The task of translating natural language questions into query languages has long been a central focus in semantic parsing.Recent advancements in Large Language Models (LLMs) have significantly accelerated progress in this field.However, existing studies typically focus on a single query language, resulting in methods with limited generalizability across different languages.In this paper, we formally define the Text-to-Query task paradigm, unifying semantic parsing tasks across various query languages.We identify query skeletons as a shared optimization target of Text-to-Query tasks, and propose a general dynamic data augmentation framework that explicitly diagnoses modelspecific weaknesses in handling these skeletons to synthesize targeted training data.Experiments on four Text-to-Query benchmarks demonstrate that our method achieves state-ofthe-art performance using only a small amount of synthesized data, highlighting the efficiency and generality of our approach and laying a solid foundation for unified research on Textto-Query tasks.We release our code

Second-Order vulnerabilities, such as second-order Cross-Site Scripting (XSS) and Server-Side Request Forgery (SSRF), occur when user-controlled inputs are stored in databases and later retrieved in different execution contexts, complicating static detection. Existing static analysis approaches struggle primarily with two challenges. First, they struggle in accurately identifying database-accessing functions defined by third-party libraries or custom data access layers, often leading to missed taint propagation paths. Second, they may fail to contextually model database operations when queries are dynamically constructed and depend on runtime parameters. To address these limitations, we propose STaint, a novel bi-directional static analysis method that integrates taint analysis with large language models (LLMs). Using semantic reasoning, STaint accurately identifies and models custom database reads and writes, effectively reconstructing comprehensive taint data flows in the database. Preliminary evaluations on ten real-world PHP applications show that STaint successfully detects 56 second-order vulnerability paths, including 7 previously unknown cases, outperforming existing techniques.

Abstract How can we estimate the result size for a given query on complex spatial objects like polygons? Estimating a query’s result size, also known as the cardinality estimation, plays a significant role in query scheduling and optimization. Accurate and fast cardinality estimation substantially improves query efficiency. Existing compatible solutions, mainly histogram-based, deal with polygons as their minimal bounding rectangles for easier processing, which leads to inaccurate estimation. To address this issue, we present PolyCard, a learned cardinality estimator for intersection queries on spatial polygons. We successfully apply learning techniques to spatial polygons with variable sizes. PolyCard has the following properties. (i) Accurate: PolyCard improves 30% accuracy compared with existing solutions, (ii) Fast: PolyCard takes only 4 microseconds for an estimation, and (iii) Stable: PolyCard is robust against datasets and queries of different cardinalities. Our experiments on four real-world datasets of millions of polygons demonstrate the efficiency and effectiveness of PolyCard.

Server-side request forgery (SSRF) vulnerabilities are inevitable in PHP web applications. Existing static tools in detecting vulnerabilities in PHP web applications neither contain SSRF-related features to enhance detection accuracy nor consider PHP’s dynamic type features. In this paper, we present Artemis , a static taint analysis tool for detecting SSRF vulnerabilities in PHP web applications. First, Artemis extracts both PHP built-in and third-party functions as candidate source and sink functions. Second, Artemis constructs both explicit and implicit call graphs to infer functions’ relationships. Third, Artemis performs taint analysis based on a set of rules that prevent over-tainting and pauses when SSRF exploitation is impossible. Fourth, Artemis analyzes the compatibility of path conditions to prune false positives. We have implemented a prototype of Artemis and evaluated it on 250 PHP web applications. Artemis reports 207 true vulnerable paths (106 true SSRFs) with 15 false positives. Of the 106 detected SSRFs, 35 are newly found and reported to developers, with 24 confirmed and assigned CVE IDs.

Server-side request forgeries (SSRFs) are inevitable in PHP web applications. Existing static taint analysis tools for PHP suffer from both high rates of false positives and false negatives in detecting SSRF because they do not incorporate application-specific sources and sinks, account for PHP's dynamic type characteristics, and include SSRF-specific taint analysis rules, leading to over-tainting and under-tainting. In this work, we propose a technique to accurately detect SSRF vulnerabilities in PHP web applications. First, we extract both PHP built-in and application-specific functions as candidate source and sink functions. Second, we extract explicit and implicit function calls to construct applications' call graphs. Third, we perform a taint analysis based on a set of rules that prevent over-tainting and under-tainting. We have implemented a prototype and evaluated it with different types of PHP web applications. Our preliminary experiment shows that we detect 24 SSRF vulnerabilities in 13 different types of applications. 20 of the vulnerabilities are known and 4 of the vulnerabilities are new.

Recently, neural implicit 3D reconstruction in indoor scenarios has become popular due to its simplicity and impressive performance. Previous works could produce complete results leveraging monocular priors of normal or depth. However, they may suffer from over-smoothed reconstructions and long-time optimization due to unbiased sampling and inaccurate monocular priors. In this paper, we propose a novel neural implicit surface reconstruction method, named FD-NeuS, to learn fine-detailed 3D models using multi-level importance sampling strategy and multi-view consistency methodology. Specifically, we leverage segmentation priors to guide region-based ray sampling, and use piecewise exponential functions as weights to pilot 3 D points sampling along the rays, ensuring more attention on important regions. In addition, we introduce multi-view feature consistency and multi-view normal consistency as supervision and uncertainty respectively, which further improve the reconstruction of details. Extensive quantitative and qualitative results show that FD-NeuS outperforms existing methods in various scenes.

In SC20, (Srivastava et al. 2020) proposed a Parallel Framework forBayesianLearning, or ramBLe, for short, which is a highly parallel and efficient framework for learning the structure of Bayesian Networks (BNs) from samples,There was a discrepancy in Bibliography in the PDF and the source file. We have followed the source file. ?> particularly large genome-scale networks. As part of our participation in the SC21 Student Cluster Competition, our task was to verify conclusions from the original work (Srivastava et al. 2020). Here we present the outcome of our experiments, which were performed on a four-node cluster from the Oracle Cloud HPC platform. We reproduce the numerical results from (Srivastava et al. 2020), namely the algorithm's performance and scaling behavior using MPI and different Python and Boost libraries on the Oracle cloud.

At the present stage, most of the packet header detection algorithms in massive access are to solve the problem of multi-user collision in the additive white gaussian noise (AWGN) channel. On the other hand, many algorithms do not consider that the number of user collisions is unknown and varies randomly. First, we propose an adaptive algorithm based on correlation. Secondly, based on the coordinate ascent variational inference (CAVI) algorithms, we present an improved header detection method in fading channels. Compared with the traditional packet head detection method based on compressive sensing reconstruction algorithm, this improved method solves the problem that the number of users who collide is unknown, and it also has good performance in fading channels. In addition, the method can also cope with the random change of the number of users in collision.