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Journal articles on the topic 'Author Topic Model'

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Author: Grafiati
Published: 14 March 2023

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1

Han, Peng, Ming Mei Chen, and Ying Nan Zhang. "A Hierarchical Bayesian Model for Text Corpora." Applied Mechanics and Materials 687-691 (November 2014): 1237–40. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1237.

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We propose a new generative probabilistic Dirich- let Author-Topic (DAT) Model for extracting information about authors and topics from large text collections. DAT is a three-level hierarchical Bayesian model. The model builds on the Author Topic (AT) model, adding the key attribute that distribution over author is conditioned on a Dirichlet prior. The probability distribution over topics in a multi-author document is a mixture of the distributions associated with the authors. The three level distributions including document-author, author-topic and topic-word are learned from data in an unsupervised manner using a Gibbs sampling algorithm. We give results on a large corpus which contains 1740 papers from the Neural Information Processing Systems Conference (NIPS). Experiments based on perplexity scores for test documents are used to illustrate systematic differences between the proposed model and a number of alternatives.
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Jin, Jian, Qian Geng, Haikun Mou, and Chong Chen. "Author–Subject–Topic model for reviewer recommendation." Journal of Information Science 45, no. 4 (October 19, 2018): 554–70. http://dx.doi.org/10.1177/0165551518806116.

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Interdisciplinary studies are becoming increasingly popular, and research domains of many experts are becoming diverse. This phenomenon brings difficulty in recommending experts to review interdisciplinary submissions. In this study, an Author–Subject–Topic (AST) model is proposed with two versions. In the model, reviewers’ subject information is embedded to analyse topic distributions of submissions and reviewers’ publications. The major difference between the AST and Author–Topic models lies in the introduction of a ‘Subject’ layer, which supervises the generation of hierarchical topics and allows sharing of subjects among authors. To evaluate the performance of the AST model, papers in Information System and Management (a typical interdisciplinary domain) in a famous Chinese academic library are investigated. Comparative experiments are conducted, which show the effectiveness of the AST model in topic distribution analysis and reviewer recommendation for interdisciplinary studies.
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Hoi, Huynh Tan. "FINDING OUT THE INFLUENCE OF TOPIC BASED OBJECT ACCORDING TO SOCIAL NETWORKS." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 15, 2021): 948–53. http://dx.doi.org/10.17762/itii.v9i1.222.

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In this research paper, the author mainly focuses on researching models in social networks analysis and then use them to develop the system which is able to allow users to search right topics as well as right research papers of other authors. In this paper, the author do research on Author – Conference –Topic model for the topic relevant to users’ requirements. With each of these topics, the system will create a graph of Topical Affinity Propagation (TAP) with the aim to analyze the topics based influence of authors. The results on the corpus extracted from Microsoft Academic Research include 34,330 authors, 19,921 articles, 1,335 conference and 424,501 co-author relations.
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Phan, Truong Ho Viet, Thanh Trung Ho, and Phuc Do. "Topic based object Influence analysis in Social networks." Science and Technology Development Journal 16, no. 4 (December 31, 2013): 68–78. http://dx.doi.org/10.32508/stdj.v16i4.1586.

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In this article, we focus on researching models in social networks analysis and use them to develop the system which enables users to exactly find topics as well as necessary research of authors. In this research, we study Author – Conference – Topic (ACT) model to find out the topic relevant to users’ requirements. With each of these topics, the system will create a graph of Topical Affinity Propagation (TAP) in purpose of analyzing the topic based influence of authors. Experimental results on the corpus extracted from Microsoft Academic Research include 34,330 authors, 19,921 articles, 1,335 conference and 424,501 co-author relations.
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Seroussi, Yanir, Ingrid Zukerman, and Fabian Bohnert. "Authorship Attribution with Topic Models." Computational Linguistics 40, no. 2 (June 2014): 269–310. http://dx.doi.org/10.1162/coli_a_00173.

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Authorship attribution deals with identifying the authors of anonymous texts. Traditionally, research in this field has focused on formal texts, such as essays and novels, but recently more attention has been given to texts generated by on-line users, such as e-mails and blogs. Authorship attribution of such on-line texts is a more challenging task than traditional authorship attribution, because such texts tend to be short, and the number of candidate authors is often larger than in traditional settings. We address this challenge by using topic models to obtain author representations. In addition to exploring novel ways of applying two popular topic models to this task, we test our new model that projects authors and documents to two disjoint topic spaces. Utilizing our model in authorship attribution yields state-of-the-art performance on several data sets, containing either formal texts written by a few authors or informal texts generated by tens to thousands of on-line users. We also present experimental results that demonstrate the applicability of topical author representations to two other problems: inferring the sentiment polarity of texts, and predicting the ratings that users would give to items such as movies.
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Rifqi Nafis, Rizky Parlika, and Sugiarto. "MENGELOMPOKKAN TOPIK TWEET AKUN POLITISI DENGAN PEMODELAN TOPIK METODE AUTHOR-TOPIC MODELS." Jurnal Informatika dan Sistem Informasi 2, no. 1 (March 23, 2021): 99–105. http://dx.doi.org/10.33005/jifosi.v2i1.268.

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Twitter merupakan sebuah platform untuk berhubungan satu sama lain agar tetap berkomunikasi dan tetap terhubung dengan mengirim pesan cepat dan daring. Pada Twitter juga dapat membuat tweet yang berisi foto, video, tautan, atau teks. Pada aplikasi Twitter pengguna cenderung lebih mengutarakan isi hati yang sebenarnya dibandingkan dengan platform media sosial lainnya. Pada tahun 2020 ini telah terjadi wabah atau pandemi Covid-19 dan pada akhir tahun bulan Desember akan diselenggarakan pilkada serentak seluruh Indonesia. Dari hal tersebut dibutuhkannya sebuah platform yang mampu memberikan informasi visual terhadap aktivitas politisi dalam hal mengutarakan isi hatinya di media sosial Twitter dengan melakukan topic modelling terhadap aktivitas politisi tersebut ketika membuat sebuah tulisan atau tweet menggunakan metode Author-Topic Models. Penelitian ini dikhususkan untuk menganalisa data tweet akun Twitter politisi di Indonesia, proses pengambilan data dilakukan dengan cara crawling data pada user timeline aplikasi Twitter. Setelah data didapatkan kemudian akan dianalisa dengan menggunakan Author-Topic Models dan dilakukan visualisasi terhadap topik pembahasan berdasarkan tweet yang telah dibuat oleh masing-masing akun Twitter politisi. Melalui proses topic modelling, telah didapatkan hasil terbaik berupa 3 topik. Model 3 topik tersebut dikatakan terbaik karena memiliki nilai perplexity terendah diantara jumlah topik lain yang diuji. 3 topik yang terbentuk dianalisis dan diterjemahkan ke dalam label kategori, yaitu “Rakyat Tolak RUU Cipta Kerja”, “Ucapan Syukur saat Pandemi”, “Kondisi Ekonomi saat Pandemi” dengan jumlah data tweet yang digunakan adalah 24846 data tweet dari 36 akun Twitter politisi.
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Liu, Sanya, Cheng Ni, Zhi Liu, Xian Peng, and Hercy N. H. Cheng. "Mining Individual Learning Topics in Course Reviews Based on Author Topic Model." International Journal of Distance Education Technologies 15, no. 3 (July 2017): 1–14. http://dx.doi.org/10.4018/ijdet.2017070101.

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Nowadays, Massive Open Online Courses (MOOC) has obtained a rapid development and drawn much attention from the areas of learning analytics and artificial intelligence. There are lots of unstructured data being generated in online reviews area. The learning behavioral data become more and more diverse, and they prompt the emergence of big data in education. To mine useful information from these data, we need to use educational data mining and learning analysis technique to study the learning feelings and discussed topics among learners. This paper aims to mine and analyze topic information hidden in the unstructured reviews data in MOOC, a novel author topic model based on an unsupervised learning idea is proposed to extract learning topics for the each learner. According to the experimental results, we will analyze and focuses of interests of learners, which facilitates further personalized course recommendation and improve the quality of online courses.
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Kim, Jooyeon, Dongwoo Kim, and Alice Oh. "Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora." Transactions of the Association for Computational Linguistics 5 (December 2017): 191–204. http://dx.doi.org/10.1162/tacl_a_00055.

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Much of scientific progress stems from previously published findings, but searching through the vast sea of scientific publications is difficult. We often rely on metrics of scholarly authority to find the prominent authors but these authority indices do not differentiate authority based on research topics. We present Latent Topical-Authority Indexing (LTAI) for jointly modeling the topics, citations, and topical authority in a corpus of academic papers. Compared to previous models, LTAI differs in two main aspects. First, it explicitly models the generative process of the citations, rather than treating the citations as given. Second, it models each author’s influence on citations of a paper based on the topics of the cited papers, as well as the citing papers. We fit LTAI into four academic corpora: CORA, Arxiv Physics, PNAS, and Citeseer. We compare the performance of LTAI against various baselines, starting with the latent Dirichlet allocation, to the more advanced models including author-link topic model and dynamic author citation topic model. The results show that LTAI achieves improved accuracy over other similar models when predicting words, citations and authors of publications.
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Rakhmawati, Nur Aini, N. F. Faiz, Irmasari Hafidz, Indra Raditya, Pande Dinatha, and Andrianto Suwignyo. "Clustering student Instagram accounts using author-topic model." International Journal of Business Intelligence and Data Mining 19, no. 1 (2021): 70. http://dx.doi.org/10.1504/ijbidm.2021.115954.

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McCallum, A., X. Wang, and A. Corrada-Emmanuel. "Topic and Role Discovery in Social Networks with Experiments on Enron and Academic Email." Journal of Artificial Intelligence Research 30 (October 13, 2007): 249–72. http://dx.doi.org/10.1613/jair.2229.

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Previous work in social network analysis (SNA) has modeled the existence of links from one entity to another, but not the attributes such as language content or topics on those links. We present the Author-Recipient-Topic (ART) model for social network analysis, which learns topic distributions based on the direction-sensitive messages sent between entities. The model builds on Latent Dirichlet Allocation (LDA) and the Author-Topic (AT) model, adding the key attribute that distribution over topics is conditioned distinctly on both the sender and recipient---steering the discovery of topics according to the relationships between people. We give results on both the Enron email corpus and a researcher's email archive, providing evidence not only that clearly relevant topics are discovered, but that the ART model better predicts people's roles and gives lower perplexity on previously unseen messages. We also present the Role-Author-Recipient-Topic (RART) model, an extension to ART that explicitly represents people's roles.
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Paul, Michael J., and Mark Dredze. "Sprite: Generalizing Topic Models with Structured Priors." Transactions of the Association for Computational Linguistics 3 (December 2015): 43–57. http://dx.doi.org/10.1162/tacl_a_00121.

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We introduce Sprite, a family of topic models that incorporates structure into model priors as a function of underlying components. The structured priors can be constrained to model topic hierarchies, factorizations, correlations, and supervision, allowing Sprite to be tailored to particular settings. We demonstrate this flexibility by constructing a Sprite-based model to jointly infer topic hierarchies and author perspective, which we apply to corpora of political debates and online reviews. We show that the model learns intuitive topics, outperforming several other topic models at predictive tasks.
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Chen, Mo. "Model of Network Topic Detection Based on Web Usage Behaviour Mode Analysis and Mining Technology." International Journal of Computers Communications & Control 12, no. 2 (March 1, 2017): 183. http://dx.doi.org/10.15837/ijccc.2017.2.2599.

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This research has caught researchers’ wide attention for detecting network topic exactly with the arrival of big data era characterized by semi-structured or unstructured text. This paper proposes a model of network topic detection based on web usage behaviour mode analysis and mining technology taking Web news as object of research. The author elaborates main function and method proposed in this model, which include the analysis module of Web news instance clicking mode, the analysis module of Web news instance retrieval mode, the analysis module of Web news instance seed and the analysis module of similar Web news instance supporting topics. Based on these functions and methods, the author elaborates main algorithm proposed in this model, which include the mining algorithm of Web news seed instances and the mining algorithm of similar Web news instances supporting topics. These functional algorithms have been applied in processing module of model, and focus on how to detect network topic efficiently from a large number of web usage behaviour towards to Web news instances, in order to explore a research method for network topic detection. The process of experimental analysis includes three steps, firstly, the author analyses the precision of topic detection under different method, secondly, the author completes the impact analysis of Web news topic detection quality from the number of Web news instances concerned and seed threshold, finally, the author completes the quality impact analysis of Web news instances mined supporting topic from the number of Web news instances concerned and probability threshold. The results of experimental analysis show the feasibility, validity and superiority of model design and play an important role in constructing topic-focused Web news corpus so as to provide a real-time data source for topic evolution tracking.
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Li, Chunshan, William K. Cheung, Yunming Ye, Xiaofeng Zhang, Dianhui Chu, and Xin Li. "The Author-Topic-Community model for author interest profiling and community discovery." Knowledge and Information Systems 44, no. 2 (July 20, 2014): 359–83. http://dx.doi.org/10.1007/s10115-014-0764-9.

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Ho, Thanh, and Phuc Do. "Social Network Analysis Based on Topic Model with Temporal Factor." International Journal of Knowledge and Systems Science 9, no. 1 (January 2018): 82–97. http://dx.doi.org/10.4018/ijkss.2018010105.

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On social networks, each message has many features where the interested topics and the actors sending and receiving topics are important features. Unlike the traditional approach, which views each message belonging to a topic, the topic model is based on the approach, which indicates that each message has a mixture of many topics. However, topic model has limitations about discovering interested topics of actors with temporal factor and labelling latent topics. The article proposes a temporal-author-recipient-topic (TART) model based on: (i) discovering interested topics and analyzing the role of actors on social networks with the temporal factor; (ii) labelling the latent topics from topic model based on topic taxonomy; (iii) applying the temporal factor for finding the relation among factors in model; and (iv) finding out the variation of interested topics of actors with each period of time. An experimenting TART model on two corpora with 1,004,396 messages in Vietnamese and 25,009 actors by the software is built for SNA.
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Hafidz, Irmasari, Indra Raditya, Pande Dinatha, Andrianto Suwignyo, Nur Rakhmawati, and Faiz NF. "Clustering Student Instagram accounts using Author-Topic Model Based." International Journal of Business Intelligence and Data Mining 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijbidm.2020.10020280.

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Wen, Zhi, Pratheeksha Nair, Chih-Ying Deng, Xing Han Lu, Edward Moseley, Naomi George, Charlotta Lindvall, and Yue Li. "Mining heterogeneous clinical notes by multi-modal latent topic model." PLOS ONE 16, no. 4 (April 8, 2021): e0249622. http://dx.doi.org/10.1371/journal.pone.0249622.

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Latent knowledge can be extracted from the electronic notes that are recorded during patient encounters with the health system. Using these clinical notes to decipher a patient’s underlying comorbidites, symptom burdens, and treatment courses is an ongoing challenge. Latent topic model as an efficient Bayesian method can be used to model each patient’s clinical notes as “documents” and the words in the notes as “tokens”. However, standard latent topic models assume that all of the notes follow the same topic distribution, regardless of the type of note or the domain expertise of the author (such as doctors or nurses). We propose a novel application of latent topic modeling, using multi-note topic model (MNTM) to jointly infer distinct topic distributions of notes of different types. We applied our model to clinical notes from the MIMIC-III dataset to infer distinct topic distributions over the physician and nursing note types. Based on manual assessments made by clinicians, we observed a significant improvement in topic interpretability using MNTM modeling over the baseline single-note topic models that ignore the note types. Moreover, our MNTM model led to a significantly higher prediction accuracy for prolonged mechanical ventilation and mortality using only the first 48 hours of patient data. By correlating the patients’ topic mixture with hospital mortality and prolonged mechanical ventilation, we identified several diagnostic topics that are associated with poor outcomes. Because of its elegant and intuitive formation, we envision a broad application of our approach in mining multi-modality text-based healthcare information that goes beyond clinical notes. Code available at https://github.com/li-lab-mcgill/heterogeneous_ehr.
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Luo, Wang, Hongliang Li, Guanghui Liu, and Liaoyuan Zeng. "Semantic Annotation of Satellite Images Using Author–Genre–Topic Model." IEEE Transactions on Geoscience and Remote Sensing 52, no. 2 (February 2014): 1356–68. http://dx.doi.org/10.1109/tgrs.2013.2250978.

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Wang Luo, Hongliang Li, and Guanghui Liu. "Automatic Annotation of Multispectral Satellite Images Using Author–Topic Model." IEEE Geoscience and Remote Sensing Letters 9, no. 4 (July 2012): 634–38. http://dx.doi.org/10.1109/lgrs.2011.2177064.

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Trung Thanh, Ho, Nguyen Quang Hung, and Tran Duy Thanh. "Applying topic model combined with Kohonen networks to discover and visualize communities on social networks." Science & Technology Development Journal - Economics - Law and Management 3, no. 3 (January 8, 2020): 311–26. http://dx.doi.org/10.32508/stdjelm.v3i3.572.

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Users are members of communities on social networks. Users’ interested topics keep changing, resulting in the change of their communities’ interested topics as well. Level, period of time, and interested topics represent features of a community which (i) change upon preferences of each user on social networks for making friends or being interested in topics (based on message content); (ii) are formed or change from online groups of friends or the suggestions to make friends. Hence, the link of users in communities can be viewed as a network of users by their features in social network communities. In this paper, the author studies and proposes a new model for discovering communities using Temporal-Author-Recipient-Topic (TART) model combined with Kohonen neural networks to discover communities of users with the same interested topics over different periods of time. The research goal is achieved through testing models on two Vietnamese datasets (collected from social networks at universities and online newspapers).
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Alvarez-Melis, David, and Martin Saveski. "Topic Modeling in Twitter: Aggregating Tweets by Conversations." Proceedings of the International AAAI Conference on Web and Social Media 10, no. 1 (August 4, 2021): 519–22. http://dx.doi.org/10.1609/icwsm.v10i1.14817.

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We propose a new pooling technique for topic modeling in Twitter, which groups together tweets occurring in the same user-to-user conversation. Under this scheme, tweets and their replies are aggregated into a single document and the users who posted them are considered co-authors. To compare this new scheme against existing ones, we train topic models using Latent Dirichlet Allocation (LDA) and the Author-Topic Model (ATM) on datasets consisting of tweets pooled according to the different methods. Using the underlying categories of the tweets in this dataset as a noisy ground truth, we show that this new technique outperforms other pooling methods in terms of clustering quality and document retrieval.
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ZHOU, Xiuze, and Shunxiang WU. "The Biterm Author Topic in the Sentences Model for E-Mail Analysis." IEICE Transactions on Information and Systems E100.D, no. 8 (2017): 1852–59. http://dx.doi.org/10.1587/transinf.2016edp7382.

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Wen, Qi, Maoshan Qiang, Bingqing Xia, and Nan An. "Discovering regulatory concerns on bridge management: An author-topic model based approach." Transport Policy 75 (March 2019): 161–70. http://dx.doi.org/10.1016/j.tranpol.2017.04.012.

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Cohen, Eli. "Created Realities: A Model." Informing Science: The International Journal of an Emerging Transdiscipline 24 (2021): 031–49. http://dx.doi.org/10.28945/4800.

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The purpose of this paper is to provide a model to help explain why ideas about reality differ. Misinformation is an important topic that in the past several years has gained prominence. The author developed a model of informing.
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Zhang, Chengzhi, Hua Zhao, Xuehua Chi, and Shuitian Ma. "Information Organization Patterns from Online Users in a Social Network." KNOWLEDGE ORGANIZATION 46, no. 2 (2019): 90–103. http://dx.doi.org/10.5771/0943-7444-2019-2-90.

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Recent years have seen the rise of user-generated contents (UGCs) in online social media. Diverse UGC sources and information overload are making it increasingly difficult to satisfy personalized information needs. To organize UGCs in a user-centered way, we should not only map them based on textual topics but also link them with users and even user communities. We propose a multi-dimensional framework to organize information by connecting UGCs, users, and user communities. First, we use a topic model to generate a topic hierarchy from UGCs. Second, an author-topic model is applied to learn user interests. Third, user communities are detected through a label propagation algorithm. Finally, a multi-dimensional information organization pattern is formulated based on similarities among the topic hierarchies of UGCs, user interests, and user communities. The results reveal that: 1) our proposed framework can organize information from multiple sources in a user-centered way; 2) hierarchical topic structures can provide comprehensive and in-depth topics for users; and, 3) user communities are efficient in helping people to connect with others who have similar interests.
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Irwanto, Rachmad. "WHAT ACTUALLY DRIVE CONSTRUCTION PROJECT PERFORMANCE FROM OPERATIONS MANAGEMENT PERSPECTIVE." International Journal of Civil Engineering and Infrastructure 2, no. 1 (August 18, 2022): 32. http://dx.doi.org/10.24853/ijcei.2.1.32-40.

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Construction industry is always an interesting discussion topic. Some have it approached from its planning and scheduling view accommodating risk analysis. Other interesting topics related to construction industry are its business model, organization and operations. The author argues that construction is a function of them all. An emphasized study on a specific topic above would be less relevant given the fact that construction industry is highly fragmented. From literature review, factors such project integration, communications, knowledge and understanding, human resources, safety, financial, and cost management drive more significant impact on project scheduling performance
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Старкова, Е., E. Starkova, Светлана Веретехина, and Svetlana Veretekhina. "Social and Economic Model of Labor Effectiveness." Management of the Personnel and Intellectual Resources in Russia 6, no. 4 (September 27, 2017): 47–51. http://dx.doi.org/10.12737/article_59a53cc5373157.02045513.

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People and their abilities become main resources in the terms of modern post-industrial world. Due to this fact the topic of labor efficiency becomes the main direction of successful economy development. In this article the author describes the main aspects and elements of labor efficiency, explores the changes of labor economy efficiency in terms of computer science development, describes what professions will be cancelled and what competency will be useful at labor market in few years. Based on this data the author forms a model of labor efficiency increase.
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QIN Yilin, and SHI Guodong. "Research on The Automatic Annotation of Remote Sensing Images Based on Author-Topic Model." International Journal of Digital Content Technology and its Applications 6, no. 22 (December 31, 2012): 277–83. http://dx.doi.org/10.4156/jdcta.vol6.issue22.31.

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Zhang, Jinsong, and Xiaozhong Liu. "Citation Oriented AuthorRank for Scientific Publication Ranking." Applied Sciences 12, no. 9 (April 25, 2022): 4345. http://dx.doi.org/10.3390/app12094345.

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It is now generally accepted that an article written by influential authors often deserves a higher ranking in information retrieval. However, it is a challenging task to determine an author’s relative influence since information about the author is, much of the time, inaccessible. Actually, in scientific publications, the author is an important metadata item, which has been widely used in previous studies. In this paper, we bring an optimized AuthorRank, which is a topic-sensitive algorithm calculated by citation context, into citation analysis for testing whether and how topical AuthorRank can replace or enhance classical PageRank for publication ranking. For this purpose, we first propose a PageRank with Priors (PRP) algorithm to rank publications and authors. PRP is an optimized PageRank algorithm supervised by the Labeled Latent Dirichlet Allocation (Labeled-LDA) topic model with full-text information extraction. We then compared four methods of generating an AuthorRank score, looking, respectively, at the first author, the last author, the most famous author, and the “average” author (of a publication). Additionally, two combination methods (Linear and Cobb–Douglas) of AuthorRank and PRP were compared with several baselines. Finally, as shown in our evaluation results, the performance of AuthorRank combined with PRP is better (p < 0.001) than other baselines for publication ranking.
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Zhang, Jinsong, and Xiaozhong Liu. "Citation Oriented AuthorRank for Scientific Publication Ranking." Applied Sciences 12, no. 9 (April 25, 2022): 4345. http://dx.doi.org/10.3390/app12094345.

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It is now generally accepted that an article written by influential authors often deserves a higher ranking in information retrieval. However, it is a challenging task to determine an author’s relative influence since information about the author is, much of the time, inaccessible. Actually, in scientific publications, the author is an important metadata item, which has been widely used in previous studies. In this paper, we bring an optimized AuthorRank, which is a topic-sensitive algorithm calculated by citation context, into citation analysis for testing whether and how topical AuthorRank can replace or enhance classical PageRank for publication ranking. For this purpose, we first propose a PageRank with Priors (PRP) algorithm to rank publications and authors. PRP is an optimized PageRank algorithm supervised by the Labeled Latent Dirichlet Allocation (Labeled-LDA) topic model with full-text information extraction. We then compared four methods of generating an AuthorRank score, looking, respectively, at the first author, the last author, the most famous author, and the “average” author (of a publication). Additionally, two combination methods (Linear and Cobb–Douglas) of AuthorRank and PRP were compared with several baselines. Finally, as shown in our evaluation results, the performance of AuthorRank combined with PRP is better (p < 0.001) than other baselines for publication ranking.
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McCafferty, Paul. "Developing a model for supervising social work students in groups." Journal of Practice Teaching and Learning 6, no. 2 (December 20, 2012): 24–42. http://dx.doi.org/10.1921/jpts.v6i2.326.

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Much is written about group supervision in other health care fields; however less attention is paid to this topic in social work. This lack of scholarly attention became obvious to the author when he attempted to begin supervising students in groups and was unable to reference a suitable model or template of group supervision from which to gain ideas or direction. The author therefore decided to develop his own model of group supervision. The following article gives an account of how the author developed this model by critically appraising the relevant policy and theory from a local and national perspective to inform his practice.
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Wei, Penghui, Wenji Mao, and Guandan Chen. "A Topic-Aware Reinforced Model for Weakly Supervised Stance Detection." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7249–56. http://dx.doi.org/10.1609/aaai.v33i01.33017249.

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Analyzing public attitudes plays an important role in opinion mining systems. Stance detection aims to determine from a text whether its author is in favor of, against, or neutral towards a given target. One challenge of this task is that a text may not explicitly express an attitude towards the target, but existing approaches utilize target content alone to build models. Moreover, although weakly supervised approaches have been proposed to ease the burden of manually annotating largescale training data, such approaches are confronted with noisy labeling problem. To address the above two issues, in this paper, we propose a Topic-Aware Reinforced Model (TARM) for weakly supervised stance detection. Our model consists of two complementary components: (1) a detection network that incorporates target-related topic information into representation learning for identifying stance effectively; (2) a policy network that learns to eliminate noisy instances from auto-labeled data based on off-policy reinforcement learning. Two networks are alternately optimized to improve each other’s performances. Experimental results demonstrate that our proposed model TARM outperforms the state-of-the-art approaches.
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Toubasi, Salem, Ahmad Alzubaidi, and Mushir Abahra. "The Problems of Arab Solidarity and the Impact of Arab Spring on It: Study on the Model of the GCC Security." Journal of Politics and Law 13, no. 3 (August 20, 2020): 160. http://dx.doi.org/10.5539/jpl.v13n3p160.

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The author presents a brief study of the Arab solidarity. Furthermore, From the point of view of the author the Arab cooperation is one of the most controversial topics in the Arab world, whereas this idea extends to the first history of the Arab countries, we can also mention the creation of the Gulf Cooperation Council, and then the Maghreb Union of Arab Countries. In this article, the topic of Arab solidarity will be presented, through the views of many thinkers, and based on many analytical researches of Arab public opinion, the view of Arab solidarity and analysis of many ideas put forward for this project. There are new positive ideas possibly can be implemented to achieve the Arab solidarity, which is still a very important project for a wide sector of Arabs, this analytical study will present the case of the Gulf Cooperation Council states as example of positive implementation of Arab solidarity. This article includes the results of Arab public opinion and shed the light on the Arab spring which affected many international and local events. The author asks if Arab solidarity will a real case in the future. This study will investigate these issues and provide the readers with a modern perspective on this topic, furthermore this research approaches this subject from three different areas of discussion: the realism approach, the functionalism approach, and the idealism approach.
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Dai, Jianhua, and Jingxin Xu. "Knowledge Graph Construction for Intelligent Media Based on Mobile Internet." Wireless Communications and Mobile Computing 2022 (January 11, 2022): 1–14. http://dx.doi.org/10.1155/2022/4867220.

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Mobile Internet-based intelligent media has become a popular academic topic. This study uses the CiteSpace visualisation tool and Scientific Citation Index Expanded database to comb the existing research in the field of intelligent media from a quantitative perspective. A total of 7248 English papers were published on the topic of “intelligent media” from 2012 to 2021, and 145 highly cited papers refined were analysed. Scientific knowledge graphs were analysed from six dimensions: annual publication quantity, country of publication, institution of publication, author, keywords, and cited references. In the last 10 years, the research literature on intelligent media has been found to increase annually. Presently, the People’s Republic of China and the United States of America have a high proportion of documents in this field. Chinese universities and institutions have achieved significantly in terms of the quantity and quality of documents. From the perspective of the whole intelligent media discipline, the high-yield author group has not been formed, and there is minimal cooperation amongst authors. Popular intelligent media topics include film, social media, machine learning, swarming motility, data mining, and artificial intelligence. Subject words of the main research directions are event recognition, fake news, Cable News Network model, reconfigurable intelligent surface, comprehensive survey, microblog message, strain sensor, and traffic event. Combined with popular topics and time zone maps, the future research frontier in the field of smart media is identified.
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Mroczyński-Szmaj, Łukasz. "Nowe europejskie prawo sygnalizowania nieprawidłowości. Rozwiązania modelowe a polski projekt ustawy o ochronie osób zgłaszających naruszenia prawa." Prawo w Działaniu 52 (2022): 208–32. http://dx.doi.org/10.32041/pwd.5209.

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Due presentation the topic “The new European whistleblowing law. Model Solutions and the Polish Draft Act on the Protection of Whistleblowers” the author set himself three goals. First, he briefly describes the most important and most characteristic provisions of the new Directive on whistleblowers to give the reader at least a general idea of the model solution prepared by the EU legislator. Second, the paper gives a brief presentation of the draft law on the protection of whistleblowers that implements the Directive in the Polish legal system, taking into account the differences and divergences from the model solution noticed by the literature, the legal literature, and the author himself, so as to provide the reader with knowledge about the most important problems of implementation. Finally, the author aims to present individual conclusions and postulates de lege ferenda within the presented topic. In this last element, the author pays special attention to opportunities and threats connected with the new legislation.
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Peng, Qing Xi. "Classifying Sentiment Based on LDA Model." Applied Mechanics and Materials 427-429 (September 2013): 2614–17. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2614.

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Online reviews as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. Although supersized methods have obtained good results, a large amount of corpus should be trained beforehand. Recently, topic models have been introduced for the simultaneous analysis for sentiment in the document. However, the LDA model makes the assumption that, given the parameters the words in the document are all independent. It obviously isnt the case. The words in the document express the sentiment of the author. This paper proposes a model to solve the problem. We assume that the sentiments are related to the topic in the documents. A sentiment layer is added to the LDA model to improve it. Experimental result in the dataset demonstrates the advantage of the proposed model.
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Yang, Yang, and Feifei Wang. "Author topic model for co-occurring normal documents and short texts to explore individual user preferences." Information Sciences 570 (September 2021): 185–99. http://dx.doi.org/10.1016/j.ins.2021.04.060.

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Makarov, Ilya, Olga Gerasimova, Pavel Sulimov, and Leonid E. Zhukov. "Dual network embedding for representing research interests in the link prediction problem on co-authorship networks." PeerJ Computer Science 5 (January 21, 2019): e172. http://dx.doi.org/10.7717/peerj-cs.172.

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We present a study on co-authorship network representation based on network embedding together with additional information on topic modeling of research papers and new edge embedding operator. We use the link prediction (LP) model for constructing a recommender system for searching collaborators with similar research interests. Extracting topics for each paper, we construct keywords co-occurrence network and use its embedding for further generalizing author attributes. Standard graph feature engineering and network embedding methods were combined for constructing co-author recommender system formulated as LP problem and prediction of future graph structure. We evaluate our survey on the dataset containing temporal information on National Research University Higher School of Economics over 25 years of research articles indexed in Russian Science Citation Index and Scopus. Our model of network representation shows better performance for stated binary classification tasks on several co-authorship networks.
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Usat, Yahya. "Kepemimpinan Blusukan: Model Kepemimpinan Kristen Yang Membumi." Integritas: Jurnal Teologi 1, no. 2 (December 18, 2019): 93–100. http://dx.doi.org/10.47628/ijt.v1i2.10.

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Leadership is generally full of rhetoric, while the life of ministry requires leadership that touches the real needs of the person being led. Therefore, the authors conducted a library research to discuss this topic. From the results of the analysis and synthesis of several literary sources by the author, the leadership that touches the real needs of the people being led is grounded leadership. The real form of leadership that is down to earth is leadership that really touches the needs of followers or subordinates through the approach of character and commendable traits where there is sincerity to serve and sacrifice for the benefit of all those who are led fairly. The grounded leadership is inherent in leaders who do not see themselves as rulers or governments that rule over people who are led arbitrarily, but servant-hearted leaders where they carry out their duties on the principle of serving and not to be served.
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Barkley, Russell A. "The North American Perspective On Attention Deficit Hyperactivity Disorder." Australian Educational and Developmental Psychologist 13, no. 1 (May 1996): 2–23. http://dx.doi.org/10.1017/s0816512200027358.

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AbstractThe purpose of the present paper is to (1) provide an overview of the nature of attention-deficit hyperactivity disorder (ADHD) as it seems to be viewed by North American clinicians and clinical scientists; (2) describe its diagnostic criteria as they are applied in that region; (3) discuss the prevalence of ADHD in the region; and (4) briefly present a new theoretical model of the authors emerging from that North American perspective. Some of the critical issues related to these matters will be raised along the way. Given the thousands of scientific papers on this topic, however, an overview of these various topics is all that space here can afford. Readers wishing a more thorough treatment of these topics as well as those pertaining to history, developmental courses, associated risks, assessment, and treatments for ADHD are directed to other writings by the author (Barkley, 1990).
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40

Tseng, Yu-Ching. "A syntactic-semantic optimality theoretic model on Hakka topic-comment construction." Studies in English Language and Education 8, no. 2 (May 3, 2021): 759–78. http://dx.doi.org/10.24815/siele.v8i2.18723.

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The purpose of this paper is to show how the basic Topic-Comment ordering pattern of the Hakka can be accounted for by the constraint-based optimality theory. Part of the linguistic data used in this paper is adopted from Xu (2002), while those examples presented to show syntactic tests are created by the author. These sentences have been further checked and confirmed by a native speaker of Hakka. This paper proposes an Optimality Theoretic (OT) model that takes into account both syntactic and semantic considerations. It shows that semantic information comes into play successively at different points of OT grammar. First, integrating semantic information into the schema of OT syntax works precisely to describe the Hakka topic-initial sentence pattern. The alignment constraints incorporate information about the semantically defined topic and comment constructions into the constraint design, which interacts with other markedness constraints to filter linguistic constructions during production. Second, semantic constraints are formed to further evaluate form-meaning pairs during the process of interpretation. In this aspect, semantic notions including contrastiveness and markedness are incorporated into the theoretical plan with the purpose of pairing syntactically well-formed sentences with appropriate meaning. The paper successfully presents an optimization model illustrating how syntax and semantics cooperate to pair meanings with linguistic constructions in forming linguistic expressions.
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Trabelsi, Amine, and Osmar R. Zaïane. "PhAITV: A Phrase Author Interaction Topic Viewpoint Model for the Summarization of Reasons Expressed by Polarized Stances." Proceedings of the International AAAI Conference on Web and Social Media 13 (July 6, 2019): 482–92. http://dx.doi.org/10.1609/icwsm.v13i01.3246.

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This work tackles the problem of unsupervised modeling and extraction of the main contrastive sentential reasons conveyed by divergent viewpoints in text documents. It proposes a pipeline framework that is centered around the detection and clustering of phrases, assimilated to argument facets using a novel Phrase Author Interaction Topic-Viewpoint (PhAITV) model. The evaluation is conducted on all the components of the framework. It is mainly based on the informativeness, the relevance and the clustering accuracy of extracted reasons. The framework shows a significant improvement over several configurations and state-of-the-art methods in contrastive summarization on online debate datasets.
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42

Napolskikh, Dmitrii Leonidovich. "Spatial aspect of development of the economic clustering model of Volga Region." Теоретическая и прикладная экономика, no. 2 (February 2020): 28–37. http://dx.doi.org/10.25136/2409-8647.2020.2.32676.

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The subject of this research is the internal and external boundaries of Volga Region as a macroregion applicable to the task of development economic clustering model of its regions. The object of this research is the correlation between administrative-territorial boundaries of Volga Region and its economic zoning. The author examines the historical and other prerequisites for emergence of the set of contradictions between economic zoning, defined by the economic-geographical factors, and administrative-territorial division. Special attention is paid to the theoretical concepts and practice of economic zoning in the territory of Volga Region, presenting the results of clarification of the categorical apparatus applicable to the topic of research. The main conclusions of the conducted study consists in presence of the basis for formation of economic space of the Volga Region, as well as Volga &ndash; Pre-Urals vector of economic integration. The article also determines the instability of spatial boundaries of Volga Region as a macroregion during the XX &ndash; XXI centuries and highlights the key contradictions. The author&rsquo;s contribution into research of this topic consists in determining two main approaches towards spatial selection of interregional clusters of economic integration within Volga Region.
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Yao, Vej. "«Divided audience» – a new education model as a reform of the education process in Chinese universities." Interactive science, no. 10 (44) (October 19, 2019): 27–29. http://dx.doi.org/10.21661/r-508381.

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The article is devoted to the idea of a «divided audience» which was proposed by a Chinese scientist, Professor Chang Xuexin. The author outlines that at the moment, it is used as a new educational model in Chinese universities and it is considered reform of the educational process. The author comes to the conclusion that this model has changed the traditional Chinese pedagogical thought – the role of students has become dominant in teaching. It is practically confirmed by improvements in the quality of education in Chinese universities and is acute topic of discussion among teachers and scientists in China.
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KOLEVATYKH, Dmitry A. "URBOTROPY." Urban construction and architecture 9, no. 1 (March 15, 2019): 85–91. http://dx.doi.org/10.17673/vestnik.2019.01.14.

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The hypothesis of phenotypological causes of city formation is considered. It is proposed to look at the "city" as the most convenient model for the survival of modern man. In parallel, the author explains the reason for the emergence of this model, perceiving man himself as a representative of one of the biological species of our planet. The topic of professional ethics in the work of modern architects and city planners regarding the historical and architectural environment of the city is raised. The author introduces a number of terms to describe the observed phenomena. The study topic is supported by illustrative material. The arguments set forth in the article formulate a general thesis about the need to integrate related disciplines of a wide range into the professional fi eld of architects solving modern urban planning tasks.
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Liu, Wen-Chien. "Trade-off theory of capital structure: evidence from estimations of non-parametric and semi-parametric panel fixed effect models." Investment Management and Financial Innovations 14, no. 1 (March 31, 2017): 115–23. http://dx.doi.org/10.21511/imfi.14(1).2017.12.

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A firm’s capital structure decisions constitute an essential research topic academically and practically. In this study, the author uses the data of US listed firms to test the traditional trade-off theory of capital structure, which posits that firms should balance the benefit of tax shields and costs of financial distress to purse an optimal debt ratio. Therefore, to determine the complex relationship between firm value and debt ratio and avoid the problem of model misspecification, the author adopts the non-parametric fixed effect model and semi-parametric (partially linear) fixed effect model. Our empirical results reveal that a nonlinear and asymmetric relationship exists between firm value and market debt ratio, thus, considerably supporting trade-off theory. Moreover, the use of different definitions of key variables and various kernel functions engenders robust results. Overall, the author suggests that firm managers should employ financial leverages appropriately to maximize firm value.
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LAYTON, ROBERT, PAUL A. WATTERS, and RICHARD DAZELEY. "Authorship analysis of aliases: Does topic influence accuracy?" Natural Language Engineering 21, no. 4 (October 8, 2013): 497–518. http://dx.doi.org/10.1017/s1351324913000272.

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AbstractAliasesplay an important role in online environments by facilitating anonymity, but also can be used to hide the identity of cybercriminals. Previous studies have investigated this alias matching problem in an attempt to identify whether two aliases are shared by an author, which can assist with identifying users. Those studies create their training data by randomly splitting the documents associated with an alias into two sub-aliases. Models have been built that can regularly achieve over 90% accuracy for recovering the linkage between these ‘random sub-aliases’. In this paper, random sub-alias generation is shown to enable these high accuracies, and thus does not adequately model the real-world problem. In contrast, creating sub-aliases using topic-based splitting drastically reduces the accuracy of all authorship methods tested. We then present a methodology that can be performed on non-topic controlled datasets, to produce topic-based sub-aliases that are more difficult to match. Finally, we present an experimental comparison between many authorship methods to see which methods better match aliases under these conditions, finding that localn-gram methods perform better than others.
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Almuhanna, Abrar A., Wael M. S. Yafooz, and Abdullah Alsaeedi. "An Interactive Scholarly Collaborative Network Based on Academic Relationships and Research Collaborations." Applied Sciences 12, no. 2 (January 17, 2022): 915. http://dx.doi.org/10.3390/app12020915.

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In this era of digital transformation, when the amount of scholarly literature is rapidly growing, hundreds of papers are published online daily with regard to different fields, especially in relation to academic subjects. Therefore, it difficult to find an expert/author to collaborate with from a specific research area. This is thought to be one of the most challenging activities in academia, and few people have considered authors’ multi-factors as an enhanced method to find potential collaborators or to identify the expert among them; consequently, this research aims to propose a novel model to improve the process of recommending authors. This is based on the authors’ similarity measurements by extracting their explicit and implicit topics of interest from their academic literature. The proposed model mainly consists of three factors: author-selected keywords, the extraction of a topic’s distribution from their publications, and their publication-based statistics. Furthermore, an enhanced approach for identifying expert authors by extracting evidence of expertise has been proposed based on the topic-modeling principle. Subsequently, an interactive network has been constructed that represents the predicted authors’ collaborative relationship, including the top-k potential collaborators for each individual. Three experiments have been conducted on the collected data; they demonstrated that the most influential factor for accurately recommending a collaborator was the topic’s distribution, which had an accuracy rate of 88.4%. Future work could involve building a heterogeneous co-collaboration network that includes both the authors with their affiliations and computing their similarities. In addition, the recommendations would be improved if potential and real collaborations were combined in a single network.
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48

Parnes, Dror. "The search for an optimal RBC regulatory system." Journal of Financial Economic Policy 6, no. 1 (April 1, 2014): 78–92. http://dx.doi.org/10.1108/jfep-05-2013-0021.

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Purpose – The author assembles three hypothetical regulatory regimes and deploys computer simulations to contrast different banking systems based on conventional strategies for appointing risk-based capital minimum thresholds. The paper aims to discuss these issues. Design/methodology/approach – The author instigates cascading failure models within numerous directed graphs and measures the inflicted costs, the accumulated bank failures, and the general robustness of the networks following various economic shocks. Findings – The author finds that a homogeneous regulatory regime is an inferior approach. However, a selected too-big-to-fail scheme portrays the best defensive banking model with the lowest number of total bank failures and the fewest banks' costs and social costs. Research limitations/implications – The author can only theoretically examine this topic. Originality/value – The author overcomes some obstacles in prior studies including the use of a large and complex network and the proportional allocation of funds upon a bank failure.
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Hufiah, Atika, Afandi Afandi, and Eko Sri Wahyuni. "ANALISIS BIBLIOMETRIK DOMAIN KETERAMPILAN BERPIKIR TINGKAT TINGGI DALAM PENDIDIKAN ABAD 21 MENGGUNAKAN VOSVIEWER." JS (JURNAL SEKOLAH) 6, no. 1 (December 20, 2021): 1. http://dx.doi.org/10.24114/js.v6i1.29841.

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Abstract : Bibliometric Analysis of Higher Order Thinking Skills (HOTS) Domain in 21st Century Education Using Vosviewer.This study aims to determine the development of research on the domain of higher order thinking skills in Scopus indexed journals or proceedings from 2010-2020. Data collection is done using the Publish or Perish application with the keywords critical thinking, creative thinking, and problem solving. Microsoft Excel was used to analyze the development of the number of publications, author productivity, and top articles cited by Microsoft Excel, while to analyze the relationship between research topics, research trends, and research topics that were most frequently researched, the VOSViewer application was used. The results showed that the highest publication occurred in 2020, which was 404 publications (14.92%). The 3 most productive researchers are Dwyer, C, and Fahmi, M who produced 7 journal articles. Creative thinking is the most cited topic. Network visualization is divided into 6 clusters. The latest topics based on article data in this study include models, technology, emotional intelligence, active learning, and higher education which were most widely published in 2017-2020. The most frequently researched topic is critical thinking, followed by creative thinking, strategy, practice, and case study topics..Keyword : Bibliometric, higher order thinking skill, scopus, VOSViewer Abstrak : Analisis Bibliometrik Domain Keterampilan Berpikir Tingkat Tinggi dalam Pendidikan Abad 21 Menggunakan Vosviewer.Penelitian inibertujuan untuk mengetahui perkembanganriset-risettentang domain keterampilan berpikir tingkat tinggi padajurnalatauprosidingterindeks Scopus dari tahun 2010-2020. Pengumpulan data dilakukan menggunakan aplikasi Publish or Perish dengan kata kunci critical thinking, creative thinking, dan problem solvilng. Untuk menganalisis perkembangan jumlah publikasi, produktivitas penulis, dan top artikel yang paling banyak disitasi digunakan Microsoft Excel, sedangkan untukmenganalisishubugan antar topik riset, tren riset, dan topik riset yang paling sering diteliti digunakan aplikasi VOSViewer. Hasil penelitian menunjukkan bahwa publikasi tertinggi terjadi pada tahun 2020 yaitu sebanyak 404 publikasi (14,92%). 3 peneliti paling produktif adalah Dwyer, C dan Fahmi, M yang menghasilkan 7 artikel jurnal. Creative thinking merupakan topik yang paling banyak dikutip. Network visualization terbagi menjadi 6 klaster. Topik terbaru berdasarkan data artikel pada penelitian ini diantaranya model, technology, emotional intelligence, active learning, dan higher education paling banyak dipublikasi pada tahun 2017-2020. Topik yang paling sering diteliti adalah critical thinking, disusul dengan topik creative thinking, strategy, practice dan case study. Kata kunci : Bibliometrik, keterampilan berpikir tingkat tinggi, jurnal, scopus, VOSViewer
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Плужникова, Наталья, and Natalya Pluzhnikova. "Transformation of the model HOMO ECONOMICUS in the context of teaching the science of service in the universities." Universities for Tourism and Service Association Bulletin 10, no. 4 (December 19, 2016): 69–75. http://dx.doi.org/10.12737/23578.

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The article examines the transformation of the model «homo economicus», which underlies the modern science of service. This model is considered as a theoretical design that allows explaining the nature of human needs. The Foundation model was the concept of classical political economy, represented in the works of A. Smith and D. Ricardo. There are also the disadvantages of this model applied to the study of contemporary culture and the needs of modern man. The author focuses on the concept of personality in modern psychological science, which can be defined as a multi-potential person or a man-scanner. In this regard, the author resorts to the concept of a man-scanner in the works of famous American coach-trainer B. Sher. The author studies the prospects of us- ing this model in modern culture, and also with regard to the typology of needs according to Maslow. It’s shown the relationship of this model with the needs of modern man and his value orientations. On the basis of it there are selected methodological recommendations for teaching of science of service at the University. In particular, there are noticed problematic topics in the teaching of modern science of service, and also shown the methodi- cal part – conduct lectures with students on the topic «Practical aspects of service.» It includes the purpose and objectives of lectures, educational technology, and questions for discussion. As necessary components of learning the science of service the author identifies practical orientation, as well as the existential needs of man in different situations. In this regard, the attention is paid not so much to economic as to philosophical component of the discipline «science of service».
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