3,194 research outputs found

    Improved Approximate String Matching and Regular Expression Matching on Ziv-Lempel Compressed Texts

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    We study the approximate string matching and regular expression matching problem for the case when the text to be searched is compressed with the Ziv-Lempel adaptive dictionary compression schemes. We present a time-space trade-off that leads to algorithms improving the previously known complexities for both problems. In particular, we significantly improve the space bounds, which in practical applications are likely to be a bottleneck

    Law Smells - Defining and Detecting Problematic Patterns in Legal Drafting

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    A survey on opinion summarization technique s for social media

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    The volume of data on the social media is huge and even keeps increasing. The need for efficient processing of this extensive information resulted in increasing research interest in knowledge engineering tasks such as Opinion Summarization. This survey shows the current opinion summarization challenges for social media, then the necessary pre-summarization steps like preprocessing, features extraction, noise elimination, and handling of synonym features. Next, it covers the various approaches used in opinion summarization like Visualization, Abstractive, Aspect based, Query-focused, Real Time, Update Summarization, and highlight other Opinion Summarization approaches such as Contrastive, Concept-based, Community Detection, Domain Specific, Bilingual, Social Bookmarking, and Social Media Sampling. It covers the different datasets used in opinion summarization and future work suggested in each technique. Finally, it provides different ways for evaluating opinion summarization

    Faster subsequence recognition in compressed strings

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    Computation on compressed strings is one of the key approaches to processing massive data sets. We consider local subsequence recognition problems on strings compressed by straight-line programs (SLP), which is closely related to Lempel--Ziv compression. For an SLP-compressed text of length mˉ\bar m, and an uncompressed pattern of length nn, C{\'e}gielski et al. gave an algorithm for local subsequence recognition running in time O(mˉn2logn)O(\bar mn^2 \log n). We improve the running time to O(mˉn1.5)O(\bar mn^{1.5}). Our algorithm can also be used to compute the longest common subsequence between a compressed text and an uncompressed pattern in time O(mˉn1.5)O(\bar mn^{1.5}); the same problem with a compressed pattern is known to be NP-hard

    Trans-Inpainter: Wireless Channel Information- Guided Image Restoration via Multimodal Transformer

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    Image inpainting is a critical computer vision task to restore missing or damaged image regions. In this paper, we propose Trans-Inpainter, a novel multimodal image inpainting method guided by Channel State Information (CSI) data. Leveraging the power of transformer architectures, Trans-Inpainter effectively extracts visual information from CSI time sequences, enabling high-quality and realistic image inpainting. To evaluate its performance, we compare Trans-Inpainter with RF-Inpainter, the state-of-the-art radio frequency (RF) signal-based image inpainting technique. Through comprehensive experiments, Trans-Inpainter consistently demonstrates superior performance in various scenarios. Additionally, we investigate the impact of CSI data variations on Trans-Inpainter's imaging ability, analyzing individual sensor data, fused data from multiple sensors, and altered CSI matrix dimensions. These insights provide valuable references for future wireless sensing and computer vision studies
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