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Journal articles on the topic 'Semantic similarity metric'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Parveen, Suraiya, and Ranjit Biswas. "An ontology-based semantic similarity metric to empower semantic search." International Journal of Engineering & Technology 7, no. 4 (2018): 2161. http://dx.doi.org/10.14419/ijet.v7i4.14153.

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Heterogeneity in documents is a challenge for information Retrieval. The keyword search focuses on matching the keywords with web repositories. It does not consider the synonyms or semantically similar words. The heterogeneity of the content makes retrieval inadequate. Semantic search helps to capture more appropriate results using domain ontology. Keyword search is extended with the help of similar concepts of ontology. Similarity between the ontological concepts is recognized to get appropriate search results. Once the semantic similarity among the concepts is known, more relevant documents

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Wang, Xiao-dong, Lei Guo, Jun Fang, and Shu-Fu Dong. "An EMD-Based Metric for Document Semantic Similarity." Journal of Electronics & Information Technology 30, no. 9 (2011): 2156–61. http://dx.doi.org/10.3724/sp.j.1146.2007.00177.

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Ming Che Lee, Jia Wei Chang, Tung Cheng Hsieh, Hui Hui Chen, and Ching Hui Chen. "A Sentence Similarity Metric Based on Semantic Patterns." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 18 (2012): 576–85. http://dx.doi.org/10.4156/aiss.vol4.issue18.71.

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Wang, Huibing, Lin Feng, Jing Zhang, and Yang Liu. "Semantic Discriminative Metric Learning for Image Similarity Measurement." IEEE Transactions on Multimedia 18, no. 8 (2016): 1579–89. http://dx.doi.org/10.1109/tmm.2016.2569412.

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Slam, Nady, Wushour Slamu, and Pei Wang. "A Case Representation and Similarity Measurement Model with Experience-Grounded Semantics." International Journal of Software Engineering and Knowledge Engineering 30, no. 01 (2020): 119–46. http://dx.doi.org/10.1142/s0218194020500060.

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Case-based reasoning heavily depends on the structure and content of the cases, and semantics is essential to effectively represent cases. In the field of structured case representation, most of the works regarding case representation and measurement of semantic similarity between cases are based on model-theoretic semantics and their extensions. The purpose of this study is to explore the potential of experienced-grounded semantics in case representation and semantic similarity measurement. The main contents in this study are as follows: (i) a case representation model based on experience-gro

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Hua, Yan, Yingyun Yang, and Jianhe Du. "Deep Multi-Modal Metric Learning with Multi-Scale Correlation for Image-Text Retrieval." Electronics 9, no. 3 (2020): 466. http://dx.doi.org/10.3390/electronics9030466.

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Multi-modal retrieval is a challenge due to heterogeneous gap and a complex semantic relationship between different modal data. Typical research map different modalities into a common subspace with a one-to-one correspondence or similarity/dissimilarity relationship of inter-modal data, in which the distances of heterogeneous data can be compared directly; thus, inter-modal retrieval can be achieved by the nearest neighboring search. However, most of them ignore intra-modal relations and complicated semantics between multi-modal data. In this paper, we propose a deep multi-modal metric learnin

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Zhang, Pei Ying. "Semantic Similarity Metric and its Application in Text Classification." Applied Mechanics and Materials 170-173 (May 2012): 3711–14. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.3711.

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Text classification is the task of assigning natural language textual documents to predefined categories based on their context. The main concern is this paper is to improve the accuracy of text classification system combined an improved CHI method and semantic similarity metric. Firstly, use an improved CHI method to select features from the raw features aim to reduce the dimensions of the features. Secondly, calculates the semantic distance between text feature vector and categorization feature vector so as to determine the document categorization. Finally, we carried out a series of experim

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Pirró, Giuseppe. "A semantic similarity metric combining features and intrinsic information content." Data & Knowledge Engineering 68, no. 11 (2009): 1289–308. http://dx.doi.org/10.1016/j.datak.2009.06.008.

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Stanchev, Lubomir. "Fine-Tuning an Algorithm for Semantic Document Clustering Using a Similarity Graph." International Journal of Semantic Computing 10, no. 04 (2016): 527–55. http://dx.doi.org/10.1142/s1793351x16400195.

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In this article, we examine an algorithm for document clustering using a similarity graph. The graph stores words and common phrases from the English language as nodes and it can be used to compute the degree of semantic similarity between any two phrases. One application of the similarity graph is semantic document clustering, that is, grouping documents based on the meaning of the words in them. Since our algorithm for semantic document clustering relies on multiple parameters, we examine how fine-tuning these values affects the quality of the result. Specifically, we use the Reuters-21578 b

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Zhou, Shiyuan, and Yinglin Wang. "Clustering Services Based on Community Detection in Service Networks." Mathematical Problems in Engineering 2019 (December 2, 2019): 1–11. http://dx.doi.org/10.1155/2019/1495676.

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Service-oriented computing has become a promising way to develop software by composing existing services on the Internet. However, with the increasing number of services on the Internet, how to match requirements and services becomes a difficult problem. Service clustering has been regarded as one of the effective ways to improve service matching. Related work shows that structure-related similarity metrics perform better than semantic-related similarity metrics in clustering services. Therefore, it is of great importance to propose much more useful structure-related similarity metrics to impr

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Liu, Jingyi, Caijuan Shi, Dongjing Tu, Ze Shi, and Yazhi Liu. "Zero-Shot Image Classification Based on a Learnable Deep Metric." Sensors 21, no. 9 (2021): 3241. http://dx.doi.org/10.3390/s21093241.

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The supervised model based on deep learning has made great achievements in the field of image classification after training with a large number of labeled samples. However, there are many categories without or only with a few labeled training samples in practice, and some categories even have no training samples at all. The proposed zero-shot learning greatly reduces the dependence on labeled training samples for image classification models. Nevertheless, there are limitations in learning the similarity of visual features and semantic features with a predefined fixed metric (e.g., as Euclidean

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Sun, Jinyong, Tianlong Gu, and Junyan Qian. "A Behavioral Similarity Metric for Semantic Workflows Based on Semantic Task Adjacency Relations With Importance." IEEE Access 5 (2017): 15609–18. http://dx.doi.org/10.1109/access.2017.2731378.

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Zeng, Zhi Hao, Fu Lu Guo, and Qi Sun. "SDMM Based Ranking Mechanism for Semantic Web Services Search." Applied Mechanics and Materials 373-375 (August 2013): 1853–58. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1853.

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For search of semantic Web services, a semantic Web services matching results ranking mechanism based on SDMM (semantic distance metric model) is proposed. The calculation of semantic similarity measure can be realized by using this three-dimensional SDMM which is for presenting the semantic relationship of objects defined in ontology, therefore, the semantic Web Service matchmaking results can be ranked in accordance with the semantic similarity measure. The approach based on SDMM significantly improves search accuracy of semantic Web service matchmaking, and enhance users experience of seman

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Zhang, Pei Ying. "Sentence Similarity Metric and its Application in FAQ System." Advanced Materials Research 718-720 (July 2013): 2248–51. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2248.

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FAQ system is a question answering system which finds the question sentence from question-answer collection and then returns its corresponding answer to user. The task of matching questions to corresponding question-answer pairs has become a major challenge in FAQ system. This paper proposes a method for sentence similarity metric between questions according to its semantic similarity as well as the length of question length. Experiments show that this method can improve the accuracy and intelligence of answering system, has some practical value.

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Huang, Hongtao, Cunliang Liang, and Haizhi Ye. "A Semantic Information Content Based Method for Evaluating FCA Concept Similarity." International Journal of Cognitive Informatics and Natural Intelligence 12, no. 2 (2018): 77–93. http://dx.doi.org/10.4018/ijcini.2018040106.

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Probability information content-based FCA concepts similarity computation method relies on the frequency of concepts in corpus, it takes only the occurrence probability as information content metric to compute FCA concept similarity, which leads to lower accuracy. This article introduces a semantic information content-based method for FCA concept similarity evaluation, in addition to the occurrence probability, it takes the superordinate and subordinate semantic relationship of concepts to measure information content, which makes the generic and specific degree of concepts more accurate. Then

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Hashimoto, Tatsunori B., David Alvarez-Melis, and Tommi S. Jaakkola. "Word Embeddings as Metric Recovery in Semantic Spaces." Transactions of the Association for Computational Linguistics 4 (December 2016): 273–86. http://dx.doi.org/10.1162/tacl_a_00098.

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Continuous word representations have been remarkably useful across NLP tasks but remain poorly understood. We ground word embeddings in semantic spaces studied in the cognitive-psychometric literature, taking these spaces as the primary objects to recover. To this end, we relate log co-occurrences of words in large corpora to semantic similarity assessments and show that co-occurrences are indeed consistent with an Euclidean semantic space hypothesis. Framing word embedding as metric recovery of a semantic space unifies existing word embedding algorithms, ties them to manifold learning, and de

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ZHU, SONGHAO, ZHIWEI LIANG, and XIAOYUAN JING. "VIDEO RETRIEVAL VIA LEARNING COLLABORATIVE SEMANTIC DISTANCE." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 04 (2011): 475–90. http://dx.doi.org/10.1142/s0218001411008944.

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Graph-based semi-supervised learning approaches have been proven effective and efficient in solving the problem of the inefficiency of labeled data in many real-world application areas, such as video annotation. However, the pairwise similarity metric, a significant factor of existing approaches, has not been fully investigated. That is, these graph-based semi-supervised approaches estimate the pairwise similarity between samples mainly according to the spatial property of video data. On the other hand, temporal property, an essential characteristic of video data, is not embedded into the pair

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Colombo-Mendoza, Luis Omar, Rafael Valencia-García, Alejandro Rodríguez-González, Ricardo Colomo-Palacios, and Giner Alor-Hernández. "Towards a knowledge-based probabilistic and context-aware social recommender system." Journal of Information Science 44, no. 4 (2017): 464–90. http://dx.doi.org/10.1177/0165551517698787.

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In this article, we propose (1) a knowledge-based probabilistic collaborative filtering (CF) recommendation approach using both an ontology-based semantic similarity metric and a latent Dirichlet allocation (LDA) model-based recommendation technique and (2) a context-aware software architecture and system with the objective of validating the recommendation approach in the eating domain (foodservice places). The ontology on which the similarity metric is based is additionally leveraged to model and reason about users’ contexts; the proposed LDA model also guides the users’ context modelling to

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Huang, Yan, Yang Long, and Liang Wang. "Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8489–96. http://dx.doi.org/10.1609/aaai.v33i01.33018489.

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Although image and sentence matching has been widely studied, its intrinsic few-shot problem is commonly ignored, which has become a bottleneck for further performance improvement. In this work, we focus on this challenging problem of few-shot image and sentence matching, and propose a Gated Visual-Semantic Embedding (GVSE) model to deal with it. The model consists of three corporative modules in terms of uncommon VSE, common VSE, and gated metric fusion. The uncommon VSE exploits external auxiliary resources to extract generic features for representing uncommon instances and words in images a

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Chatterjee, Kasturi, and Shu-Ching Chen. "Hybrid Query Refinement." International Journal of Multimedia Data Engineering and Management 2, no. 3 (2011): 52–71. http://dx.doi.org/10.4018/jmdem.2011070104.

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This paper proposes a hybrid query refinement model for distance-based index structures supporting content-based image retrievals. The framework refines a query by considering both the low-level feature space as well as the high-level semantic interpretations separately. Thus, it successfully handles queries where the gap between the feature components and the semantics is large. It refines the low-level feature space, indexed by the distance based index structure, in multiple iterations by introducing the concept of multipoint query in a metric space. It refines the high-level semantic space

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Hall, Kathleen Currie, Claire Allen, Tess Fairburn, Michael Fry, Michael McAuliffe, and Kevin McMullin. "Measuring perceived morphological relatedness." Canadian Journal of Linguistics/Revue canadienne de linguistique 61, no. 1 (2016): 31–67. http://dx.doi.org/10.1017/cnj.2016.2.

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AbstractThis paper provides a metric for determining whether a given pair of English words is perceived to be morphologically related, based on objective measurements of the words’ orthographic, phonetic, and semantic similarity to each other. The metric is developed on the basis of results from a behavioural study in which participants were asked to judge the relative similarity of pairs of words. The metric is intended to help researchers determine which forms in a language plausibly have segments that alternate; as an example, it is applied to the lexicon of English to illustrate its utilit

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Nguyen, Phuc, Khai Nguyen, Ryutaro Ichise, and Hideaki Takeda. "EmbNum+: Effective, Efficient, and Robust Semantic Labeling for Numerical Values." New Generation Computing 37, no. 4 (2019): 393–427. http://dx.doi.org/10.1007/s00354-019-00076-w.

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Abstract In recent years, there has been an increasing interest in numerical semantic labeling, in which the meaning of an unknown numerical column is assigned by the label of the most relevant columns in predefined knowledge bases. Previous methods used the p value of a statistical hypothesis test to estimate the relevance and thus strongly depend on the distribution and data domain. In other words, they are unstable for general cases, when such knowledge is undefined. Our goal is solving semantic labeling without using such information while guaranteeing high accuracy. We propose EmbNum+, a

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Mazandu, Gaston K., and Nicola J. Mulder. "Information Content-Based Gene Ontology Semantic Similarity Approaches: Toward a Unified Framework Theory." BioMed Research International 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/292063.

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Several approaches have been proposed for computing term information content (IC) and semantic similarity scores within the gene ontology (GO) directed acyclic graph (DAG). These approaches contributed to improving protein analyses at the functional level. Considering the recent proliferation of these approaches, a unified theory in a well-defined mathematical framework is necessary in order to provide a theoretical basis for validating these approaches. We review the existing IC-based ontological similarity approaches developed in the context of biomedical and bioinformatics fields to propose

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Butusov, Igor, and Aleksandr Romanov. "Prevention of Information Security Incidents in Automated Information System." Voprosy kiberbezopasnosti, no. 5(39) (2020): 45–51. http://dx.doi.org/10.21681/2311-3456-2020-05-45-51.

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The purpose of the article is to support the processes of preventing information security incidents in conditions of high uncertainty. Method: methods of mathematical (theoretical) computer science and fuzzy set theory. Result: an information security Incident, including a computer incident, is considered as a violation or termination of the functioning of an automated information system and (or) a violation of information stored and processed in this system, including those caused by a computer attack. Information descriptions are presented in the form of structured data about signs of comput

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Huang, Meiyan, Wei Yang, Mei Yu, Zhentai Lu, Qianjin Feng, and Wufan Chen. "Retrieval of Brain Tumors with Region-Specific Bag-of-Visual-Words Representations in Contrast-Enhanced MRI Images." Computational and Mathematical Methods in Medicine 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/280538.

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A content-based image retrieval (CBIR) system is proposed for the retrieval of T1-weighted contrast-enhanced MRI (CE-MRI) images of brain tumors. In this CBIR system, spatial information in the bag-of-visual-words model and domain knowledge on the brain tumor images are considered for the representation of brain tumor images. A similarity metric is learned through a distance metric learning algorithm to reduce the gap between the visual features and the semantic concepts in an image. The learned similarity metric is then used to measure the similarity between two images and then retrieve the m

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Xu, Peigang, Yadong Wang, and Bo Liu. "A differentor-based adaptive ontology-matching approach." Journal of Information Science 38, no. 5 (2012): 459–75. http://dx.doi.org/10.1177/0165551512447906.

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Ontology matching, aimed at finding semantically related entities from different ontologies, plays an important role in establishing interoperation among Semantic Web applications. Recently, many similarity measures have been proposed to explore the lexical, structural or semantic features of ontologies. However, a key problem is how to integrate various similarities automatically. In this paper, we define a novel metric termed a “differentor” to assess the probability that a similarity measure can find the one-to-one mappings between two ontologies at the entity level, and use it to integrate

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Et.al, Dr R. Rooba. "Webpage Recommendation System Based on the Social Media Semantic Details of the Website." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 237–43. http://dx.doi.org/10.17762/turcomat.v12i6.1358.

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The web page recommendation is generated by using the navigational history from web server log files. Semantic Variable Length Markov Chain Model (SVLMC) is a web page recommendation system used to generate recommendation by combining a higher order Markov model with rich semantic data. The problem of state space complexity and time complexity in SVLMC was resolved by Semantic Variable Length confidence pruned Markov Chain Model (SVLCPMC) and Support vector machine based SVLCPMC (SSVLCPMC) meth-ods respectively. The recommendation accuracy was further improved by quickest change detection usin

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Kang, Jian, Rubén Fernández-Beltrán, Zhen Ye, Xiaohua Tong, Pedram Ghamisi, and Antonio Plaza. "High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery." Remote Sensing 12, no. 16 (2020): 2603. http://dx.doi.org/10.3390/rs12162603.

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Deep metric learning has recently received special attention in the field of remote sensing (RS) scene characterization, owing to its prominent capabilities for modeling distances among RS images based on their semantic information. Most of the existing deep metric learning methods exploit pairwise and triplet losses to learn the feature embeddings with the preservation of semantic-similarity, which requires the construction of image pairs and triplets based on the supervised information (e.g., class labels). However, generating such semantic annotations becomes a completely unaffordable task

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Gu, Geonmo, and Byungsoo Ko. "Symmetrical Synthesis for Deep Metric Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 10853–60. http://dx.doi.org/10.1609/aaai.v34i07.6716.

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Deep metric learning aims to learn embeddings that contain semantic similarity information among data points. To learn better embeddings, methods to generate synthetic hard samples have been proposed. Existing methods of synthetic hard sample generation are adopting autoencoders or generative adversarial networks, but this leads to more hyper-parameters, harder optimization, and slower training speed. In this paper, we address these problems by proposing a novel method of synthetic hard sample generation called symmetrical synthesis. Given two original feature points from the same class, the p

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Aguilar, Jose, Camilo Salazar, Henry Velasco, Julian Monsalve-Pulido, and Edwin Montoya. "Comparison and Evaluation of Different Methods for the Feature Extraction from Educational Contents." Computation 8, no. 2 (2020): 30. http://dx.doi.org/10.3390/computation8020030.

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This paper analyses the capabilities of different techniques to build a semantic representation of educational digital resources. Educational digital resources are modeled using the Learning Object Metadata (LOM) standard, and these semantic representations can be obtained from different LOM fields, like the title, description, among others, in order to extract the features/characteristics from the digital resources. The feature extraction methods used in this paper are the Best Matching 25 (BM25), the Latent Semantic Analysis (LSA), Doc2Vec, and the Latent Dirichlet allocation (LDA). The util

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Abdelkader, Mostefai, and Mekour Mansour. "A Method Based on a New Word Embedding Approach for Process Model Matching." International Journal of Artificial Intelligence and Machine Learning 11, no. 1 (2021): 1–14. http://dx.doi.org/10.4018/ijaiml.2021010101.

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This paper proposes a method based on a new word embedding approach for matching business process model. The proposed method aligns two process models in four steps. First activity labels are extracted and pre-processed to remove meaningless words, then each word composing an activity label and using a semantic similarity metric based on WordNet is represented with an n-dimensional vector in the space of the vocabulary of the two labels to be compared. Based on these representations, a vector representation of each activity label is computed by averaging the vectors representing words found in

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Liu, Hailin, Ling Xu, Mengning Yang, Meng Yan, and Xiaohong Zhang. "Predicting Component Failures Using Latent Dirichlet Allocation." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/562716.

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Latent Dirichlet Allocation (LDA) is a statistical topic model that has been widely used to abstract semantic information from software source code. Failure refers to an observable error in the program behavior. This work investigates whether semantic information and failures recorded in the history can be used to predict component failures. We use LDA to abstract topics from source code and a new metric (topic failure density) is proposed by mapping failures to these topics. Exploring the basic information of topics from neighboring versions of a system, we obtain a similarity matrix. Multipl

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Singh, Anjali, Ruhi Sharma Mittal, Shubham Atreja, et al. "Automatic Generation of Leveled Visual Assessments for Young Learners." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9713–20. http://dx.doi.org/10.1609/aaai.v33i01.33019713.

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Images are an essential tool for communicating with children, particularly at younger ages when they are still developing their emergent literacy skills. Hence, assessments that use images to assess their conceptual knowledge and visual literacy, are an important component of their learning process. Creating assessments at scale is a challenging task, which has led to several techniques being proposed for automatic generation of textual assessments. However, none of them focuses on generating image-based assessments. To understand the manual process of creating visual assessments, we interview

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Yoo, Yongmin, Tak-Sung Heo, Yeongjoon Park, and Kyungsun Kim. "A Novel Hybrid Methodology of Measuring Sentence Similarity." Symmetry 13, no. 8 (2021): 1442. http://dx.doi.org/10.3390/sym13081442.

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The problem of measuring sentence similarity is an essential issue in the natural language processing area. It is necessary to measure the similarity between sentences accurately. Sentence similarity measuring is the task of finding semantic symmetry between two sentences, regardless of word order and context of the words. There are many approaches to measuring sentence similarity. Deep learning methodology shows a state-of-the-art performance in many natural language processing fields and is used a lot in sentence similarity measurement methods. However, in the natural language processing fie

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MARKOV, ZDRAVKO. "AN ALGEBRAIC APPROACH TO INDUCTIVE LEARNING." International Journal on Artificial Intelligence Tools 10, no. 01n02 (2001): 257–72. http://dx.doi.org/10.1142/s0218213001000519.

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The paper presents a framework to induction of concept hierarchies based on consistent integration of metric and similarity-based approaches. The hierarchies used are subsumption lattices induced by the least general generalization operator (lgg) commonly used in inductive learning. Using some basic results from lattice theory the paper introduces a semantic distance measure between objects in concept hierarchies and discusses its applications for solving concept learning and conceptual clustering tasks. Experiments with well known ML datasets represented in three types of languages - proposit

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Garcia Castro, Leyla Jael, Rafael Berlanga, and Alexander Garcia. "In the pursuit of a semantic similarity metric based on UMLS annotations for articles in PubMed Central Open Access." Journal of Biomedical Informatics 57 (October 2015): 204–18. http://dx.doi.org/10.1016/j.jbi.2015.07.015.

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CHATTERJEE, KASTURI, and SHU-CHING CHEN. "A NOVEL INDEXING AND ACCESS MECHANISM USING AFFINITY HYBRID TREE FOR CONTENT-BASED IMAGE RETRIEVAL IN MULTIMEDIA DATABASES." International Journal of Semantic Computing 01, no. 02 (2007): 147–70. http://dx.doi.org/10.1142/s1793351x07000093.

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An efficient access and indexing framework, called Affinity Hybrid Tree (AH-Tree), is proposed which combines feature and metric spaces in a novel way. The proposed framework helps to organize large image databases and support popular multimedia retrieval mechanisms like Content-Based Image Retrieval (CBIR). It is efficient in terms of computational overhead and fairly accurate in producing query results close to human perception. AH-Tree, by being able to introduce the high level semantic image relationship as it is in its index structure, solves the problem of translating the content-similar

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Yi, Kwan. "A Semantic Similarity Approach for Linking Tweet Messages to Library of Congress Subject Headings using Linked Resources: A Pilot Study." Advances in Classification Research Online 24, no. 1 (2014): 43. http://dx.doi.org/10.7152/acro.v24i1.14676.

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The objective of this study is to propose, implement, and test a framework of assigning relevant Library of Congress (LC) subject headings to tweet messages. In this study, the task of assigning LC headings is considered an automatic classification task that identifies relevant LC subject headings for given tweets. The classification task is conducted in two stages. In the first stage, tweets are clustered so that similar tweets are grouped together. In the second stage, the degree of similarity between a cluster of tweets and LC subject headings is measured by a popular similarity metric, Jac

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Vivas, Fulvio Yesid, Oscar Mauricio Caicedo, and Juan Carlos Nieves. "A Semantic and Knowledge-Based Approach for Handover Management." Sensors 21, no. 12 (2021): 4234. http://dx.doi.org/10.3390/s21124234.

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Handover Management (HM) is pivotal for providing service continuity, enormous reliability and extreme-low latency, and meeting sky-high data rates, in wireless communications. Current HM approaches based on a single criterion may lead to unnecessary and frequent handovers due to a partial network view that is constrained to information about link quality. In turn, HM approaches based on multicriteria may present a failure of handovers and wrong network selection, decreasing the throughput and increasing the packet loss in the network. This paper proposes SIM-Know, an approach for improving HM

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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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Fan, Lili, Hongwei Zhao, Haoyu Zhao, Pingping Liu, and Huangshui Hu. "Image Retrieval Based on Learning to Rank and Multiple Loss." ISPRS International Journal of Geo-Information 8, no. 9 (2019): 393. http://dx.doi.org/10.3390/ijgi8090393.

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Image retrieval applying deep convolutional features has achieved the most advanced performance in most standard benchmark tests. In image retrieval, deep metric learning (DML) plays a key role and aims to capture semantic similarity information carried by data points. However, two factors may impede the accuracy of image retrieval. First, when learning the similarity of negative examples, current methods separate negative pairs into equal distance in the embedding space. Thus, the intraclass data distribution might be missed. Second, given a query, either a fraction of data points, or all of

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Zhu, Hai, Xingsi Xue, Chengcai Jiang, and He Ren. "Multiobjective Sensor Ontology Matching Technique with User Preference Metrics." Wireless Communications and Mobile Computing 2021 (March 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/5594553.

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Due to the problem of data heterogeneity in the semantic sensor networks, the communications among different sensor network applications are seriously hampered. Although sensor ontology is regarded as the state-of-the-art knowledge model for exchanging sensor information, there also exists the heterogeneity problem between different sensor ontologies. Ontology matching is an effective method to deal with the sensor ontology heterogeneity problem, whose kernel technique is the similarity measure. How to integrate different similarity measures to determine the alignment of high quality for the u

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Hernandez, Julio, Heidy M. Marin-Castro, and Miguel Morales-Sandoval. "A Semantic Focused Web Crawler Based on a Knowledge Representation Schema." Applied Sciences 10, no. 11 (2020): 3837. http://dx.doi.org/10.3390/app10113837.

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The Web has become the main source of information in the digital world, expanding to heterogeneous domains and continuously growing. By means of a search engine, users can systematically search over the web for particular information based on a text query, on the basis of a domain-unaware web search tool that maintains real-time information. One type of web search tool is the semantic focused web crawler (SFWC); it exploits the semantics of the Web based on some ontology heuristics to determine which web pages belong to the domain defined by the query. An SFWC is highly dependent on the ontolo

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Liu, Zheng. "An Efficient Web Image Annotation Ranking Algorithm." Advanced Materials Research 108-111 (May 2010): 81–87. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.81.

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Existing image annotation approaches mainly concentrate on achieving annotation results. Annotation order has not been taken into account carefully. As orderly annotation list could enhance the performance of image retrieval system, it is of great importance to rank annotations. This paper presents an algorithm to rank Web image annotating results. For an annotated Web image, we firstly partition the image by a region growing method. Secondly, relevance degree between two annotations is estimated through considering both semantic similarity and image content. Next, the regions of unlabeled ima

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Wang, Xinshao, Yang Hua, Elyor Kodirov, Guosheng Hu, and Neil M. Robertson. "Deep Metric Learning by Online Soft Mining and Class-Aware Attention." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5361–68. http://dx.doi.org/10.1609/aaai.v33i01.33015361.

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Deep metric learning aims to learn a deep embedding that can capture the semantic similarity of data points. Given the availability of massive training samples, deep metric learning is known to suffer from slow convergence due to a large fraction of trivial samples. Therefore, most existing methods generally resort to sample mining strategies for selecting nontrivial samples to accelerate convergence and improve performance. In this work, we identify two critical limitations of the sample mining methods, and provide solutions for both of them. First, previous mining methods assign one binary s

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Jing, Ya, Chenyang Si, Junbo Wang, Wei Wang, Liang Wang, and Tieniu Tan. "Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11189–96. http://dx.doi.org/10.1609/aaai.v34i07.6777.

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Text-based person search aims to retrieve the corresponding person images in an image database by virtue of a describing sentence about the person, which poses great potential for various applications such as video surveillance. Extracting visual contents corresponding to the human description is the key to this cross-modal matching problem. Moreover, correlated images and descriptions involve different granularities of semantic relevance, which is usually ignored in previous methods. To exploit the multilevel corresponding visual contents, we propose a pose-guided multi-granularity attention

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Shen, J., and T. Cheng. "CLUSTERING ANALYSIS OF OFFICER'S BEHAVIOURS IN LONDON POLICE FOOT PATROL ACTIVITIES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4/W2 (July 10, 2015): 143–46. http://dx.doi.org/10.5194/isprsannals-ii-4-w2-143-2015.

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In this small paper we aim at presenting a framework of conceptual representation and clustering analysis of police officers’ patrol pattern obtained from mining their raw movement trajectory data. This have been achieved by a model developed to accounts for the spatio-temporal dynamics human movements by incorporating both the behaviour features of the travellers and the semantic meaning of the environment they are moving in. Hence, the similarity metric of traveller behaviours is jointly defined according to the stay time allocation in each Spatio-temporal region of interests (ST-ROI) to sup

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Diakogiannis, Foivos I., François Waldner, and Peter Caccetta. "Looking for Change? Roll the Dice and Demand Attention." Remote Sensing 13, no. 18 (2021): 3707. http://dx.doi.org/10.3390/rs13183707.

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Change detection, i.e., the identification per pixel of changes for some classes of interest from a set of bi-temporal co-registered images, is a fundamental task in the field of remote sensing. It remains challenging due to unrelated forms of change that appear at different times in input images. Here, we propose a deep learning framework for the task of semantic change detection in very high-resolution aerial images. Our framework consists of a new loss function, a new attention module, new feature extraction building blocks, and a new backbone architecture that is tailored for the task of s

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Wang, Guangcong, Jianhuang Lai, Peigen Huang, and Xiaohua Xie. "Spatial-Temporal Person Re-Identification." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8933–40. http://dx.doi.org/10.1609/aaai.v33i01.33018933.

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Most of current person re-identification (ReID) methods neglect a spatial-temporal constraint. Given a query image, conventional methods compute the feature distances between the query image and all the gallery images and return a similarity ranked table. When the gallery database is very large in practice, these approaches fail to obtain a good performance due to appearance ambiguity across different camera views. In this paper, we propose a novel two-stream spatial-temporal person ReID (st-ReID) framework that mines both visual semantic information and spatial-temporal information. To this e

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Cancian, Pierandrea, Nina Cortese, Matteo Donadon, et al. "Development of a Deep-Learning Pipeline to Recognize and Characterize Macrophages in Colo-Rectal Liver Metastasis." Cancers 13, no. 13 (2021): 3313. http://dx.doi.org/10.3390/cancers13133313.

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Quantitative analysis of Tumor Microenvironment (TME) provides prognostic and predictive information in several human cancers but, with few exceptions, it is not performed in daily clinical practice since it is extremely time-consuming. We recently showed that the morphology of Tumor Associated Macrophages (TAMs) correlates with outcome in patients with Colo-Rectal Liver Metastases (CLM). However, as for other TME components, recognizing and characterizing hundreds of TAMs in a single histopathological slide is unfeasible. To fasten this process, we explored a deep-learning based solution. We

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