In this article, we consider a generalized longest common subsequence (LCS) problem with multiple substring inclusive constraints. For the two input sequences X and Y of lengths n and m , and a set of d constraints of total length r , the problem is to find a common subsequence Z of X and Y including each of constraint string in P as a substring and the length of Z is maximized. A new dynamic programming solution to this problem is presented in this article. The correctness of the new algorithm is proved. The time complexity of our algorithm is . In the case of the number of constraint strings is fixed, our new algorithm for the generalized LCS problem with multiple substring inclusive constraints requires \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6} $$\textbf{\textit{O ( nmr )}}$$ time and space.

This paper studies the string-excluding (STR-EC)-constrained longest common subsequence (LCS) problem, a generalized LCS problem. For the two input sequences, X and Y , of lengths n andmand a constraint string, P, of length r, the goal is to find the longest common subsequence, Z, of X and Y that excludes P as a substring. The problem and its solution were first proposed by Chen and Chao, but we found that their algorithm cannot solve the problem correctly. A new dynamic programming solution for the STR-EC-LCS problem is then presented in this paper, and the correctness of the new algorithm is proven. The time complexity of the new algorithm is O(nmr).

Convolutional neural network (CNN) has achieved impressive success in lightweight image super-resolution (SR) methods, yet the nature of its local operations constrains the SR performance. Recent Transformer has attracted increasing attention in lightweight SR methods owing to its remarkable global feature extraction capacity. However, the huge computational cost makes it challenging for lightweight SR methods to efficiently utilize Transformer to exploit global contextual information from shallowto intermediate layers. To address these issues, we propose a novel lightweight Transformer and CNN interaction network for image Super-Resolution (TCSR), which fully leverages the complementary strengths of Transformer and CNN. Specifically, an efficient lightweight Transformer and CNN Interaction Block (TCIB) is designed to extract local and global features at various stages of the network, resulting in favorable hybrid features that significantly improve the quality of reconstructed images. Then, we construct a lightweight Reversed UNet (RUNet) to progressively aggregate hybrid features as well as to better trade-off the reconstruction accuracy and efficiency. Furthermore, we introduce a Refinement module to further refine edge and texture details with global information. Experimental results on numerous benchmarks validate that the proposed TCSR achieves superior performance with fewer parameters and less computational overhead than state-of-the-art lightweight methods.

Location-based services (LBS) generate large volumes of spatiotemporal data, raising significant privacy concerns. Yet the field lacks a systematic survey that examines security risks across the full LBS data lifecycle and provides a comprehensive comparison of privacy-preserving mechanisms. This scoping review advances LBS privacy by offering a lifecycle-aware synthesis, a rigorous comparative framework, and a forward-looking agenda for research and deployment. Specifically, we: (1) map attack surfaces and quantify privacy–utility trade-offs across collection, storage, transmission, processing, and sharing, with particular attention to server-side threats and untrusted providers; (2) develop a multidimensional framework that compares leading techniques-k-anonymity, differential privacy, perturbation/obfuscation, and encrypted search-in terms of protection strength, computational/communication overhead, and deployment suitability; and (3) identify technical bottlenecks and outline research directions aligned with emerging scenarios and industry trends. Together, these contributions provide actionable criteria for selecting, combining, and deploying robust, efficient LBS privacy solutions.

The digital transformation of educational systems has significantly optimized administrative workflows and enhanced the user experience for educators and learners. However, the accumulation of sensitive personal data on educational websites has made them prime targets for cyber threats. Despite growing awareness of these security challenges, the technical roots of vulnerabilities within such platforms remain insufficiently explored. To address this gap, we introduce LLM-EduAttackGraph, a specialized tool designed to assist in vulnerability detection by leveraging large language models (LLMs). Rather than serving as a fully automated monitoring system, LLM-EduAttackGraph operates as a human-in-the-loop assistant, combining expert knowledge with the analytical capabilities of LLMs to help identify potential penetration paths based on network fingerprint information. Using LLM-EduAttackGraph, we have so far identified 961 penetration vulnerabilities across educational websites in mainland China—a number that continues to grow as analysis progresses. These findings demonstrate the tool’s practical value in augmenting cybersecurity research and efforts. Our in-depth analysis of the discovered vulnerabilities reveals that limited developer experience and a heavy dependence on outsourced website development are key contributing factors. By shedding light on these root causes, our research offers actionable strategies and insights aimed at improving the cybersecurity posture of educational platforms and ensuring the sustainable development of online education. Furthermore, we have compared LLM-EduAttackGraph with several existing large model penetration tools to demonstrate the performance of LLM-EduAttackGraph. Such strengths include its low demand for hardware resources and having undergone empirical verification.