The increasing prevalence of large language models (LLMs) has significantly advanced text generation, but the human-like quality of LLM outputs presents major challenges in reliably distinguishing between human-authored and LLM-generated texts. Existing detection benchmarks are constrained by their reliance on static datasets, scenario-specific tasks (e.g., question answering and text refinement), and a primary focus on English, overlooking the diverse linguistic and operational subtleties of LLMs. To address these gaps, we propose CUDRT, a comprehensive evaluation framework and bilingual benchmark in Chinese and English, categorizing LLM activities into five key operations: Create, Update, Delete, Rewrite, and Translate. CUDRT provides extensive datasets tailored to each operation, featuring outputs from state-of-the-art LLMs to assess the reliability of LLM-generated text detectors. This framework supports scalable, reproducible experiments and enables in-depth analysis of how operational diversity, bilingual training sets, and LLM architectures influence detection performance. Our extensive experiments demonstrate the framework’s capacity to optimize detection systems and provide practical guidance for training model-based detectors, revealing that training on specific operations and outputs from certain LLMs significantly improves model-based detector generalization. By advancing robust methodologies for identifying LLM-generated texts, this work contributes to the development of intelligent systems capable of meeting real-world bilingual detection challenges. Source code and dataset are available at GitHub.

When deploying network nodes, there are many redundant nodes, low network coverage and high energy consumption of network nodes, the node deployment method of wireless sensor networks(WSN) based on mobile edge computing is studied. WSN nodes are divided into anchor nodes and unknown nodes. Taking the location information of anchor nodes as a reference, the specific location of unknown nodes is obtained by trilateral measurement. Minimizing the node distance error is taken as the objective function, and the cuckoo search algorithm is used to solve it to obtain the final location result of the node. The mobile edge computing method is used to design the node deployment method of WSN to complete the node deployment. Simulation results show that the number of redundant nodes in this method is 3, maximum network coverage is 89%, maximum energy consumption of network nodes is 34.3J.

Recent research has shown that the performances of super-resolution methods can be significantly boosted using deep convolutional neural networks. However, current superresolution methods continue to exhibit relatively low performances for video, partly because they ignore certain crucial inter-frame information from the original low-resolution frame sequence or the hierarchical features of deep networks. In this paper, we propose a novel method for video super resolution named dense-connected residual network (DCRnet) to address the above drawbacks. The DCRnet can preserve the low -frequency contents of motion compensated frames, and facilitate the restoration of high-frequency details by exploiting the hierarchical features from all the convolutional layers. Specifically, we propose a dense-connected residual block (DCRB) as a basic component. The output of one DCRB is the compressed concatenation of all preceding DCRBs features and each residual block features of the current DCRB. Extensive experimentation demonstrates that our method is superior to the current state-of-the-art methods in both quantitative and qualitative metrics.

Given the edge list of a social network, the node embedding method learns the structural features for every node and embeds the features into a vector space. The current related work on node embedding exploits only a portion of existing networks, e.g., static networks. However, social networks are inherently hierarchical and dynamic systems in which the topology changes constantly and the strength of influence of information among neighbors varies with different numbers of hops. We propose a highly efficient node embedding method, DNPS, that is faster and more accurate than state-of-the-art methods and that can further boost the training progress, especially under dynamic conditions. In this paper, we attempt to model the hierarchical and dynamic features of social networks by designing a damping-based sampling algorithm corresponding to a local search-based incremental learning algorithm, which can easily be extended to large-scale scenarios. We conduct extensive experiments on six real-world social networks with three challenging tasks, including missing link prediction, dynamic link prediction, and multi-label classification. The results of the experiments on these tasks demonstrate that the proposed method significantly outperforms the existing methods with different settings.

This paper studies the problem of learning node embeddings (a.k.a. distributed representations) for dynamic networks. The embedding methods allocate each node in network with a d-dimensions vector, which can generalize across various tasks, such as item recommendation, node labeling, and link prediction. In practice, many real-world networks are evolving with nodes/links added or deleted. However, most of the proposed methods are focusing on static networks. Although some previous researches have shown some promising results to handle the dynamic scenario, they just considered the added links and ignored the deleted ones. In this work, we designed a joint learning of added and deleted links model, named RDEM, for dynamic network embedding.