Image captioning is crucial for multimodal understanding, bridging visual content and natural language. Despite recent advancements in Large Multimodal Models (LMMs), when faced with unseen entities or scenes in the open world, even when attempting to leverage learned knowledge, models still struggle with vague and inaccurate descriptions, and may even generate knowledge hallucinations. A key reason is that the model fails to effectively integrate knowledge with visual information, limiting its understanding of visual content. Thus, we propose Adaptive Knowledge Graph-guided Multimodal Alignment (AKGMA) for image captioning, which enhances semantic understanding in open-world scenes through visual knowledge reasoning, reducing knowledge hallucinations and improving caption quality. It consist three key components: Entity-guided Knowledge Aligner (EKA), Adaptive Knowledge Graph Construction (AKGC), and Scene-Context Knowledge Adapter (SCKA). EKA connects visual entities to knowledge graphs, providing structured knowledge to a small language model, which interacts with a visual encoder to acquire visual knowledge. AKGC uses reinforcement learning to build image-relevant subgraphs to optimize knowledge prompts and improve knowledge hallucinations. SCKA leverages scene graph annotations to extract visual contextual knowledge and inject it into Large Language Models (LLMs), ensuring the generated descriptions are consistent with the image's details. Additionally, we introduce UniKnowCap, a new image knowledge description dataset spanning various open-world knowledge domains, designed to evaluate the knowledge accuracy and detail consistency of model-generated descriptions. Extensive experiments show our model outperforms baselines across multiple metrics.

Ethereum, a foundational blockchain platform enabling decentralized applications, has catalyzed the development of myriad applications through smart contracts. However, vulnerabilities within these contracts have precipitated notable financial losses, garnering keen interest from both industry and academia. While existing vulnerability detection research has progressed in addressing traditional challenges, it increasingly falls short of comprehensively securing smart contracts against evolving attack patterns and new vulnerabilities. Distinguishing itself from prior surveys, this review conducts an in-depth analysis of actual attack incidents, delving into the characteristics of recent vulnerabilities and thoroughly exploring the present research difficulties and challenges. The paper aims to offer a holistic view on smart contract security research, advance the depth of vulnerability detection technologies, and suggest directions for future inquiries.

The risk of road accidents is rising rapidly. Distracted driving remains one of the leading causes of traffic accidents. Therefore, the identifying of the distracted driving become significant. Extensive methods based on the convolutional neural network (CNN) have been applied to the detection of the distracted driving. Within Convolutional Neural Network (CNN), the convolution operations are good at extracting local features but experience difficulty to capture global representations. Within visual transformer (ViT), the cascaded self-attention modules perform surpassingly in capturing content-based global interactions but unfortunately deteriorate local feature details. In order to address those challenges mentioned before, we propose a new distracted driving detection method that utilizes the driver and related object cues as guidance and combines CNN with ViT as a backbone to capture the local and global features. Besides, the simulation module is introduced to obtain the result of classification during a certain time period in the stage of inference. Under the widely used StateFarm benchmark, our proposed method presents the best performance.

Due to the ever-increasing complexity of modern communication networks, network operators are making tremendous efforts on achieving objectives for the network to meet the diversified requirements of many real-world applications. However, network operators are repeatedly taking a lot of time on some common tasks shared by different networks. In order to reduce repetitive human efforts on network management, advanced machine learning paradigms, such as deep reinforcement learning, has received numerous attention in the networking community. Nevertheless, it encounters great difficulty in transferring learned policies to new environments, resulting in new model training and testing for each changed environment setting. To tackle this important issue, in this paper we propose a new framework that is the first of its kind to enable an agent to have transferable knowledge for network management, specifically, for network path selection tasks. Through this framework, an agent can efficiently learn and express the transferable network knowledge for achieving task objectives. Extensive experimental results show that the learned knowledge through the proposed framework can realize some common objectives of path selection tasks across different network environments. In addition, the knowledge learned from one network task can significantly improve the learning performance of another similar but different task.

Electronic medical record (EMR) text word segmentation is the basis of natural language processing in medicine. Due to the characteristics of EMR, such as strong specialization, high cost of annotation, special writing style and sustained growth of terminology, the current Chinese word segmentation (CWS) methods cannot fully meet the requirements of the application of EMR. In order to solve this problem, an EMR word segmentation model based on Graph Neural Network (GNN), bidirectional Long Short-Term Memory network (Bi-LSTM) and conditional random field (CRF) is designed in this paper to improve the segmentation effect and reduce the dependence on data set. In the model, GNN based on the domain lexicon is used to learn the local composition features, Bi-LSTM is used to capture the long-term dependence and context sequence information, and CRF is used to obtain the optimal annotation sequence based on the sentence level label information. Through multi-feature interaction, the ambiguity resolution and new word recognition in the EMR word segmentation are effectively carried out. Compared with CWS tools such as Jieba and Pkuseg, as well as baseline models and state-of-the-art methods, the precision and recall rate of the model in this paper have been significantly improved.

DHT (Distributed Hash Table) algorithms are very efficient for distributed data storage and retrieval. As one kind of P2P overlay, DHT overlay also has the advantages of high reliability, high scalability and low cost. DHT has not only been applied to form user nodes' overlays, but also been proposed to form DHT-based server farms, such as DHT-based SIP server farm, HSS server farm, DNS server farm, CDN server farm, etc. However, seldom DHT algorithms consider server farm's stringent requirement on system capacity, and only a handful of DHT algorithms take server into consideration. This paper presents our DHT algorithm called SFDHT for high throughput DHT server farm. Compared with existing DHTs, SFDHT considers the characters and requirements of DHT server farm and maximizes system capacity. SFDHT is a one-hop DHT with novel built-in load balancing solution. The proposed load balancing solution produces much less overhead that existing solutions do. Both theoretical analysis and simulation results show that SFDHT can reduce overhead, balance load and improve system capacity.