Spatial transcriptomics (ST) helps us understand cell interactions, developmental processes, and disease progression within tissues by analyzing gene expression while preserving spatial information on tissue sections. However, the spatial distribution patterns of the same cell population may differ in different slice samples, and a single slice is difficult to adapt to spatial changes, making multi-slice integration methods a research hotspot in recent years. Traditional graph convolution relies on a fixed graph structure, whose adjacency relationships remain fixed during training. It cannot be adaptively updated according to feature changes and is difficult to reflect the spatial distribution differences between different slices. Dynamic graph convolutional neural networks (DGCNN), on the other hand, adaptively update based on node embeddings or features during training to capture complex spatial relationships. Therefore, we propose DGAE, a framework based on DGCNN for multi-slice ST data alignment and data enhancement. DGAE consists of two modules: DGAE_align and DGAE_recog. DGAE_align combines K-nearest neighbor (KNN) and r-radius to build a hybrid graph, and integrates the spatial information of different slices to achieve accurate spatial alignment. DGAE_recog aggregates the information of adjacent slices into the target slice for data enhancement, achieving effective transmission of information between different slices. Experimental results show that DGAE outperforms existing methods in multi-slice ST data alignment and also demonstrates superior performance in data enhancement tasks. In addition, DGAE has shown well adaptability and stability in spatial domain recognition, denoising and disease research, demonstrating the wide applicability and scalability of DGAE as a method for multi-slice ST data alignment and data enhancement.

In-network aggregation (INA) has emerged as a key technology to alleviate communication bottlenecks in large-scale distributed training, but its performance is often hindered by suboptimal routing. Existing INA-aware routing algorithms suffer from certain limitations: they either lack a global, multi-job coordination mechanism, or operate on incomplete network models that ignore key hardware constraints such as switch processing capacity. These deficiencies lead to network congestion and inefficient resource utilization, ultimately undermining the full potential of INA. To address these challenges, we present INARouting, a novel framework that holistically solves the multi-job hierarchical aggregation routing problem. We propose TINA, a hierarchical aggregation protocol that supports multi-job in-network aggregation. To address different deployment scenarios, we develop two variants: INARouting-Opt that provides optimal solutions for moderate-scale networks, and INARouting-Relax, a fast and effective heuristic using LP-relaxation and a greedy score-based rounding algorithm for large-scale deployments. Through extensive experiments on various scales of Fat-Tree and Spine-Leaf topologies, we demonstrate that INARouting significantly outperforms state-of-the-art methods. INARouting- Opt achieves provably optimal solutions, reducing average job completion time by up to 56% compared to existing methods. Meanwhile, INARouting-Relax outperforms existing algorithms while being 5× faster in solving time, enabling efficient routing in large-scale, dynamic environments.

Human-Object Interaction (HOI) detection is a fundamental task in understanding human-object relationships. However, existing methods struggle with long-tail data distributions and capturing global context, leading to poor performance in detecting rare classes. To address these challenges, we propose a novel HOI detector named VTHOI, which integrates visual and textual embeddings to improve the model’s global perception and its ability to detect rare classes. First, the image’s global context is extracted and fused with local human-object pair features during decoding to generate vision logits prompts. Subsequently, the proposed Adaptive Logits Fusion Module (ALFM) integrates the vision logits prompts into the backbone, enhancing global contextual understanding. Additionally, the consistency constraints of the language model are employed to learn textual descriptions and semantic relationships of non-rare classes, thereby enabling the model to better capture the features of rare classes. Our approach outperforms state-of-the-art methods on the HICO-DET and V-COCO datasets, achieving significant improvements, particularly in rare class detection. Our code is available at: https://github.com/CrystalCao9/VTHOI.

Android malware detection faces critical challenges due to evolving evasion techniques and data privacy constraints that hinder centralized data sharing. Existing static and dynamic detection methods exhibit complementary limitations: static approaches lack robustness against obfuscation, while dynamic methods fail to capture comprehensive behavioral patterns. To address these issues, we propose a hybrid dynamic-static detection model (CBBA) and a federated learning framework (DW-FL-CBBA) with dynamic weight optimization. The CBBA model integrates static features (permissions/APIs) and dynamic behavioral sequences via a three-channel architecture combining CNN, BPNN, and BiLSTM with attention mechanisms, achieving 98.69% detection accuracy. The DW-FL-CBBA framework implements federated learning with dynamic weighted averaging, reducing performance degradation from low-quality local models by 1 % compared to conventional federated baselines. Our solution effectively mitigates data silos while maintaining GDPRcompliant privacy preservation, offering a deployable framework for collaborative threat intelligence in distributed environments.

BCube is a modular data center network.Compared with other topologies, BCube has natural advantages, such as lower deployment costs and stronger failure recovery capabilities.However, RDMA technology used in BCube still faces challenges, including high retransmission overhead, Head-of-Line Blocking (HoLB) and deadlock problems.Existing solutions for traditional data centers cannot simultaneously address these issues due to the unique topology and server transmission characteristics of BCube.In this paper, we propose a per-port flow control named PortFC for BCube.PortFC addresses the above problems through the designs of a Pause/Resume control signal, a per-port queue allocation method, an egress-detecting per-port flow control mechanism, and a serveraware queue scheduling method.Our evaluation shows that PortFC is free from retransmission, capable of eliminating HoLB and avoiding deadlocks.PortFC achieves 1.7-8.0times higher throughput and reduces latency by 11.7%-87.7%compared to the state-of-the-art

Based on the analysis of user behavior on microvideo platforms, we find that the more times users have explicit interactions with the videos posted by vloggers, the higher the possibility of explicit interactions between users and vloggers. However, the existing recommendation models mainly rely on the direct interests between users and items to model user preferences, and fail to explore the potential interests between users and items from multiple perspectives, resulting in incomplete modeling of user preferences. In response to the above problems, this paper proposes a multi-view augmented graph neural network model for micro-video vlogger recommendation (MAGNN). Specifically, we simultaneously construct bipartite graphs of the user-vlogger interaction relationship and the uservideo interaction relationship to fully capture the direct and potential vlogger preferences of users from different views. Furthermore, we have designed a bi-directional cross-attention with cross-dot-product fusion module. It adaptively learns the correlation between the preference features of different views through the dual paths of forward propagation and backward propagation, and optimizes the attention mechanism by using the cross-dot-product mechanism to enhance the discriminative ability of the attention mechanism. We conducted a large number of experiments on two public datasets. The experimental results fully verify the effectiveness of the method we propose in the recommendation task of micro-video vloggers.

The technology for detecting vehicles holds significant importance in achieving automated monitoring and AI-assisted driving systems.The advanced technique for object detection, specifically a category within YOLOv8, is frequently employed for detecting vehicles. This paper proposes an improved lightweight YOLOv8 detection method that aims to strike a balance between computational load and detection ratio. The method is specifically designed for edge computing platforms to detect vehicle. In this method, a multi-scale convolution block is designed in YOLOv8 backbone network, and an efficient multi-scale attention module (EMA) is introduced to improve the detection accuracy of the algorithm. GSconv and VoVGSCSP modules are incorporated into the YOLOv8 neck network with the aim of minimizing the floating-point operations (FLOPs). In addition, a new high-speed detector module is designed to improve the detection speed and reduce the number of parameters. To assess the method’s performance, we conducted experiments using the PASCAL VOC dataset and the MS COCO dataset. the proposed model exhibits a notable 3.5% enhancement in detection precision, along with a substantial 42.68% reduction in FLOPs, and an impressive 30.50% decrease in the quantity of model parameters in comparison to the existing YOLOv8. Additionally,the average processing time has decreased by 25%. Comprehensive case studies and comparative analyses demonstrate the method’s effectiveness and its superiority over existing approaches.

Survival prediction serves as a pivotal component in precision oncology, enabling the optimization of treatment strategies through mortality risk assessment. While the integration of histopathological images and genomic profiles offers enhanced potential for patient stratification, existing methodologies are constrained by two fundamental limitations: (i) insufficient attention to fine-grained local features in favor of global representations, and (ii) suboptimal cross-modal fusion strategies that either neglect intrinsic correlations or discard modality-specific information. To address these challenges, we propose DSCASurv, a novel cross-modal fusion alignment framework designed to explore and integrate intrinsic correlations across multimodal data, thereby improving the accuracy of survival prediction. Specifically, DSCASurv leverages the local feature extraction capabilities of convolutional layers and the long-range dependency modeling of scanning state space models to extract intra-modal representations, while generating cross-modal representations through dual parallel mixer architectures. A cross-modal attention module functions as a bridge for inter-modal information exchange and complementary information transfer. The framework ultimately integrates all intra-modal representations to generate survival predictions by enhancing and recalibrating complementary information. Extensive experiments on five benchmark cancer datasets demonstrate the superior performance of our approach compared to existing methods.

Advancements in spatial transcriptomics (ST) technology have enabled the analysis of gene expression while preserving cellular spatial information, greatly enhancing our understanding of cellular interactions within tissues. Accurate identification of spatial domains is crucial for comprehending tissue organization. However, the effective integration of spatial location and gene expression still faces significant challenges. To address this challenge, we propose a novel self-supervised graph representation learning framework named stHGC for identifying spatial domains. Firstly, a hybrid neighbor graph is constructed by integrating different similarity metrics to represent spatial proximity and high-dimensional gene expression features. Secondly, a self-supervised graph representation learning framework is introduced to learn the representation of spots in ST data. Within this framework, the graph attention mechanism is utilized to characterize relationships between adjacent spots, and the self-supervised method ensures distinct representations for non-neighboring spots. Lastly, a spatial regularization constraint is employed to enable the model to retain the structural information of spatial neighbors. Experimental results demonstrate that stHGC outperforms state-of-the-art methods in identifying spatial domains across ST datasets with different resolutions. Furthermore, stHGC has been proven to be beneficial for downstream tasks such as denoising and trajectory inference, showcasing its scalability in handling ST data.

The booming of artificial intelligence (AI) agents has brought about promising business scenarios for sixth-generation (6G) mobile networks, while simultaneously posing significant challenges to network functionalities and infrastructure. These AI agents can be deployed on end devices (e.g., intelligent robots and intelligent cars) or as digital entities (e.g., personal AI assistants). As novel service entities with autonomous decision-making and task execution capabilities, AI agents introduce potential risks of uncontrollable actions and privacy disclosures. AI agents also require new 6G capabilities beyond traditional communication, including multimodality information interaction (e.g., AI models and tokens) and support for service requirements (e.g., computing and sensing of data). In this article, we introduce the concept of AI-agent communication network (ACN), a new paradigm to enable global information interaction and on-demand capability provisioning for single or multiple AI agents. We first introduce the vision and architectural framework of ACN. Then, key technologies and future research directions related to ACN are discussed. Furthermore, we provide potential use cases to elaborate on how ACN can expand the service capabilities of 6G networks.

Circular RNA (circRNA) is a class of noncoding RNA that is highly conserved and exhibit exceptional stability. Due to its function as a microRNA sponge, circRNA has gained significant attention as an essential biomarker and potential drug target in the pathogenesis of several cancers. Although many circRNAs have been identified to play a role in cancer resistance, traditional methods are time-consuming and expensive. In this context, computational methods offer a promising way to facilitate the discovery process. However, most existing prediction models focus on the association between circRNAs and drug resistance, without considering the corresponding disease-related information in the circRNA-drug resistance association. Incorporating disease-related information into the prediction of circRNA-drug resistance associations could potentially improve the efficiency and speed of discovering and developing circRNA-targeting drugs. We propose a computational framework, named GraphCDD, for predicting the association between circRNA and drug resistance. Our model utilizes data from three sources, namely circRNA, disease, and drug, to construct three similarity networks that represent the features of circRNA, disease, and drug, respectively. We utilize a multimodal graph neural network to acquire efficient representations of circRNAs, diseases, and drugs by integrating various types of information, and establish a predictive model. The experimental results have validated the effectiveness of our model and provided a promising method in predicting potential associations between circRNA and drug resistance.

Recently, Computing Force Networks (CFNs) have emerged to deeply integrate and flexibly schedule multi-layer, multi-domain, distributed, and heterogeneous computing force resources. CFNs build a resources trading platform between consumers and providers, facilitating efficient resource sharing. Therefore, resources trading is an important issue but it faces some challenges. Firstly, because all kinds of large-scale and small-scale resource providers are distributed in a wide area and the number of consumers is larger compared with edge/cloud computing scenarios, the credibility of consumers and providers is hard to guarantee. Secondly, due to market monopolies by large resource providers, fixed pricing strategies, and information asymmetry, both consumers and providers exhibit a low willingness to engage in resources trading. To solve these challenges, the paper proposes an incentive mechanism for trust-driven resources trading to guarantee trusted and efficient resources trading. We first design a trust guarantee scheme based on reputation evaluation, blockchain, and trust threshold setting. Then, the proposed incentive scheme can dynamically adjust prices and enable the platform to provide appropriate rewards based on providers’ classified types and contributions. We formulate an optimization problem aiming at maximizing the trading platform’s utility and obtaining an optimal contract based on individual rationality and incentive compatible constraints. Simulation results verify the feasibility and effectiveness of our scheme, highlighting its potential to reshape the future of computing resource management, increase overall economic efficiency, and foster innovation and competitiveness in the digital economy.

Weed detection is crucial for the healthy growth of crops, yet existing detection models struggle to perform high-accuracy real-time detection of weeds in natural environments on edge computing devices. This paper introduces EMPA-YOLO, a model designed for rapid, accurate, real-time weed detection on low-performance edge computing devices. It incorporates an efficient multi-scale convolutional structure, C3EMSC, and a lightweight, adaptive weight subsampling layer, LAWDS, into YOLOv5s. Additionally, a logical distillation algorithm, AlignSoftTarget, is proposed for knowledge distillation. Validation on a mixed dataset of crops and weeds showed that EMPA-YOLO improved mAP50 by 11.2%, reduced parameter count by 4.8M, decreased computational load by 8.7GFLOPs, and increased inference frame rate by 58% compared to the original YOLOv5s algorithm. When compared to YOLOv3, YOLOv5s, YOLOv6, YOLOv8s, and RT-DETR, inference speed improved by 89.2%, 60.8%, 77.5%, 76.5%, and 94.7%, respectively, with mean accuracy enhancements of 7.3%, 11.2%, 16.7%, 0.9%, and 1.2%. Real-world testing on edge computing devices met real-time detection requirements, proving its efficacy and practicality in weed detection.

网络资源的覆盖范围日益广泛, 算力资源也逐渐成为能够提供泛在计算服务的基础设施. 然而, 在广域网络, 底层网络和计算资源缺乏密切的研究或协同设计, 仍然存在计算服务调度缓慢、 数据分发不灵活、 数据传输效率低等问题. 本文提出算力感知网络(CAN)的系统架构设计, 其核心贡献在于引入感知平面来收集、 管理并综合计算和网络的信息. 这样, 感知平面、控制平面和数据平面组成一个闭环控制系统, 增强了整个系统的感知能力、 决策能力和数据转发功能. 为了使能CAN系统, 本文提出三项关键技术: 算力路由、 弹性广播和广域高吞吐传输. 本文以人工智能(AI)模型训练、 推理和离线参数传输为例, 展示CAN的适用性, 并指出未来的一些研究方向.

Predicting potential drug-disease associations (RDAs) plays a pivotal role in elucidating therapeutic strategies for diseases and facilitating drug repositioning, making it of paramount importance. However, existing methods are constrained and rely heavily on limited domain-specific knowledge, impeding their ability to effectively predict candidate associations between drugs and diseases. Moreover, the simplistic definition of unknown information pertaining to drug-disease relationships as negative samples presents inherent limitations. To overcome these challenges, we introduce a novel hierarchical negative sampling-based graph contrastive model, termed HSGCLRDA, which aims to forecast latent associations between drugs and diseases. In this study, HSGCLRDA integrates the association information as well as similarity between drugs, diseases and proteins. Meanwhile, the model constructs a drug-disease-protein heterogeneous network. Subsequently, employing a hierarchical structural sampling technique, we establish reliable negative drug-disease samples utilizing PageRank algorithms. Utilizing meta-path aggregation within the heterogeneous network, we derive low-dimensional representations for drugs and diseases, thereby constructing global and local feature graphs that capture their interactions comprehensively. To obtain representation information, we adopt a self-supervised graph contrastive approach that leverages graph convolutional networks (GCNs) and second-order GCNs to extract feature graph information. Furthermore, we integrate a contrastive cost function derived from the cross-entropy cost function, facilitating holistic model optimization. Experimental results obtained from benchmark datasets not only showcase the superior performance of HSGCLRDA compared to various baseline methods in predicting RDAs but also emphasize its practical utility in identifying novel potential diseases associated with existing drugs through meticulous case studies.

Studies have shown that IncRNA-miRNA interactions can affect cellular expression at the level of gene molecules through a variety of regulatory mechanisms and have important effects on the biological activities of living organisms. Several biomolecular network-based approaches have been proposed to accelerate the identification of lncRNA-miRNA interactions. However, most of the methods cannot fully utilize the structural and topological information of the lncRNA-miRNA interaction network. In this article, we proposed a new method, ISLMI, a prediction model based on information injection and second order graph convolution network(SOGCN). The model calculated the sequence similarity and Gaussian interaction profile kernel similarity between lncRNA and miRNA, fused them to enhance the intrinsic interaction between the nodes, using SOGCN to learn second-order representations of similarity matrix information. At the same time, multiple feature representations obtain using different graph embedding methods were also injected into the second-order graph representation. Finally, matrix complementation was used to increase the model accuracy. The model combined the advantages of different methods and achieved reliable performance in 5-fold cross-validation, significantly improved the performance of predicting lncRNA-miRNA interactions. In addition, our model successfully confirmed the superiority of ISLMI by comparing it with several other model algorithm.

In this paper, we analyze the service information informing problem in the Computing Aware Networking (CAN). We introduce the motivation and the key use case of the Computing Aware Networking. For the service information informing, we describe the related mechanisms and an example procedure. Meanwhile, we explore the way to measure the computing in CAN. This paper provides a general overview of service information informing in CAN, explores the key technologies, and targets to work as a reference for the further study of CAN.

Facing the development trend of computing and network deep convergence, this paper proposes a computing-aware routing protocol(CARP), which introduces computing information as a cost into the network. CARP provides the capability to jointly schedule service requests to optimal service endpoints along network path, and guarantee the quality of service (QoS) for end users. Moreover, in order to make the network aware of the computing resource information, we develop the computing resource discovery and advertisement scheme, which achieves a better trade-off between network resource consumed by routing path and signaling load for computing resource information updates. Finally, The test results show that under the ideal network load, the system capacity of the proposed system can be increased by 33.17% compared with the existing MEC system, and the end-to-end average latency can be increased by 35.29%. Therefore, the performance analyses show that our proposed scheme can substantially achieve the joint optimization of computing and networking resources through a tighter integration of computing and networking.

MOTIVATION: CircularRNA (circRNA) is a class of noncoding RNA with high conservation and stability, which is considered as an important disease biomarker and drug target. Accumulating pieces of evidence have indicated that circRNA plays a crucial role in the pathogenesis and progression of many complex diseases. As the biological experiments are time-consuming and labor-intensive, developing an accurate computational prediction method has become indispensable to identify disease-related circRNAs. RESULTS: We presented a hybrid graph representation learning framework, named GraphCDA, for predicting the potential circRNA-disease associations. Firstly, the circRNA-circRNA similarity network and disease-disease similarity network were constructed to characterize the relationships of circRNAs and diseases, respectively. Secondly, a hybrid graph embedding model combining Graph Convolutional Networks and Graph Attention Networks was introduced to learn the feature representations of circRNAs and diseases simultaneously. Finally, the learned representations were concatenated and employed to build the prediction model for identifying the circRNA-disease associations. A series of experimental results demonstrated that GraphCDA outperformed other state-of-the-art methods on several public databases. Moreover, GraphCDA could achieve good performance when only using a small number of known circRNA-disease associations as the training set. Besides, case studies conducted on several human diseases further confirmed the prediction capability of GraphCDA for predicting potential disease-related circRNAs. In conclusion, extensive experimental results indicated that GraphCDA could serve as a reliable tool for exploring the regulatory role of circRNAs in complex diseases.

MOTIVATION: Accumulating evidences have indicated that microRNA (miRNA) plays a crucial role in the pathogenesis and progression of various complex diseases. Inferring disease-associated miRNAs is significant to explore the etiology, diagnosis and treatment of human diseases. As the biological experiments are time-consuming and labor-intensive, developing effective computational methods has become indispensable to identify associations between miRNAs and diseases. RESULTS: We present an Ensemble learning framework with Resampling method for MiRNA-Disease Association (ERMDA) prediction to discover potential disease-related miRNAs. Firstly, the resampling strategy is proposed for building multiple different balanced training subsets to address the challenge of sample imbalance within the database. Then, ERMDA extracts miRNA and disease feature representations by integrating miRNA-miRNA similarities, disease-disease similarities and experimentally verified miRNA-disease association information. Next, the feature selection approach is applied to reduce the redundant information and increase the diversity among these subsets. Lastly, ERMDA constructs an individual learner on each subset to yield primitive outcomes, and the soft voting method is introduced for making the final decision based on the prediction results of individual learners. A series of experimental results demonstrates that ERMDA outperforms other state-of-the-art methods on both balanced and unbalanced testing sets. Besides, case studies conducted on the three human diseases further confirm the ERMDA's prediction capability for identifying potential disease-related miRNAs. In conclusion, these experimental results demonstrate that our method can serve as an effective and reliable tool for researchers to explore the regulatory role of miRNAs in complex diseases.

Long non-coding RNA(lncRNA) can interact with microRNA(miRNA) and play an important role in inhibiting or activating the expression of target genes and the occurrence and development of tumors. Accumulating studies focus on the prediction of miRNA-lncRNA interaction, and mostly are concerned with biological experiments and machine learning methods. These methods are found with long cycles, high costs, and requiring over much human intervention. In this paper, a data-driven hierarchical deep learning framework was proposed, which was composed of a capsule network, an independent recurrent neural network with attention mechanism and bi-directional long short-term memory network. This framework combines the advantages of different networks, uses multiple sequence-derived features of the original sequence and features of secondary structure to mine the dependency between features, and devotes to obtain better results. In the experiment, five-fold cross-validation was used to evaluate the performance of the model, and the zea mays data set was compared with the different model to obtain better classification effect. In addition, sorghum, brachypodium distachyon and bryophyte data sets were used to test the model, and the accuracy reached 0.9850, 0.9859 and 0.9777, respectively, which verified the model's good generalization ability.

RNA binding protein (RBP) is extensively involved in various cellular regulatory processes through the interaction with RNAs. Capturing the RBP binding preferences is fundamental for revealing the pathogenesis of complex diseases. Many experimental detection techniques are still time-consuming and labor-intensive, therefore, it is indispensable to develop a computational method with convincing accuracy. In this study, we proposed a CNN-BLSTM hybrid deep learning framework, named DeepDW, for predicting the RBP binding sites on RNAs with high-order encoding features of RNA sequence and secondary structure. The high-order encoding strategy was used to characterize the dependencies among adjacency nucleotides. For CNN-BLSTM hybrid model, DeepDW first employed two 1-D convolutional neural networks (CNNs) for learning the local features from high-order encoded matrices of RNA sequence and structure separately, and then applied two bidirectional long short-term memory networks (BLSTMs) to capture the global information in a higher level. Moreover, a series of experiments were carried out on 31 public datasets to evaluate our proposed framework, and DeepDW achieved superior performance than the state-of-the-art methods. The results indicated that the combination of high-order encoding method and CNN-BLSTM hybrid model had advantages in identifying RBP-RNA binding sites.

Recent studies have found that lncRNA (long non-coding RNA) in ncRNA (non-coding RNA) is not only involved in many biological processes, but also abnormally expressed in many complex diseases. Identification of lncRNA-disease associations accurately is of great significance for understanding the function of lncRNA and disease mechanism. In this paper, a deep learning framework consisting of stacked autoencoder(SAE), multi-scale ResNet and stacked ensemble module, named DHNLDA, was constructed to predict lncRNA-disease associations, which integrates multiple biological data sources and constructing feature matrices. Among them, the biological data including the similarity and the interaction of lncRNAs, diseases and miRNAs are integrated. The feature matrices are obtained by node2vec embedding and feature extraction respectively. Then, the SAE and the multi-scale ResNet are used to learn the complementary information between nodes, and the high-level features of node attributes are obtained. Finally, the fusion of high-level feature is input into the stacked ensemble module to obtain the prediction results of lncRNA-disease associations. The experimental results of five-fold cross-validation show that the AUC of DHNLDA reaches 0.975 better than the existing methods. Case studies of stomach cancer, breast cancer and lung cancer have shown the great ability of DHNLDA to discover the potential lncRNA-disease associations.

Wang–Mendel (WM) fuzzy system is an effective and interpretable model for solving tabular data classification problem. However, original WM fuzzy system is weak in handling dataset with high dimensionality or large volume. Meanwhile, its capability of characterizing data is narrow, which results from lacking hierarchical transformation of features like deep learning-based models. In this article, we propose a deep fuzzy rule-based classification system (DFRBCS) based on improved WM method, in which fuzzy technique and deep learning strategy are combined to make a desirable tradeoff between model’s interpretability and prediction accuracy. We first redefine the consequent part of fuzzy rule in WM fuzzy system with class probability vector, which endows the improved WM fuzzy system with capacity for serving as building block of deep model. The model structure of DFRBCS is designed in layer-by-layer manner, where raw features can be transformed hierarchically. For every layer in DFRBCS, it contains many improved WM fuzzy systems whose input spaces are generated by shuffling and sliding window operation on concatenated outputs of fuzzy systems in previous layer. Comparative experiments are conducted on 45 real-world datasets with various sizes and dimensionality between our method, five baseline models, and the other deep fuzzy classifiers (D-TSK-FC, HID-TSK-FC, FCCI-TSK, DSA-FC, and MEEFIS). The experimental results show that DFRBCS is competitive in classification performance and promising in model’s interpretability.

The interaction between ncRNA and protein is a kind of crucial molecular activities in a cell. Developing computational methods to predict ncRNA-protein interactions has attracted increasing attentions in recent years. In this work, a novel stacked ensemble learning framework is presented for predicting ncRNA-protein interaction based on heterogeneous feature combinations, named HFC-RPI. Firstly, the compositional features of k-mer with different orders were extracted from the primary sequence and secondary structure of RNA and protein respectively. Secondly, we trained a set of base learners using a variety of heterogeneous combinations of the extracted features respectively. Thirdly, the prediction results of these base learners were employed to train the stacked learner, which output the final prediction result at the higher layer in HFC-RPI. Moreover, in order to improve the generalization of HFC-RPI, when training the base learners, a cross-validation based method was applied. Extensive experimental results showed that the proposed learning framework HFC-RPI was effective and feasible for predicting the interaction of ncRNA and protein. By comparing with state-of-the-art methods, HFC-RPI was superior to them on most performance evaluation metrics.

In this paper, we propose a gated iterative capsule network model for the ADR detection task, named GICN. To alleviate the impact caused by abbreviations and misspelled words, we add character embedding as part of the input. Most ADRs consist of multiple words, e.g., short-term memory dysfunction. Hence, we apply a convolutional neural network (CNN) to obtain the complete phrase information. To effectively extract deep semantic information, we introduce a capsule network with a gated iteration unit that clusters features from underlying to high capsules. The gated iteration mechanism can remember contextual information, which will be introduced when clustering features. Experimental results show that our approach can achieve significant performance improvement for ADR detection from social media text compared with other state-of-the-art works.

Different facial components carry different amount of information being conveyed for 3D dynamic expression recognition. Hence, identifying facial components that are highly relevant to specific expression changes is crucial for discriminative facial expression recognition. This work aims to learn expression-sensitive features, which are expected to not only yield comparable recognition performance with the state-of-the-art ones, but also can be interpreted by human. Firstly, spatio-temporal features (HOG3D) are extracted from local depth patch-sequences to represent facial expression dynamics. A two-phase feature selection process is then proposed to determine the facial components that can best distinguish the expressions. In order to verify the effectiveness of the resulting facial components, the expression-sensitive features from the corresponding area are fed into a hierarchical classifier for facial expression recognition. The proposed method is evaluated on the BU-4DFE benchmark database, and results show that learned expression-sensitive features can achieve a comparable recognition performance with existing methods. Additionally, the resulting HOG3D features after feature selection can be used to generate semantic interpretation of the expression dynamics.

This paper proposes a semantic concept method to recognize multi-ethnic people based on Axiomatic fuzzy set (AFS) theory with application to image analysis. There are two advantages of the proposed approach: (i) It can convert the facial features to semantic concepts and in such a way we bridge the semantic gap between low level pixel features and interpretable concepts. (ii) It can implement the logical operation of semantic concepts in the AFS framework. Technically, we first construct facial features utilizing the facial landmarks such as eyes, nose, mouth, and face contour. Second, we establish some corresponding semantic concepts to describe facial features. Finally, a set of the semantic concept rules are extracted to form a classifier aimed at identifying facial ethnic attributes. The efficacy of the proposed approach is verified on Chinese Multi-ethnic face database (CMFD), FEI and CK[Formula: see text]. Meanwhile, we first demonstrate that the selected features have two obvious advantages: (1) these features can achieve better performance for ethical recognition than the features based on pixel values directly. (2) The selected features can be obtained via facial landmark detector regardless of the image resolutions. Then, we compare the proposed approach with some existing classifiers using the selected features, such as principal component analysis (PCA), C4.5, Decision table, Cart, Fuzzy Decision Tree (FDT) and Repeated Incremental Pruning to Produce Error Reduction (Ripper), extensive experiments show that our method exhibits a similar performance with these methods, which is demonstrated by Friedman test, however, our proposed approach can provide interpretability and comprehension capability.

In this study, we present a comprehensible classifier AFSNN that embeds a new type of coherence membership function, which builds upon the theoretical findings of the axiomatic fuzzy set (AFS) theory into the hidden layer of neural network with random weights (NNRWs). Borrowing from the idea of NNRWs that employs the random initialization technique, the relation among attributes, simple concepts, and complex concepts are randomly determined. Complex concepts are generated through the combination of randomly selected simple concepts by AFS logic operation. The output weights of NNRWs are utilized to evaluate the confidence of each complex concept for every target class, which means that the feasibility of complex concepts for every class is determined analytically rather than through the tuning parameters of constraint conditions such as in conventional AFS-based classifiers. For the proposed method, compared to other neural-network-based classification methods, the fuzzy descriptions generated from complex concepts in hidden layer make classification result human understandable. We have experimented with several benchmark datasets and compared the results with other neural network-based classifiers. We show that our method outperforms Ensemble, EvRBFN, NNEP, LVQ, and iRProp+ in the seven out of ten datasets. The results show that the performance of AFSNN is competitive in terms of classification accuracy and the network shows a distinctive capability of providing explicit knowledge in the form of linguistic description.

One potential problem in real classification applications is that the amount of labeled training data is insufficient since it is usually time-consuming to label data manually. When multiple modalities are available, it is possible to train an initial classifier for each modality using a small amount of labeled data, and then re-train each classifier using unlabeled data associated with the labels generated from the other modalities. This can be achieved by the well-known CO-training algorithm. Assuming that two modalities are available, it only takes the information from the other modality but not that from the self modality into account when choosing data, which usually results in slow convergence of classification accuracy. This may make the CO-training procedure time-consuming. To overcome this, we present a novel modification to the original CO-training algorithm, which is concerned with how new samples are chosen at each iteration to re-train the classifiers in order to improve the convergence of classification accuracy. In our method, the new data is chosen based on the weighted scores which are generated from both modalities instead of only the scores from the other modality as in the original CO-training. We apply both the modified and original CO-training methods on multi-modal person identification task using speech and vision. Experiments on a publicly available database show that our method outperforms the original CO-training by a large margin, in terms of convergence of classification accuracy on a separate testing data set.

The most fundamental problem of local feature based kinship verification methods is that a local feature can capture the variations of environmental conditions and the differences between two persons having a kin relation, which can significantly decrease the performance. To address this problem, we propose a feature subtraction method to remove the kinship unrelated part from the local feature through a linear function of which only one parameter, namely a subtraction matrix, needs to be inferred from training data. This is done by using a gradient descent method to simultaneously minimize the feature distance between face image pairs with kinship and maximize the distance between non-kinship pairs. Based on the subtracted feature, the verification is realized through a simple Gaussian based distance comparison method. Experiments on two public databases show that the feature subtraction method outperforms or is comparable to state-of-the-art kinship verification methods.

In this paper, we present a local feature learning method for face recognition to deal with varying poses. As opposed to the commonly used approaches of recovering frontal face images from profile views, the proposed method extracts the subject related part from a local feature by removing the pose related part in it on the basis of a pose feature. The method has a closed-form solution, hence being time efficient. For performance evaluation, cross pose face recognition experiments are conducted on two public face recognition databases FERET and FEI. The proposed method shows a significant recognition improvement under varying poses over general local feature approaches and outperforms or is comparable with related state-of-the-art pose invariant face recognition approaches.

In P2P multicast applications, membership management protocols are the basic utilities. In this context, gossip-based protocols have emerged as attractive ones for that they are highly reliable, scalable and simple. Existing gossip-based membership protocols either ignore the underlying topology and the heterogeneity nature of peer nodes or consume lots of control overhead. In this paper, first, we present a modified scalable membership protocol, MSCAMP, to account for node heterogeneity. Then a DHT-aid, gossip-based heterogeneous peer-to-peer membership protocol, called DIGOM, is proposed. DIGOM groups nearby nodes into clusters and takes advantage of MSCAMP as an intra-cluster membership protocol. A DHT structure is built to aid node subscription and inter-cluster link building. From the theoretical analysis and simulation results, both the inter-and intra-cluster fanout in DIGOM can satisfy the requirements for reliable dissemination. Specially, DIGOM achieves a good quality of load balance and requires no synchronization.

Traffic metrics at application level are critical for protocol research, abnormity detection, accounting and network operation. There are great challenges to identify packets at application level since dynamic protocol ports and packet encryption are deployed popularly. There are several different methods of traffic identification being proposed in recently research for corresponding applications. It is impossible to identify traffic with any one method alone. A methodology of online traffic identification at application level named multi-phases identification (MPI) based on packet and flow is proposed in this paper. There are two stages in the methodology. The traffic classification is based on packet characteristic in the first stage and based on flow feature in the second stage to correct the results in the first stage. There are several advantages in MPI: (1) these existing traffic identification methods can be easily integrated into MPI to improve the identification accuracy, (2) the corresponding new identification method for the new application can be inserted into MPI feasibly with scripts of the identification rule, and (3) efficiency of identification can be improved with the mechanism of adaptive justification for the sequence of methods and implemented on multi-CPUs platform. MPI has been implemented a general purpose CPU platform with OC-48 POS and 10 GE network interface. Experiment on an OC-48 POS backbone link shows MPI is accurate and effective for traffic identification.

Web community detection is one of the important ways to enhance retrieval quality of web search engine. How to design one highly effective algorithm to partition network community with few domain knowledge is the key to network community detection. Traditional algorithms, such as Wu-Huberman algorithm, need priori information to detect community, the Radichi algorithm relies on the triangle number in the network, the extremal optimization algorithm proposed by Duch J. is extremely sensitive to the initial solution, easy to fall into the local optimum. This article proposes a new model based on particle swarm optimization to detect network community, and with different scale network chart, Zachary, Krebs and dolphins network architecture to test the algorithm, the experimental results indicate this model can effectively find web communities of network structure without any domain information.