Visual Imaging of Benthic Carbonate-Mixed Factories in the Ross Sea Region Marine Protected Area, Antarctica

Marine biogenic skeletal production is the prevalent source of Ca-carbonate in today’s Antarctic seas. Most information, however, derives from the post-mortem legacy of calcifying organisms. Prior imagery and evaluation of Antarctic habitats hosting calcifying benthic organisms are poorly present in the literature, therefore, a Remotely Operated Vehicle survey was carried out in the Ross Sea region Marine Protected Area during the 2013–2014 austral summer. Two video surveys of the seafloor were conducted along transects between 30 and 120 m (Adelie Cove) and 230 and 260 m (Terra Nova Bay “Canyon”), respectively. We quantified the relative abundance of calcifiers vs non-calcifiers in the macro- and mega-epibenthos. Furthermore, we considered the typology of the carbonate polymorphs represented by the skeletonized organisms. The combined evidence from the two sites reveals the widespread existence of carbonate-mixed factories in the area, with an overwhelming abundance of both low-Mg and (especially) high-Mg calcite calcifiers. Echinoids, serpulids, bryozoans, pectinid bivalves and octocorals prove to be the most abundant animal producers in terms of abundance. The shallower Adelie Cove site also showed evidence of seabed coverage by coralline algae. Our results will help in refining paleoenvironmental analyses since many of the megabenthic calcifiers occur in the Quaternary record of Antarctica. We set a baseline to monitor the future response of these polar biota in a rapidly changing ocean.

ScatT-LOOP: scattering tetrolet-LOOP descriptor and optimized NN for iris recognition at-a-distance

Iris Recognition at-a Distance (IAAD) is a major challenge for researchers due to the defects associated with the visual imaging and poor image quality in dynamic environments, which imposed bad impacts on the accuracy of recognition. Thus, in order to enable the effective IAAD, this paper proposes a new method, named, Chronological Monarch Butterfly Optimization (Chronological MBO)-enabled Neural Network (NN). The recognition of iris using NN is trained with the proposed Chronological MBO, which is developed through the combination of Chronological theory in Monarch Butterfly Optimization (MBO). The recognition becomes effective with the automatic segmentation and the normalization of iris image on the basis of Hough Transform (HT) and Daugman’s rubber sheet model followed with the process of feature extraction with the developed ScatT-LOOP descriptor, which is the integration of scattering transform (ST), Local Optimal Oriented Pattern (LOOP) descriptor, and Tetrolet transform (TT). The developed ScatT-LOOP descriptor extracts the texture as well as the orientation details of image for effective recognition. The analysis is evaluated with the CASIA Iris dataset with respect to the evaluation metrics, accuracy, False Acceptance Rate (FAR), and False Rejection Rate (FRR). The proposed method has the accuracy, FRR, and FAR of 0.97, 0.005, and 0.005, respectively. The experimental results proved that the proposed method is effective than the existing methods of iris recognition.

Struktur Puisi Karya John Dami Mukese Serta Relevansinya Dalam Pembelajaran Sastra

This study aims to describe the structure of the poem "Autumn" by John Dami Mukese which consists of physical and inner structures and their relevance to the study of literature in high school. This study uses a qualitative approach with qualitative descriptive methods. The data in this study are in the form of words that contain physical and inner structure in the poem "Autumn" by John Dami Mukese. The data source is a collection of books "Jelata Poel Jelata" by John Dami Mukese. Data collection procedures in this study are reading, identifying, modifying, and classifying in accordance with the focus of the study. Data analysis uses in-depth understanding techniques. Checking the validity of the data is done through triangulation techniques. The results of the poem builder research structure consisting of physical structures in the form of: connotative and denotative diction; visual imaging; concrete words; style of personification and repetition; rhyme; and typography. The inner structure of themes; feeling (taste); tone; and mandate. The structure of the builders of "Autumn" poetry by John Dami Mukese can be used as teaching material in the study of literature in high school specifically at KD 3.17 analyzing the elements of poetry builders. Abstrak: Penelitian ini bertujuan untuk mendeskripsikan struktur puisi “Musim Gugur” karya John Dami Mukese yang terdiri atas struktur fisik dan struktur batin dan relevansinya dengan pembelajaran sastra di SMA. Penelitian ini menggunakan pendekatan kualitatif dengan metode deskriptif kualitatif. Data dalam penelitian ini berupa kata-kata yang mengandung struktur fisik dan batin dalam puisi “Musim Gugur” karya John Dami Mukese. Sumber data adalah buku kumpulan “Puisi-Puisi Jelata” karya John Dami Mukese. Prosedur pengumpulan data dalam penelitian ini adalah membaca, mengidentifikasi, mengodifikasi, dan mengklasifikasikan sesuai dengan fokus penelitian. Analisis data menggunakan teknik pemahaman arti secara mendalam. Pengecekan keabsahan data dilakukan melalui teknik triangulasi. Hasil penelitian struktur pembangun puisi yang terdiri atas struktur fisik berupa: diksi konotatif dan denotatif; pengimajian visual; kata konkret; gaya bahasa personifikasi dan repetisi; rima; dan tipografi. Struktur batin berupa tema; feeling (rasa); nada; dan amanat. Struktur pembangun puisi “Musim Gugur” karya John Dami Mukese dapat digunakan sebagai bahan ajar dalam pembelajaran sastra di SMA secara khusus pada KD 3.17 menganalisis unsur pembangun puisi.

Review and Evaluation of Systems Supporting Data Journalism

In recent years, a new form of journalism has developed a strong dynamic: journalism based on data. The success of the move is so great that technologies and applications that support it have begun to emerge. Although technologies are in a period of mature productivity, their evaluation is an area lagging in development. It is a fact that lack of evaluation based on a reliable methodology is evident in the literature. Finding a suitable methodology for this purpose is particularly important in order to (a) evaluate systems supporting data journalism resulting in a specific ranking of potential, and (b) make it easier for the user to choose the application that best suits his / her requirements and cognitive level. A comparative evaluation of 9 applications related to the visual imaging component was attempted with usability and functionality criteria. The aim of this study is to provide a quick evaluation method, open to proposed improvements and further refinement, in order to establish a framework for qualitative assessment of data journalism applications.

A simple model to detect atrial fibrillation via visual imaging

Automatic detection of atrial fibrillation (AF) is a challenging issue. In this study we proposed and validated a model to identify AF by using facial video recordings. We analyzed photoplethysmographic imaging (PPGi) signals, extracted from video of a subject’s face. Sixty-eight patients were included: 30 in sinus rhythm (SR), 25 in AF and 13 presenting with atrial flutter or frequent ectopic beats (ARR). Twenty-six indexes were computed. The dataset was divided in three subsets: the training, validation, and test set, containing, respectively, 58, 29, and 13% of the data. Mean of inter-systolic interval series (M), Local Maxima Similarity (LMS), and pulse harmonic strength (PHS) indexes were significantly different among all groups. Variability and irregularity parameters had the lowest values in SR, the highest in AF, with intermediate values in ARR. The PHS was higher in SR than in ARR, and higher in ARR than in AF. The LMS index was the highest in SR, intermediate in ARR and the lowest in AF. Similarity indexes were higher in SR than in AF and ARR. A model with three features, namely M, Similarity1 and LMS was chosen. With this model, the accuracy for the validation set was 0.947±0.007 for SR, 0.954±0.004 for AF and 0.919±0.006 for ARR; for the test set (never-seen data), accuracy was 0.876±0.021 for SR, 0.870±0.030 for AF and 0.863±0.029 for ARR. A contactless video-based monitoring can be used to detect AF, differentiating it from SR and from frequent ectopies.