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Добірка наукової літератури з теми "Aggregate entity"

Автор: Grafiati

Опубліковано: 13 квітня 2024

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Статті в журналах з теми "Aggregate entity"

Hirose, Shoichi, and Junji Shikata. "Aggregate Entity Authentication Identifying Invalid Entities with Group Testing." Electronics 12, no. 11 (2023): 2479. http://dx.doi.org/10.3390/electronics12112479.

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It is common to implement challenge-response entity authentication with a MAC function. In such an entity authentication scheme, aggregate MAC is effective when a server needs to authenticate many entities. Aggregate MAC aggregates multiple tags (responses to a challenge) generated by entities into one short aggregate tag so that the entities can be authenticated simultaneously regarding only the aggregate tag. Then, all associated entities are valid if the pair of a challenge and the aggregate tag is valid. However, a drawback of this approach is that invalid entities cannot be identified when they exist. To resolve the drawback, we propose group-testing aggregate entity authentication by incorporating group testing into entity authentication using aggregate MAC. We first formalize the security requirements and present a generic construction. Then, we reduce the security of the generic construction to that of aggregate MAC and group testing. We also enhance the generic construction to instantiate a secure scheme from a simple and practical but weaker aggregate MAC scheme. Finally, we show some results on performance evaluation.

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Guangyu, Lei, and Han Jichang. "Based on the CT Image Rebuilding the Micromechanics Hierarchical Model of Concrete." Advances in Civil Engineering 2022 (October 12, 2022): 1–12. http://dx.doi.org/10.1155/2022/2445901.

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Establishing a mesoscopic numerical model to investigate the mechanical properties of concrete has very important significance. This paper considers the random distribution of aggregate in concrete. The aggregate is assumed to be spherical, respectively, to simulate the interface layer as the entity unit or the contact elements. The random aggregate model and the interface model of random aggregate were established. Based on the CT image and the application of MATLAB and MIMICS software, the different characteristics of the concrete model for 3D reconstruction were set up. Through comparative analysis of the advantages and disadvantages of different models, considering the CT number included in the CT images, this paper establishes the reconstruction model, which includes the shape of concrete aggregates, gradation, holes, etc. The analysis results have shown that the model can infer realistic concrete behavior, providing a new approach for studying concrete properties at the mesoscale.

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Korkontzelos, Ioannis, Dimitrios Piliouras, Andrew W. Dowsey, and Sophia Ananiadou. "Boosting drug named entity recognition using an aggregate classifier." Artificial Intelligence in Medicine 65, no. 2 (2015): 145–53. http://dx.doi.org/10.1016/j.artmed.2015.05.007.

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Cui, Xiaohui, Xiaolong Qu, Dongmei Li, Yu Yang, Yuxun Li, and Xiaoping Zhang. "MKGCN: Multi-Modal Knowledge Graph Convolutional Network for Music Recommender Systems." Electronics 12, no. 12 (2023): 2688. http://dx.doi.org/10.3390/electronics12122688.

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With the emergence of online music platforms, music recommender systems are becoming increasingly crucial in music information retrieval. Knowledge graphs (KGs) are a rich source of semantic information for entities and relations, allowing for improved modeling and analysis of entity relations to enhance recommendations. Existing research has primarily focused on the modeling and analysis of structural triples, while largely ignoring the representation and information processing capabilities of multi-modal data such as music videos and lyrics, which has hindered the improvement and user experience of music recommender systems. To address these issues, we propose a Multi-modal Knowledge Graph Convolutional Network (MKGCN) to enhance music recommendation by leveraging the multi-modal knowledge of music items and their high-order structural and semantic information. Specifically, there are three aggregators in MKGCN: the multi-modal aggregator aggregates the text, image, audio, and sentiment features of each music item in a multi-modal knowledge graph (MMKG); the user aggregator and item aggregator use graph convolutional networks to aggregate multi-hop neighboring nodes on MMKGs to model high-order representations of user preferences and music items, respectively. Finally, we utilize the aggregated embedding representations for recommendation. In training MKGCN, we adopt the ratio negative sampling strategy to generate high-quality negative samples. We construct four different-sized music MMKGs using the public dataset Last-FM and conduct extensive experiments on them. The experimental results demonstrate that MKGCN achieves significant improvements and outperforms several state-of-the-art baselines.

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Aberbach, Adin, Mayank Kejriwal, and Ke Shen. "Multipartite Entity Resolution: Motivating a K-Tuple Perspective (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23434–35. http://dx.doi.org/10.1609/aaai.v38i21.30417.

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Анотація:

Entity Resolution (ER) is the problem of algorithmically matching records, mentions, or entries that refer to the same underlying real-world entity. Traditionally, the problem assumes (at most) two datasets, between which records need to be matched. There is considerably less research in ER when k > 2 datasets are involved. The evaluation of such multipartite ER (M-ER) is especially complex, since the usual ER metrics assume (whether implicitly or explicitly) k < 3. This paper takes the first step towards motivating a k-tuple approach for evaluating M-ER. Using standard algorithms and k-tuple versions of metrics like precision and recall, our preliminary results suggest a significant difference compared to aggregated pairwise evaluation, which would first decompose the M-ER problem into independent bipartite problems and then aggregate their metrics. Hence, M-ER may be more challenging and warrant more novel approaches than current decomposition-based pairwise approaches would suggest.

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Tsukui, Kazuo, and Kenji Tadokoro. "Affinity Association between Polynucleotide, Glycoprotein, or Sulfated Polysaccharides and Disease-Associated Prion Protein." Microbiology Insights 2 (January 2009): MBI.S3103. http://dx.doi.org/10.4137/mbi.s3103.

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Proteinase-K resistant prion protein (PrPres) has the property to aggregate in TSE-injured animal tissues. We have developed a test method to discriminate scrapie-infected and mock-infected hamsters by detecting the PrPres in plasma. It seemed that aggregation of the PrPres with some heterogeneous molecule(s) enabled successful detection by this method. In order to investigate which molecule became the partner in the PrPres aggregates; we examined some molecules that could presumably have this ability. As a result, we found synthetic Poly-A RNA, especially in its denatured form, to be the most effective entity although glycoprotein, sulfated polysaccharide showed less effectiveness. DNA in the denatured form also has a high affinity, although in the presence of protein the effectiveness unsuccessful. On the basis of this result, it is possible that the PrPres aggregate in scrapie-infected hamster plasma is composed of PrPres and RNA.

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SEID, DAWIT, and SHARAD MEHROTRA. "AGGREGATE QUERY PROCESSING FOR SEMANTIC WEB DATABASES: AN ALGEBRAIC APPROACH." International Journal of Semantic Computing 01, no. 04 (2007): 479–95. http://dx.doi.org/10.1142/s1793351x07000226.

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As a growing number of applications represent data as semantic graphs like RDF (Resource Description Format) and the many entity-attribute-value formats, query languages for such data are being required to support operations beyond graph pattern matching and inference queries. Specifically the ability to express aggregate queries is an important feature which is either lacking or is implemented with little attention to the peculiarities of the data model. In this paper, we study the meaning and implementation of grouping and aggregate queries over RDF graphs. We first define grouping and aggregate operators algebraically and then show how the SPARQL query language can be extended to express grouping and aggregate queries.

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Yi, Liu, Diao Xing-chun, Cao Jian-jun, Zhou Xing, and Shang Yu-ling. "A Method for Entity Resolution in High Dimensional Data Using Ensemble Classifiers." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/4953280.

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Анотація:

In order to improve utilization rate of high dimensional data features, an ensemble learning method based on feature selection for entity resolution is developed. Entity resolution is regarded as a binary classification problem, an optimization model is designed to maximize each classifier’s classification accuracy and dissimilarity between classifiers and minimize cardinality of features. A modified multiobjective ant colony optimization algorithm is employed to solve the model for each base classifier, two pheromone matrices are set up, weighted product method is applied to aggregate values of two pheromone matrices, and feature’s Fisher discriminant rate of records’ similarity vector is calculated as heuristic information. A solution which is called complementary subset is selected from Pareto archive according to the descending order of three objectives to train the given base classifier. After training all base classifiers, their classification outputs are aggregated by max-wins voting method to obtain the ensemble classifiers’ final result. A simulation experiment is carried out on three classical datasets. The results show the effectiveness of our method, as well as a better performance compared with the other two methods.

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Yuan, Xu, Qihang Lei, Shuo Yu, Chengchuan Xu, and Zhikui Chen. "Fine-grained relational learning for few-shot knowledge graph completion." ACM SIGAPP Applied Computing Review 22, no. 3 (2022): 25–38. http://dx.doi.org/10.1145/3570733.3570735.

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Few-shot knowledge graph completion (FKGC) task aims to infer missing entities or relations by using few-shot support instances in the knowledge graph. Existing FKGC methods focus on the learning of few-shot relation representations, which are obtained by aggregating the neighbor information of each entity. However, most of these models take the entity's neighbor relations and entities as the same hierarchy and do not make fine-grained distinctions, resulting in entity embeddings with low expressiveness, which may further decrease the quality of learned few-shot relation embeddings. Moreover, many of those models directly use the concatenation of the entity embeddings as the relation representations, and neglect the valuable interaction between relations. In this paper, we propose a fine-grained relational learning framework IDEAL for few-shot knowledge graph completion task. Specifically, we first propose a unique hierarchical attention encoder to aggregate the neighbor information of each entity from two levels, i.e., the entity-relation level and the relation-entity level. Then a relation recoding validator is proposed to formulate the interaction between different relations. Instead of obtaining the few-shot relation representations by using the entity embeddings, the relation recoding validator module aggregates the neighbor relations of each entity to encode the few-shot relation, which can reduce the over-dependence on specific entities in the few-shot relation encoding phase. The relation recoding module is also extended with respect to the excellent performance of the transformer in modeling sequence information. We then introduce a transformer encoder to extract underlying and valuable sequence information between relations. Extensive experiments are conducted on two datasets, i.e., NELL and Wiki. The experimental results demonstrate that our model outperforms state-of-the-art FKGC methods. Besides, we devise the ablation study to demonstrate the effectiveness of each key component. The case study also shows the interpretability of our model intuitively.

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Hager, Liesl. "The Insolvency Act’s deviation from the common law: Juristic ghost or aggregate approach?" South African Law Journal 138, no. 1 (2021): 152–70. http://dx.doi.org/10.47348/salj/v138/i1a7.

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Анотація:

In this article I engage with the provisions of the Insolvency Act 24 of 1936 regulating the dissolution of the universal partnership upon insolvency. Our common law prefers an aggregate approach to partnerships, meaning that a partnership enjoys no separate legal personality distinct from its composing partners. The lack of separate legal personality of a partnership is described by some academics as a ‘remarkable defect’. The Insolvency Act however creates an exception to this general rule by deeming a partnership to be a separate legal entity. The Insolvency Act’s deviation from the common-law rule and creation of a ‘juristic ghost’ is explored in this article. The ‘dual priorities’ rule, the aggregate theory and the entity theory are explained in this article. Furthermore, the judicial debates about the Act’s deviation are discussed. In conclusion, it is suggested that the presumption that legislation does not intend to change existing law should not apply when dealing with the Insolvency Act, as the legislature has expressly deviated from the common-law aggregate approach.

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