performance degradation. To this end, we propose an elastic multimodal sleep staging network (ElaSleepNet), consisting of multimodal information completion (MIC) and adaptive cross-modal (ACM) interaction. Specifically, MIC maximizes the consistency of multimodal signals on intra-epoch temporal level and inter-epoch contextual level, thereby enhancing the reasoning and completion abilities of the available modalities for the unavailable modalities. Moreover, we introduce learnable parameters and design the ACM attention mechanism, which allows handling multimodal information interaction while maintaining robustness in the absence of certain modalities. Our ElaSleepNet demonstrates its state-of-the-art on three multimodal sleep datasets. Compared with previous methods, ElaSleepNet can achieve better performance with fewer testing modalities, making it flexible for daily monitoring.

Gene regulatory network (GRN) reconstruction remains a great challenge in computational biology and bioinformatics. However, most existing methods focus on inferring GRNs from a single type of dataset, rather than try to integrate multi-type datasets. In this work, GRN inferences based on decision tree (DT) model are improved to handle multi-type gene expression datasets and a novel inference named BFMDDT for GRN reconstruction from multi-type datasets is proposed. In BFMDDT, a specific data-integration strategy is performed on different types of datasets to extract two types of training sets, first. Then specific DT models are applied to learn the feature importance from these training sets in two ways. On the one hand, which gene(s) would regulate a specific gene is determined by one ensemble DT model. On the other hand, which gene(s) are regulated by the specific gene is determined by one ensemble DT model of the same type. At last, by mixing the feature importance gained via the two models, a global feature importance ranking is acquired and the GRN can be constructed according to the ranking. BFMDDT shows confidence in extracting information from multi-type datasets and the extensibility by combining with other technologies. Numerous experiments are conducted which show the effectiveness of data integration, the GRN reconstruction accuracy, efficiency and extensibility of BFMDDT, respectively.

Multimodal physiological signals, such as EEG, EOG and EMG, provide rich and reliable physiological information for automated sleep staging (ASS). However, in the real world, the completeness of various modalities is difficult to guarantee, which seriously affects the performance of ASS based on multimodal learning. Furthermore, the exploration of temporal context information within PTSs is also a serious challenge. To this end, we propose a robust multimodal sleep staging framework named contrastive imagination modality sleep network (CIMSleepNet). Specifically, CIMSleepNet handles the issue of arbitrary modal missing through the combination of modal awareness imagination module (MAIM) and semantic & modal calibration contrastive learning (SMCCL). Among them, MAIM can capture the interaction among modalities by learning the shared representation distribution of all modalities. Meanwhile, SMCCL introduces prior information of semantics and modalities to check semantic consistency while maintaining the uniqueness of each modality. Utilizing the calibration of SMCCL, the data distribution recovered by MAIM is aligned with the real data distribution. We further design a multi-level cross-branch temporal attention mechanism, which can facilitate the mining of interactive temporal context representations at both the intra-epoch and inter-epoch levels. Extensive experiments on five multimodal sleep datasets demonstrate that CIMSleepNet remarkably outperforms other competitive methods under various missing modality patterns. The source code is available at: https://github.com/SQAIYY/CIMSleepNet.

Reconstructing gene regulatory networks(GRNs) is an increasingly hot topic in bioinformatics. Dynamic Bayesian network(DBN) is a stochastic graph model commonly used as a vital model for GRN reconstruction. But probabilistic characteristics of biological networks and the existence of data noise bring great challenges to GRN reconstruction and always lead to many false positive/negative edges.${Score}_{Lasso}$is a hybrid DBN score function combining DBN and linear regression with good performance. Its performance is, however, limited by first-order assumption and ignorance of the initial network of DBN. In this article, an integrated model based on higher-order DBN model, higher-order Lasso linear regression model and Pearson correlation model is proposed. Based on this, a hybrid higher-order DBN score function for GRN reconstruction is proposed, namely BIC-LP. BIC-LP score function is constructed by adding terms based on Lasso linear regression coefficients and Pearson correlation coefficients on classical BIC score function. Therefore, it could capture more information from dataset and curb information loss, compared with both many existing Bayesian family score functions and many state-of-the-art methods for GRN reconstruction. Experimental results show that BIC-LP can reasonably eliminate some false positive edges while retaining most true positive edges, so as to achieve better GRN reconstruction performance.

Transformer models exploit multi-head self-attention to capture long-range information. Further, window-based self-attention solves the problem of quadratic computational complexity and provides a new solution for dense prediction of 3D images. However, Transformers miss structural information due to the naive tokenization scheme. Furthermore, single-scale attention fails to achieve a balance between feature representation and semantic information. Aiming at the above problems, we propose a window-based dynamic crossscale cross-attention transformer (DCS-Former) for precise representation of the diversity features. DCS-former first constructs dual-compound feature representations through Antehoc Structure-aware Module and Post-hoc Class-aware Module. Then, the bidirectional attention structure is designed to interactively fuse structural features with class representations. The experimental results show that our method outperforms various competing segmentation models on three different public datasets.

The graph kernel of the functional brain network is an effective method in the field of neuropsychiatric disease diagnosis like Alzheimer's Disease (AD). The traditional static brain networks cannot reflect dynamic changes of brain activities, but evolving brain networks, which are a series of brain networks over time, are able to seize such dynamic changes. As far as we know, the graph kernel method is effective for calculating the differences among networks. Therefore, it has a great potential to understand the dynamic changes of evolving brain networks, which are a series of chronological differences. However, if the conventional graph kernel methods which are built for static networks are applied directly to evolving networks, the evolving information will be lost and accurate diagnostic results will be far from reach. We propose an effective method, called Global Matching based Graph Kernels (GM-GK), which captures dynamic changes of evolving brain networks and significantly improves classification accuracy. At the same time, in order to reflect the natural properties of the brain activity of the evolving brain network neglected by the GM-GK method, we also propose a Local Matching based Graph Kernel (LM-GK), which allows the order of the evolving brain network to be locally fine-tuned. Finally, the experiments are conducted on real data sets and the results show that the proposed methods can significantly improve the neuropsychiatric disease diagnostic accuracy.

Community search on bipartite graphs has been extensively studied in suspicious-group detection and team formation. However, the existing studies focused on the cohesiveness of the community, but ignored the size constraint on bipartite graphs , potentially leading to large community sizes and high costs. In this study, a size-constrained ( α , β )–community (SCC) containing a query vertex on a bipartite graph was investigated, where the upper layer size of the community cannot exceed threshold s and the lower layer size cannot exceed threshold t . For supporting SCC search in different situations, two search methods—peeling and expansion—are proposed by peeling from the ( α , β )-core containing the query vertex and expanding from the query vertex respectively. An efficient lower bound based on degree gap is proposed by terminating unpromising search branches early to increase the efficiency of the community search. The experimental results indicated that the proposed methods can be used to find communities within the size thresholds, with the efficiency of the search increased based on the lower bound.

Interoperable Blockchain Database (IBD) can enable users to execute transactions for sharing data stored in various blockchains maintained by different organizations or communities in a transparent manner. However, compared to traditional distributed databases, IBD can hardly provide high-level security and scalability, which are caused by many factors, such as system architecture, consensus protocol, and interactive pattern. Among them, the consensus protocol is the most critical factor, since the credibility of consensus nodes inside the corresponding blockchains are difficult to be guaranteed. Additionally, the consensus protocol directly affects the verification efficiency for given transactions in IBD. In this paper, we formally concern the problem of secure data sharing in IBD. We present a scheme namedHybridchainto execute transactions for sharing data securely and efficiently. We first propose a novel concept namedInteroperable Consensus Group(ICG) which organizes a set of basic consensus nodes into a group, each of which is responsible for managing at least one local blockchain. Then, we present an interoperable cross-chains consensus protocol to achieve eventual consistency of blockchain transactions. We conduct extensive experiments, and the evaluation results show that our proposed approach achieves superior performance.

SimRank is a significant metric to measure the similarity of nodes in graph data analysis. The problem of SimRank computation has been studied extensively, however there is no existing work that can provide one unified algorithm to support the SimRank computation both on static and temporal graphs. In this work, we first propose CrashSim, an index-free algorithm for single-source SimRank computation in static graphs. CrashSim can provide provable approximation guarantees for the computational results in an efficient way. In addition, as the reallife graphs are often represented as temporal graphs, CrashSim enables efficient computation of SimRank in temporal graphs. We formally define two typical SimRank queries in temporal graphs, and then solve them by developing an efficient algorithm based on CrashSim, called CrashSim-T. From the extensive experimental evaluation using five real-life and synthetic datasets, it can be seen that the CrashSim algorithm and CrashSim-T algorithm substantially improve the efficiency of the state-of-the-art SimRank algorithms by about 30%, while achieving the precision of the result set with about 97%.

In the area of social network, bioinformatics, e-commerce and so on, the graph model is widely used to present the certain objects and the relations among them. Taking such graph model as the source data, it is significant to extract the compact informative subgraph which can best explain how the given query points are connected. Existing work considers only the connection between individual objects and the returned subgraph is unfavorable when the given query points are far distant each other in the initial graph. In the paper, we will first simplify and present the initial graph by the PeakGraph model in which the graph nodes are divided into different groups based on the density of linkages. Based on the PeakGraph model, we further extract the informative subgraph by two steps, namely local search and global search. In the former step, the local optimal subgraph is extracted for each group; in the latter step, we connect these local optimal subgraphs by some heuristic rules. Our experiments show that our algorithm achieves good performance in both accuracy and efficiency for all kinds of queries.

A representative skylinecontains$k$skyline points that can represent its corresponding full skyline. The existing measuring criteria of$k$representative skylines are specifically designed for static data, and they cannot effectively handle streaming data. In this paper, we focus on the problem of calculating the$k$representative skyline over data streams. First, we propose a new criterion to choose$k$skyline points as the$k$representative skyline for data stream environments, termed the$k$largest dominance skyline ($k$-LDS), which is representative to the entire data set and is highly stable over the streaming data. Second, we propose an efficient exact algorithm, called Prefix-based Algorithm (PBA), to solve the$k$-LDS problem in a 2-dimensional space. The time complexity of PBA is only$\mathcal {O}((M-k)\times k)$where$M$is the size of the full skyline set. Third, the$k$-LDS problem for a$d$-dimensional ($d\ge 3$) space turns out to be very complex. Therefore, a greedy algorithm is designed to answer$k$-LDS queries. To further accelerate the calculation, we propose a$\epsilon$-greedy algorithm which can achieve an approximate factor of$\frac{1}{(1+\epsilon)}(1-\frac{1}{\sqrt{e}})$. Experimental results on both synthetic and real-world data show that our$k$-LDS significantly outperforms its competitors in data stream environments. Furthermore, we demonstrate that the proposed$\epsilon$-greedy algorithm can solve$k$-LDS efficiently and with a competitive accuracy.

Global skyline, as an important variant of skyline, has been widely applied in multiple criteria decision making, business planning and data mining, while there are no previous studies on the global skyline query in the subspace. Hence in this paper we propose subspace global skyline (SGS) query, which is concerned about global skyline in ad hoc subspace. Firstly, we propose an appropriate index structure RB-tree to rapidly find the initial scan positions of query. Secondly, by making analysis of basic properties of SGS, we propose a single SGS algorithm based on RB-tree (SSRB) to compute SGS points. Then an optimized single SGS algorithm based on RB-tree (OSSRB) is proposed, which can reduce the scan space and improve the computation efficiency in contrast to SSRB. Next, by sharing the scan space of different queries, a multiple SGS algorithm based on RB-tree (MSRB) is proposed to compute multiple SGS (MSGS). Finally, the performances of our proposed algorithms are verified through a large number of simulation experiments.

Reverse skyline query plays an important role in many sensing applications, such as environmental monitoring, habitat monitoring, and battlefield monitoring. Due to the limited power supplies of wireless sensor nodes, the existing centralized approaches, which do not consider energy efficiency, cannot be directly applied to the distributed sensor environment. In this paper, we investigate how to process reverse skyline queries energy efficiently in wireless sensor networks. Initially, we theoretically analyzed the properties of reverse skyline query and proposed a skyband-based approach to tackle the problem of reverse skyline query answering over wireless sensor networks. Then, an energy-efficient approach is proposed to minimize the communication cost among sensor nodes of evaluating range reverse skyline query. Moreover, optimization mechanisms to improve the performance of multiple reverse skylines are also discussed. Extensive experiments on both real-world data and synthetic data have demonstrated the efficiency and effectiveness of our proposed approaches with various experimental settings.

A significant number of applications on graph require the key relations among a group of query nodes. Given a relational graph such as social network or biochemical interaction, an informative subgraph is urgent, which can best explain the relationships among a group of given query nodes. Based on Particle Swarm Optimization (PSO), a new framework of SISP (Searching the Informative Subgraph based on PSO) is proposed. SISP contains three key stages. In the initialization stage, a random spreading method is proposed, which can effectively guarantee the connectivity of the nodes in each particle; In the calculating stage of fitness, a fitness function is designed by incorporating a sign function with the goodness score; In the update stage, the intersection-based particle extension method and rule-based particle compression method are proposed. To evaluate the qualities of returned subgraphs, the appropriate calculating of goodness score is studied. Considering the importance and relevance of a node together, we present the PNR method, which makes the definition of informativeness more reliable and the returned subgraph more satisfying. At last, we present experiments on a real dataset and a synthetic dataset separately. The experimental results confirm that the proposed methods achieve increased accuracy and are efficient for any query set.

Traditional databases manage only deterministic information, but now many applications that use databases involve uncertain data. For example, it is infeasible for a sensor database to contain only the exact value of each sensor at all points in time. The uncertainty is inherent in these systems due to measurement and sampling errors, and resource limitations. This paper aims at the query processing algorithm of twig patterns on probabilistic XML documents. The existing algorithms evaluate twig patterns in a traversal way. The main shortcoming of this way is scanning the whole probabilistic XML document to get the final results. In this paper, we first represent a probabilistic XML document in the form of probabilistic tag streams and then match them in a holistic way. Extensive experiments are conducted and show that the proposed holistic way has the higher performance than the traversal way.