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1
Thomsen, Bodil Marie Stavning. "Tag a keyword, tag two..." K&K - Kultur og Klasse 34, no. 102 (October 2, 2006): 135–36. http://dx.doi.org/10.7146/kok.v34i102.22321.
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Wu, Chung-Hsien, and Yeou-Jiunn Chen. "Multi-keyword spotting of telephone speech using a fuzzy search algorithm and keyword-driven two-level CBSM." Speech Communication 33, no. 3 (February 2001): 197–212. http://dx.doi.org/10.1016/s0167-6393(00)00016-9.
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Campos, Alfredo, Angeles Amor, and María Angeles González. "The Importance of the Keyword-Generation Method in Keyword Mnemonics." Experimental Psychology 51, no. 2 (January 2004): 125–31. http://dx.doi.org/10.1027/1618-3169.51.2.125.
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Abstract. Keyword mnemonics is under certain conditions an effective approach for learning foreign-language vocabulary. It appears to be effective for words with high image vividness but not for words with low image vividness. In this study, two experiments were performed to assess the efficacy of a new keyword-generation procedure (peer generation). In Experiment 1, a sample of 363 high-school students was randomly divided into four groups. The subjects were required to learn L1 equivalents of a list of 16 Latin words (8 with high image vividness, 8 with low image vividness), using a) the rote method, or the keyword method with b) keywords and images generated and supplied by the experimenter, c) keywords and images generated by themselves, or d) keywords and images previously generated by peers (i.e., subjects with similar sociodemographic characteristics). Recall was tested immediately and one week later. For high-vividness words, recall was significantly better in the keyword groups than the rote method group. For low-vividness words, learning method had no significant effect. Experiment 2 was basically identical, except that the word lists comprised 32 words (16 high-vividness, 16 low-vividness). In this experiment, the peer-generated-keyword group showed significantly better recall of high-vividness words than the rote method groups and the subject generated keyword group; again, however, learning method had no significant effect on recall of low-vividness words.
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Salter, James, and Nick Antonopoulos. "An optimized two-tier P2P architecture for contextualized keyword searches." Future Generation Computer Systems 23, no. 2 (February 2007): 241–51. http://dx.doi.org/10.1016/j.future.2006.07.014.
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ITO, SEIJI, and SIGERU OMATU. "KEYWORDS SPECIFICATION FOR IMAGES USING SANDGLASS-TYPE NEURAL NETWORKS." International Journal of Computational Intelligence and Applications 04, no. 02 (June 2004): 143–52. http://dx.doi.org/10.1142/s1469026804001203.
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We propose a keyword specification method for images, which can be used to retrieve an image by a keyword. In order to specify a keyword for a sub-region of the image, images are segmented in some regions. Here, we consider ten keywords to specify the regions. The image segmentation method consists of the maximum-distance algorithm, labeling, and merging the small regions. We provide training regions for each keyword. Important features of the keyword are selected using the Factor Analysis (FA). The features are compressed into a two-dimensional space using a Sandglass-type Neural Network (SNN). We train the SNN using the training dataset. Then 60 samples of the testing dataset are evaluated by keywords extraction.
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Helmreich, Stefan, and Sophia Roosth. "Life Forms: A Keyword Entry." Representations 112, no. 1 (2010): 27–53. http://dx.doi.org/10.1525/rep.2010.112.1.27.
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We deliver a "keyword" account of the term life form as it has been used in natural philosophy and biology over the last two hundred years, beginning with its appearance in German as Lebensform. We argue that life form has, since its earliest enunciations, pointed to a space of possibility within which life might take shape, but that the way that space is imagined and theorized in biology has transformed substantially; life form originated as a term referring to idealized, aesthetic possibilities, then transformed to describe biogeographic and evolutionary potentialities, and today, in the age of synthetic biology and astrobiology, has come to signal conjectural and future possibilities.
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Chen, Zijun, Tingting Zhao, and Wenyuan Liu. "Time-aware collective spatial keyword query." Computer Science and Information Systems 18, no. 3 (2021): 1077–100. http://dx.doi.org/10.2298/csis200131034c.
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The collective spatial keyword query is a hot research topic in the database community in recent years, which considers both the positional relevance to the query location and textual relevance to the query keywords. However, in real life, the temporal information of object is not always valid. Based on this, we define a new query, namely time-aware collective spatial keyword query (TCoSKQ), which considers the positional relevance, textual relevance, and temporal relevance between objects and query at the same time. Two evaluation functions are defined to meet different needs of users, for each of which we propose an algorithm. Effective pruning strategies are proposed to improve query efficiency based on the two algorithms. Finally, the experimental results show that the proposed algorithms are efficient and scalable.
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Fakas, Georgios J., and Zhi Cai. "Object summaries for keyword search." Encyclopedia with Semantic Computing and Robotic Intelligence 02, no. 02 (November 14, 2018): 1750002. http://dx.doi.org/10.1142/s2529737617500022.
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The abundance and ubiquity of graphs (e.g., semantic knowledge graphs, such as Google’s knowledge graph, DBpedia; online social networks such as Google[Formula: see text], Facebook; bibliographic graphs such as DBLP, etc.) necessitates the effective and efficient search over them. Thus, we propose a novel keyword search paradigm, where the result of a search is an Object Summary (OS). More precisely, given a set of keywords that can identify a Data Subject (DS), our paradigm produces a set of OSs as results. An OS is a tree structure rooted at the DS node (i.e., a node containing the keywords) with surrounding nodes that summarize all data held on the graph about the DS. An OS can potentially be very large in size and therefore unfriendly for users who wish to view synoptic information about the data subject. Thus, we investigate the effective and efficient retrieval of concise and informative OS snippets (denoted as size-[Formula: see text] OSs). A size-[Formula: see text] OS is a partial OS containing [Formula: see text] nodes such that the summation of their importance scores results in the maximum possible total score. However, the set of nodes that maximize the total importance score may result in an uninformative size-[Formula: see text] OSs, as very important nodes may be repeated in it, dominating other representative information. In view of this limitation, we investigate the effective and efficient generation of two novel types of OS snippets, i.e., diverse and proportional size-[Formula: see text] OSs, denoted as DSize-[Formula: see text] and PSize-[Formula: see text] OSs. Namely, besides the importance of each node, we also consider its pairwise relevance (similarity) to the other nodes in the OS and the snippet. We conduct an extensive evaluation on two real graphs (DBLP and Google[Formula: see text]). We verify effectiveness by collecting user feedback, e.g., by asking DBLP authors (i.e., the DSs themselves) to evaluate our results. In addition, we verify the efficiency of our algorithms and evaluate quality of the snippets that they produce.
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Takase, Haruhiko, Hiroharu Kawanaka, and Shinji Tsuruoka. "Supporting System for Quiz in Large Class – Automatic Keyword Extraction and Browsing Interface –." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 1 (January 20, 2015): 152–57. http://dx.doi.org/10.20965/jaciii.2015.p0152.
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We focus on developing an e-learning system that supports the grasping of misunderstanding from descriptive answers. We propose real-time keyword extraction and an interface for grasping misunderstanding based on extracted keywords. The system extracts keywords without extra information. Teachers find majormisunderstandings by using the proposed interface, which consists of two views – keyword and description. Using these views, teachers browse answers in three steps – finding keywords, reading around keywords, and reading full answers. We use experiments to demonstrate the effectiveness of our system, this proposed keyword extraction extracts expected words. Subjects evaluate the proposed interface for its effectiveness in grasping misunderstandings. Using our proposed, teachers found major misunderstandings quickly and easily.
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Li, Huajiao, Haizhong An, Yue Wang, Jiachen Huang, and Xiangyun Gao. "Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network." Physica A: Statistical Mechanics and its Applications 450 (May 2016): 657–69. http://dx.doi.org/10.1016/j.physa.2016.01.017.
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HAN, Chang Woo, Shin Jae KANG, and Nam Soo KIM. "Estimation of Phone Mismatch Penalty Matrices for Two-Stage Keyword Spotting." IEICE Transactions on Information and Systems E93.D, no. 8 (2010): 2331–35. http://dx.doi.org/10.1587/transinf.e93.d.2331.
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Millett, Ronald P. "Integration of taxonomy and keyword searches: A comparison of two implementations." Bulletin of the American Society for Information Science and Technology 36, no. 1 (October 2009): 31–35. http://dx.doi.org/10.1002/bult.2009.1720360109.
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Takada, Tomoki, Mizuki Arai, and Tomohiro Takagi. "Automatic Keyword Annotation System Using Newspapers." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 3 (May 20, 2014): 340–46. http://dx.doi.org/10.20965/jaciii.2014.p0340.
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Nowadays, an increasingly large amount of information exists on the web. Therefore, a method is needed that enables us to find necessary information quickly because this is becoming increasingly difficult for users. To solve this problem, information retrieval systems like Google and recommendation systems like that on Amazon are used. In this paper, we focus on information retrieval systems. These retrieval systems require index terms, which affect the precision of retrieval. Two methods generally decide index terms. One is analyzing a text using natural language processing and deciding index terms using varying amounts of statistics. The other is someone choosing document keywords as index terms. However, the latter method requires too much time and effort and becomes more impractical as information grows. Therefore, we propose the Nikkei annotator system, which is based on the model of the human brain and learns patterns of past keyword annotation and automatically outputs keywords that users prefer. The purposes of the proposed method are automating manual keyword annotation and achieving high speed and high accuracy keyword annotation. Experimental results showed that the proposed method is more accurate than TFIDF and Naive Bayes in P@5 and P@10. Moreover, these results also showed that the proposed method could annotate about 19 times faster than Naive Bayes.
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Mamta, Brij B. Gupta, and Syed Taqi Ali. "ISEkFT." Journal of Information Technology Research 12, no. 3 (July 2019): 133–53. http://dx.doi.org/10.4018/jitr.2019070108.
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Public-key encryption with keyword search (PEKS) is a well-known technique which allows searching on encrypted data using the public key system. However, this technique suffers from the keyword guessing attack (KGA). To address this problem, a modified version of PEKS called public key encryption with fuzzy keyword search (PEFKS) has been introduced where each keyword is associated with an exact search trapdoor (EST) and a fuzzy search trapdoor (FST) which is provided to the cloud server. PEFKS prevents KGA in such a way that two or maximum three keywords share the same FST. Hence, even if the cloud server knows the FST it cannot link it to the corresponding keyword. But, with a probability of 1/3 the malicious cloud server can still guess the keyword corresponding to FST. Therefore, in this article, the authors present an approach which can improve the security of the PEFKS technique by reducing the probability of guessing the keyword to 1/k where k is the number of keywords that share the same FST, thus enhancing the overall reliability. In addition, the authors have used an identity-based encryption (IBE) as an underlying technique to construct the searchable encryption scheme and proved its security in the standard model.
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Li, Luping, Stephen Petschulat, Guanting Tang, Jian Pei, and Wo-Shun Luk. "Efficient and Effective Aggregate Keyword Search on Relational Databases." International Journal of Data Warehousing and Mining 8, no. 4 (October 2012): 41–81. http://dx.doi.org/10.4018/jdwm.2012100103.
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Keyword search on relational databases is useful and popular for many users without technical background. Recently, aggregate keyword search on relational databases was proposed and has attracted interest. However, two important problems still remain. First, aggregate keyword search can be very costly on large relational databases, partly due to the lack of efficient indexes. Second, finding the top-k answers to an aggregate keyword query has not been addressed systematically, including both the ranking model and the efficient evaluation methods. In this paper, the authors tackle these two problems to improve the efficiency and effectiveness of aggregate keyword search on large relational databases. They designed indexes efficient in both size and construction time. The authors propose a general ranking model and an efficient ranking algorithm. They also report a systematic performance evaluation using real data sets.
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Chen, Yang, Wenmin Li, Fei Gao, Kaitai Liang, Hua Zhang, and Qiaoyan Wen. "Practical Attribute-Based Conjunctive Keyword Search Scheme." Computer Journal 63, no. 8 (December 9, 2019): 1203–15. http://dx.doi.org/10.1093/comjnl/bxz140.
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Abstract To date cloud computing may provide considerable storage and computational power for cloud-based applications to support cryptographic operations. Due to this benefit, attribute-based keyword search (ABKS) is able to be implemented in cloud context in order to protect the search privacy of data owner/user. ABKS is a cryptographic primitive that can provide secure search services for users but also realize fine-grained access control over data. However, there have been two potential problems that prevent the scalability of ABKS applications. First of all, most of the existing ABKS schemes suffer from the outside keyword guessing attack (KGA). Second, match privacy should be considered while supporting multi-keyword search. In this paper, we design an efficient method to combine the keyword search process in ABKS with inner product encryption and deploy several proposed techniques to ensure the flexibility of retrieval mode, the security and efficiency of our scheme. We later put forward an attribute-based conjunctive keyword search scheme against outside KGA to solve the aforementioned problems. We provide security notions for two types of adversaries and our construction is proved secure against chosen keyword attack and outside KGA. Finally, all-side simulation with real-world data set is implemented for the proposed scheme, and the results of the simulation show that our scheme achieves stronger security without yielding significant cost of storage and computation.
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Pojanapunya, Punjaporn, and Richard Watson Todd. "Log-likelihood and odds ratio: Keyness statistics for different purposes of keyword analysis." Corpus Linguistics and Linguistic Theory 14, no. 1 (April 25, 2018): 133–67. http://dx.doi.org/10.1515/cllt-2015-0030.
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AbstractKeyword analysis is used in a range of sub-disciplines of applied linguistics from genre analyses to critically-oriented studies for different purposes ranging from producing a general characterization of a genre to identifying text-specific ideological issues. This study compares the use of log-likelihood (LL), a probability statistic, and odds ratio (OR), an effect size statistic, for keyword identification and argues that the two methods produce different keywords applicable to research focusing on different purposes. Through two case studies, keyword analyses of advance fee scams against the British National Corpus and research articles in applied linguistics against research articles from other academic disciplines, we show that both the LL and OR keywords concern the aboutness of the corpus, but differ in their specificity and pervasiveness through the corpus. LL highlights words which are relatively common in general use serving genre purposes, whereas OR highlights more specialized words serving critically-oriented purposes. Methodological and practical contributions to keyword analysis are discussed.
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Pan, Jin Kun, and Dong Sheng Li. "Research on Efficient Spatial Keyword Queries Supporting Wildcard." Applied Mechanics and Materials 427-429 (September 2013): 2554–57. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2554.
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With the popularity of location-based services, Web contents are being geo-tagged and spatial keyword queries that retrieve objects satisfying both spatial and keyword conditions are gaining in prevalence. The existing spatial keyword queries focus on exact match or prefix match of the keywords cannot satisfy the demand of wildcard based imprecise match in many realistic scenes. Aiming to solve this problem, two methods which are fit for different situation are put forward: the inverted file and R-tree integrated index which fits for the situation that requires high time efficiency and the Prefix Bloom Filter and R-tree integrated index which fits for the situation requiring high space efficiency. The effectiveness of the two indexes is valid through experiments.
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Bertels, Ann, and Dirk Speelman. "‘Keywords Method’ versus ‘Calcul des Spécificités’." International Journal of Corpus Linguistics 18, no. 4 (December 5, 2013): 536–60. http://dx.doi.org/10.1075/ijcl.18.4.04ber.
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This paper explores two tools and methods for keyword extraction. As several tools are available, it makes a comparison of two widely used tools, namely Lexico3 (Lamalle et al. 2003) and WordSmith Tools (Scott 2013). It shows the importance of keywords and discusses recent studies involving keyword extraction. Since no previous study has attempted to compare two different tools, used by different language communities and which use different methodologies to extract keywords, this paper aims at filling the gap by comparing not only the tools and their practical use, but also the underlying methodologies and statistics. By means of a comparative study on a small test corpus, this paper shows major similarities and differences between the tools. The similarities mainly concern the most typical keywords, whereas the differences concern the total number of significant keywords extracted, the granularity of both probability value and typicality coefficient and the type of the reference corpus.
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Rammal, Mahmoud, Zeinab Bahsoun, and Mona Al Achkar Jabbour. "Keyword extraction from Arabic legal texts." Interactive Technology and Smart Education 12, no. 1 (April 20, 2015): 62–71. http://dx.doi.org/10.1108/itse-11-2013-0030.
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Purpose – The purpose of this paper is to apply local grammar (LG) to develop an indexing system which automatically extracts keywords from titles of Lebanese official journals. Design/methodology/approach – To build LG for our system, the first word that plays the determinant role in understanding the meaning of a title is analyzed and grouped as the initial state. These steps are repeated recursively for the whole words. As a new title is introduced, the first word determines which LG should be applied to suggest or generate further potential keywords based on a set of features calculated for each node of a title. Findings – The overall performance of our system is 67 per cent, which means that 67 per cent of the keywords extracted manually have been extracted by our system. This empirical result shows the validity of this study’s approach after taking into consideration the below-mentioned limitations. Research limitations/implications – The system has two limitations. First, it is applied to a sample of 5,747 titles and it can be developed to generate all finite state automata for all titles. The other limitation is that named entities are not processed due to their varieties that require specific ontology. Originality/value – Almost all keyword extraction systems apply statistical, linguistic or hybrid approaches to extract keywords from texts. This paper contributes to the development of an automatic indexing system to replace the expensive human indexing by taking advantages of LG, which is mainly applied to extract time, date and proper names from texts.
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McCabe, Jennifer, and Steven Wise. "It’s All Fun and Games until Someone Learns Something: Assessing the Learning Outcomes of Two Educational Games." Evidence Based Library and Information Practice 4, no. 4 (December 17, 2009): 6. http://dx.doi.org/10.18438/b8fc8m.
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Objective – To determine whether educational games can be designed that are both fun and effective in improving information seeking skills.
Methods – Two skills that are known to be particularly difficult for students taking a required information literacy test were identified. These skills are the ability to identify citations and the ability to search databases with keywords. Educational games were designed to address these two skills. The first game, Citation Tic Tac Toe, placed commonly used bibliographic citations into a tick tac toe style grid. Students were required to play the Tic Tac Toe game and subsequently given citation identification exercises. The second game arranged key concepts related to search phrases in a Magnetic Keyword interface. Students were observed searching databases before and after playing the Magnetic Keyword game and their pre- and post-play searches were analyzed.
Results – Students who played the Tic Tac Toe game improved more from pretest to posttest than students who only took an online tutorial. In addition, students who played the Magnetic Keyword game demonstrated quicker database searching for their topics and expressed increased satisfaction with their results.
Conclusions – Games can be created which have measurable educational outcomes and are fun. It is important, however, to establish the educational objective prior to beginning game design.
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Yang, Shu Xin, Wei Tang, and Bi Bo He. "A Parallel Query Processing Technique for Keyword Search over Data Graph." Applied Mechanics and Materials 380-384 (August 2013): 2609–13. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2609.
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As a hot topic in the field of database research, keyword search on data graph has attracted many attentions. However, most of existing works are mainly studied on CPU. An important problem is how to efficiently generate answers for keyword search. In this paper, a parallel approach of keyword search based on interval coding is investigated. The approach includes two main tasks, which are finding root nodes and getting shortest paths from root to keyword nodes. To find root nodes quickly, we adopt a strategy of reachability judging between any two nodes with interval assigned to every node. Meanwhile, aim to speed up finding root nodes and getting shortest paths from root to keyword nodes, we provide data parallel processing for compute-intensive tasks by using graphical processing unit. Experiment results show the high performance of the proposed solution both on CPU and graphical processing unit.
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Geluso, Joe, and Roz Hirch. "The reference corpus matters." Register Studies 1, no. 2 (September 25, 2019): 209–42. http://dx.doi.org/10.1075/rs.18001.gel.
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Abstract This study investigates the effect that reference corpora of different registers have on the content of keyword lists. The study focusses on two target corpora and the keyword lists generated for each when using three distinct reference corpora. The two target corpora consist of published research by faculty at two PhD-granting programs in applied linguistics in North America. The reference corpora comprise published research in applied linguistics, newspaper and magazine articles, and fiction texts, respectively. The findings suggest that while common keywords representing each target corpus emerge regardless of the reference corpus used in the analysis, there are also substantial differences. Primarily, using a reference corpus of the same sub-register as the target corpus better highlights content unique to each target corpus while using a reference corpus of a different register better uncovers words that reflect the register that the target corpora represent. Implications for conducting keyword analysis are discussed.
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Jung, Yong-Bok, and Eui-Seob Park. "Keyword Analysis of Two SCI Journals on Rock Engineering by using Text Mining." Journal of Korean Society For Rock Mechanics 25, no. 4 (August 31, 2015): 303–19. http://dx.doi.org/10.7474/tus.2015.25.4.303.
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Zhang, Yinghui, Robert H. Deng, Jiangang Shu, Kan Yang, and Dong Zheng. "TKSE: Trustworthy Keyword Search Over Encrypted Data With Two-Side Verifiability via Blockchain." IEEE Access 6 (2018): 31077–87. http://dx.doi.org/10.1109/access.2018.2844400.
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Campos, Alfredo, and Leticia Ameijide. "The accuracy of judgments of learning (JOLs) and the mnemonic keyword method in elderly adults." International Journal of Psychological Research 4, no. 2 (December 30, 2011): 64–71. http://dx.doi.org/10.21500/20112084.782.
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The aim was to assess the efficacy of the keyword mnemonic method and its impact on the accuracy of Judgments of Learning (JOLs) in elderly adults. The sample consisted of a hundred participants aged 65 to 86, who were subdivided into two age groups: 65-75 years and 76-86 years. Each group had to learn, using either their own habitual method of learning or the mnemonic keyword method, the Spanish meaning of a list of keywords in Latin that had high image vividness scores. The 76-86 year age group, who tended to use their own habitual method as opposed to the mnemonic keyword method, obtained higher accuracy scores than the 65-75 year age group.
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Selvaganesan, S., Su-Cheng Haw, and Lay-Ki Soon. "XDMA: A Dual Indexing and Mutual Summation Based Keyword Search Algorithm for XML Databases." International Journal of Software Engineering and Knowledge Engineering 24, no. 04 (May 2014): 591–615. http://dx.doi.org/10.1142/s0218194014500223.
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Achieving the effectiveness in relation to the relevance of query result is the most crucial part of XML keyword search. Developing an XML Keyword search approach which addresses the user search intention, keyword ambiguity problems and query/search result grading (ranking) problem is still challenging. In this paper, we propose a novel approach called XDMA for keyword search in XML databases that builds two indices to resolve these problems. Then, a keyword search technique based on two-level matching between two indices is presented. Further, by utilizing the logarithmic and probability functions, a terminology that defines the Mutual Score to find the desired T-typed node is put forward. We also introduce the similarity measure to retrieve the exact data through the selected T-typed node. In addition, grading for the query results having comparable relevance scores is employed. Finally, we demonstrate the effectiveness of our proposed approach, XDMA with a comprehensive experimental evaluation using the datasets of DBLP, WSU and eBay.
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Zeng, Fugeng, and Chunxiang Xu. "A Novel Model for Lattice-Based Authorized Searchable Encryption with Special Keyword." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/314621.
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Data stored in the cloud servers, keyword search, and access controls are two important capabilities which should be supported. Public-keyword encryption with keyword search (PEKS) and attribute based encryption (ABE) are corresponding solutions. Meanwhile, as we step into postquantum era, pairing related assumption is fragile. Lattice is an ideal choice for building secure encryption scheme against quantum attack. Based on this, we propose the first mathematical model for lattice-based authorized searchable encryption. Data owners can sort the ciphertext by specific keywords such as time; data users satisfying the access control hand the trapdoor generated with the keyword to the cloud sever; the cloud sever sends back the corresponding ciphertext. The security of our schemes is based on the worst-case hardness on lattices, called learning with errors (LWE) assumption. In addition, our scheme achieves attribute-hiding, which could protect the sensitive information of data user.
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Zhao, Juan, and Hui Yun Xiong. "A Keyword Query Method Based on Digital Type Properties." Advanced Materials Research 919-921 (April 2014): 2115–18. http://dx.doi.org/10.4028/www.scientific.net/amr.919-921.2115.
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Now, the way to access the database in most still uses a simple keyword query method, it is generally based on semantic matching, rather than based on semantic matching. So it can't make full use of the semantic relationship between the data, and unable to obtain accurate results. This paper proposes a keyword query method based on digital type, it can largely improve the efficiency of user queries. We propose two query algorithm namely multi-table query and single table query in the database query time of uncertainty. Through experimental analysis, we can see that our algorithm is more effective than simple type keyword queries.
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Yeom, Sanghui, Seungyeon Choi, Jeonghee Chi, and Soyoung Park. "Blockchain-Based Employment Contract System Architecture Allowing Encrypted Keyword Searches." Electronics 10, no. 9 (May 4, 2021): 1086. http://dx.doi.org/10.3390/electronics10091086.
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We introduce a blockchain-based online employment contract system to protect the rights and interests of both employees and employers. In the proposed model, an employer and a worker can interactively create a new electronic online contract, and the mutually signed contract is saved on a contract blockchain so that the contract becomes certifiable but cannot be forged by the contract signers. In particular, the blockchain in our system provides transactional privacy to protect sensitive personal information such as social identifier, contact information, income, and so forth, contained in the contract. Since a remote cloud server must provide not only secure storage, accessibility, and availability of all signed contracts, but also increased security in the server, we propose a new encrypted keyword search mechanism with enhanced search accuracy. Each contract is associated with encrypted keywords generated from the names of contractual parties and must remain confidential and anonymous even to the server. Although, the contracts must always be accessible by the contract signers, only the cloud server should be able to retrieve each user’s contract without decrypting the contract or identifying the contract signer. To meet these requirements, we propose a new encrypted keyword search mechanism based on Gentry’s homomorphic encryption technology; the server can find each user’s contract when two encrypted arbitrary keywords are homomorphic to each other. Since the keywords in the proposed system are based on person names or business names, they are easily predictable, and, thereby, many synonyms for a keyword can exist. Therefore, the proposed encrypted keyword search takes into account not only the keywords but also the ownership of each contract; in this way, the proposed search scheme is secure against a keyword guessing attack and provides strong search accuracy against the keyword synonyms. As a result, users can only access their own contracts, and the cloud server can exactly retrieve the requester’s contracts. Implementations for the proposed system and corresponding analysis on its security and simulated performance are provided.
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Kim, Yoon, Hwang, and Jun. "Patent Keyword Analysis using Time Series and Copula Models." Applied Sciences 9, no. 19 (September 29, 2019): 4071. http://dx.doi.org/10.3390/app9194071.
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The technological keywords extracted from patent documents have much information about a developed technology. We can understand the technological structure of a product by examining the results of patent analysis. So far, much research has been done on patent data analysis. The technological keywords of patent documents contain representative information on the developed technology. As such, the patent keyword is one of the most important factors in patent data analysis. In this paper, we propose a patent data analysis model combining a integer valued time series model and copula direction dependence for integer valued patent keyword analysis over time. Most patent keywords are frequency values and keywords often change over time. However, the existing patent keywords analysis works do not account for two major factors: integer value and time. For modeling integer valued keyword data with time factor, we use a copula directional dependence model based on marginal regression with a beta logit function and integer valued generalized autoregressive conditional heteroskedasticity model. Using the proposed model, we find technological trends and relations in the target technological domain. To illustrate the performance and implication of our paper, we carry out experiments using the patent documents applied and registered by Apple company. This study contributes to the effective planning for the research and development of technologies by utilizing the evolution of technology over time.
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Chen, Rui, Bin Fang, and Patrick Shen-Pei Wang. "Chinese Handwriting Identification Method Based on Keyword Extraction." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 11 (March 31, 2017): 1753004. http://dx.doi.org/10.1142/s0218001417530044.
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Text-independent handwriting identification methods require that features such as texture are extracted from lengthy document image; while text-dependent handwriting identification methods require that the contents of the documents being compared are identical. In order to overcome these confinements, this paper presents a novel Chinese handwriting identification technique. First, Chinese characters are segmented from handwriting document, then keywords are extracted based on matching and voting of local features of character. Then the same-content keywords are used to build training sets, and these training sets of two documents are compared. Because the keywords are similar to signature, the handwriting identification problem is transformed into signature verification problem. Experiments on HIT-MW, HIT-SW and CASIA show this method outperforms many text-independent handwriting identification methods.
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Li, Jinye. "A comparative study of keyword extraction algorithms for English texts." Journal of Intelligent Systems 30, no. 1 (January 1, 2021): 808–15. http://dx.doi.org/10.1515/jisys-2021-0040.
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Abstract This study mainly analyzed the keyword extraction of English text. First, two commonly used algorithms, the term frequency–inverse document frequency (TF–IDF) algorithm and the keyphrase extraction algorithm (KEA), were introduced. Then, an improved TF–IDF algorithm was designed, which improved the calculation of word frequency, and it was combined with the position weight to improve the performance of keyword extraction. Finally, 100 English literature was selected from the British Academic Written English Corpus for the analysis experiment. The results showed that the improved TF–IDF algorithm had the shortest running time and took only 4.93 s in processing 100 texts; the precision of the algorithms decreased with the increase of the number of extracted keywords. The comparison between the two algorithms demonstrated that the improved TF–IDF algorithm had the best performance, with a precision rate of 71.2%, a recall rate of 52.98%, and an F 1 score of 60.75%, when five keywords were extracted from each article. The experimental results show that the improved TF–IDF algorithm is effective in extracting English text keywords, which can be further promoted and applied in practice.
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Attique, Muhammad, Muhammad Afzal, Farman Ali, Irfan Mehmood, Muhammad Fazal Ijaz, and Hyung-Ju Cho. "Geo-Social Top-k and Skyline Keyword Queries on Road Networks." Sensors 20, no. 3 (February 1, 2020): 798. http://dx.doi.org/10.3390/s20030798.
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The rapid growth of GPS-enabled mobile devices has popularized many location-based applications. Spatial keyword search which finds objects of interest by considering both spatial locations and textual descriptions has become very useful in these applications. The recent integration of social data with spatial keyword search opens a new service horizon for users. Few previous studies have proposed methods to combine spatial keyword queries with social data in Euclidean space. However, most real-world applications constrain the distance between query location and data objects by a road network, where distance between two points is defined by the shortest connecting path. This paper proposes geo-social top-k keyword queries and geo-social skyline keyword queries on road networks. Both queries enrich traditional spatial keyword query semantics by incorporating social relevance component. We formalize the proposed query types and appropriate indexing frameworks and algorithms to efficiently process them. The effectiveness and efficiency of the proposed approaches are evaluated using real datasets.
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Mahlberg, Michaela, and Dan McIntyre. "A case for corpus stylistics." English Text Construction 4, no. 2 (November 17, 2011): 204–27. http://dx.doi.org/10.1075/etc.4.2.03mah.
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In this article we investigate keywords and key semantic domains in Fleming’s Casino Royale. We identify groups of keywords that describe elements of the fictional world such as characters and settings as well as thematic signals. The keyword groups fall into two broad categories that are characterized as text-centred and reader-centred, with the latter providing particular clues for interpretation. We also compare the manually identified keyword groups with key semantic domains that are based on automatic semantic analysis. The comparison shows, for instance, how words that do not seem to fit a semantic domain can be seen as reader-centred keywords fulfilling specific textual functions. By linking our analysis to arguments in literary criticism, we show how quantitative and qualitative approaches can usefully complement one another.
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Kim, Yunjeong, and Kyung Wha Oh. "Which Consumer Associations Can Build a Sustainable Fashion Brand Image? Evidence from Fast Fashion Brands." Sustainability 12, no. 5 (February 25, 2020): 1703. http://dx.doi.org/10.3390/su12051703.
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Since fast fashion is often considered the opposite of sustainable fashion, this study was conducted to clarify the consumer brand associations with sustainable fashion by analyzing three fast fashion brands. Our research included two studies. First, we conducted in-depth interviews with 20 female consumers in Korea who had purchase experience with the sustainable fashion of three selected brands, H&M, Zara, and Uniqlo, to identify sustainable keyword associations. We then structured the keyword data using network analysis. The keyword associations for the three brands resulted in a network of 60 nodes and 629 links with the term “eco-friendly” as the most meaningful keyword. Second, we surveyed 200 women and quantitatively confirmed the association of “eco-friendly fabric” among the keywords suggestive of “eco-friendly” as the most important factor in building a sustainable fashion brand image. In addition, keywords, such as “marketing” and “campaign”, were ranked in the top ten in H&M and Zara, which may imply the opportunistic use of greenwash. This study contributes to the literature by applying in-depth analysis of consumer associations of fast fashion brands from a sustainability perspective through network analysis. We expect our findings to help fashion companies strategically build a sustainable fashion brand image.
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Oh, Se Eun, Shuai Li, and Nicholas Hopper. "Fingerprinting Keywords in Search Queries over Tor." Proceedings on Privacy Enhancing Technologies 2017, no. 4 (October 1, 2017): 251–70. http://dx.doi.org/10.1515/popets-2017-0048.
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AbstractSearch engine queries contain a great deal of private and potentially compromising information about users. One technique to prevent search engines from identifying the source of a query, and Internet service providers (ISPs) from identifying the contents of queries is to query the search engine over an anonymous network such as Tor.In this paper, we study the extent to which Website Fingerprinting can be extended to fingerprint individual queries or keywords to web applications, a task we call Keyword Fingerprinting (KF). We show that by augmenting traffic analysis using a two-stage approach with new task-specific feature sets, a passive network adversary can in many cases defeat the use of Tor to protect search engine queries.We explore three popular search engines, Google, Bing, and Duckduckgo, and several machine learning techniques with various experimental scenarios. Our experimental results show that KF can identify Google queries containing one of 300 targeted keywords with recall of 80% and precision of 91%, while identifying the specific monitored keyword among 300 search keywords with accuracy 48%. We also further investigate the factors that contribute to keyword fingerprintability to understand how search engines and users might protect against KF.
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AZCARRAGA, ARNULFO P., TEDDY YAP, and TAT SENG CHUA. "COMPARING KEYWORD EXTRACTION TECHNIQUES FOR WEBSOM TEXT ARCHIVES." International Journal on Artificial Intelligence Tools 11, no. 02 (June 2002): 219–32. http://dx.doi.org/10.1142/s0218213002000861.
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The WEBSOM methodology for building very large text archives has a very slow method for extracting meaningful unit labels. This is due to the fact that the method computes for the relative frequencies of all the words of all the documents associated to each unit and then compares these to the relative frequencies of all the words of other units in the map. Since maps may have more than 100,000 units and the archieve may contain up to 7 million documents, the existing WEBSOM method is not practical. A fast alternative method, referred to as the liGHtSOM method, is based on the distribution of weights in the weight vectors of the trained map, plus a simple manipulation of the random projection matrix used for input data compression. Comparison made using a WEBSOM archieve of the Reuters text collection reveal that a high percentage of keywords extracted using this method match the keywords extracted for the same map units using the original WEBSOM method. A detailed time complexity analysis of the two methods is also provided.
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Bensalem, Elias Adam. "The impact of keyword and full video captioning on listening comprehension." Global Journal of Foreign Language Teaching 6, no. 3 (August 3, 2016): 112–20. http://dx.doi.org/10.18844/gjflt.v6i3.1657.
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This study investigates the effect of two types of captioning (full and keyword captioning) on listening comprehension. Thirty-six university-level EFL students participated in the study. They were randomly assigned to watch three video clips under three conditions. The first group watched the video clips with full captions. The second group watched the same video clips with keyword captions. The control group watched the video clips without captions. After watching each clip, participants took a listening comprehension test. At the end of the experiment, participants completed a questionnaire to measure their perceptions about the use of captions and the video clips they watched. Results indicated that the full captioning group significantly outperformed both the keyword captioning and the no captioning group on the listening comprehension tests. However, this study did not find any significant difference between the keyword captioning group and the no captioning group. Results of the survey suggest that keyword captioning were a source of distraction for participants.Keywords: captions, video, listening comprehension, ESL.
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Roko, Abubakar, Shyamala Doraisamy, Azrul Hazri Jantan, and Azreen Azman. "Effective keyword query structuring using NER for XML retrieval." International Journal of Web Information Systems 11, no. 1 (April 20, 2015): 33–53. http://dx.doi.org/10.1108/ijwis-06-2014-0022.
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Purpose – The purpose of this paper is to propose and evaluate XKQSS, a query structuring method that relegates the task of generating structured queries from a user to a search engine while retaining the simple keyword search query interface. A more effective way for searching XML database is to use structured queries. However, using query languages to express queries prove to be difficult for most users since this requires learning a query language and knowledge of the underlying data schema. On the other hand, the success of Web search engines has made many users to be familiar with keyword search and, therefore, they prefer to use a keyword search query interface to search XML data. Design/methodology/approach – Existing query structuring approaches require users to provide structural hints in their input keyword queries even though their interface is keyword base. Other problems with existing systems include their inability to put keyword query ambiguities into consideration during query structuring and how to select the best generated structure query that best represents a given keyword query. To address these problems, this study allows users to submit a schema independent keyword query, use named entity recognition (NER) to categorize query keywords to resolve query ambiguities and compute semantic information for a node from its data content. Algorithms were proposed that find user search intentions and convert the intentions into a set of ranked structured queries. Findings – Experiments with Sigmod and IMDB datasets were conducted to evaluate the effectiveness of the method. The experimental result shows that the XKQSS is about 20 per cent more effective than XReal in terms of return nodes identification, a state-of-art systems for XML retrieval. Originality/value – Existing systems do not take keyword query ambiguities into account. XKSS consists of two guidelines based on NER that help to resolve these ambiguities before converting the submitted query. It also include a ranking function computes a score for each generated query by using both semantic information and data statistic, as opposed to data statistic only approach used by the existing approaches.
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Liu, Feng, Xiaodi Huang, Weidong Huang, and Sophia Xiaoxia Duan. "Performance Evaluation of Keyword Extraction Methods and Visualization for Student Online Comments." Symmetry 12, no. 11 (November 22, 2020): 1923. http://dx.doi.org/10.3390/sym12111923.
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Topic keyword extraction (as a typical task in information retrieval) refers to extracting the core keywords from document topics. In an online environment, students often post comments in subject forums. The automatic and accurate extraction of keywords from these comments are beneficial to lecturers (particular when it comes to repeatedly delivered subjects). In this paper, we compare the performance of traditional machine learning algorithms and two deep learning methods in extracting topic keywords from student comments posted in subject forums. For this purpose, we collected student comment data from a period of two years, manually tagging part of the raw data for our experiments. Based on this dataset, we comprehensively compared the five typical algorithms of naïve Bayes, logistic regression, support vector machine, convolutional neural networks, and Long Short-Term Memory with Attention (Att-LSTM). The performances were measured by the four evaluation metrics. We further examined the keywords by visualization. From the results of our experiment and visualization, we conclude that the Att-LSTM method is the best approach for topic keyword extraction from student comments. Further, the results from the algorithms and visualization are symmetry, to some degree. In particular, the extracted topics from the comments posted at the same stages of different teaching sessions are, almost, reflection symmetry.
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Jiang, Zhuoxuan, Chunyan Miao, and Xiaoming Li. "Application of keyword extraction on MOOC resources." International Journal of Crowd Science 1, no. 1 (March 6, 2017): 48–70. http://dx.doi.org/10.1108/ijcs-12-2016-0003.
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Purpose Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by learners all over the world, unprecedented massive educational resources are aggregated. The educational resources include videos, subtitles, lecture notes, quizzes, etc., on the teaching side, and forum contents, Wiki, log of learning behavior, log of homework, etc., on the learning side. However, the data are both unstructured and diverse. To facilitate knowledge management and mining on MOOCs, extracting keywords from the resources is important. This paper aims to adapt the state-of-the-art techniques to MOOC settings and evaluate the effectiveness on real data. In terms of practice, this paper also tries to answer the questions for the first time that to what extend can the MOOC resources support keyword extraction models, and how many human efforts are required to make the models work well. Design/methodology/approach Based on which side generates the data, i.e instructors or learners, the data are classified to teaching resources and learning resources, respectively. The approach used on teaching resources is based on machine learning models with labels, while the approach used on learning resources is based on graph model without labels. Findings From the teaching resources, the methods used by the authors can accurately extract keywords with only 10 per cent labeled data. The authors find a characteristic of the data that the resources of various forms, e.g. subtitles and PPTs, should be separately considered because they have the different model ability. From the learning resources, the keywords extracted from MOOC forums are not as domain-specific as those extracted from teaching resources, but they can reflect the topics which are lively discussed in forums. Then instructors can get feedback from the indication. The authors implement two applications with the extracted keywords: generating concept map and generating learning path. The visual demos show they have the potential to improve learning efficiency when they are integrated into a real MOOC platform. Research limitations/implications Conducting keyword extraction on MOOC resources is quite difficult because teaching resources are hard to be obtained due to copyrights. Also, getting labeled data is tough because usually expertise of the corresponding domain is required. Practical implications The experiment results support that MOOC resources are good enough for building models of keyword extraction, and an acceptable balance between human efforts and model accuracy can be achieved. Originality/value This paper presents a pioneer study on keyword extraction on MOOC resources and obtains some new findings.
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Lauper, Ursula, Jian-Hua Chen, and Shao Lin. "Window of Opportunity for New Disease Surveillance: Developing Keyword Lists for Monitoring Mental Health and Injury Through Syndromic Surveillance." Disaster Medicine and Public Health Preparedness 11, no. 2 (June 23, 2016): 173–78. http://dx.doi.org/10.1017/dmp.2016.99.
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AbstractStudies have documented the impact that hurricanes have on mental health and injury rates before, during, and after the event. Since timely tracking of these disease patterns is crucial to disaster planning, response, and recovery, syndromic surveillance keyword filters were developed by the New York State Department of Health to study the short- and long-term impacts of Hurricane Sandy. Emergency department syndromic surveillance is recognized as a valuable tool for informing public health activities during and immediately following a disaster. Data typically consist of daily visit reports from hospital emergency departments (EDs) of basic patient data and free-text chief complaints. To develop keyword lists, comparisons were made with existing CDC categories and then integrated with lists from the New York City and New Jersey health departments in a collaborative effort. Two comprehensive lists were developed, each containing multiple subcategories and over 100 keywords for both mental health and injury. The data classifiers using these keywords were used to assess impacts of Sandy on mental health and injuries in New York State. The lists will be validated by comparing the ED chief complaint keyword with the final ICD diagnosis code. (Disaster Med Public Health Preparedness. 2017;11:173–178)
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Selvan, Mercy Paul, A. Viji Amutha Mary, and S. Jancy. "Automatic User Domain Classification Based on Support Vector Machine (SVM)." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3327–31. http://dx.doi.org/10.1166/jctn.2019.8187.
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Finding domain of a research paper and a researcher is a crucial task and would be highly appreciable in order to provide personalized search results to the user. An automatic user domain classification technique based on SVM has been proposed in this paper in order to determine the domain of a user based on her publications. In this technique, for a given user, his specific area of domain is determined by classifying the keywords from his publication works. It consists of two phases: keyword extraction and domain classification. In keyword extraction phase, the list of publications corresponding to a user mail id is retrieved by using publish or perish tool. From each of the published papers, the keywords are extracted. In domain classification, SVM classifier is applied to determine the domain of the user. This is performed by training standard keywords from each domain into the SVM classifier. If a user belongs to more than one domain, then the primary domain with more publications will be considered.
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Cheng, Ming, Chris K. Anderson, Zhen Zhu, and S. Chan Choi. "Service online search ads: from a consumer journey view." Journal of Services Marketing 32, no. 2 (April 9, 2018): 126–41. http://dx.doi.org/10.1108/jsm-06-2016-0224.
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Purpose This study aims to address the following research questions: Do the two types of service firms (individual or aggregator) have similar competitiveness on online search ads? How should the two types of service firms select optimal branded keywords to improve search performance? In addition, how do consumers’ search queries influence the service search performance of the two types of service firms? Design/methodology/approach In this study, the authors conduct an empirical analysis by building a two-stage choice modeling on the process of search engine ranking and consumer click-through decisions. The authors estimate the parameter coefficients and test the hypotheses using maximum likelihood estimation in the logistic regression model. Findings The empirical findings suggest that consumer response rates are highly dependent upon three aspects (service types, branded keyword strategy and consumer search query). First, the authors found that service aggregators receive greater consumer responses than individual service providers. Second, depending upon the various branded keyword strategies (e.g. generic vs branded, “within-type” vs “cross-type”) implemented by service aggregators or individual firms, the expected consumer responses could be quite different. Finally, customer’s search query, being either generic or branded, also has direct effect and interactive effect with service type on how consumers would response to the sponsored ads in the service search process. Research limitations/implications The limitation of the research is twofold. First, conversion rate is not considered in the model estimation due to the nature of the data set. Second, the discussion about the keywords selection strategies is focusing on the hospitality industry. Future research shall further validate the generalizability into other industries. Practical implications First, given this competitive advantage, service aggregators should take an aggressive approach to adopting paid search strategy in acquiring new users and enhance its brand salience in the service ecosystem. Second, when considering other competitor’s brand names to include, if a firm is a service provider (e.g. hotel), a strategy that can help it receive higher consumer response would be to use “within-type” rather than “cross-type” branded keyword strategy. If a firm is a service aggregator, a better branded keyword strategy would be to use “across-type” instead of “within-type” approach. In addition, given that consumer’s brand awareness can influence the effectiveness of branded keyword strategy, online service search should target consumers in earlier stages of a decision journey. Social implications The authors believe their theoretical framework can provide actionable solutions to service firms to ease customer’s search process, increase customer’s stickiness using search engines and add value to the customer relationships with all services entities within the digital ecosystem. Originality/value This study is the first to expand online search marketing into granule examinations (main and interactive effects of three key factors) in the service search domain. First, the authors differentiate service firms into two categories – online travel aggregators and individual hotels in the model. Second, the authors introduce two sets of new classifications of branded keywords for online service search research (i.e. own versus other brand and “cross-type” versus “within-type” branded keywords). Third, this study integrates service consumers’ search word specificity into the conceptual framework which is often missing in previous online search research.
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CHIU, IVEY, and L. H. SHU. "Biomimetic design through natural language analysis to facilitate cross-domain information retrieval." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, no. 1 (January 2007): 45–59. http://dx.doi.org/10.1017/s0890060407070138.
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Biomimetic, or biologically inspired, design uses analogous biological phenomena to develop solutions for engineering problems. Several instances of biomimetic design result from personal observations of biological phenomena. However, many engineers' knowledge of biology may be limited, thus reducing the potential of biologically inspired solutions. Our approach to biomimetic design takes advantage of the large amount of biological knowledge already available in books, journals, and so forth, by performing keyword searches on these existing natural-language sources. Because of the ambiguity and imprecision of natural language, challenges inherent to natural language processing were encountered. One challenge of retrieving relevant cross-domain information involves differences in domain vocabularies, or lexicons. A keyword meaningful to biologists may not occur to engineers. For an example problem that involved cleaning, that is, removing dirt, a biochemist suggested the keyword “defend.” Defend is not an obvious keyword to most engineers for this problem, nor are the words defend and “clean/remove” directly related within lexical references. However, previous work showed that biological phenomena retrieved by the keyword defend provided useful stimuli and produced successful concepts for the clean/remove problem. In this paper, we describe a method to systematically bridge the disparate biology and engineering domains using natural language analysis. For the clean/remove example, we were able to algorithmically generate several biologically meaningful keywords, including defend, that are not obviously related to the engineering problem. We developed a method to organize and rank the set of biologically meaningful keywords identified, and confirmed that we could achieve similar results for two other examples in encapsulation and microassembly. Although we specifically address cross-domain information retrieval from biology, the bridging process presented in this paper is not limited to biology, and can be used for any other domain given the availability of appropriate domain-specific knowledge sources and references.
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Saito, Hidetoshi. "Effects of Preparation and Use of Keyword Lists on a Classroom Story-Retelling Test." JALT Journal 39, no. 1 (May 1, 2017): 5. http://dx.doi.org/10.37546/jaltjj39.1-1.
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The purpose of this quasi-experimental study was to explore the effects of test practice and keyword use on story-retelling test performance under four conditions. Eighty-two beginning and intermediate Japanese university students enrolled in English courses were required to practice orally summarizing two passages using a keyword list and also instructed to orally summarize one of two previously unseen passages without preparation. In the test session, two groups experienced two conditions that were identical and one that was different. Both groups retold one practiced passage with keywords at hand and a new passage without a keyword list. Group 1 retold another practiced passage with the keyword list withheld, whereas Group 2 read an additional new passage, made a keyword list, and retold it with the keyword list but without practice. Test practice was found to improve performance, but keyword list use induced better performance only when used with practice. テスト準備とキーワードリストは口頭要約テストに役立つか。この研究はテスト準備とキーワードリストの使用が口頭での要約テストに役立つかを調査することを目的とした。日本人大学生（初中級者）2グループ（計82名）が二つの同一条件と一つの異なる条件でそれぞれ英文要約を行った（計三条件づつ）。参加者は予め二種の英文が渡され、キーワードリストを作って練習をするように指示された。また、その場で新しい英文の要約を行うことも指示された。試験当日、両グループともまず練習した英文をリストとともに要約し、その後新しい英文の要約も行った。グループ1はさらに、準備したキーワードリストなしで練習した英文の要約を行った。グループ2はその場で新しい英文を読み、キーワードリストを作って要約を行った。結果として、練習したほうが、練習をしていない場合より良いが、キーワードリストは練習した場合のみに有効であることがわかった。
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Abdulsada, Ayad I., Dhafer G. Honi, and Salah Al-Darraji. "Efficient multi-keyword similarity search over encrypted cloud documents." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (July 1, 2021): 510. http://dx.doi.org/10.11591/ijeecs.v23.i1.pp510-518.
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Many organizations and individuals are attracted to outsource their data into remote cloud service providers. To ensure privacy, sensitive data should be encrypted be-fore being hosted. However, encryption disables the direct application of the essential data management operations like searching and indexing. Searchable encryption is acryptographic tool that gives users the ability to search the encrypted data while being encrypted. However, the existing schemes either serve a single exact search that loss the ability to handle the misspelled keywords or multi-keyword search that generate very long trapdoors. In this paper, we address the problem of designing a practical multi-keyword similarity scheme that provides short trapdoors and returns the correct results according to their similarity scores. To do so, each document is translated intoa compressed trapdoor. Trapdoors are generated using key based hash functions to en-sure their privacy. Only authorized users can issue valid trapdoors. Similarity scores of two textual documents are evaluated by computing the Hamming distance between their corresponding trapdoors. A robust security definition is provided together withits proof. Our experimental results illustrate that the proposed scheme improves thesearch efficiency compared to the existing schemes. Further more, it shows a high level of performance.
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Vishnupriya, B. A., N. Senthamarai, and S. Bharathi. "Efficient NN Spatial Keyword Search Using Spatial Inverted (SI) Index." International Journal of Engineering & Technology 7, no. 2.19 (April 17, 2018): 17. http://dx.doi.org/10.14419/ijet.v7i2.19.12101.
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"Spatial information mining", or learning revelation in spatial database, alludes to the illustration out of concealed information, spatial relations, or different examples that are not unequivocally put away in spatial databases. To get to the spatial database alongside the catchphrase another kind of inquiry called spatial watchword question is utilized. A spatial watchword inquiry get client area and client given catchphrases as contentions and gives web protests that are spatially and literarily material to these information. The current answers for such inquiries depend on IR2-tree that has a couple of inadequacies as space utilization and event of false hit is extremely huge when the question of the last outcome is far from the inquiry point .To beat this issue a novel file structure called Spatial Inverted file is proposed. Presently a-days use of portable is expanding enormously .In the versatile system an intermediary is set between base station and Location Based Server (LBS).This intermediary utilizes the Spatial modified file procedure to answer the SK inquiry by utilizing spatial data from the base station and printed data from the client question. The outcome from the SI record is given to two file structure in the intermediary called EVR Tree and Grid list. The Estimated Valid Region (EVR) for the present area of the client and required spatial articles are produced and come back to the client. On the off chance that the EVR is absent in the two file structure of intermediary it offer question to LBS. In the event that the client given inquiry is miss written or miss spelled it can be oversee by SI record utilizing n gram/2L Approximation file.
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Almaslukh, Abdulaziz, Yunfan Kang, and Amr Magdy. "Temporal Geo-Social Personalized Keyword Search Over Streaming Data." ACM Transactions on Spatial Algorithms and Systems 7, no. 4 (December 31, 2021): 1–28. http://dx.doi.org/10.1145/3473006.
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The unprecedented rise of social media platforms, combined with location-aware technologies, has led to continuously producing a significant amount of geo-social data that flows as a user-generated data stream. This data has been exploited in several important use cases in various application domains. This article supports geo-social personalized queries in streaming data environments. We define temporal geo-social queries that provide users with real-time personalized answers based on their social graph. The new queries allow incorporating keyword search to get personalized results that are relevant to certain topics. To efficiently support these queries, we propose an indexing framework that provides lightweight and effective real-time indexing to digest geo-social data in real time. The framework distinguishes highly dynamic data from relatively stable data and uses appropriate data structures and a storage tier for each. Based on this framework, we propose a novel geo-social index and adopt two baseline indexes to support the addressed queries. The query processor then employs different types of pruning to efficiently access the index content and provide a real-time query response. The extensive experimental evaluation based on real datasets has shown the superiority of our proposed techniques to index real-time data and provide low-latency queries compared to existing competitors.