Pengaruh Harga, Iklan, dan Citra Merek (Brand Image) Terhadap Keputusan Pembelian Smartphone Android Samsung Galaxy Series (Studi Kasus Pada Mahasiswa Fakutas Ekonomi dan Bisnis UMS)

The development of time and increasing needs of communication causing smartphone business industry more challenging. Samsung is one of companies engaged in electronics production sector one of it’s production is smartphone. The number of smartphone industry competition causing increasingly diverse consumer choice. And the factor affecting of consumer opinion to choose smartphone are price factor, advertisement, brand image. The formulation problem in this research is how price, advertisement, and brand image has influence by simultaneous through partial toward purchasing decision of smartphone Samsung Galaxy series in Faculty of Economic and Business Muhammadiyah Surakarta University students. The hypothesis of this research is thought to exist in partial positive influence pricing, advertising, brand image on purchasing decisions. The population of this research is Faculty of Economic and Business Muhammadiyah Surakarta University students by taking 100 respondent as sample. This research using classical assumption test, multiple regression analyst, t test method, f test method, and R2 test method. The result of multiple regression is independent variable has most influence to dependent variable is advertisement (0,345). Result of t-test proved that all of independent variable (price, advertisement, and brand image) has positive influence to dependent variable it is decision purchasing. And the coefficient of determination (adjusted R2) is 37,3% which mean could be interpret that price variable, advertisement, and brand image able to explain to the variation of the variable changes in purchasing decisions by 37,3% while the rest is 62,7% explained by other variable that are not used in this research. That means all hypothesis proven true, the variable pricing, advertisement, and brand image individual positive influence on purchasing decisions. Keyword: price, advertisement, brand image, purchasing decisio

MIDV-2020: a comprehensive benchmark dataset for identity document analysis

Identity documents recognition is an important sub-field of document analysis, which deals with tasks of robust document detection, type identification, text fields recognition, as well as identity fraud prevention and document authenticity validation given photos, scans, or video frames of an identity document capture. Significant amount of research has been published on this topic in recent years, however a chief difficulty for such research is scarcity of datasets, due to the subject matter being protected by security requirements. A few datasets of identity documents which are available lack diversity of document types, capturing conditions, or variability of document field values. In this paper, we present a dataset MIDV-2020 which consists of 1000 video clips, 2000 scanned images, and 1000 photos of 1000 unique mock identity documents, each with unique text field values and unique artificially generated faces, with rich annotation. The dataset contains 72409 annotated images in total, making it the largest publicly available identity document dataset to the date of publication. We describe the structure of the dataset, its content and annotations, and present baseline experimental results to serve as a basis for future research. For the task of document location and identification content-independent, feature-based, and semantic segmentation-based methods were evaluated. For the task of document text field recognition, the Tesseract system was evaluated on field and character levels with grouping by field alphabets and document types. For the task of face detection, the performance of Multi Task Cascaded Convolutional Neural Networks-based method was evaluated separately for different types of image input modes. The baseline evaluations show that the existing methods of identity document analysis have a lot of room for improvement given modern challenges. We believe that the proposed dataset will prove invaluable for advancement of the field of document analysis and recognition.This work is partially supported by Russian Foundation for Basic Research (projects 19-29-09066 and 19-29-09092). All source images for MIDV-2020 dataset were obtained from Wikimedia Commons. Author attributions for each source images are listed in the original MIDV-500 description table (ftp://smartengines.com/midv-500/documents.pdf). Face images by Generated Photos (https://generated.photos)

SSKSRIF: Scale and Rotation Invariant Features Based on Spatial Space of Keypoints for Camera-Based Information Spotting

International audienc

A randomized hierarchical trees indexing approach for camera-based information spotting

International audienc

A Comparison of Explicit and Implicit Graph Embedding Methods for Pattern Recognition

International audienceIn recent years graph embedding has emerged as a promising solution for enabling the expressive, convenient, powerful but computa tional expensive graph based representations to benefit from mature, less expensive and efficient state of the art machine learning models of statistical pattern recognition. In this paper we present a comparison of two implicit and three explicit state of the art graph embedding methodologies. Our preliminary experimentation on different chemoinformatics datasets illustrates that the two implicit and three explicit graph embedding approaches obtain competitive performance for the problem of graph classification

Report on the Symbol Recognition and Spotting Contest

LORIA / INPL- cole des Mines de Nanc