In cell-free multiple-input multiple-output (MIMO) systems, beam alignment is critical to achieving high spectral efficiency and reliable communication. However, traditional optimization-based methods often suffer from high computational complexity and sensitivity to dynamic channel conditions, especially in decentralized architectures with distributed access points (APs) and mobile users. To address these challenges, a novel scheme for beam alignment optimization that leverages generative diffusion models (GDMs) is proposed in this paper. By learning the underlying distribution of optimal beamforming configurations from historical channel state information (CSI) and user positioning data, the proposed approach generates high-quality precoding and combining matrices that minimize beam alignment errors while maximizing signal-to-noise ratio (SNR). The system model integrates a conditional diffusion process, where CSI and user locations serve as input to guide the generation of beamforming solutions. The framework operates in two phases: an offline training stage that learns the latent distribution of optimal beam alignment, and an online inference stage that rapidly adapts to real-time channel variations. To ensure practicality, the design incorporates power constraints and feedback mechanisms to dynamically refine beam configurations. Simulations demonstrate significant improvements in beam alignment accuracy and communication performance, particularly in high-mobility scenarios.

The rapid advancement of medical dialog generation (MDG) techniques has enabled medical dialog systems (MDSs) to generate high-quality responses rich in medical expertise by integrating diverse medical information. However, they still encounter several challenges, including generic response generation, lack of semantic precision, and imprecise dialog topic extraction. This study aims to design a novel model to address these challenges simultaneously. Correspondingly, we propose the TRL-HMIE model, which represents transformer reinforcement learning (RL) for heterogeneous medical information extraction. In particular, we incorporate GPT-3 from transformer-related models as the reference language model. Our enhancements focused on three key aspects. First, we developed a conversation-topic classifier to precisely categorize conversation topics, supporting the conversation-topic locator module in generating reliable conversation topics. Second, the model employs a multihead attention mechanism to capture crucial information from the dialog context, facilitating the extraction of key dialog information and enhancing the accuracy of heterogeneous information extraction. Finally, the model integrates RL and a reward fusion mechanism, which, combined with its ability to handle multisource information and long dialog contexts, generates optimized rewards for the TRL-HMIE model, encouraging the production of doctor responses with precise semantics and dialog topics. The experimental results demonstrate that the proposed method achieves a 6.07% improvement over the benchmark model on the MedDG and MedDialog datasets. The experimental results demonstrate that the proposed method achieves a 6.07% improvement over the benchmark model on the MedDG and MedDialog datasets.

The goal of community search is to provide effective solutions for real-time, high-quality community searches within large networks. In many practical applications, such as event organization and friend recommendations, discovering various community structures within a network is crucial for users. However, existing community search algorithms rarely address issues within temporal graphs, and those that do often have two main limitations: (1) traditional community search methods become inefficient and experience significant increases in computation time when scaled to large graphs; (2) while GNN-based community search methods for temporal graphs offer generalizability, they often focus solely on community connectivity and lack cohesiveness. Therefore, we propose a new model PK-GCN, based on Graph Neural Networks (GNNs), to identify persistent k-core communities in temporal networks. This model can handle dynamic changes in temporal graphs and identify communities that persist over time. Compared to existing community search methods, our model not only finds communities with tighter structures but also allows for dynamic queries based on user input without needing retraining. Specifically, our model constructs features by integrating k-core information from core decomposition, graph features, and query features, resulting in more expressive node representations. Additionally, we designed a flexible dynamic query mechanism that allows users to input time information to query communities. Experiments on multiple datasets demonstrate that our model outperforms other GNN-based community search algorithms in F1-score.

Memory-resident malware has become a huge threat to cybersecurity. They perform malicious operations only in memory and are difficult to detect by existing technologies. Existing malware detection solutions fail to effectively extract API sequence’s semantic features and memory data features related to malicious behaviors in memory dumps. This research paper presents a novel detection framework to address these limitations. It first extracts intrinsic semantic features of API sequences from memory data using a fine-tuned BERT, then extracts executable data features from memory dumps using a pre-trained ResNet34 neural network. It then splices the two features to train a deep neural network-based detection model. We created a high-quality dataset with 2180 benign programs and 1897 recent memory-resident malware samples. We implement MemAPIDet for Windows 10. It performs better than the state-of-the-art methods with a prediction accuracy of 97.78%

In the ever-evolving digital landscape, the complexity of web technologies has significantly increased. This complexity highlights the limitations of traditional web defense mechanisms in offering complete protection. Web shells, especially, present a formidable challenge in the field of web security. Recognizing and addressing this challenge is of paramount importance. It necessitates innovative understandings/approaches that contribute to the collective knowledge in web security. To achieve this, our paper introduces a novel type of attack: the cross-file web shell. Alongside this, we propose a detection methodology utilizing Graph Neural Networks (GNNs). Our method leverages the Function Call Graph (FCG) to generate graph embedding, capturing both the structural and semantic nuances of code. By incorporating a variety of statistics features, our approach adeptly identifies the characteristic patterns of web shells. Utilizing deep learning, this technique allows for precise classification and detection. The efficacy of our method is demonstrated by its impressive performance in detecting cross-file web shells, achieving an accuracy of 96.65% and an F1-score of 96.63%. In addition, we simulate real-world cross-file web shell attack and successfully detecte them using our method. These results underscore the potential of our approach in significantly enhancing web security measures.

Abstract Deep learning models have been widely used in hyperspectral images classification. However, the classification results are not satisfactory when the number of training samples is small. Focused on above-mentioned problem, a novel Two-stage Multi-dimensional Convolutional Stacked Autoencoder (TMC-SAE) model is proposed for hyperspectral images classification. The proposed model is composed of two sub-models SAE-1 and SAE-2. The SAE-1 is a 1D autoencoder with asymmetric structre based on full connection layers and 1D convolution layers to reduce spectral dimensionality. The SAE-2 is a hybrid autoencoder composed of 2D and 3D convolution operations to extract spectral-spatial features from the reduced dimensionality data by SAE-1. The SAE-1 is trained with raw data by unsupervised learning and the encoder of SAE-1 is employed to reduce spectral dimensionality of raw data. The data after dimension reduction is used to train the SAE-2 by unsupervised learning. The fine-tuning of SAE-2 encoder and the training of classifier are implemented simultaneously with small number of samples by supervised learning. Comparative experiments are performed on three widely used hyperspectral remote sensing data. The extensive comparative experiments demonstrate that the proposed architecture can effectively extract deep features and maintain high classification accuracy with small number of training samples.

Comprehensive analysis of multi-omics data has now garnered significant attention. However, due to the diversity of multi-omics data, integrating multi-omics information presents a formidable challenge for researchers. Moreover, the high dimensionality and sparsity characteristics in omics data further complicate multi-omics data analysis. To address these challenges and obtain high-quality representations suitable for downstream tasks, we propose a self-supervised clustering learning framework called the Multi-view Feature Hierarchical Contrastive Clustering model (MFHCC) to extract multi-level features. Firstly, the proposed model considers multi-omics as multi-modality and employs an autoencoder for each modality to integrate diverse omics information simultaneously. Secondly, it utilizes a multilevel feature extraction framework with contrastive learning methods to mitigate the impact of redundant information and null values on representation quality while capturing semantic information embedded in the data. Additionally, the model incorporates a deep clustering module to guide the representation toward downstream tasks while integrating high-level features for guidance. Through extensive experiments conducted on pan-cancer datasets, we validate the effectiveness of MFHCC. For instance, the model achieves an accuracy exceeding 76% by omics types, thus confirming its superior performance.

De novo drug design is a challenging task that involves understanding the principles of chemistry, chemical properties, and the rules that govern molecular interactions. Deep learning-based generative models, such as MolGAN, offer a promising approach for generating new molecules with the desired chemical properties from molecular graphs. Such models often combine a discrete generative adversarial network (GAN) and reinforcement learning (RL) to produce highly valid and novel molecules. However, the severe mode collapse problem leads to low performance. This study aims to alleviate and investigate the effect of multiple factors on mode collapse. We conducted experiments on different sampling methods, training epochs, and datasets of various volumes and evaluated the experimental results using performance metrics such as validity, uniqueness, novelty, and diversity. The experimental results demonstrate that noise sampling distributions, training epochs, and training data volumes affect performance. The experimental results provide a direction for mitigating the mode collapse problem for RL-based discrete GANs.

Amid escalating cyber threats, websites have emerged as predominant targets for attackers employing web shells to maintain extended control. Web shells, frequently used by Advanced Persistent Threat (APT) groups, often result in significant damage, despite the conspicuous lack of focused academic research on their detection. This paper illuminates the stealth variant of the web shell, covertly embedded within benign files, and addresses the unique detection challenges presented by their covert nature and the dearth of targeted datasets. In response to these challenges, we construct three datasets: small web shells, benign files, and stealth web shells, subsequently proposing an innovative triplet network detection model for the stealth web shell. This model excels in differentiating stealth web shells from benign files while simultaneously aligning them more closely with small web shells, thereby refining classification precision. Our methodology transforms samples into opcode sequences through a series of processing steps, and then integrates them into the specially designed triplet network. Benchmarked against a cutting-edge deep learning network model and recognized detection tools, our detection methodology yields superior performance, delivering a high accuracy of 92.56% and a robust F1-score of 89.17%. These results substantiate the potency of our approach in countering the mounting threat posed by stealth web shells.

A longstanding technological challenge exists regarding the precise assembly design and performance optimization of large optics in high power laser facilities, comprising a combination of many complex problems involving mechanical, material, and laser beam physics. In this study, an augmented virtual assembly framework based on a multiphysics analysis and digital simulation is presented for the assembly optimization of large optics. This framework focuses on the fundamental impact of the structural and assembly parameters of a product on its optical performance; three-dimensional simulation technologies improve the accuracy and measurability of the impact. Intelligent iterative computation algorithms have been developed to optimize the assembly plan of large optics, which are significantly affected by a series of constraints including dynamic loads and nonlinear ambient excitations. Finally, using a 410-mm-aperture frequency converter as the study case, we present a detailed illustration and discussion to validate the performance of the proposed system in large optics assembly and installation engineering.

Accumulating evidence has demonstrated that RNAs play an important role in identifying various complex human diseases. However, the number of known disease related RNAs is still small and many biological experiments are time-consuming and labor-intensive. Therefore, researchers have focused on developing useful computational algorithms to predict associations between diseases and RNAs. It is useful for people to identify complex human diseases at molecular level, especially in diseases diagnosis, therapy, prognosis and monitoring. In this paper, we propose a novel framework Graph Convolutional Attention Network(GCAN) to predict potential disease-RNAs associations. Facing thousands of associations, GCAN benefits from the efficiency of deep learning model. Compared to other disease-RNAs association prediction methods, GCAN operates the computation process from global structure of disease-RNAs network with graph convolution networks(GCN) and can also integrate local neighborhoods with the attention mechanism. What is more, GCAN is at the first attempt to utilize GCN to discover the feature representation of the latent nodes in disease-RNAs network. In order to evaluate the performance of GCAN, we conduct experiments on two different disease-RNAs networks: disease-miRNA and disease-lncRNA. Comparisons of several state-of-the-art methods using disease-RNAs networks show that our novel frameworks outperform baselines by a wide margin in potential disease-RNAs associations.

Opinion mining has raised growing interest both in industry and academia in the past decade. Opinion role labeling (ORL) is a task to extract opinion holder and target from natural language to answer the question “who express what”. Recent years, neural network based methods with additional lexical and syntactic features have achieved state-of-the-art performances in similar tasks. Moreover, Bidirectional Encoder Representations from Transformers (BERT) has shown impressive performances among a variety of natural language processing (NLP) tasks. To investigate BERT based end-to-end model in ORL, we propose models using BERT, Bidirectional Long short-term Memory (BiLSTM) and Conditional Random Field (CRF) to jointly extract opinion roles (e.g., opinion holder and target). Experimental results show that our models achieve remarkable scores without using extra syntactic and/or semantic features. To our best knowledge, we are among the pioneers to successfully integrate BERT in this manner. Our work contributes to the improvement of state-of-the-art aspect-level opinion mining methods and providing strong baselines for future work.

In recent years, the low-rank sparse tracker has been successfully used in object tracking by exploiting low-rank constraints to capture the underlying structure of candidate particles. It uses simple sparse error to account for occlusion and noise measured by the L1-norm, which is assumed to be following the Laplacian distribution. However, this Laplacian assumption may not be accurate to describe complex corruptions. In this letter, we propose an infrared (IR) target tracking method based on a robust low-rank sparse representation which aims to seek for the maximum-likelihood estimation solution of the residuals in the tracking framework. Experimental results on challenging IR image sequences indicate that the proposed method achieves favorable tracking performance and is more robust to various types of noise.