Historical texts often contain terminology that reflects outdated or harmful social values. Identifying such contentious terms is essential for the Galleries, Libraries, Archives, and Museums (GLAM) community, but manual annotation requires cultural expertise and is difficult to scale. This study evaluates whether large language models (LLMs) can support this process by aligning with human judgments of contentiousness in historical Dutch corpora. Using the Dutch Contentious terms in Contexts Corpus (ConConCor), we formalize the task as context-dependent binary classification and compare two LLMs across multiple prompt configurations and evaluation scenarios. The models achieve near-human-level agreement on explicit cases but diverge when contextual or historical reasoning is required. Analysis of disagreement patterns shows that LLMs capture overtly harmful expressions yet tend to over-predict contentiousness for identity-related and colonial terms and under-predict for semantically shifted or figurative uses. These findings suggest that LLMs can serve as auxiliary annotators for sensitive-language detection in historical materials, provided that human oversight and contextual interpretation remain central to annotation workflows.

Machine translation attempts to understand the semantics of the original language and automatically translate text from one language to another. However, in traditional Sequence-to-Sequence (Seq2Seq) Korean-Chinese machine translation models, the performance in long sequence generation tasks is often limited, primarily due to the model’s difficulty in effectively utilizing key information in too lengthy sequences. To address this issue, we propose a hierarchical Korean-Chinese machine translation model based on Korean sentence segmentation. Utilizing the unique linguistic features and grammatical structure of Korean, we develop a segmentation method for long sequence texts to construct a hierarchical model for translating segmented sentences. Additionally, we incorporate pre-trained knowledge to enhance the integration of linguistic knowledge with the model and strengthen the connections between contexts. Experiments conducted on four Dong-A Ilbo variant datasets demonstrate that our method significantly improves the accuracy and fluency of translation compared to existing models.

The capacity of Deep Learning to automatically learn features from source code has facilitated its extensive utilization in detecting software vulnerabilities.However, existing pre-trained models regard code snippets as token sequences, neglecting the inherent structure of the code.The utilization of graph-based code representations is constrained by the limitations of Graph Neural Networks, particularly the difficulty in capturing long-range dependencies, which renders it challenging to learn grammatical and semantic information from complex code graph representations.This paper proposes a novel multiscale software vulnerability detection method based on supervised contrastive learning.The method integrates local features obtained from code paths with structural features extracted from the Code Property Graph by GraphTrans, thereby achieving a multi-scale feature representation of the source code.Additionally, a supervised contrastive loss function is employed during training in order to fully utilize label information and address the class imbalance problem.The experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods, achieving the highest accuracy, precision, and F1 on the real-world benchmark dataset CodeXGLUE for software vulnerability detection.

Text matching is the task of matching two texts and determining the relationship between them, which has extensive applications in natural language processing tasks such as reading comprehension, and Question-Answering systems. The mainstream approach is to compute text representations or to interact with the text through attention mechanism, which is effective in text matching tasks. However, the performance of these models is insufficient for texts that require commonsense knowledge-based reasoning. To this end, in this paper, We introduce a new model for text matching called the Knowledge Enhanced Text Matching model (KETM), to enrich contextual representations with real-world common-sense knowledge from external knowledge sources to enhance our model understanding and reasoning. First, we use Wiktionary to retrieve the text word definitions as our external knowledge. Secondly, we feed text and knowledge to the text matching module to extract their feature vectors. The text matching module is used as an interaction module by integrating the encoder layer, the co-attention layer, and the aggregation layer. Specifically, the interaction process is iterated several times to obtain in-depth interaction information and extract the feature vectors of text and knowledge by multi-angle pooling. Then, we fuse text and knowledge using a gating mechanism to learn the ratio of text and knowledge fusion by a neural network that prevents noise generated by knowledge. After that, experimental validation on four datasets are carried out, and the experimental results show that our proposed model performs well on all four datasets, and the performance of our method is improved compared to the base model without adding external knowledge, which validates the effectiveness of our proposed method.

Three-dimensional (3D) NAND flash memory has high capacity and cell storage density by using the multi-bit technology and vertical stack architecture, but degrading data reliability due to high raw bit error rates (RBER) caused by program/erase (P/E) cycles and retention periods. Low-density parity-check (LDPC) codes become more popular error-correcting technologies to improve data reliability due to strong error correction capability, but introducing more decoding iterations at higher RBER. To reduce decoding iterations, this paper proposes BeLDPC: bit errors aware adaptive rate LDPC codes for 3D triple-level cell (TLC) NAND flash memory. Firstly, bit error characteristics in 3D charge trap TLC NAND flash memory are studied on a real FPGA testing platform, including asymmetric bit flipping and temporal locality of bit errors. Then, based on these characteristics, a high-efficiency LDPC code is designed. Experimental results show BeLDPC can reduce decoding iterations under different P/E cycles and retention periods.

The optimization of task scheduling in Hadoop environment is an important research topic. The result of task scheduling affects the system performance and resource utilization. The existing task scheduling algorithm is lack of consideration at the cache level, which makes the performance of the task greatly affected. Therefore, this paper proposes an improved task scheduling algorithm based on cache locality and data locality. Firstly section matrix and weighted bipartite graph are constructed according to the relation between resources and tasks. Then the bipartite graph matching is used to realize map task scheduling for optimizing the local cache and data locality and reducing the data transmission amount during task execution process. The experimental results show that the proposed algorithm can effectively improve the data locality and system performance, which is better than other two algorithms.

Improving image resolution has broad applications and is an important research topic. Recently, a hybrid method Adaptive Sparse Domain Selection (ASDS) combining a reconstruction‐based method and an example‐based method has been proposed to take advantage of the two, but may not reconstruct sufficient details. In this study, the authors propose to improve ASDS: Zeyde's method is first used to obtain an intermediate image with high‐frequency details, and then the obtained image is used to replace the autoregressive model of ASDS as the example‐based term. In addition, the authors may split the input image into patches and use different parameter settings for the patches of different amount of details. Experimental results demonstrate the improved hybrid methods can produce high‐quality images quantitatively and perceptually.

Issues about city utility services reported by citizens can provide unprecedented insights into the various aspects of such services. Analysis of these issues can improve living quality through evidence-based decision making. However, these issues are complex, because of the involvement of spatial and temporal components, in addition to having multi-dimensional and multivariate natures. Consequently, exploring utility service problems and creating visual representations are difficult. To analyze these issues, we propose a visual analytics process based on the main tasks of utility service management. We also propose an aggregate method that transforms numerous issues into legible events and provide visualizations for events. In addition, we provide a set of tools and interaction techniques to explore such issues. Our approach enables administrators to make more informed decisions.