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Journal articles on the topic 'Hierarchical search'

Author: Grafiati

Published: 23 June 2021

Last updated: 4 February 2022

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1

KOREPIN, VLADIMIR E., and YING XU. "HIERARCHICAL QUANTUM SEARCH." International Journal of Modern Physics B 21, no. 31 (December 20, 2007): 5187–205. http://dx.doi.org/10.1142/s0217979207038344.

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Database search has wide applications and is used as a subroutine in many important algorithms. In this paper, we will consider a database with a single target item. Quantum algorithm (Grover) locates the target item faster than any classical algorithm. In addition to a full (Grover) search, it frequently occurs that one is looking for a group of items (a block) containing the target item, rather than the target item itself. This problem is known as partial search. As a generalization of the full search, partial search is of particular importance in practice. Partial search trades accuracy for speed, i.e., it works faster than a full search. There exists different versions of partial search. We will study the optimized version of the algorithm discovered by Grover and Radhakrishnan and call it GRK. GRK can be applied successively (in a sequence). First, the database is partitioned into blocks and GRK is applied to find the target block. This target block is then partitioned into subblocks and GRK is used again to find the target subblock. This procedure can be repeated if the database is large enough. (This sequence of GRK's is called a hierarchy.) Another possibility is to partition the database into subblocks directly and use GRK to find the target subblock once. In this paper, we will prove that the latter is faster (makes less queries to the Oracle).

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2

Zupan, Jure, and Morton E. Munk. "Feedback search of hierarchical trees." Analytical Chemistry 58, no. 14 (December 1986): 3219–25. http://dx.doi.org/10.1021/ac00127a065.

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3

de Buy Wenniger, Gideon Maillette, and Khalil Sima’an. "Visualization, Search and Analysis of Hierarchical Translation Equivalence in Machine Translation Data." Prague Bulletin of Mathematical Linguistics 101, no. 1 (April 1, 2014): 43–54. http://dx.doi.org/10.2478/pralin-2014-0003.

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Abstract Translation equivalence constitutes the basis of all Machine Translation systems including the recent hierarchical and syntax-based systems. For hierarchical MT research it is important to have a tool that supports the qualitative and quantitative analysis of hierarchical translation equivalence relations extracted from word alignments in data. In this paper we present such a toolkit and exemplify some of its uses. The main challenges taken up in designing this tool are the efficient and compact, yet complete, representation of hierarchical translation equivalence coupled with an intuitive visualization of these hierarchical relations. We exploit a new hierarchical representation, called Hierarchical Alignment Trees (HATs), which is based on an extension of the algorithms used for factorizing n-ary branching SCFG rules into their minimally-branching equivalents. Our toolkit further provides a search capability based on hierarchically relevant properties of word alignments and/or translation equivalence relations. Finally, the tool allows detailed statistical analysis of word alignments, thereby providing a breakdown of alignment statistics according to the complexity of translation equivalence units or reordering phenomena. We illustrate this with an empirical study of the coverage of inversion-transduction grammars for a number of corpora enriched with manual or automatic word alignments, followed by a breakdown of corpus statistics to reordering complexity.

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Chen, Lin Chih. "Building a Post-Search Academic Search Engine Based on a Serial of Clustering Methods." Applied Mechanics and Materials 284-287 (January 2013): 3051–55. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3051.

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Academic search engines, such as Google Scholar and Scirus, provide a Web-based interface to effectively find relevant scientific articles to researchers. However, current academic search engines are lacking the ability to cluster the search results into a hierarchical tree structure. In this paper, we develop a post-search academic search engine by using a mixed clustering method. In this method, we first adopt a suffix tree clustering and a two-way hash mechanism to generate all meaningful labels. We then develop a divisive hierarchical clustering algorithm to organize the labels into a hierarchical tree. According to the results of experiments, we conclude that using our mixed clustering method to cluster the search results can give significant performance gains than current academic search engines. In this paper, we make two contributions. First, we present a high performance academic search engine based on our mixed clustering method. Second, we develop a divisive hierarchical clustering algorithm to organize all returned search results into a hierarchical tree structure.

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5

Zhang, Qiong. "Hierarchical Route Representation, Indexing, and Search." IEEE Pervasive Computing 7, no. 2 (April 2008): 78–84. http://dx.doi.org/10.1109/mprv.2008.31.

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6

del Campo, Jorge M., and Andreas M. Köster. "A hierarchical transition state search algorithm." Journal of Chemical Physics 129, no. 2 (July 14, 2008): 024107. http://dx.doi.org/10.1063/1.2950083.

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7

Chen, Di, Shanshan Zhang, Wanli Ouyang, Jian Yang, and Bernt Schiele. "Hierarchical Online Instance Matching for Person Search." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 10518–25. http://dx.doi.org/10.1609/aaai.v34i07.6623.

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Person Search is a challenging task which requires to retrieve a person's image and the corresponding position from an image dataset. It consists of two sub-tasks: pedestrian detection and person re-identification (re-ID). One of the key challenges is to properly combine the two sub-tasks into a unified framework. Existing works usually adopt a straightforward strategy by concatenating a detector and a re-ID model directly, either into an integrated model or into separated models. We argue that simply concatenating detection and re-ID is a sub-optimal solution, and we propose a Hierarchical Online Instance Matching (HOIM) loss which exploits the hierarchical relationship between detection and re-ID to guide the learning of our network. Our novel HOIM loss function harmonizes the objectives of the two sub-tasks and encourages better feature learning. In addition, we improve the loss update policy by introducing Selective Memory Refreshment (SMR) for unlabeled persons, which takes advantage of the potential discrimination power of unlabeled data. From the experiments on two standard person search benchmarks, i.e. CUHK-SYSU and PRW, we achieve state-of-the-art performance, which justifies the effectiveness of our proposed HOIM loss on learning robust features.

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Gies, D. R., S. J. Williams, R. A. Matson, Z. Guo, S. M. Thomas, J. A. Orosz, and G. J. Peters. "A SEARCH FOR HIERARCHICAL TRIPLES USINGKEPLERECLIPSE TIMING." Astronomical Journal 143, no. 6 (May 2, 2012): 137. http://dx.doi.org/10.1088/0004-6256/143/6/137.

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9

NAGARAJ SHENOY, U., Y. N. SRIKANT, V. P. BHATKAR, and SANDEEP KOHLI. "AUTOMATIC DATA PARTITIONING BY HIERARCHICAL GENETIC SEARCH." Parallel Algorithms and Applications 14, no. 2 (July 1999): 119–47. http://dx.doi.org/10.1080/10637199808947382.

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10

Tedmori, S., and N. Al-Najdawi. "Hierarchical stochastic fast search motion estimation algorithm." IET Computer Vision 6, no. 1 (2012): 21. http://dx.doi.org/10.1049/iet-cvi.2010.0188.

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11

Lim, Hyesook, Ha Chu, and Changhoon Yim. "Hierarchical Binary Search Tree for Packet Classification." IEEE Communications Letters 11, no. 8 (August 2007): 689–91. http://dx.doi.org/10.1109/lcomm.2007.070389.

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12

OISHI, Atsuya, Iori OKUNO, Shoji AMANO, and Shinobu YOSHIMURA. "1303 Hierarchical Contact Search for Isogeometric Analysis." Proceedings of The Computational Mechanics Conference 2013.26 (2013): _1303–1_—_1303–2_. http://dx.doi.org/10.1299/jsmecmd.2013.26._1303-1_.

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13

Fang, Xiaowen. "A Hierarchical Search History for Web Searching." International Journal of Human-Computer Interaction 12, no. 1 (May 2000): 73–88. http://dx.doi.org/10.1207/s15327590ijhc1201_3.

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14

Tian, Xinmei, Linjun Yang, Yijuan Lu, Qi Tian, and Dacheng Tao. "Image Search Reranking With Hierarchical Topic Awareness." IEEE Transactions on Cybernetics 45, no. 10 (October 2015): 2177–89. http://dx.doi.org/10.1109/tcyb.2014.2366740.

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15

Zhen He. "Hierarchical Colorant-Based Direct Binary Search Halftoning." IEEE Transactions on Image Processing 19, no. 7 (July 2010): 1824–36. http://dx.doi.org/10.1109/tip.2010.2045690.

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16

Mihm, Jürgen, Christoph H. Loch, Dennis Wilkinson, and Bernardo A. Huberman. "Hierarchical Structure and Search in Complex Organizations." Management Science 56, no. 5 (May 2010): 831–48. http://dx.doi.org/10.1287/mnsc.1100.1148.

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17

Montgomery, Thomas A., and Edmund H. Durfee. "Search reduction in hierarchical distributed problem solving." Group Decision and Negotiation 2, no. 3 (September 1993): 301–17. http://dx.doi.org/10.1007/bf01384251.

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18

Li, Wei, Shaogang Gong, and Xiatian Zhu. "Hierarchical distillation learning for scalable person search." Pattern Recognition 114 (June 2021): 107862. http://dx.doi.org/10.1016/j.patcog.2021.107862.

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19

Prakash, Miss Pawar Pratima. "A Review on Enabling Synonym Based Fined-grained Multi-keyword Search Using Hierarchical Clustering." International journal of Emerging Trends in Science and Technology 03, no. 12 (December 20, 2016): 4866–70. http://dx.doi.org/10.18535/ijetst/v3i12.11.

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20

Holm, Liisa. "Benchmarking fold detection by DaliLite v.5." Bioinformatics 35, no. 24 (July 2, 2019): 5326–27. http://dx.doi.org/10.1093/bioinformatics/btz536.

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Abstract Motivation Protein structure comparison plays a fundamental role in understanding the evolutionary relationships between proteins. Here, we release a new version of the DaliLite standalone software. The novelties are hierarchical search of the structure database organized into sequence based clusters, and remote access to our knowledge base of structural neighbors. The detection of fold, superfamily and family level similarities by DaliLite and state-of-the-art competitors was benchmarked against a manually curated structural classification. Results Database search strategies were evaluated using Fmax with query-specific thresholds. DaliLite and DeepAlign outperformed TM-score based methods at all levels of the benchmark, and DaliLite outperformed DeepAlign at fold level. Hierarchical and knowledge-based searches got close to the performance of systematic pairwise comparison. The knowledge-based search was four times as efficient as the hierarchical search. The knowledge-based search dynamically adjusts the depth of the search, enabling a trade-off between speed and recall. Availability and implementation http://ekhidna2.biocenter.helsinki.fi/dali/README.v5.html. Supplementary information Supplementary data are available at Bioinformatics online.

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21

Nie, Mengdie, Zhi-Jie Wang, Chunjing Gan, Zhe Quan, Bin Yao, and Jian Yin. "An Improved Hierarchical Datastructure for Nearest Neighbor Search." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10001–2. http://dx.doi.org/10.1609/aaai.v33i01.330110001.

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Nearest neighbor search is a fundamental computational tool and has wide applications. In past decades, many datastructures have been developed to speed up this operation. In this paper, we propose a novel hierarchical datastructure for nearest neighbor search in moderately high dimension. Our proposed method maintains good run time guarantees, and it outperforms several state-of-the-art methods in practice.

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22

Parikh, N. P., C. Y. Lo, A. Singhal, and K. W. Wu. "HS: a hierarchical search package for CAD data." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 12, no. 1 (1993): 1–5. http://dx.doi.org/10.1109/43.184838.

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23

Serrano, D. P., J. M. Escola, and P. Pizarro. "Synthesis strategies in the search for hierarchical zeolites." Chem. Soc. Rev. 42, no. 9 (2013): 4004–35. http://dx.doi.org/10.1039/c2cs35330j.

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24

Zaid, Norasykin Mohd, Sim Kim Lau, and Mohd Nihra Haruzuan Mohamad Said. "Ontology-based Search System Using Hierarchical Structure Design." Procedia - Social and Behavioral Sciences 97 (November 2013): 566–70. http://dx.doi.org/10.1016/j.sbspro.2013.10.274.

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25

Jau-Yuen Chen, C. A. Bouman, and J. C. Dalton. "Hierarchical browsing and search of large image databases." IEEE Transactions on Image Processing 9, no. 3 (March 2000): 442–55. http://dx.doi.org/10.1109/83.826781.

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26

SHIMIZU, Yoshiaki, and Takeshi WADA. "Hybrid Tabu Search Approach for Hierarchical Logistics Optimization." Transactions of the Institute of Systems, Control and Information Engineers 17, no. 6 (2004): 241–48. http://dx.doi.org/10.5687/iscie.17.241.

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27

Frasca, S., and C. Palomba. "Spectral filtering for hierarchical search of periodic sources." Classical and Quantum Gravity 21, no. 20 (September 25, 2004): S1645—S1654. http://dx.doi.org/10.1088/0264-9381/21/20/007.

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28

SASSA, Fumihiro, and Manabu HASEGAWA. "20606 A Hierarchical Design of Iterated Local Search." Proceedings of Conference of Kanto Branch 2006.12 (2006): 439–40. http://dx.doi.org/10.1299/jsmekanto.2006.12.439.

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29

Hutchinson, Hilary Browne. "Children's interface design for hierarchical search and browse." ACM SIGCAPH Computers and the Physically Handicapped, no. 75 (January 2003): 11–12. http://dx.doi.org/10.1145/976261.976265.

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30

WADA, Takeshi, Shigeharu MATSUDA, and Yoshiaki SHIMIZU. "Hybrid Tabu Search Approach for Hierarchical Logistics Optimization." Proceedings of Manufacturing Systems Division Conference 2003 (2003): 25–26. http://dx.doi.org/10.1299/jsmemsd.2003.25.

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31

Svanberg, K., and M. Werme. "A hierarchical neighbourhood search method for topology optimization." Structural and Multidisciplinary Optimization 29, no. 5 (January 7, 2005): 325–40. http://dx.doi.org/10.1007/s00158-004-0493-x.

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32

Tran, D. A., and T. Nguyen. "Hierarchical multidimensional search in peer-to-peer networks." Computer Communications 31, no. 2 (February 2008): 346–57. http://dx.doi.org/10.1016/j.comcom.2007.08.007.

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33

Tsay, Jyh-Jong, and Chi-Hsiang Lin. "Hierarchical directory mapping for category-constrained meta-search." Journal of Intelligent Information Systems 42, no. 1 (July 25, 2013): 75–94. http://dx.doi.org/10.1007/s10844-013-0256-5.

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34

Xu, Jiajie, Yunjun Gao, Chengfei Liu, Lei Zhao, and Zhiming Ding. "Efficient route search on hierarchical dynamic road networks." Distributed and Parallel Databases 33, no. 2 (March 2, 2014): 227–52. http://dx.doi.org/10.1007/s10619-014-7146-x.

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35

Girill, T. R., and Clement H. Luk. "Hierarchical search support for hypertext on-line documentation." International Journal of Man-Machine Studies 36, no. 4 (April 1992): 571–85. http://dx.doi.org/10.1016/0020-7373(92)90097-5.

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36

WAH, BENJAMIN W., and LON-CHAN CHU. "COMBINATORIAL SEARCH ALGORITHMS WITH META-CONTROL: MODELING AND IMPLEMENTATIONS." International Journal on Artificial Intelligence Tools 01, no. 03 (September 1992): 369–97. http://dx.doi.org/10.1142/s0218213092000259.

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In this paper, we model search algorithms with meta-control, allowing resource constraints, approximation, and parallel processing to be incorporated easily in the search process. The basic building block of the model is a hierarchical search process (HSP) consisting of context-free and context-sensitive grammars classified according to problem-independent and problem-dependent parts. The context-sensitive components are used mainly for evaluating decision parameters and in ordering production rules in the context-free grammar. The execution of the grammars for given initial conditions may invoke other HSPs already defined in the system. We describe ISE (acronym for Integrated Search Environment), a tool that implements hierarchical searches with meta-control. By separating the problem-dependent and problem-independent components in ISE, new search methods based on a combination of existing methods can be developed easily by coding a single master control program. Further, new applications solved by searches can be developed by coding the problem-dependent parts and reusing the problem-independent parts already developed. We describe the organization of ISE and present experiments carried out on the system.

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37

Elkawkagy*, Mohamed, and Elbeh Heba. "Reduce Artificial Intelligence Planning Effort by using Map-Reduce Paradigm." Regular issue 10, no. 7 (May 30, 2021): 24–32. http://dx.doi.org/10.35940/ijitee.g8902.0510721.

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While several approaches have been developed to enhance the efficiency of hierarchical Artificial Intelligence planning (AI-planning), complex problems in AI-planning are challenging to overcome. To find a solution plan, the hierarchical planner produces a huge search space that may be infinite. A planner whose small search space is likely to be more efficient than a planner produces a large search space. In this paper, we will present a new approach to integrating hierarchical AI-planning with the map-reduce paradigm. In the mapping part, we will apply the proposed clustering technique to divide the hierarchical planning problem into smaller problems, so-called sub-problems. A pre-processing technique is conducted for each sub-problem to reduce a declarative hierarchical planning domain model and then find an individual solution for each so-called sub-problem sub-plan. In the reduction part, the conflict between sub-plans is resolved to provide a general solution plan to the given hierarchical AI-planning problem. Preprocessing phase helps the planner cut off the hierarchical planning search space for each sub-problem by removing the compulsory literal elements that help the hierarchical planner seek a solution. The proposed approach has been fully implemented successfully, and some experimental results findings will be provided as proof of our approach's substantial improvement inefficiency.

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38

El-Dsouky, Ali I., Hesham A. Ali, and Rabab Samy Rashed. "Ranking Documents Based on the Semantic Relations Using Analytical Hierarchy Process." International Journal of Information Retrieval Research 7, no. 3 (July 2017): 22–37. http://dx.doi.org/10.4018/ijirr.2017070102.

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With the rapid growth of the World Wide Web comes the need for a fast and accurate way to reach the information required. Search engines play an important role in retrieving the required information for users. Ranking algorithms are an important step in search engines so that the user could retrieve the pages most relevant to his query In this work, the authors present a method for utilizing genealogical information from ontology to find the suitable hierarchical concepts for query extension, and ranking web pages based on semantic relations of the hierarchical concepts related to query terms, taking into consideration the hierarchical relations of domain searched (sibling, synonyms and hyponyms) by different weighting based on AHP method. So, it provides an accurate solution for ranking documents when compared to the three common methods.

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39

Fisher, D. "Iterative Optimization and Simplification of Hierarchical Clusterings." Journal of Artificial Intelligence Research 4 (April 1, 1996): 147–78. http://dx.doi.org/10.1613/jair.276.

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Clustering is often used for discovering structure in data. Clustering systems differ in the objective function used to evaluate clustering quality and the control strategy used to search the space of clusterings. Ideally, the search strategy should consistently construct clusterings of high quality, but be computationally inexpensive as well. In general, we cannot have it both ways, but we can partition the search so that a system inexpensively constructs a `tentative' clustering for initial examination, followed by iterative optimization, which continues to search in background for improved clusterings. Given this motivation, we evaluate an inexpensive strategy for creating initial clusterings, coupled with several control strategies for iterative optimization, each of which repeatedly modifies an initial clustering in search of a better one. One of these methods appears novel as an iterative optimization strategy in clustering contexts. Once a clustering has been constructed it is judged by analysts -- often according to task-specific criteria. Several authors have abstracted these criteria and posited a generic performance task akin to pattern completion, where the error rate over completed patterns is used to `externally' judge clustering utility. Given this performance task, we adapt resampling-based pruning strategies used by supervised learning systems to the task of simplifying hierarchical clusterings, thus promising to ease post-clustering analysis. Finally, we propose a number of objective functions, based on attribute-selection measures for decision-tree induction, that might perform well on the error rate and simplicity dimensions.

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40

Unger, Christoph. "The scope of discourse connectives: implications for discourse organization." Journal of Linguistics 32, no. 2 (September 1996): 403–38. http://dx.doi.org/10.1017/s0022226700015942.

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The main aim of this paper is to discuss the claim that discourse connectives are best treated as indicators of coherence relations between hierarchically organized discourse units. It will be argued that coherence relations cannot be seen as cognitively real entities. Furthermore, there is no evidence for hierarchical organization in discourse. The intuitions underlying the notion of hierarchical discourse structure are instead explained in terms of consequences of processing a text in the search for optimal relevance. This account draws attention to a hitherto not widely discussed set of data.

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41

Yoon, Hyo-Sun, and Mi-Young Kim. "Fast Hierarchical Search Method for Multi-view Video Coding." KIPS Transactions on Software and Data Engineering 2, no. 7 (July 31, 2013): 495–502. http://dx.doi.org/10.3745/ktsde.2013.2.7.495.

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42

Sun, Bo, and Paolo Gardoni. "Directional search algorithm for hierarchical model development and selection." Reliability Engineering & System Safety 182 (February 2019): 194–207. http://dx.doi.org/10.1016/j.ress.2018.09.013.

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43

Laguna, Manuel, Rafael Martí, and Vicente Valls. "Arc crossing minimization in hierarchical digraphs with tabu search." Computers & Operations Research 24, no. 12 (December 1997): 1175–86. http://dx.doi.org/10.1016/s0305-0548(96)00083-4.

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44

Peng, Tao, Qin Liu, Baishuang Hu, Jierong Liu, and Jiawei Zhu. "Dynamic Keyword Search With Hierarchical Attributes in Cloud Computing." IEEE Access 6 (2018): 68948–60. http://dx.doi.org/10.1109/access.2018.2878268.

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45

Durfee, E. H., and T. A. Montgomery. "Coordination as distributed search in a hierarchical behavior space." IEEE Transactions on Systems, Man, and Cybernetics 21, no. 6 (1991): 1363–78. http://dx.doi.org/10.1109/21.135682.

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46

Lyu, Bingqing, Lu Qin, Xuemin Lin, Lijun Chang, and Jeffrey Xu Yu. "Supergraph Search in Graph Databases via Hierarchical Feature-Tree." IEEE Transactions on Knowledge and Data Engineering 31, no. 2 (February 1, 2019): 385–400. http://dx.doi.org/10.1109/tkde.2018.2833124.

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47

Theiss, Justin, and Michael Silver. "Modeling attention during visual search with hierarchical Bayesian inference." Journal of Vision 19, no. 10 (September 6, 2019): 107a. http://dx.doi.org/10.1167/19.10.107a.

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48

Castillo Valdivieso, Pedro A., and Salma Gaou. "Hierarchical classification of web search results to detect users." International Journal of Artificial Intelligence and Soft Computing 6, no. 4 (2017): 287. http://dx.doi.org/10.1504/ijaisc.2017.10018302.

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Gaou, Salma, and Pedro A. Castillo Valdivieso. "Hierarchical classification of web search results to detect users." International Journal of Artificial Intelligence and Soft Computing 6, no. 4 (2018): 287. http://dx.doi.org/10.1504/ijaisc.2018.097281.

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50

Silva, Jorge, Alberto Speranzon, João Borges de Sousa, and Karl Henrik Johansson. "Hierarchical search strategy for a team of autonomous vehicles." IFAC Proceedings Volumes 37, no. 8 (July 2004): 567–72. http://dx.doi.org/10.1016/s1474-6670(17)32038-4.

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