A hybrid SDN network contains both traditional and SDN network, which combines the robustness of traditional protocols with the flexibility of SDN while avoiding their limitations and incompatibility. However, a hybrid SDN network comes with its own set of challenges, including error-prone deployment processes, risks of inconsistency, and complex incremental deployment strategies. In this paper, we present a survey of the deployment solutions and optimization strategies for hybrid SDN networks. We systematically review solutions to control plane and data plane deployments, and describe typical use cases of hybrid SDN networks. We discuss and compare various optimization strategies from perspectives of traffic engineering, resource saving, network control capacity, and network security. This paper aims to provide insights to researchers into the future development of hybrid SDN networks and inspire more efforts in this area.

Aspect-level sentiment analysis aims at identifying the sentiment polarity of target in the context. In most of the previous sentiment analysis models, there usually exists the problem of insufficient extraction capability of local features and long-distance dependency features. To solve the above problem, in this paper, we propose an improved model (called ADeCNN) for aspect-level sentiment analysis, by incorporating the attention mechanism into the deformable CNN model. In ADeCNN, we use deformable convolutional layers and bi-directional long short-term memory network (Bi-LSTM), combined with sentence-level attention, to extract sentiment features, and to break through the limitations of the model’s long-distance dependency feature extraction capability. We then use a gated end-to-end memory network (GMemN2N) to integrate the target into the sentiment feature extraction process, so as to obtain sentiment features. And finally, we obtain the corresponding sentiment analysis results through the classifier. In addition, in order to solve the problem that the same words have large differences in the polarity of sentiments expressed in different targets, the model is also constructed with the ability to generate different attention weights based on target to assist sentiment analysis, with the aim of further enhancing the correlation between the target and the words in the sentence. We setup experiments to demonstrate the functionality effectiveness and performance gains of ADeCNN, based on the SemEval 2014 Task4 and SemEval 2017 Task4 datasets. Extensive experimental results show that ADeCNN outperforms its competitors, producing an arresting increase of the classification accuracy on all the three datasets of Laptop, Restaurant, and Twitter.

Federal Learning (FL) is highly respected for protecting data privacy in a distributed environment. However, the correlation between the updated gradient and the training data opens up the possibility of data reconstruction for malicious attackers, thus threatening the basic privacy requirements of FL. Previous research on such attacks mainly focuses on two main perspectives: one exclusively relies on gradient attacks, which performs well on small-scale data but falter with large-scale data; the other incorporates images prior but faces practical implementation challenges. So far, the effectiveness of privacy leakage attacks in FL is still far from satisfactory. In this paper, we introduce the Gradient Guided Diffusion Model (GGDM), a novel learning-free approach based on a pre-trained unconditional Denoising Diffusion Probabilistic Models (DDPM), aimed at improving the effectiveness and reducing the difficulty of implementing gradient based privacy attacks on complex networks and high-resolution images. To the best of our knowledge, this is the first work to employ the DDPM for privacy leakage attacks of FL. GGDM capitalizes on the unique nature of gradients and guides DDPM to ensure that reconstructed images closely mirror the original data. In addition, in GGDM, we elegantly combine the gradient similarity function with the Stochastic Differential Equation (SDE) to guide the DDPM sampling process based on theoretical analysis, and further reveal the impact of common similarity functions on data reconstruction. Extensive evaluation results demonstrate the excellent generalization ability of GGDM. Specifically, compared with state-of-the-art methods, GGDM shows clear superiority in both quantitative metrics and visualization, significantly enhancing the reconstruction quality of privacy attacks.

Techniques that map the entities and relations of the knowledge graph (KG) into a lowdimensional continuous space are called KG embedding or knowledge representation learning. However, most existing techniques learn the embeddings based on the facts in KG alone, suffering from the issues of imperfection and spareness of KG. Recently, the research on textual information in KG embedding has attracted much attention due to the rich semantic information supplied by the texts. Thus, in this paper, a survey of techniques for textual information based KG embedding is proposed. Firstly, we introduce the techniques for encoding the textual information to represent the entities and relations from perspectives of encoding models and scoring functions, respectively. Secondly, methods for incorporating the textual information in the existing embedding techniques are summarized. Thirdly, we discuss the training procedure of textual information based KG embedding techniques. Finally, applications of KG embedding with textual information in the specific tasks such as KG completion in zero-shot scenario, multilingual entity alignment, relation extraction and recommender system are explored. We hope that this survey will give insights to researchers into textual information based KG embedding.

The knowledge graph (KG) is an efficient form of knowledge organization and expression, providing prior knowledge support for various downstream tasks, and has received extensive attention in natural language processing. However, existing large-scale KGs have many hidden facts that need to be discovered. How to effectively use the structure information of KG is an important research direction of knowledge reasoning. Structure-Information-based reasoning over the KG is a technique used to find the missing facts by the structure information of KG. This survey summarizes the methods and applications of Structure-Information-based reasoning and hopes to be helpful to the research in this field. First, we introduced the definition of knowledge reasoning and the conceptual description of related tasks. Then, we reviewed the methods of Structure-Information-based reasoning. Specifically, we categorized them into four representative classes: PRA-based reasoning, Path-Embedding-based reasoning, RL-based reasoning, and GNN-based reasoning. We compared the motivations and details between practices in the same category. After that, we described the application of Structure-Information-based knowledge reasoning in the KG Completion, Question Answering System, Recommendation System, and other fields. Finally, we discussed the future research directions of Structure-Information-based reasoning.