Recently, publishers of offensive comments are increasingly employing strategies such as metaphors, abbreviations, and homophones to obscure the aggressive nature of their comments. These strategies pose a significant challenge for existing detection models. At present, many studies mainly focused on the detection of explicit offensive speech, and there were few studies on implicit offensive speech. Our research aims to analyze implicit offensive speech on Chinese social platforms and achieve high detection performance. Firstly, we have collected data from one of the largest Chinese social networking platforms, Weibo, and constructed the first Chinese implicit offensive speech dataset, which contains 54,714 comments. Subsequently, we introduce Enhanced-BERT-Mate-Ambiguity (EBMA), a novel fuzzy semantic interpretation framework that leverages BERT and knowledge graphs. Specifically, this model detects implicit offensive speech by extracting semantic, emotional, metaphorical, and ambiguity features. Finally, extensive experiments were conducted, including comparison tests, robustness tests, and ablation studies, to validate our approach. We tested our model against state-of-the-art models in the field, and an accuracy of 95.83% and an F1-score of 95.52% confirmed its best performance. The performance of our model is visually illustrated through visualization. Moreover, we provide an analysis of error cases to explore the limitations of our model.

Medical image segmentation based on neural networks is pivotal in promoting digital health equity. The attention mechanism increasingly serves as a key component in modern neural networks, as it enables the network to focus on regions of interest, thus improving the segmentation accuracy in medical images. However, current attention mechanisms confront an accuracy-complexity trade-off paradox: accuracy gains demand higher computational costs, while reducing complexity sacrifices model accuracy. Such a contradiction inherently restricts the real-world deployment of attention mechanisms in resource-limited settings, thus exacerbating healthcare disparities. To overcome this dilemma, we propose a parameter-free Neighborhood Self-Dissimilarity Attention (NSDA), inspired by radiologists' diagnostic patterns of prioritizing regions exhibiting substantial differences during clinical image interpretation. Unlike pairwise-similarity-based self-attention mechanisms, NSDA constructs a size-adaptive local dissimilarity measure that quantifies element-neighborhood differences. By assigning higher attention weights to regions with larger feature differences, NSDA directs the neural network to focus on high-discrepancy regions, thus improving segmentation accuracy without adding trainable parameters directly related to computational complexity. The experimental results demonstrate the effectiveness and generalization of our method. This study presents a parameter-free attention paradigm, designed with clinical prior knowledge, to improve neural network performance for medical image analysis and contribute to digital health equity in low-resource settings. The code is available at [https://github.com/ChenJunren-Lab/Neighborhood-Self-Dissimilarity-Attention](https://github.com/ChenJunren-Lab/Neighborhood-Self-Dissimilarity-Attention).

Through the use of sarcastic sentences on social media, people can express their strong emotions. Therefore, the detection of sarcasm in social media has received more and more attention over the past years. Classifying a sentence as sarcastic or nonsarcastic heavily relies on the contextual information of the sentence. However, only focusing on the features of target text is the main solution of most existing research. Moreover, the scale of publicly available Chinese sarcasm dataset is very small and does not contain the contextual information. To address the issues mentioned above, we build a Chinese sarcasm dataset from Bilibili, which is one of the most widely used social network platforms in China and has a significant number of sarcastic comments and contextual information. As far as we know, our dataset is the first publicly available large-scale Chinese sarcasm dataset including contextual information. Additionally, we have proposed a novel retrospective reading method for detecting sarcasm that leverages contextual information to improve model's performance. The experimental results show the effectiveness of the proposed model and the significance of contextual information for Chinese sarcasm detection: achieving the highest F-score of 0.6942, outperforming existing state-of-the-art (SOTA) approaches. The study presented in this article offers approaches and ideas for future Chinese sarcasm detection studies.

Multidimensional scaling (MDS) is an attractive method for location-related applications due to its robustness against noise. This paper applies MDS to time difference of arrival (TDOA) localization in the modified polar representation (MPR) for integrating near-field and far-field localizations. The new MDS formulation yields a constrained optimization problem in terms of the source position. We then propose a computationally efficient and noise robust solution to solve this problem. The solution is closed-form and asymptotically unbiased. It can achieve better mean-square error (MSE) in the large noise region and possibly lower bias than the closed-form solutions from the literature, and also has attractive complexity.

Mobile edge computing (MEC) can sufficiently meet the computing demands of complex application consists of multiple interdependent tasks which can be represented by a directed acyclic graph (DAG). For tasks in a DAG, different scheduling orders and offloading decisions will generate different completion time, which further affects the Quality of Experiences (QoEs). So it is important to study the scheduling and offloading schemes for tasks in MEC scenarios. To this end, we first designed a scheme that schedules tasks with the highest response ratio and offloads tasks to the optimal processor with the optimization method for a DAG, which is termed as HRRO algorithm. Then, considering the complexity of the reality, we extended the HRRO to the ultradense MEC system and achieved the optimal joint scheduling and offloading scheme for multi-DAG based on the genetic algorithm, which can be concluded as HRRO based on the genetic algorithm (HRRO-GA). Subsequently, to evaluate the performance of the algorithms, we conducted amounts of the simulation experiments and compared the results with several state-of-the-art algorithms, including distributed earliest finish-time offloading (DEFO), potential game-based offloading algorithm (PGOA), and GA-based multiuser earliest finish time (GA-MEFT). Meanwhile, we selected some random strategies to verify the schemes of HRRO-GA are the best. Finally, we concluded that HRRO-GA is more suitable for the ultradense MEC system.

Spam reviews have long polluted review systems, undermining their industries. Detecting spam movie reviews faces some brand-new challenges compared to traditional spam detection. These include coordinated spamming attacks during premieres or at advance screenings. However, most of existing studies only use inherent relations among reviews, movies, and users, they do not fully exploit explicit and implicit relations between reviews in coordinated spamming attacks. To address these novel challenges, we propose a spam movie review detection method based on mining explicit and implicit relation semantics and fusing multi-view semantics. To the best of our knowledge, we are the first to enhance spam movie review detection by exploiting both explicit and implicit relations between reviews in coordinated spamming attacks. First, we build an explicit relation movie-review graph with movie synopses and high-quality external reviews. We extract movie factual knowledge embeddings using a Heterogeneous Graph Transformer (HGT) network. Next, we input the factual knowledge embeddings with corresponding review embeddings into a contrastive network to get review credibility features. Additionally, we build an implicit relation graph between reviews using metadata and semantic similarities. We extract relation-enhanced review semantics via another HGT network. Finally, we fuse the three review semantic features through an attention layer before making classification. Experiments show our method achieves higher performance and robustness over state-of-the-art methods.

Recent research has shown that using the 1-D signal arrival angles observed by linear arrays can locate a 3-D source in unique co-ordinates. Current methods to solve this localization problem are based on semidefinite programming (SDP) or gradient-based iteration, which are either computationally demanding or facing divergence or local convergence issues. This paper reformulates the maxi-mum likelihood (ML) estimation of the 3-D localization problem using the factor graph model, where an effective algorithm is designed through message passing. Although iterative, the proposed solution is more robust to measurement noise than the Gauss-Newton (GN) iterative solution, and the complexity is lower than the SDP solution without the need to introduce semidefinite relaxation error. Simulations validate the analytical performance and complexity, and con-firm the superiority on the convergence of the proposed solution.

With the development of big data and blockchain, an increasing number of scholars have begun to study blockchain for data sharing. By studying data sharing models that are based on blockchain, we find that almost all of them have the following problems: 1) it is difficult to protect the privacy and integrity of users’ data, along with users’ data ownership; 2) the storage burden of blockchain is heavy, and blockchain lacks a mechanism for dealing with data with diverse types and inconsistent formats; and 3) the consensus mechanism has low fairness or low efficiency. Therefore, we propose a data-sharing privacy protection model (DS2PM) that is based on blockchain and a federated learning mechanism for solving these problems. The safety analysis and experimental results show that the DS2PM outperforms the previously established schemes.

Nowadays, more and more people decide whether to watch a certain movie by reading online movie reviews. Driven by large commercial interest, a growing number of spammers try to manipulate the online word-of-mouth of movies by publishing spam reviews. This has severely destroyed the credibility of movie review platforms and affected the healthy development of the lm industry. However, there is little research on the detection of spam movie reviews. Meanwhile, there are still great challenges for spam movie review detection, such as the lack of publicly available datasets, insufficient features, and low-performance detection models. In this paper, we firstly construct a dataset for spam movie reviews with our proposed labeling strategy and release it publicly. Secondly, we design 28 features to detect spam movie reviews, 13 of which are completely new features. Thirdly, in order to mine the characteristics of collusive attack behavior deeply, we propose a Graph-aware Feature Interactional Model (GAIM), which combines TextCNN, MLP (Multilayer Perceptron), and GAT (Graph Attention Network). After performance evaluation, the experimental results show that GAIM is more effective than the state-of-the-art baselines with an F1-score of 91.88% for spam movie review.

Mobile-edge computing (MEC) has been garnering considerable level of interests by processing computation tasks nearby mobile devices (MDs). With limited computation and communication resources and strict task deadline, balancing the energy consumption and time delay of computational tasks will be highly focused. MDs deployed energy harvesting (EH) modules can always provide service to continuous task requests, and finer-grained offloading schemes of the MEC system will significantly affect the time delay of computation tasks. However, when combined them together, the energy causal constraint and the coupling between offloading ratios and resources allocation will cause new challenges for the computation offloading problem. To address these issues, we investigate the partial computation offloading schemes for multiple MDs enabled by harvested energy in MEC. Specifically, we build models for two computing modes and EH process. Subsequently, we formulate a nonconvex optimization problem by minimizing the energy consumption of all the MDs while satisfying the constraint of time delay. Furthermore, we propose and design a novel algorithm based on the Lyapunov optimization to achieve optimal solution, that is, Lyapunov-optimization-based partial computation offloading for multiuser (LOMUCO). Then, we take the long-term average energy consumption and the discarding ratio of computation tasks as the quantitative metrics and conduct extended simulation experiments to confirm the performance of LOMUCO. Finally, compared to several baseline or state-of-the-art algorithms, including local computing all (LCA), offloading computing all (OCA), randomly partial computation offloading (RPCO), and Lyapunov-optimization-based dynamic computation offloading (LODCO), we can demonstrate the superiority of LOMUCO.