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Journal articles on the topic 'Semantic matching for testing'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

GIUNCHIGLIA, FAUSTO, and PAVEL SHVAIKO. "Semantic matching." Knowledge Engineering Review 18, no. 3 (2003): 265–80. http://dx.doi.org/10.1017/s0269888904000074.

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We think of match as an operator that takes two graph-like structures (e.g. database schemas or ontologies) and produces a mapping between elements of the two graphs that correspond semantically to each other. The goal of this paper is to propose a new approach to matching, called semantic matching. As its name indicates, in semantic matching the key intuition is to exploit the model-theoretic information, which is codified in the nodes and the structure of graphs. The contributions of this paper are (i) a rational reconstruction of the major matching problems and their articulation in terms o

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Gorbatsevich, V., Y. Vizilter, V. Knyaz, and A. Moiseenko. "SINGLE-SHOT SEMANTIC MATCHER FOR UNSEEN OBJECT DETECTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 379–84. http://dx.doi.org/10.5194/isprs-archives-xlii-2-379-2018.

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In this paper we combine the ideas of image matching, object detection, image retrieval and zero-shot learning for stating and solving the semantic matching problem. Semantic matcher takes two images (test and request) as input and returns detected objects (bounding boxes) on test image corresponding to semantic class represented by request (sample) image. We implement our single-shot semantic matcher CNN architecture based on GoogleNet and YOLO/DetectNet architectures. We propose the detection-by-request training and testing protocols for semantic matching algorithms. We train and test our CN

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Zhao, Xuanpu, Dianmo Sheng, Zhentao Tan, et al. "Training-free Open-Vocabulary Semantic Segmentation via Diverse Prototype Construction and Sub-region Matching." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10474–82. https://doi.org/10.1609/aaai.v39i10.33137.

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Open-vocabulary semantic segmentation (OVSS) aims to segment images of arbitrary categories specified by class labels. While previous approaches relied on extensive image-text pairs or dense semantic annotations, recent training-free methods attempted to overcome these limitations by constructing semantic prototypes in the construction stage and image-to-image matching (i.e., prototype matching) during testing. However, these methods often struggle to effectively capture the visual characteristics of categories and fail to utilize local features during prototype matching. To deal with these pr

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Elhaj, Elamin. "A SEMANTIC RESOURCE BASED APPROACH FOR STAR SCHEMAS MATCHING." International Journal of Database Management Systems (IJDMS ) 10, no. 6 (2019): 01–14. https://doi.org/10.5281/zenodo.3515941.

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The hybrid approach is widely used in constructing data warehouse (DW) schemas. It relies on a complex process for matching two sets of multidimensional star schemas: schemas built from business requirements (BR-Star schemas) and schemas constructed on the organization data source (DS-Star schemas). Using a semantic resource during this matching helps solving heterogeneity problems. This paper suggests a semiautomatic approach for the construction of approved star schemas by matching DS-Star schemas with BRStars, and by using WordNet as a semantic resource for solving heterogeneity issues. Thi

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Renoult, Louis, and J. Bruno Debruille. "N400-like Potentials and Reaction Times Index Semantic Relations between Highly Repeated Individual Words." Journal of Cognitive Neuroscience 23, no. 4 (2011): 905–22. http://dx.doi.org/10.1162/jocn.2009.21410.

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The N400 ERP is an electrophysiological index of semantic processing. Its amplitude varies with the semantic category of words, their concreteness, or whether their meaning matches that of a preceding context. The results of a number of studies suggest that these effects could be markedly reduced or suppressed for stimuli that are repeated. Nevertheless, we have recently shown that significant effects of semantic matching and category could be obtained on N400-like potentials elicited by massively repeated target words in a prime–target semantic categorization task. If such effects could be ob

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CHIEN, BEEN-CHIAN, and SHIANG-YI HE. "A LEXICAL DECISION TREE SCHEME FOR SUPPORTING SCHEMA MATCHING." International Journal of Information Technology & Decision Making 10, no. 03 (2011): 519–37. http://dx.doi.org/10.1142/s0219622011004439.

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To manipulate semantic web and integrate different data sources efficiently, automatic schema matching plays a key role. A generic schema matching method generally includes two phases: the linguistic similarity matching phase and the structural similarity matching phase. Since linguistic matching is an essential step for effective schema matching, developing a high accurate linguistic similarity matching scheme is required. In this paper, a schema matching approach called Similarity Yield Matcher (SYM) is proposed. In SYM, a lexical decision tree is presented to determine the linguistic simila

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Wang, Ya Ning, and Lan Nan Xiao. "Research on Automatic Scoring Methods for Programs Based on Program Understanding." Applied Mechanics and Materials 513-517 (February 2014): 2054–58. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2054.

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This paper discusses three general automatic scoring algorithms including software quality metric algorithm, dynamic testing algorithm and static analysis algorithm based on program understanding, analyzes and designs their rationality, practicality and maneuverability. Therefore, a new automatic scoring strategy which combines dynamic testing and pattern matching based on program understanding is proposed. This method focuses on the accurate expressions on program algorithm and semantic points in student program, enhances the performance of the automatic scoring system and matches the idea of

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Pang, Zongshang, Yuta Nakashima, Mayu Otani, and Hajime Nagahara. "PixCon: Pixel-Level Contrastive Learning Revisited." Electronics 14, no. 8 (2025): 1623. https://doi.org/10.3390/electronics14081623.

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Contrastive image representation learning has been essential for pre-training vision foundation models to deliver excellent transfer learning performance. It was originally developed based on instance discrimination, which focuses on instance-level recognition tasks. Lately, the focus has shifted to directly working on the dense spatial features to improve transfer performance on dense prediction tasks such as object detection and semantic segmentation, for which pixel-level and region-level contrastive learning methods have been proposed. Region-level methods usually employ region-mining algo

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Yang, Shuaihu, Min Feng, and Danping Guan. "Intelligent Scheduling System for Production Line Automatic Matching Based on DSSM-XGBoost." Journal of Physics: Conference Series 2203, no. 1 (2022): 012072. http://dx.doi.org/10.1088/1742-6596/2203/1/012072.

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Abstract Intelligent production scheduling is an important part of intelligent production, and its production time and intensity are reasonably arranged, which can fully improve production efficiency. This article provides an intelligent model for engine manufacturing and scheduling. After the model decomposes the order into processes, the DSSM algorithm solves the semantic similarity of the text in the production content of the process, and generates a semantic vector to convert unstructured data into structured data. Based on XGBoost algorithm, the required production line is marked to estab

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Susyanto, Teguh, and Khabib Mustofa. "Pencarian Lowongan Pekerjaan Berbasis Agen Berdasarkan Profil Pencari Kerja dengan Pendekatan Semantic Web Service." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 10, no. 1 (2016): 103. http://dx.doi.org/10.22146/ijccs.12734.

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Currently, job searching service still has many weaknesses and often fails to provide relevant job information that matches the needs of job seekers. This is due to the searching method applied in the search engines still uses the syntax-based matching and the lack of integration among the job service providers. Therefore it’s difficult for the job seekers to get the desired information. To overcome these weaknesses, a prototype of a job vacancy searching by involving a web service as a job information provider is proposed.This thesis is aimed to create job search based on the personalization

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Xue, Xingsi, Xiaojing Wu, Jie Zhang, Lingyu Zhang, Hai Zhu, and Guojun Mao. "Aggregating Heterogeneous Sensor Ontologies with Fuzzy Debate Mechanism." Security and Communication Networks 2021 (May 26, 2021): 1–12. http://dx.doi.org/10.1155/2021/2878684.

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Aiming at enhancing the communication and information security between the next generation of Industrial Internet of Things (Nx-IIoT) sensor networks, it is critical to aggregate heterogeneous sensor data in the sensor ontologies by establishing semantic connections in diverse sensor ontologies. Sensor ontology matching technology is devoted to determining heterogeneous sensor concept pairs in two distinct sensor ontologies, which is an effective method of addressing the heterogeneity problem. The existing matching techniques neglect the relationships among different entity mapping, which make

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Li, Jinming, Peng Wang, Cui Ni, and Wen Rong. "Loop Closure Detection Based on Image Semantic Segmentation in Indoor Environment." Mathematical Problems in Engineering 2022 (March 10, 2022): 1–14. http://dx.doi.org/10.1155/2022/7765479.

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When mobile robots run in indoor environment, a large number of similar images are easy to appear in the images collected, probably causing false-positive judgment in loop closure detection based on simultaneous localization and mapping (SLAM). To solve this problem, a loop closure detection algorithm for visual SLAM based on image semantic segmentation is proposed in this paper. Specifically, the current frame is semantically segmented by optimized DeepLabv3+ model to obtain semantic labels in the image. The 3D semantic node coordinates corresponding to each semantic label are then extracted

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Krapchatova, Yaroslava A. "THE USE OF COMPUTER TESTING FOR FUTURE INTERPRETERS’ SELF-ASSESSMENT OF ENGLISH LISTENING COMPETENCE." Information Technologies and Learning Tools 60, no. 4 (2017): 205. http://dx.doi.org/10.33407/itlt.v60i4.1725.

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The methodology of self-assessment of English listening competence for future interpreters is highlighted in the article. Computer exercises, heuristic discussion, listening tips and self-assessment checklists are offered to improve students’ listening skills and abilities. Various computer exercises are used to check and evaluate lexical and grammatical skills, language and semantic anticipation, understanding of a gist, details and specific information of the text. The following types of computer exercises: multiple choice, multi-select, gap-filling, matching, crossword and jumbled words are

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Mansurova, Madina, Vladimir Barakhnin, Assel Ospan, and Roman Titkov. "Ontology-Driven Semantic Analysis of Tabular Data: An Iterative Approach with Advanced Entity Recognition." Applied Sciences 13, no. 19 (2023): 10918. http://dx.doi.org/10.3390/app131910918.

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This study focuses on the extraction and semantic analysis of data from tables, emphasizing the importance of understanding the semantics of tables to obtain useful information. The main goal was to develop a technology using the ontology for the semantic analysis of tables. An iterative algorithm has been proposed that can parse the contents of a table and determine cell types based on the ontology. The study presents an automated method for extracting data in various languages in various fields, subject to the availability of an appropriate ontology. Advanced techniques such as cosine distan

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Huang, S., F. Nex, Y. Lin, and M. Y. Yang. "SEMANTIC SEGMENTATION OF BUILDING IN AIRBORNE IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 35–42. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-35-2019.

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<p><strong>Abstract.</strong> Building is a key component to the reconstructing of LoD3 city modelling. Compared to terrestrial view, airborne datasets have more occlusions at street level but can cover larger area in the urban areas. With the popularity of the Deep Learning, many tasks in the field of computer vision can be solved in easier and efficiency way. In this paper, we propose a method to apply deep neural networks to building façade segmentation. In particular, the FC-DenseNet and the DeepLabV3+ algorithms are used to segment the building from airborne images and g

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Grey, Sarah, John N. Williams, and Patrick Rebuschat. "INCIDENTAL EXPOSURE AND L3 LEARNING OF MORPHOSYNTAX." Studies in Second Language Acquisition 36, no. 4 (2014): 611–45. http://dx.doi.org/10.1017/s0272263113000727.

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Evidence of learning following incidental exposure has been found for aspects of nonnative syntax in adults (Rebuschat & Williams, 2006, 2012; Williams & Kuribara, 2008). However, little research has tested delayed effects of learning under an incidental condition or moved beyond word order. This study investigated learning of third language (L3) morphosyntax (word order and case marking) under an incidental exposure condition. Participants were second language Spanish learners exposed auditorily to a semiartificial language, Japlish, during a semantic plausibility judgment task. Perfo

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Park, Jisun, and Kyungeun Cho. "Neural Rendering-Based 3D Scene Style Transfer Method via Semantic Understanding Using a Single Style Image." Mathematics 11, no. 14 (2023): 3243. http://dx.doi.org/10.3390/math11143243.

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In the rapidly emerging era of untact (“contact-free”) technologies, the requirement for three-dimensional (3D) virtual environments utilized in virtual reality (VR)/augmented reality (AR) and the metaverse has seen significant growth, owing to their extensive application across various domains. Current research focuses on the automatic transfer of the style of rendering images within a 3D virtual environment using artificial intelligence, which aims to minimize human intervention. However, the prevalent studies on rendering-based 3D environment-style transfers have certain inherent limitation

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A. Jalil, M., F. Mohd, C. P. Ling, and N. M. M. Noor. "Criminal intelligence analysis based on ontological knowledge representation model." International Journal of Engineering & Technology 7, no. 2.28 (2018): 187. http://dx.doi.org/10.14419/ijet.v7i2.28.12908.

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Nowadays, community security is an issue which is given higher priority by all agencies, aiming to reduce crime incidence. As knowledge representation is the appropriate way to apply on complex crime analysis information, hence ontology-based case matching model is proposed to represent the relationships among the knowledge. Therefore, in this study, the ontology model is developed using semantic web modelling tool, TopBraid Composer Standard Edition in order to represent the crime information with the well-defined classes and relationships. The advantage of TopBraid is the ability in offering

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Serratosa, Francesc. "A Methodology to Generate Attributed Graphs with a Bounded Graph Edit Distance for Graph-Matching Testing." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 11 (2018): 1850038. http://dx.doi.org/10.1142/s0218001418500386.

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This paper presents a methodology for generating pairs of attributed graphs with a lower and upper- bounded graph edit distance (GED). It is independent of the type of attributes on nodes and edges. The algorithm is composed of three steps: randomly generating a graph, generating another graph as a sub-graph of the first, and adding structural and semantic noise to both. These graphs, together with their bounded distances, can be used to manufacture synthetic databases of large graphs. The exact GED between large graphs cannot be obtained for runtime reasons since it has to be computed through

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Xue, Xingsi, Qi Wu, Miao Ye, and Jianhui Lv. "Efficient Ontology Meta-Matching Based on Interpolation Model Assisted Evolutionary Algorithm." Mathematics 10, no. 17 (2022): 3212. http://dx.doi.org/10.3390/math10173212.

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Ontology is the kernel technique of the Semantic Web (SW), which models the domain knowledge in a formal and machine-understandable way. To ensure different ontologies’ communications, the cutting-edge technology is to determine the heterogeneous entity mappings through the ontology matching process. During this procedure, it is of utmost importance to integrate different similarity measures to distinguish heterogeneous entity correspondence. The way to find the most appropriate aggregating weights to enhance the ontology alignment’s quality is called ontology meta-matching problem, and recent

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Sun, Qingwei, Jiangang Chao, Wanhong Lin, Zhenying Xu, Wei Chen, and Ning He. "Learn to Few-Shot Segment Remote Sensing Images from Irrelevant Data." Remote Sensing 15, no. 20 (2023): 4937. http://dx.doi.org/10.3390/rs15204937.

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Few-shot semantic segmentation (FSS) is committed to segmenting new classes with only a few labels. Generally, FSS assumes that base classes and novel classes belong to the same domain, which limits FSS’s application in a wide range of areas. In particular, since annotation is time-consuming, it is not cost-effective to process remote sensing images using FSS. To address this issue, we designed a feature transformation network (FTNet) for learning to few-shot segment remote sensing images from irrelevant data (FSS-RSI). The main idea is to train networks on irrelevant, already labeled data but

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Wu, Guangyao, Xiaoming Xu, and Yiting Kang. "AI-Driven Automated Test Generation Framework for VCU: A Multidimensional Coupling Approach Integrating Requirements, Variables and Logic." World Electric Vehicle Journal 16, no. 8 (2025): 417. https://doi.org/10.3390/wevj16080417.

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This paper proposes an AI-driven automated test generation framework for vehicle control units (VCUs), integrating natural language processing (NLP) and dynamic variable binding. To address the critical limitation of traditional AI-generated test cases lacking executable variables, the framework establishes a closed-loop transformation from requirements to executable code through a five-layer architecture: (1) structured parsing of PDF requirements using domain-adaptive prompt engineering; (2) construction of a multidimensional variable knowledge graph; (3) semantic atomic decomposition of req

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Ma, Suqiang, Chun Liu, Zheng Li, and Wei Yang. "Integrating Adversarial Generative Network with Variational Autoencoders towards Cross-Modal Alignment for Zero-Shot Remote Sensing Image Scene Classification." Remote Sensing 14, no. 18 (2022): 4533. http://dx.doi.org/10.3390/rs14184533.

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Remote sensing image scene classification takes image blocks as classification units and predicts their semantic descriptors. Because it is difficult to obtain enough labeled samples for all classes of remote sensing image scenes, zero-shot classification methods which can recognize image scenes that are not seen in the training stage are of great significance. By projecting the image visual features and the class semantic features into the latent space and ensuring their alignment, the variational autoencoder (VAE) generative model has been applied to address remote-sensing image scene classi

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Touzani, Samir, and Jessica Granderson. "Open Data and Deep Semantic Segmentation for Automated Extraction of Building Footprints." Remote Sensing 13, no. 13 (2021): 2578. http://dx.doi.org/10.3390/rs13132578.

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Advances in machine learning and computer vision, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics, cost-effectively, and at scale. These characteristics are relevant to a variety of urban and energy applications, yet are time consuming and costly to acquire with today’s manual methods. Several recent research studies have shown that in comparison to more traditional methods that are based on features engineering approach, an end-to-end learning approach based on deep learning algorithm

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Guijarro-Fuentes, Pedro, Acrisio Pires, and Will Nediger. "Delay in the acquisition of Differential Object Marking by Spanish monolingual and bilingual teenagers." International Journal of Bilingualism 21, no. 2 (2016): 159–77. http://dx.doi.org/10.1177/1367006915601249.

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Aims and Objectives/Purpose/Research Questions: This study investigated the acquisition of Spanish Differential Object Marking (DOM) by bilingual and monolingual Spanish teenagers, evaluating to which extent their knowledge of DOM can be explained by different theories of acquisition. Design/Methodology/Approach: Two experiments with bilingual and monolingual Spanish teenagers (ages 10 to 15) were conducted. The experiments included an Elicited Production Completion Task, in which a space was to either be filled with an object marker or left blank, and a Context-Matching Acceptability Judgment

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Tshering, Younten, Suyogya Ratna Tamrakar, and Sai Preetham Kamishetty. "Developing programming language with compilers using JFlex in NetBean: Expanding and testing simple operators by implementing a calculator." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (2022): 1992–2005. http://dx.doi.org/10.22214/ijraset.2022.46665.

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Abstract: JFlex is a lexical analyzer generator and takes input requirements with a set of regular expressions and corresponding actions. It creates a program (a lexer) that reads input, matches the input against the regular expressions, and runs the matching action. This paper shows how Programming Language can be developed. This work was done to develop a simple programming language with compilers using JFlex in NetBean so that it can support assignment statements, if then else, while do and type checking and its execution. The data type included are int, real, char, and Boolean/String. The

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Liu, Yong, Xuexin Qi, Jiali Zhang, Hui Li, Xin Ge, and Jun Ai. "Automatic Bug Triaging via Deep Reinforcement Learning." Applied Sciences 12, no. 7 (2022): 3565. http://dx.doi.org/10.3390/app12073565.

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Software maintenance and evolution account for approximately 90% of the software development process (e.g., implementation, testing, and maintenance). Bug triaging refers to an activity where developers diagnose, fix, test, and document bug reports during software development and maintenance to improve the speed of bug repair and project progress. However, the large number of bug reports submitted daily increases the triaging workload, and open-source software has a long maintenance cycle. Meanwhile, the developer activity is not stable and changes significantly during software development. He

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Zhao, Kaiqi, Xiaochen Yuan, Zhiyao Xie, Yan Xiang, Guoheng Huang, and Li Feng. "SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection." Sensors 23, no. 14 (2023): 6430. http://dx.doi.org/10.3390/s23146430.

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With the wide application of visual sensors and development of digital image processing technology, image copy-move forgery detection (CMFD) has become more and more prevalent. Copy-move forgery is copying one or several areas of an image and pasting them into another part of the same image, and CMFD is an efficient means to expose this. There are improper uses of forged images in industry, the military, and daily life. In this paper, we present an efficient end-to-end deep learning approach for CMFD, using a span-partial structure and attention mechanism (SPA-Net). The SPA-Net extracts featur

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Padmavathi Pragada. "Automated LSTM Based Deep Learning Model for Handwritten Telugu Answer Script Analysis." Communications on Applied Nonlinear Analysis 32, no. 8s (2025): 745–62. https://doi.org/10.52783/cana.v32.3796.

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The growing demand for automated evaluation systems in educational environments, especially for languages with complex scripts like Telugu, drives the motivation for this research. Traditional handwriting recognition methods for Telugu have faced challenges with limited accuracy and adaptability, particularly in real-world educational scenarios. These limitations often result in reduced precision in character and sentence recognition, along with increased processing delays. This study proposes a novel system for the automated evaluation of handwritten Telugu answer scripts. The model incorpora

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Gutiérrez-Mangado, M. Juncal, and María Martínez-Adrián. "CLIL at the linguistic interfaces." Journal of Immersion and Content-Based Language Education 6, no. 1 (2018): 85–112. http://dx.doi.org/10.1075/jicb.17002.gut.

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Abstract This study explores the effect of CLIL on the acquisition of nominal morphology (syntax-morphology interface) and article use (syntax-semantics-discourse-interface), linguistic areas that have been scarcely investigated in CLIL settings. Here we compare article omission and overuse errors in an oral production task performed by L1 Basque-Spanish learners of L3 English in two CLIL and non-CLIL groups matching in age at testing time and amount of exposure. Results indicate that as regards nominal morphology, CLIL and non-CLIL learners are equal in terms of the omission of the definite a

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Sun, Xiangzhou. "GazeLink: A multi-language low-cost mobile eye-gesture communication system with large language models for people with amyotrophic lateral sclerosis." Applied and Computational Engineering 88, no. 1 (2024): 93–109. http://dx.doi.org/10.54254/2755-2721/88/20241639.

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Amyotrophic Lateral Sclerosis (ALS) patients who have severe motor and speech impairments mostly rely on their eyes and assistive technology to communicate. However, existing high-tech products are expensive and hard to access, while low-tech products are inefficient and restrictive. To mitigate the limitations, this research proposes GazeLink, a multi-language low-cost mobile application for ALS patients to communicate efficiently with only eye movements. First, the system recognizes user eye gestures like left or up with machine learning and a template-matching algorithm. Then, it converts t

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Liu, Hang, Qin Ye, Hairui Wang, Liang Chen, and Jian Yang. "A Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving." Remote Sensing 11, no. 11 (2019): 1348. http://dx.doi.org/10.3390/rs11111348.

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Real-time and high-precision localization information is vital for many modules of unmanned vehicles. At present, a high-cost RTK (Real Time Kinematic) and IMU (Integrated Measurement Unit) integrated navigation system is often used, but its accuracy cannot meet the requirements and even fails in many scenes. In order to reduce the costs and improve the localization accuracy and stability, we propose a precise and robust segmentation-based Lidar (Light Detection and Ranging) localization system aided with MEMS (Micro-Electro-Mechanical System) IMU and designed for high level autonomous driving

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Zhang, Xiaohan, Xingyu Li, Waqas Sultani, Yi Zhou, and Safwan Wshah. "Cross-View Geo-Localization via Learning Disentangled Geometric Layout Correspondence." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3480–88. http://dx.doi.org/10.1609/aaai.v37i3.25457.

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Cross-view geo-localization aims to estimate the location of a query ground image by matching it to a reference geo-tagged aerial images database. As an extremely challenging task, its difficulties root in the drastic view changes and different capturing time between two views. Despite these difficulties, recent works achieve outstanding progress on cross-view geo-localization benchmarks. However, existing methods still suffer from poor performance on the cross-area benchmarks, in which the training and testing data are captured from two different regions. We attribute this deficiency to the l

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Tunç, Hünkar Can, Parosh Aziz Abdulla, Soham Chakraborty, Shankaranarayanan Krishna, Umang Mathur, and Andreas Pavlogiannis. "Optimal Reads-From Consistency Checking for C11-Style Memory Models." Proceedings of the ACM on Programming Languages 7, PLDI (2023): 761–85. http://dx.doi.org/10.1145/3591251.

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Over the years, several memory models have been proposed to capture the subtle concurrency semantics of C/C++. One of the most fundamental problems associated with a memory model M is consistency checking: given an execution X , is X consistent with M ? This problem lies at the heart of numerous applications, including specification testing and litmus tests, stateless model checking, and dynamic analyses. As such, it has been explored extensively and its complexity is well-understood for traditional models like SC and TSO. However, less is known for the numerous model variants of C/C++, for wh

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Chen, Hui, Zanmei Xu, Lijuan Chen, et al. "Abstract 2315: AI-enabled precision oncology era: Advanced and interactive interpretation of next-gneneration sequencing (NGS) reports." Cancer Research 84, no. 6_Supplement (2024): 2315. http://dx.doi.org/10.1158/1538-7445.am2024-2315.

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Abstract Background: Next-generation sequencing (NGS) has been widely adopted in clinical practice, but there are still unmet needs among physicians regarding the interpretation and application of NGS reports. These needs include selecting multi-targeted and combination therapies, keeping up with evolving treatment options and clinical trial results, and incorporating personalized medicine based on real clinical cases. Methods: To address these challenges, we propose using large language model (LLM) to analyze and interpret clinical questions, perform semantic searches in a high-quality databa

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Лебедь, С. В., та С. В. Ибрагимова. "Анализ методов и инструментов обнаружения чувствительной информации в исходном коде: проблемы точности и полноты". Международный научный журнал "Современные информационные технологии и ИТ-образование" 21, № 1 (2025): 13–24. https://doi.org/10.25559/sitito.021.202501.13-24.

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В условиях повсеместного внедрения DevOps-практик и роста сложности программных систем, проблема утечки чувствительной информации (секретов), такой как API-ключи, пароли и токены, непосредственно из исходного кода и конфигурационных файлов, приобретает критическую важность. Утечка секретов может привести к серьезным инцидентам безопасности, финансовым и репутационным потерям. Статья посвящена анализу проблемы обнаружения секретов в коде. Рассматриваются типы секретов, места их возможного обнаружения и риски, связанные с их компрометацией. Проводится детальный обзор и критический анализ существ

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Chida, Nariyoshi, and Tachio Terauchi. "Repairing Regular Expressions for Extraction." Proceedings of the ACM on Programming Languages 7, PLDI (2023): 1633–56. http://dx.doi.org/10.1145/3591287.

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While synthesizing and repairing regular expressions (regexes) based on Programming-by-Examples (PBE) methods have seen rapid progress in recent years, all existing works only support synthesizing or repairing regexes for membership testing, and the support for extraction is still an open problem. This paper fills the void by proposing the first PBE-based method for synthesizing and repairing regexes for extraction. Our work supports regexes that have real-world extensions such as backreferences and lookarounds. The extensions significantly affect the PBE-based synthesis and repair problem. In

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Olteanu-Raimond, Ana-Maria, Mattia Bunel, Catherine Dominguès, et al. "A lightweight ontology for landmarks to assist rescue in mountainous areas." Advances in Cartography and GIScience of the ICA 4 (August 7, 2023): 1–9. http://dx.doi.org/10.5194/ica-adv-4-15-2023.

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Abstract. When people are injured or lost in mountains during outdoor activities and when web-based locations are not available, they locate themselves by describing their environment, routes and activities. The description of their location is done using landmarks and spatial locations (e.g., “I am located in front of Punay Lake”, “I am near a protected area”). Landmarks used can be named (e.g., “Punay Lake”) or unnamed if the landmark has no name or if the victim does not know it (e.g., "area lake"). Landmarks are represented in geographic databases by name (if possible), type and geometry.

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Li, Han, Yash Govind, Sidharth Mudgal, Theodoros Rekatsinas, and AnHai Doan. "DEEP LEARNING FOR SEMANTIC MATCHING: A SURVEY." Journal of Computer Science and Cybernetics 37, no. 4 (2021): 365–402. http://dx.doi.org/10.15625/1813-9663/37/4/16151.

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Semantic matching finds certain types of semantic relationships among schema/data constructs. Examples include entity matching, entity linking, coreference resolution, schema/ontology matching, semantic text similarity, textual entailment, question answering, tagging, etc. Semantic matching has received much attention in the database, AI, KDD, Web, and Semantic Web communities. Recently, many works have also applied deep learning (DL) to semantic matching. In this paper we survey this fast growing topic. We define the semantic matching problem, categorize its variations into a taxonomy, and de

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Xu, Huaiyuan, Xiaodong Chen, Huaiyu Cai, Yi Wang, Haitao Liang, and Haotian Li. "Semantic Matching Based on Semantic Segmentation and Neighborhood Consensus." Applied Sciences 11, no. 10 (2021): 4648. http://dx.doi.org/10.3390/app11104648.

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Establishing dense correspondences across semantically similar images is a challenging task, due to the large intra-class variation caused by the unconstrained setting of images, which is prone to cause matching errors. To suppress potential matching ambiguity, NCNet explores the neighborhood consensus pattern in the 4D space of all possible correspondences, which is based on the assumption that the correspondence is continuous in space. We retain the neighborhood consensus constraint, while introducing semantic segmentation information into the features, which makes them more distinguishable

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Zhang, Shihua, Zhenjie Zhu, Zizhuo Li, Tao Lu, and Jiayi Ma. "Matching While Perceiving: Enhance Image Feature Matching with Applicable Semantic Amalgamation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10094–102. https://doi.org/10.1609/aaai.v39i10.33095.

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Image feature matching is a cardinal problem in computer vision, aiming to establish accurate correspondences between two-view images. Existing methods are constrained by the performance of feature extractors and struggle to capture local information affected by sparse texture or occlusions. Recognizing that human eyes consider not only similar local geometric features but also high-level semantic information of scene objects when matching images, this paper introduces SemaGlue. This novel algorithm perceives and incorporates semantic information into the matching process. In contrast to recen

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Li, Hang, and Jun Xu. "Semantic Matching in Search." Foundations and Trends® in Information Retrieval 7, no. 5 (2014): 343–469. http://dx.doi.org/10.1561/1500000035.

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Zhou, Jing Tao, Hai Cheng Yang, Ming Wei Wang, and Rong Mo. "P2P Mapping Creation by Semantic Discovering and Mapping." Advanced Materials Research 186 (January 2011): 631–35. http://dx.doi.org/10.4028/www.scientific.net/amr.186.631.

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To fully fulfill the modularity and loosely coupled characteristics of P2P semantic mapping paradigm proposed in our previous work[1], a mapping creation method based on semantic discovery is presented to avoid a time-consuming and labor-intensive artificial mapping creation process. This method creates semantic mapping between peer node models by establishing the semantic relations between elements from different peer node models. Semantic relative candidates are captured through the correspondence semantic matching process including concepts matching process, attributes matching process, rel

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Pekunov, Vladimir Viktorovich. "New built-in tools for extending the Planning C language." Программные системы и вычислительные методы, no. 1 (January 2022): 32–41. http://dx.doi.org/10.7256/2454-0714.2022.1.37240.

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In this paper, the problem of developing language extensions of Planning C (a dialect of C++) is considered. The review of existing external programs and solutions built into languages that allow translating new constructions introduced into the language into the output code is carried out. Based on the analysis, it is concluded that the most natural solution built into the language will be some combination of improved regular expressions (to highlight new constructions) with code generators based on procedural and syntactic macros. At the same time, it is advisable to use elements of direct l

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., D. Elangovan, and K. Nirmala . "Semantic Matching Concept Using Semi-Automated Semantic Algorithm." International Journal of Computer Sciences and Engineering 7, no. 2 (2019): 203–6. http://dx.doi.org/10.26438/ijcse/v7i2.203206.

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Qiang, Baohua, Guangyong Xi, Yufeng Wang, Xianyi Yang, and Yuemeng Wang. "Siamese Interaction and Fine-Tuning Representation of Chinese Semantic Matching Algorithm Based on RoBERTa-wwm-ext." Mobile Information Systems 2023 (April 15, 2023): 1–9. http://dx.doi.org/10.1155/2023/8704278.

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Semantic matching research is the cornerstone of research in the fields of natural language similarity measurement and sensor ontology matching (OM). In the existing Chinese semantic matching methods, there are some shortcomings, such as the single dimension of semantic expression, the insufficient expression of context semantic relations, and the insufficient interaction of semantic information between different sentences. This paper proposes a Chinese semantic matching algorithm based on RoBERTa-wwm-ext with Siamese interaction and fine-tuning representation (RSIFR). The RSIFR model initiali

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Auclair-Ouellet, Noémie, Marion Fossard, Joël Macoir, and Robert Laforce. "The Nonverbal Processing of Actions Is an Area of Relative Strength in the Semantic Variant of Primary Progressive Aphasia." Journal of Speech, Language, and Hearing Research 63, no. 2 (2020): 569–84. http://dx.doi.org/10.1044/2019_jslhr-19-00271.

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Purpose Better performance for actions compared to objects has been reported in the semantic variant of primary progressive aphasia (svPPA). This study investigated the influence of the assessment task (naming, semantic picture matching) over the dissociation between objects and actions. Method Ten individuals with svPPA and 17 matched controls completed object and action naming tests, and object and action semantic picture matching tests. Performance was compared between the svPPA and control groups, within the svPPA group, and for each participant with svPPA versus the control group individu

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Xiang, Li, and Li ZongXun. "Context-Aware Text Matching Algorithm for Korean Peninsula Language Knowledge Base Based on Density Clustering." Mobile Information Systems 2021 (October 7, 2021): 1–9. http://dx.doi.org/10.1155/2021/5775146.

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The majority of the traditional methods deal with text matching at the word level which remains uncertain as the text semantic features are ignored. This also leads to the problems of low recall and high space utilization of text matching while the comprehensiveness of matching results is poor. The resultant method, thus, cannot process long text and short text simultaneously. The current study proposes a text matching algorithm for Korean Peninsula language knowledge base based on density clustering. Using the deep multiview semantic document representation model, the semantic vector of the t

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Gillani, Saira, Muhammad Naeem, Raja Habibullah, and Amir Qayyum. "Semantic Schema Matching Using DBpedia." International Journal of Intelligent Systems and Applications 5, no. 4 (2013): 72–80. http://dx.doi.org/10.5815/ijisa.2013.04.07.

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Jean-Mary, Yves R., E. Patrick Shironoshita, and Mansur R. Kabuka. "Ontology matching with semantic verification." Journal of Web Semantics 7, no. 3 (2009): 235–51. http://dx.doi.org/10.1016/j.websem.2009.04.001.

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