Online reputation systems play an important role in reducing consumers’ purchase uncertainty in online shopping. However, some sellers manipulate reviews for their own interests, which reduces the effectiveness of the reputation system. Unlike the previous studies, which focus on features of reviews and reviewers, this study establishes a game model to analyze sellers’ manipulation behavior and identifies what kind of sellers or under what scenario sellers are motivated to manipulate reviews. Our study provides a new perspective for platform to detect fake reviews and helps consumers to make good use of online reviews without getting trapped in some sellers’ fraudulent manipulation.

Online reviews mitigate uncertainty about product quality that is caused by information asymmetry. However, low-quality online reviews are not effective, while high-quality online reviews may raise costs. Unlike the previous studies, this paper develops a game-theoretic model to examine the feasibility and pricing of online review supervision mechanism for operators of platforms that are based on the ‘network externality’. The results reveal that platforms are not always benefited from online reviews. We provide a new perspective and give some preventive suggestions for platforms with a view to furthering sustainable development.

With the continuous development of science and information technology, mixed criticality system(MCS) is more widely used in the embedded devices such as autonomous driving vehicles. The number and variety of programs with different importance in the system are increasing. Previous mixed criticality systems placed applications with different critical levels on different hardware platforms, which was a spatial isolation technique. However, this approach creates additional problems, such as high power consumption and complex hardware design. To reduce the design complexity and cost of mixed criticality systems, embedded operating systems should satisfy the demand that applications with different critical levels would run simultaneously on unique hardware platform such as system on chip(soc). In this paper, we propose a mixed criticality system model and a scheduling strategy with low overhead, high performance, and real-time features for embedded operating system. We implement and experiment them on our own microkernel operating system named Mginkgo. The test results show that our design comes with minimal overhead, and supports temporal-isolated and spatial-isolated.

Multimodal hate speech detection aims to integrate various modalities—such as text and images—to identify complex and implicit hateful content, thereby contributing to a healthier online environment. Despite notable progress in fusion techniques, existing approaches still struggle with modeling both local and global semantics and achieving effective cross-modal integration. To address these limitations, we propose MLCA, a novel multimodal hate speech detection framework. Our method employs a Twitter-based RoBERTa model and the Swin Transformer V2 to encode textual and visual modalities, respectively. These modality-specific representations are subsequently fused using a multi-level cross-modal attention mechanism. In addition, a dynamic gating module is introduced to adaptively integrate attention features across different semantic levels. We conduct comprehensive evaluations on two benchmark datasets and compare our model with a wide range of state-of-the-art unimodal and multimodal baselines. Experimental results show that our framework consistently surpasses state-of-the-art methods on both datasets.