VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

Analysis Students' Critical Thinking Skills in Solving Problems in Terms of Cognitive Style

Tujuan dari penelitian ini adalah untuk menganalisis kemampuan berpikir kritis siswa dalam menyelesaikan masalah yang ditinjau dari gaya kognitif. Penelitian ini termasuk dalam Penelitian Kualitatif dengan metode deskriptif kualitatif. Subjek penelitian ini adalah siswa kelas VIII MTSN 3 Rokan Hulu. Teknik pengumpulan data dalam penelitian ini menggunakan tes GEFT (Group Embredded Figure Test) yang dikembangkan oleh Witkin dan tes kemampuan berpikir kritis berupa soal uraian. Teknik analisis data dilakukan dengan reduksi data, pemaparan data, analisis data pada kedua kelompok subjek dan penarikan kesimpulan. Analisis kemampuan berpikir kritis dalam penelitian ini menggunakan indikator dari Ennis yaitu merumuskan strategi, memberikan alasan, dan menyimpulkan. Penelitian ini menunjukkan perbedaan gaya kognitif siswa mempengaruhi kemampuan berpikir kritis siswa. Hasil penelitian menunjukkan bahwa siswa dengan gaya kognitif field independent memiliki kemampuan berpikir kritis lebih baik daripada siswa dengan gaya kognitif field dependent. Kata Kunci : Berpikir Kritis, Gaya Kogniti

Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page

An Uncommon Textbook: Review of \u3cem\u3eCommon Sense Mathematics\u3c/em\u3e by Ethan Bolker and Maura Mast

Ethan D. Bolker and Maura B. Mast. 2016. Common Sense Mathematics.(Washington DC.: Mathematics Association of America) ISBN-13: 978-1-93951-210-9. Common Sense Mathematics is an integrative quantitative reasoning (QR) textbook that is built around scores of exercises derived from authentic circumstances from public media and other public sources. The exercises elicit responses from students requiring extensive communication and analyses and distinguish the book from ones typically encountered in a mathematics or science course. Responses to exercises often require one-half page or more of writing and can occupy considerable class time in discussion. The book has material for a one- or two-semester course. Use of the Internet for information is assumed, and the use of spreadsheet technology is incorporated but can be avoided for portions of the latter chapters

The Effectiveness of the Guided Discovery Learning (GDL) Method Using a Contextual Approach Reviewed From Mathematical Critical Thinking Ability of Senior High School in Muna District

Penelitian ini bertujuan untuk menguji efektivitas metode pembelajaran penemuan terbimbing menggunakan pendekatan kontekstual dalam hal keterampilan berpikir kritis siswa SMP. Jenis penelitian yang digunakan adalah eksperimen semu. Populasi dalam penelitian ini adalah semua siswa kelas VIII Sekolah Menengah Atas di Kecamatan Kontukowuna, Kabupaten Muna, Sulawesi Tenggara pada tahun 2016/2017. Pengumpulan data diperoleh melalui penyediaan empat item pertanyaan instrumen tes keterampilan berpikir kritis, di mana setiap pertanyaan mewakili indikator kemampuan berpikir kritis dan lembar observasi pelaksanaan pembelajaran. Hasil penelitian dianalisis menggunakan uji t satu sampel. Temuan menunjukkan bahwa nilai uji t 2,719> (t_0,05,27) = 2,0518, yang dapat disimpulkan bahwa metode pembelajaran penemuan terbimbing efektif dalam hal kemampuan berpikir kritis siswa SMP. Hasil ini didukung oleh peningkatan rata-rata pretest 27,66 ke posttest 76,00 yang lebih tinggi dari kriteria penguasaan mengajar minimum (KKM) dari 70

Two-Stage Bagging Pruning for Reducing the Ensemble Size and Improving the Classification Performance

Ensemble methods, such as the traditional bagging algorithm, can usually improve the performance of a single classifier. However, they usually require large storage space as well as relatively time-consuming predictions. Many approaches were developed to reduce the ensemble size and improve the classification performance by pruning the traditional bagging algorithms. In this article, we proposed a two-stage strategy to prune the traditional bagging algorithm by combining two simple approaches: accuracy-based pruning (AP) and distance-based pruning (DP). These two methods, as well as their two combinations, “AP+DP” and “DP+AP” as the two-stage pruning strategy, were all examined. Comparing with the single pruning methods, we found that the two-stage pruning methods can furthermore reduce the ensemble size and improve the classification. “AP+DP” method generally performs better than the “DP+AP” method when using four base classifiers: decision tree, Gaussian naive Bayes, K-nearest neighbor, and logistic regression. Moreover, as compared to the traditional bagging, the two-stage method “AP+DP” improved the classification accuracy by 0.88%, 4.06%, 1.26%, and 0.96%, respectively, averaged over 28 datasets under the four base classifiers. It was also observed that “AP+DP” outperformed other three existing algorithms Brag, Nice, and TB assessed on 8 common datasets. In summary, the proposed two-stage pruning methods are simple and promising approaches, which can both reduce the ensemble size and improve the classification accuracy