1,720,961 research outputs found

    Development of supervisorship system with tracking progress and the use of artificial intelligence

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    The Supervisorship service has been developed with the feature to match students to supervisors based on psychological perceptions through multidimensional analysis of matching algorithms and the feature to track students’ progress. This study explores the utilization of four distinct algorithms for the purpose of student-supervisor matching. A comprehensive evaluation of these algorithms is conducted, encompassing a variety of metrics including preference satisfaction, workload balance, time and space complexities, minimum and maximum workload, and compatibility scores which this work introduced

    Algorithm Comparison for Student-Supervisor Matching in Supervisorship System Development: K-Means vs. One-to-Many Gale-Shapley

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    This paper presents a comparative analysis of the K-Means and Gale-Shapley algorithms for matching students to supervisors. The two algorithms are evaluated based on their performance in terms of preference satisfaction, balance of workload, time complexity, space complexity, and maximum and minimum workloads. The experimental results show that the Gale-Shapley algorithm outperforms the K-Means algorithm on all criteria, as shown in Table 4. Specifically, the Gale-Shapley algorithm achieves a preference satisfaction score of 0.74 and a balance of workload score of 0.5, compared to 0.34 and 0.2 for the K-Means algorithm. Additionally, the Gale-Shapley algorithm has a time complexity of O(num students × num supervisors) and a space complexity of O(num students + num supervisors), which is comparable to the K-Means algorithm. Finally, the Gale-Shapley algorithm has a maximum workload of 6 and a minimum workload of 3, compared to 15 and 3 for the K-Means algorithm. Based on the experimental results, the paper concludes that the Gale-Shapley algorithm is the superior algorithm for matching students to supervisors. It achieves a higher level of preference satisfaction and balance of workload than the K-Means algorithm, and it is still relatively efficient to run. The paper also discusses the advantages and disadvantages of both algorithms and provides recommendations for future work

    Optimizing preference satisfaction with genetic algorithm in matching students to supervisors

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    The allocation of students to supervisors is a crucial aspect of higher education, impacting the quality of guidance and support students receive for their academic projects. This paper explores the application of a genetic algorithm to optimize the matching process. The algorithm considers considers psychological compatibility between student and supervisor, and aims for maximization of preference satisfaction of students and supervisors regarding the match. Experimental results demonstrate high preference satisfaction (0.91), indicating effective alignment with students’ preferences. The algorithm’s time and space complexities show scalability, making it a promising solution for large-scale applications. Additionally, the workload distribution results highlight the algorithm’s ability to balance the student load among supervisors

    Going Beyond Counting First Authors in Author Co-citation Analysis

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    The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed

    Variations on the Author

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    “Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship

    Appropriate Similarity Measures for Author Cocitation Analysis

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    We provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis

    Dispelling the Myths Behind First-author Citation Counts

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    We conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more sophisticated methods

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    koamabayili/VECTRON-author-checklist: VECTRON author checklist

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    We have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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