In the area of agriculture and livestock management, the integration of the Internet of Things (IoT) has emerged as a groundbreaking strategy to enhance operational efficiency and advance intelligent process management. However, this sector faces significant challenges, including ambiguity in product origins and limited regulatory oversight of IoT devices. This paper explores the innovative integration of blockchain technology within the agricultural and livestock IoT, highlighting how this convergence significantly enhances operational security and transparency. We provide an in-depth review of the latest applications and advancements of blockchain in these domains, offering a comprehensive analysis of the current state of technology and its implications. Furthermore, this paper discusses the potential future development trajectories in agricultural and livestock IoT, emphasizing blockchain’s role in addressing current challenges and shaping future innovations. The findings suggest that blockchain technology not only improves data security and trustworthiness but also opens new avenues for efficient and transparent management in agriculture and animal husbandry.

In the advanced 5G and beyond networks, multi-access edge computing (MEC) is increasingly recognized as a promising technology, offering the dual advantages of reducing energy utilization in cloud data centers while catering to the demands for reliability and real-time responsiveness in end devices. However, the inherent complexity and variability of MEC networks pose significant challenges in computational offloading decisions. To tackle this problem, we propose a proximal policy optimization (PPO)-based Device-to-Device (D2D)-assisted computation offloading and resource allocation scheme. We construct a realistic MEC network environment and develop a Markov decision process (MDP) model that minimizes time loss and energy consumption. The integration of a D2D communication-based offloading framework allows for collaborative task offloading between end devices and MEC servers, enhancing both resource utilization and computational efficiency. The MDP model is solved using the PPO algorithm in deep reinforcement learning to derive an optimal policy for offloading and resource allocation. Extensive comparative analysis with three benchmarked approaches has confirmed our scheme’s superior performance in latency, energy consumption, and algorithmic convergence, demonstrating its potential to improve MEC network operations in the context of emerging 5G and beyond technologies.

The research on multi-robot systems has been divided into various fields, such as communication, navigation, task allocation, and collaborative transport. While significant progress has been made in each area, there has been limited research integrating these fields to build a fully autonomous multi-robot collaborative transport system. Therefore, we identify the key issues and propose a multi-robot collaborative transport system founded on ROS1 and conduct validation in a simulated environment, laying a solid foundation for the system to run on real robots. The primary contributions of this study include three key areas: (1) modeling and validating robot collaborative transport, (2) developing a visual task allocation system leveraging FastDDS service, and (3) resolving path collision issues in multi-robot navigation through both traditional methods and reinforcement learning techniques. Extensive experimental evaluations demonstrate that the proposed intelligent multi-robot collaborative transport system can autonomously navigate to target points for collaborative transport task. Performance assessments, based on the error between the target point and the object’s arrival point as well as the transport trajectory error, reveal that the system effectively completes the assigned tasks.

Entity relationship extraction tasks conducted in low-resource language domains (such as the medical and military domains) have long faced significant challenges, and data augmentation (DA) is considered an effective solution for addressing this issue. Compared with monolingual DA methods, cross-lingual augmentation methods more effectively enhance the diversity of data in low-resource language settings by leveraging and extending data resources derived from other languages. Unlike traditional DA methods, large language model (LLM)-based DA reduces the reliance on manual evaluations and generates more fluent and diverse data. The superior performance of LLMs is attributed to their large-scale corpora and computational resources, but these advantages also limit their applicability in cases involving low-resource languages. To address these issues, this paper proposes LS-CLDARE, an entity relationship extraction framework based on collaborative cross-lingual DA that employs both large and small models. Specifically, the small language model (SLM) is responsible for extracting entity information, while the LLM generates cross-lingual samples by combining this entity information and its high-resource language description via chain-of-thought (CoT) prompts, which guide the model through step-by-step reasoning to better handle complex tasks. To achieve enhanced cross-lingual transfer performance, the generated cross-lingual samples are combined with cross-lingual soft prompts and input into an SLM pretrained on a high-resource language domain dataset. Through transfer learning and data expansion, the entity recognition and relation extraction capabilities for the low-resource languages of the SLM are continuously improved. Extensive experiments conducted on ultralow-resource languages in the Mongolian medical domain and classical Chinese texts validate the effectiveness of LS-CLDARE. Compared with those of other DA methods, the F1 score was improved by 3.52% to 9.42%, and compared with those of other prompt-based relation extraction methods, the F1 score was improved by 1.8% to 16.93%.

In this paper, we propose an adversarial transfer learning method to solve the lack of data resources for named entity recognition (NER) tasks in spoken language understanding. In the framework, we use bi-directional long short-term memory with self-attention and conditional random field (BiLSTM-Attention-CRF) model which combines character and word information as the baseline model to train source domain and target domain corpus jointly. Shared features between domains are extracted by a shared feature extractor. This paper uses two different sharing patterns simultaneously: full sharing mode and private sharing mode. On this basis, an adversarial discriminator is added to the shared feature extractor to simulate generative adversarial networks (GAN) and eliminate domain-dependent features. This paper compares ordinary adversarial discriminator (OAD) and generalized resource-adversarial discriminator (GRAD) through experiments. The experimental results show that the transfer effect of GRAD is better than other methods. The F1 score reaches 92.99% at the highest, with a relative increase of 12.89%. It can effectively improve the performance of NER tasks in resource shortage fields and solve the problem of negative transfer.

Multi-faceted controllable text generation can be viewed as an extension and combination of controllable text generation tasks. It requires the generation of fluent text while controlling multiple different attributes (e.g., negative emotions and environmental protection in themes). Current research either estimates compact latent spaces for multiple attributes, reducing interference between different attributes but making it difficult to control the balance between multiple attributes, or controls the balance between multiple attributes but requires complex searches for decoding. Based on these issues, we propose a new method called MacHa, which trains an attribute latent space using multiple loss functions and establishes a mapping between the attribute latent space and attributes in sentences using a VAE network. An energy model based on the Hamilton function is defined in the potential space to control the balance between multiple attributes. Subsequently, in order to reduce the complexity of the decoding process, we extract samples using the RL sampling method and send them to the VAE decoder to generate the final text. The experimental results show that the MacHa method generates text with higher accuracy than the baseline models after balancing multiple attributes and has a fast decoding speed.