In the upcoming era of 6G, the accelerated development of the Internet of Everything and high-speed communication is poised to provide people with an efficient and intelligent life experience. However, the exponential growth in data traffic is expected to pose substantial task processing challenges. Relying solely on the computational resources of individual devices may struggle to meet the demand for low latency. Additionally, the lack of trust between different devices poses a limitation to the development of 6G networks. In response to this issue, this study proposes a blockchain-based 6G task offloading and collaborative computational resource allocation (CERMTOB) algorithm. The proposed first designs a blockchain-based 6G cloud-network-edge collaborative task offloading model. It incorporates a blockchain network on the edge layer to improve trust between terminals and blockchain nodes. Subsequently, the optimization objective is established to minimize the total latency of offloading, computation, and blockchain consensus. The optimal offloading scheme is determined using the wolf fish collaborative search algorithm(WF-CSA) to minimize the total delay. Simulation results show that the WF-CSA algorithm significantly reduces the total delay by up to 42.58% compared to the fish swarm algorithm, wolf pack algorithm and binary particle swarm optimisation algorithm. Furthermore, the introduction of blockchain to the cloud-side-end offloading system improves the communication success rate by a maximum of 14.93% compared to the blockchain-free system.

Next generation optical metro networks need to serve heterogeneous access traffic with guaranteed quality of service (QoS) and lower CAPEX and OPEX. In this context, an integrated network infrastructure enabling multiple services access and network slicing is necessary. In this paper, we investigate recent research efforts on network slicing in optical metro networks, the fgMTN and fgOTN. These technologies have limitations in flexible network resource slicing and efficient bandwidth utilization. Therefore, we propose a new all optical metro network SiMON for achieving flexibility in network slicing and efficiency in bandwidth utilization. In addition, we theoretically investigate the SiMON in achieving end-to-end deterministic latency. A jitter reduction method for SiMON network slices is proposed by formulating the latency components of its communication paths. Theoretical analysis and numerical studies support that the SiMON outperforms the fgMTN and fgOTN in flexible network resource slicing, and achieves higher bandwidth efficiency. Moreover, an experimental setup of the SiMON system has been implemented by FPGA. Experimental results show that the SiMON achieves 96.5% of bandwidth utilization with dynamic adjustable network slicing. Below 2 $\mu$ s jitter is achieved in the SiMON network slices under burst traffic, and below 0.2 $\mu$ s jitter has been achieved for constant frame rate traffic with short frame lengths.

Network virtualization (NV) technology is the cornerstone of modern network architectures, offering significant advantages in resource utilization, flexibility, security, and streamlined management. By enabling the deployment of multiple virtual network requests (VNRs) within a single base network through virtual network embedding (VNE), NV technology can substantially reduce the operational costs and energy consumption. However, the existing algorithms for energy-efficient VNE have limitations, including manual tuning for heuristic routing policies, inefficient feature extraction in traditional intelligent algorithms, and a lack of consideration of periodic traffic fluctuations. To address these limitations, this paper introduces a novel approach that leverages deep reinforcement learning (DRL) to enhance the efficiency of traditional methods. We employ graph convolutional networks (GCNs) for feature extraction, capturing the nuances of network graph structures, and integrate periodic traffic fluctuations as a key constraint in our model. This allows for the predictive embedding of VNRs that is both energy-efficient and responsive to dynamic network conditions. Our research aims to develop an energy-efficient VNE algorithm that dynamically adapts to network traffic patterns, thereby optimizing resource allocation and reducing energy consumption. Extensive simulation experiments demonstrate that our proposed algorithm achieves an average reduction of 22.4% in energy consumption and 41.0% in active substrate nodes, along with a 23.4% improvement in the acceptance rate compared to other algorithms.

As a specific form of knowledge distillation (KD), self-knowledge distillation enables a student network to progressively distill its own knowledge without relying on a pretrained, complex teacher network; however, recent studies of self-KD have discovered that additional dark knowledge captured by auxiliary architecture or data augmentation could create better soft targets for enhancing the network but at the cost of significantly more computations and/or parameters. Moreover, most existing self-KD methods extract the soft label as a supervisory signal from individual input samples, which overlooks the knowledge of relationships among categories. Inspired by human associative learning, we propose a simple yet effective self-KD method named associative learning for self-distillation (ALSD), which progressively distills richer knowledge regarding the relationships between categories across independent samples. Specifically, in the process of distillation, the propagation of knowledge is weighted based on the intersample relationship between associated samples generated in different minibatches, which are progressively estimated with the current network. In this way, our ALSD framework achieves knowledge ensembling progressively across multiple samples using a single network, resulting in minimal computational and memory overhead compared to existing ensembling methods. Extensive experiments demonstrate that our ALSD method consistently boosts the classification performance of various architectures on multiple datasets. Notably, ALSD pushes forward the self-KD performance to 80.10% on CIFAR-100, which exceeds the standard backpropagation by 4.81%. Furthermore, we observe that the proposed method shows comparable performance with the state-of-the-art knowledge distillation methods without the pretrained teacher network.

Cross-domain Aspect Sentiment Triplet Extraction (ASTE) aims to extract fine-grained sentiment elements from target domain sentences by leveraging the knowledge acquired from the source domain. Due to the absence of labeled data in the target domain, recent studies tend to rely on pre-trained language models to generate large amounts of synthetic data for training purposes. However, these approaches entail additional computational costs associated with the generation process. Different from them, we discover a striking resemblance between table-filling methods in ASTE and two-stage Object Detection (OD) in computer vision, which inspires us to revisit the cross-domain ASTE task and approach it from an OD standpoint. This allows the model to benefit from the OD extraction paradigm and region-level alignment. Building upon this premise, we propose a novel method named Table-Filling via Mean Teacher (TFMT). Specifically, the table-filling methods encode the sentence into a 2D table to detect word relations, while TFMT treats the table as a feature map and utilizes a region consistency to enhance the quality of those generated pseudo labels. Additionally, considering the existence of the domain gap, a cross-domain consistency based on Maximum Mean Discrepancy is designed to alleviate domain shift problems. Our method achieves state-of-the-art performance with minimal parameters and computational costs, making it a strong baseline for cross-domain ASTE.