Medication recommendation systems aim to provide personalized and safe medication options based on individual patient records. However, existing approaches often face challenges related to inadequate modeling of complex relationships within Electronic Health Records (EHRs), data sparsity, and a lack of explainability for recommendations. In this paper, we present a Knowledge-enhanced Explainable HyperGraph Convolution Network (KEHGCN) that constructs a hierarchical hypergraph structure to capture the multi-level relationships within EHR data. By incorporating external knowledge graphs, our approach introduces additional positive relations that help alleviate the impact of data sparsity on model learning. Furthermore, by performing generalized metapath construction and selection on the knowledge graph, our approach achieves effective knowledge filtering and extracts semantically meaningful metapaths, thereby further enhancing the explainability of the recommendation results. We also explicitly introduce negative relations present in the domain knowledge to improve the safety of medication recommendation. Extensive experiments on different hospital departments of MIMIC-III and MIMIC-IV datasets demonstrate that KEHGCN outperforms other state-of-the-art baselines.

The development of artificial intelligence and medical informatics has empowered the medication recommendation systems with enhanced capabilities. However, existing methods struggle with the data imbalance problem in Electronic Health Records (EHRs), where the majority of records are concentrated on a limited subset of common diagnoses, procedures, and medications. It hampers the models’ ability to recommend appropriate medications when dealing with uncommon or multifaceted cases. In addition, existing approaches often fail to adequately model the complex relationships inherent in heterogeneous medical data sources, especially medication molecular structure information. This gap restricts the potential for uncovering meaningful associations among diverse clinical entities. To address these issues, we design a hierarchical attention-based pretraining strategy, leveraging the semantic hierarchies of medical entity codes to facilitate knowledge transfer, so as to alleviate the challenge of data imbalance. Furthermore, we design a novel bilateral hierarchical heterogeneous hypergraph convolution network for medication recommendation. Specifically, we construct specialized hypergraphs for both EHR data and medication molecular structure data, enabling hypergraph convolution to capture high-order relationships while promoting bilateral knowledge enhancement between these heterogeneous data sources. This comprehensive integration allows the model to effectively capture the relationships among clinical and molecular information. Experimental results on different hospital departments of MIMIC-III and MIMIC-IV datasets demonstrate the superior performance of our model compared to state-of-the-art methods. Our source code is released at: https://github.com/LusiaZ/BH3-MedRec .

We propose an innovative fusion algorithm that combines a dense-beam LiDAR and a camera using a post-fusion strategy. This approach utilizes detection results from both images and point clouds and performs target matching based on the uniqueness of each target. Unlike traditional feature-layer fusion networks, our approach is trained using established single-sensor detection algorithms, eliminating the need for designing a feature-layer fusion network. Through experimental validation, our method surpasses the performance improvement achieved by feature-level fusion methods. Specifically, our approach achieves a significant 4.28% increase in sensing accuracy and a remarkable 25.48% improvement in pedestrian recall compared to the single-sensor system. Moreover, our method consumes fewer computational resources and is easy to implement, thereby enhancing efficiency.

Identifying which software versions are affected by a vulnerability is critical for patching, risk mitigation. Despite a growing body of tools, their real-world effectiveness remains unclear due to narrow evaluation scopes—often limited to early SZZ variants, outdated techniques, and small or coarse-grained datasets. In this paper, we present the first comprehensive empirical study of vulnerability-affected versions identification. We curate a high-quality benchmark of 1,128 real-world C/C++ vulnerabilities and systematically evaluate 12 representative tools from both tracing and matching paradigms across four dimensions: effectiveness at both vulnerability and version levels, root causes of false positives and negatives, sensitivity to patch characteristics, and ensemble potential. Our findings reveal fundamental limitations: no tool exceeds 45.0% accuracy, with key challenges stemming from heuristic dependence, limited semantic reasoning, and rigid matching logic. Patch structures such as add-only and cross-file changes further hinder performance. Although ensemble strategies can improve results by up to 10.1%, overall accuracy remains below 60.0%, highlighting the need for fundamentally new approaches. Moreover, our study offers actionable insights to guide tool development, combination strategies, and future research in this critical area. Finally, we release the replicated code and benchmark on our website to encourage future contributions.

Machine learning techniques can help us deal with many difficult problems in the real world. Proper ensemble of multiple learners can improve the predictive performance. Each base learner usually has different predictive ability on different instances or in different instance regions. However, existing ensemble methods often assume that base learners have the same predictive ability for all instances without consideration of the specificity of different instances or categories. To address these issues, we propose an adaptive ensemble learning framework with category-aware attention and local contrastive loss, which can adaptively adjust the ensemble weight of each base classifier according to the characteristics of each instance. Specifically, we design a category-aware attention mechanism to learn the predictive ability of each classifier on different categories. Furthermore, we design a local contrastive loss to capture local similarities between instances and further enhance the model’s ability to discern fine-grained patterns in the data. Extensive experiments on 20 public datasets demonstrate the effectiveness of the proposed model.

Session-based recommendation (SBR) aims to predict user behaviors based on anonymous sessions. Compared with traditional user-based recommendation, SBR has a wider range of applications, but also suffers from more severe data sparsity problems because of the absence of user-profiles and limited short-term interactions. Furthermore, both users and items in the real world have a multifaceted nature. Users may exhibit multiple intents within a session, while items may have different semantics in different contexts. Unfortunately, existing approaches often overlook or only consider one aspect of them. To address these issues, we propose a novel hypergraph-based framework for session-based recommendation, called Hyperedge Interactional Convolution Network (HICN). Each session is represented as a sequential hyperedge, and multiple modules are designed to model and make use of the multifaceted nature of items and users. In addition, two inter-hyperedge modeling modules are designed to leverage related auxiliary information from other sessions with consideration of the existence of noise, which can help alleviate the data sparsity problem. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model HICN.

In order to ensure that the web application can continue to provide high-quality services after implementing the bandwidth management schemes, the administrators usually need to predict the Quality of Service(QoS) in advance according to the hypothetical changes of the web system. In the existing research, few pay attention to the prediction of QoS in bandwidth-driven evolutionary scenarios. In this paper, we propose a solution comprised of automated data mining skills and transfer learning techniques to predict the response time of web services in bandwidth-driven evolutionary scenarios. We choose a suitable approach of domain adaptation according to the characteristics of the QoS prediction problems in this paper. There are mainly three contributions in this paper: 1) we adopt an automated data mining approach to extract features for prediction. 2) To our knowledge, it is the first time to apply domain adaptation to the QoS prediction of web services in evolutionary scenarios. 3) We perform some experiments to evaluate the effectiveness and stability of the proposed solution, including two types of evolutionary scenarios under a realworld web application.

For resource management purpose, administrators usually need to perform what-if analyses to predict the impact of any workload growths or planned changes on the performance of web services. A what-if analysis requires not only the design of system models, but also the workload models that represent the real-world user behavior. Existing methods of workload characterization based on probabilistic graphical models are quite complex if there are many web services provided by a system. Meanwhile, bandwidth resource is usually not taken into account in many related works, though it is a relatively expensive resource in cloud markets. In fact, it's very challenging to predict the network throughput of modern web services due to the factors of client-side caching, miscellaneous service responses and complex network transportation. In this paper we propose a methodology of what-if analysis named Log2Sim for the bandwidth management of web systems. We use a lightweight workload model to describe user behavior, an automated mining approach to obtain characteristics of workloads and responses from massive web logs, and traffic-aware simulations to predict the impact on the network throughput and the response time within changing contexts of user behavior. We also choose a real-life web system as use case to evaluate the effectiveness, accuracy and stability of this methodology.

To maintain a complex modern web-based system, the what-if analysis is quite useful for predicting the impact of any workload growth or planned change on the performance of individual components, even the entire system. In this paper we propose an approach based on log mining to build what-if models of web-based systems and validate them by using OPNET simulation tool. Three contributions of this paper are: (1) the construction of what-if models based on log mining to automate the task of modeling and validation, (2) the technique to address complexity of user behaviors including users' browser caching, clustering of requests and users, etc., (3) and two real-life cases to evaluate the effectiveness and accuracy of this approach. The preliminary results show that the relative errors between logged data and simulation results are less than 18% in most cases including number of requests and network throughput.