The influence maximization (IM) problem seeks to identify a set of influential seed nodes to maximise information diffusion in a network. While most existing approaches focus solely on maximizing influence spread, they often neglect fairness in the diffusion of diverse aspects of information across different communities. This oversight can lead to a biased public understanding or the exclusion of minority interests in real-world applications, such as public health messaging, political discourse, or content recommendations. To address these challenges, we define the aspect-aware fair multiobjective influence maximization (AFMOIM) problem that jointly considers three objectives: influence coverage, intercommunity fairness, and the equitable dissemination of multiple information aspects. We propose a multiobjective discrete tree seed algorithm (MODTSA) to solve the AFMOIM problem effectively. Extensive experiments on real-world networks validate the effectiveness of MODTSA, demonstrating its ability to achieve well-balanced Pareto-optimal solutions that deliver both high diffusion performance and fairness across communities and information aspects.

Symbolic music, represented as MIDI files, encapsulates intricate performance details and complex temporal and structural dependencies. Effectively modelling ultra-long MIDI sequences is essential for understanding sophisticated compositions and advancing tasks like music generation, classification, and performance analysis. However, these long MIDI sequences pose significant challenges due to their complexity, multi-track concurrency, and extensive temporal relationships. This paper introduces a novel model named LongMIDI-Net that enhances the capability of bar-level pretrained large-scale MIDI sequence understanding models, extending their effectiveness to handle complete and long MIDI sequences. The proposed approach integrates structure-sensitive models for processing bar-level segments with temporal-sensitive models to capture global relationships across entire sequences. This hierarchical design significantly reduces sequence length while maintaining high model performance. Comprehensive experiments on classification tasks across multiple datasets demonstrate the superior effectiveness of the proposed model, achieving consistently strong results. Furthermore, ablation studies highlight the advantages of bar-level segmentation over random slicing, showcasing its ability to provide a more effective and structurally coherent representation of MIDI sequences. These findings underline the importance of combining local and global information for advancing symbolic music understanding.

Detecting social media bots has become increasingly critical due to their detrimental impact on online environments. With the emergence of sophisticated large language models (LLM) such as ChatGPT, bot detection faces new challenges. These bots based on LLMs exhibit human-like behaviors, and it is difficult for traditional detection approaches to identify them effectively. Such conventional methods struggle with the advanced features associated with LLM-driven bots, which possess contextual understanding and mimic human interaction patterns. The significance of detecting LLM-driven bots lies in their increased difficulty of detection and their potential to inflict more covert harm compared with traditional bots. To address these challenges, we propose LLM-BotGuard, a novel detection model that is capable of capturing the unique features of LLM-driven bots alongside other bot characteristics through three key modules, i.e., pattern-informed feature extraction module, mixture of experts module, and graph module with graph sample and aggregation networks. Extensive experiments have been conducted to evaluate the performance of the proposed LLM-BotGuard. The results demonstrate that LLM-BotGuard significantly outperforms baseline methods in detecting LLM-driven bots. The proposed LLM-BotGuard offers a robust solution for identifying sophisticated LLM-driven bots in online social networks.

Abstract Recently, diffusion models have been proven to perform remarkably well in text-to-image synthesis tasks in a number of studies, immediately presenting new study opportunities for image generation. Google’s Imagen follows this research trend and outperforms DALLE2 as the best model for text-to-image generation. However, Imagen merely uses a T5 language model for text processing, which cannot ensure learning the semantic information of the text. Furthermore, the Efficient UNet leveraged by Imagen is not the best choice in image processing. To address these issues, we propose the Swinv2-Imagen, a novel text-to-image diffusion model based on a Hierarchical Visual Transformer and a Scene Graph incorporating a semantic layout. In the proposed model, the feature vectors of entities and relationships are extracted and involved in the diffusion model, effectively improving the quality of generated images. On top of that, we also introduce a Swin-Transformer-based UNet architecture, called Swinv2-Unet, which can address the problems stemming from the CNN convolution operations. Extensive experiments are conducted to evaluate the performance of the proposed model by using three real-world datasets, i.e. MSCOCO, CUB and MM-CelebA-HQ. The experimental results show that the proposed Swinv2-Imagen model outperforms several popular state-of-the-art methods.

As a key task of fine-grained sentiment analysis, aspect-based sentiment analysis aims to analyse people’s opinions at the aspect level from user-generated texts. Various sub-tasks have been defined according to different scenarios, extracting aspect terms, opinion terms, and the corresponding sentiment. However, most existing studies merely focus on a specific sub-task or a subset of sub-tasks, having many complicated models designed and developed. This hinders the practical applications of aspect-based sentiment analysis. Therefore, some unified frameworks are proposed to handle all the subtasks, but most of them suffer from two limitations. First, the syntactic features are neglected, but such features have been proven effective for aspect-based sentiment analysis. Second, very few efficient mechanisms are developed to leverage important syntactic features, e.g., dependency relations, dependency relation types, and part-of-speech tags. To address these challenges, in this paper, we propose a novel unified framework to handle all defined sub-tasks for aspect-based sentiment analysis. Specifically, based on the graph convolutional network, a multi-layer semantic model is designed to capture the semantic relations between aspect and opinion terms. Moreover, a multi-layer syntax model is proposed to learn explicit dependency relations from different layers. To facilitate the sub-tasks, the learned semantic features are propagated to the syntax model with better semantic guidance to learn the syntactic representations comprehensively. Different from the conventional syntactic model, the proposed framework introduces two attention mechanisms. One is to model dependency relation and type, and the other is to encode part-of-speech tags for detecting aspect and opinion term boundaries. Extensive experiments are conducted to evaluate the proposed novel unified framework, and the experimental results on four groups of real-world datasets explicitly demonstrate the superiority of the proposed framework over a range of baselines.

With the development of the Social Internet of Things (SIoT) and mobile technologies in recent years, movie recommendation systems have become popular in online movie recommendation that users may like to watch based on their historical movie viewing data monitored by the SIoT. This technology can bring considerable profits to online movie providers and has attracted the attention of a large number of related scholars. However, previous movie recommendation models based on autoencoders have insufficient model learning ability due to their defective features. Due to the errors in users’ operation, the user’s movie rating data will have some errors. The previous models cannot deal with this corrupted information, which leads to the degradation of their generalization performance. Presently, graph convolutional networks have made full progress in many fields, and they can outperform traditional methods. Therefore, in this work, we introduce a principal component analysis and denoising autoencoder integrated graph convolutional networks (PCA-DAEGCNs) for movie recommendation in the SIoT. The PCA-DAEGCN model uses the network structure of the graph autoencoder to obtain effective hidden features and, subsequently, uses denoising autoencoders to handle small changes in the feedback information. Finally, the captured hidden features of users and movies are used to derive the finally predicted scores. Comprehensive experiments show that the proposed PCA-DAEGCN is able to obtain far better efficiency than many comparative models.

Intersection is one of the most accident-prone urban scenarios for autonomous driving wherein making safe and computationally efficient decisions is non-trivial. Current research mainly focuses on the simplified traffic conditions while ignoring the existence of mixed traffic flows, i.e., vehicles, cyclists and pedestrians. For urban roads, different participants lead to a quite dynamic and complex interaction, posing great difficulty to learn an intelligent policy. This paper develops the dynamic permutation state representation in the framework of integrated decision and control (IDC) to handle signalized intersections with mixed traffic flows. Specially, this representation introduces an encoding function and summation operator to construct driving states from environmental observation, capable of dealing with different types and variant number of traffic participants. A constrained optimal control problem is built wherein the objective involves tracking performance and the constraints for different participants, roads and signal lights are designed respectively to assure safety. We solve this problem by gradient-based optimization, wherein the reasonable state will be given by the encoding function and then served as the input of policy and value function. An off-policy training is designed to reuse observations from driving environment and backpropagation through time is utilized to update the policy function and encoding function jointly. Verification result shows that the dynamic permutation state representation can enhance the driving performance of IDC, including comfort, decision compliance and safety with a large margin. The trained driving policy can realize efficient and smooth passing in the complex intersection, guaranteeing driving intelligence and safety simultaneously.

The density peaks clustering (DPC) is known as an excellent approach to detect some complicated-shaped clusters with high-dimensionality. However, it is not able to detect outliers, hub nodes and boundary nodes, or form low-density clusters. Therefore, halo is adopted to improve the performance of DPC in processing low-density nodes. This paper explores the potential reasons for adopting halos instead of low-density nodes, and proposes an improved recognition method on Halo node for Density Peak Clustering algorithm (HaloDPC). The proposed HaloDPC has improved the ability to deal with varying densities, irregular shapes, the number of clusters, outlier and hub node detection. This paper presents the advantages of the HaloDPC algorithm on several test cases.

In Wireless Local Area Networks (WLANs), cochannel deployment can bound channel access delay and improve network capacity due to mitigating the collision and interference among different Access Points (APs). In this paper, we present a network model and an interference model for multiple APs cochannel deployment and propose a channel assignment mechanism which formulates the channel assignment problem into a time slot allocation problem. Meanwhile, we assign the channel based on the vertex coloring algorithm and make extra polls by utilizing the time slot reservation strategy to improve the channel assignment. Furthermore, we optimize the polling list of APs through classifying the clients to improve the channel utilization. The simulation results show that our proposed algorithm can improve the performance in terms of network throughput, transmission delay, and packet loss rate compared with the DCF (Distributed Coordination Function) and TMCA algorithms.