Geometric Similarity Measurement Method for Micro Scene Generalization

Geometric similarity plays an important role in geographic information retrieval, map matching, and data updating. Many approaches have been developed to calculate the similarity between simple features. However, complex group objects are common in map and spatial database systems. With a micro scene that contains different types of geographic features, calculating similarity is difficult. In addition, few studies have paid attention to the changes in a scene’s geometric similarity in the process of generalization. In this study, we developed a method for measuring the geometric similarity of micro scene generalization based on shape, direction, and position. We calculated shape similarity using the hybrid feature description, and we constructed a direction Voronoi diagram and a position graph to measure the direction similarity and position similarity. The experiments involved similarity calculation and quality evaluation to verify the usability and effectiveness of the proposed method. The experiments showed that this approach can be used to effectively measure the geometric similarity between micro scenes. Moreover, the proposed method accounts for the relationships amongst the geometrical shape, direction, and position of micro scenes during cartographic generalization. The simplification operation leads to obvious changes in position similarity, whereas delete and merge operations lead to changes in direction and position similarity. In the process of generalization, the river + islands scene changed mainly in shape and position, the similarity change in river + lakes occurred due to the direction and location, and the direction similarity of rivers + buildings and roads + buildings changed little.

Less Is More: Audience Cognition of Comic Simplification in the Characters of Peking Opera

A simplified approach was used to determine if “Less is More” is still a trend in comics’ Peking opera characters. There were 225 website volunteers who took part in the study. Via a questionnaire survey and analysis, this study explored the feasibility of “simplification” in comedy. The results indicate that the proposed “simplifying” approach can be applied to the creation of dramatic characters, but the scale of simplification must be adjusted flexibly to suit different subjects. For audiences, there is not much recognition and sympathy for the works that are simplified in the extreme and the current symbolization. The simplification used in this study is merely the first step in testing the usefulness of simplification as an approach. It is used as a means of understanding the cognition of the audience to accept the simple features of Peking opera characters. In subsequent studies, the proposed “simplified” approach is necessary to adapt and improve with a view to practical application. It also requires an in-depth analysis of the cognitive differences of the different participants according to the cognitive and communication theories of artistic creation.

Aplikasi Adventure Game Berbasis Mobile Pada Komunitas Pendaki Gunung bandung

Perkembangan teknologi saat ini semakin pesat, teknologi bereperan baik sebagai media interaksi sosial, sarana edukasi, maupun sarana hiburan. Dengan menerapkan teknologi dalam kehidupan sehari-hari, diharapkan dapat membantu dan mempermudah pekerjaan. Salah satu bentuk kegiatan adalah pendakian dan petulangan, yaitu sebuah kegiatan dengan mengkombinasikan olahraga dan rekreasi yang sangat digemari oleh kalangan muda. Komunitas Pendaki Gunung Bandung (KPGB) merupakan Komunitas dengan kegiatan pendakian atau petualangan yang berperan sebagai media komunikasi dan informasi antar pegiat alam. Komunitas ini diharapkan menjadi jalur komunikasi terjalinya silaturahmi untuk membentuk kokoh rasa kekeluargaan antar sesama pendaki khususnya di Kota Bandung. Kesibukan dan padatnya jam kerja menyebabkan para anggota kesulitan dalam mengatur jadwal untuk melakukan aktivitas pendakian. Banyaknya rintangan dan tingkat kesulitan di medan pendakian, membuat banyak orang merasa takut untuk mendaki. Para pendaki yang mengalami kecelakaan dan hilang, bahkan meninggal dunia, menambah rasa takut bagi orang-orang yang ingin melakukan pendakian. Dari permasalahan yang ada perlu adanya gambaran tentang bagaimana menyelesaikan masalah ketika banyaknya aktivitas dari semua anggota yang tidak dapat melakukan pendakian. Maka aplikasi game yang bertemakan petualangan pendakian ini, dibentuk untuk memenuhi kepuasan para anggota Komunitas Pendaki Gunung Bandung, dengan pemrograman Cocos 2d-x berbasis game engine Android menggunakan smartphone yang menghasilkan game petulangan pendakian yang berkarakter 2D (dua dimensi). Aplikasi ini dirancang dengan fitur yang sederhana, oleh karena itu pengguna dapat memainkan game petualangan ini dengan nyaman. Terdapat pula fitur yang akan menghubungkan langsung pengguna dengan website KPGB, yang didalamnya terdapat informasi mengenai perlengkapan mendaki. Aplikasi ini diharapkan dapat memberikan kesenangan bagi anggota Komunitas Pendaki Gunung Bandung yang tidak bisa melakukan aktivitas berpetualang atau mendaki secara langsung. The development of technology is currently increasingly rapid, technology plays a role both as a medium of social interaction, a means of education, and a means of entertainment. By applying technology in everyday life, it is hoped that it can help and make work easier. One form of activity is climbing and adventure, which is an activity that combines sports and recreation which is very popular with young people. The Bandung Mountaineering Community (KPGB) is a community with climbing or adventure activities that act as a medium of communication and information between nature activists. This community is expected to be a communication line for establishing friendships to form a strong sense of kinship between fellow climbers, especially in the city of Bandung. Busy and busy working hours cause the members to find it difficult to arrange a schedule for climbing activities. The number of obstacles and levels of difficulty in the climbing field, makes many people feel afraid to climb. The climbers who had accidents and disappeared, and even died, added to the fear of those who wanted to climb. From the existing problems, it is necessary to have an idea of how to solve the problem when there are many activities from all members who cannot climb. So this game application with the theme of climbing adventure, was formed to meet the satisfaction of members of the Bandung Mountaineering Community, with Cocos 2d-x programming based on the Android game engine using a smartphone that produces climbing adventure games with 2D (two-dimensional) characters. This application is designed with simple features, therefore users can play this adventure game comfortably. There is also a feature that will connect users directly to the KPGB website, in which there is information about climbing equipment. This application is expected to provide pleasure for members of the Bandung Mountaineering Community who cannot carry out adventure or climbing activities directly.

Review Paper on Machine learning-based prediction of Corona Virus

Effective contact tracing of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and might mitigate the burden on healthcare system. Prediction models that combine several features to approximate the danger of infection are developed. These aim to help medical examiners worldwide in treatment of patients, especially within the context of limited healthcare resources. They established a machine learning approach that trained on records from 51,831 tested individuals (of whom 4769 were confirmed to own COVID-19 coronavirus). Test set contained data from the upcoming week (47,401 tested individuals of whom 3624 were confirmed to own COVID-19 disease). Their model predicted COVID-19 test results with highest accuracy using only eight binary features: sex, age ≥60 years, known contact with infected patients, and also the appearance of 5 initial clinical symptoms appeared. Generally, supported the nationwide data publicly reported by the Israeli Ministry of Health, they developed a model that detects COVID-19 cases by simple features accessed by asking basic inquiries to the affected patient. Their framework may be used, among other considerations, to prioritize testing for COVID-19 when testing resources are limited and important. Keywords: Machine Learning, SARS-COV-2, COVID-19, Coronavirus.

Is Post-Transplant Cyclophosphamide the New Methotrexate?

Introducing post-transplant, cyclophosphamide (PT-Cy) graft-versus-host disease (GVHD) prophylaxis in the setting of haploidentical donor transplantation has marked the most important advance in allogeneic hematopoietic cell transplantation (alloHCT) within the past 15 years. The efficacy of this procedure and its simple features have allowed for the significantly widespread application of alloHCT worldwide. Indeed, the procedure’s effectiveness in reducing immunological complications in the haploidentical setting has even challenged the status quo use of calcineurin-inhibitor, methotrexate-based GVHD prophylaxis in the setting of HLA-identical donors. Currently, however, prospective clinical trials in support of PT-Cy-based GVHD prophylaxis in the HLA-matched setting are striving to resolve the matter of its potential role. This review will briefly report the overall outcomes of PT-Cy-based GVHD prophylaxis in the haploidentical setting and summarize results obtained in the HLA-identical field. We will present future perspectives at the end of the manuscript.

Exposing Manipulated Photos and Videos in Digital Forensics Analysis

Tampered multimedia content is being increasingly used in a broad range of cybercrime activities. The spread of fake news, misinformation, digital kidnapping, and ransomware-related crimes are amongst the most recurrent crimes in which manipulated digital photos and videos are the perpetrating and disseminating medium. Criminal investigation has been challenged in applying machine learning techniques to automatically distinguish between fake and genuine seized photos and videos. Despite the pertinent need for manual validation, easy-to-use platforms for digital forensics are essential to automate and facilitate the detection of tampered content and to help criminal investigators with their work. This paper presents a machine learning Support Vector Machines (SVM) based method to distinguish between genuine and fake multimedia files, namely digital photos and videos, which may indicate the presence of deepfake content. The method was implemented in Python and integrated as new modules in the widely used digital forensics application Autopsy. The implemented approach extracts a set of simple features resulting from the application of a Discrete Fourier Transform (DFT) to digital photos and video frames. The model was evaluated with a large dataset of classified multimedia files containing both legitimate and fake photos and frames extracted from videos. Regarding deepfake detection in videos, the Celeb-DFv1 dataset was used, featuring 590 original videos collected from YouTube, and covering different subjects. The results obtained with the 5-fold cross-validation outperformed those SVM-based methods documented in the literature, by achieving an average F1-score of 99.53%, 79.55%, and 89.10%, respectively for photos, videos, and a mixture of both types of content. A benchmark with state-of-the-art methods was also done, by comparing the proposed SVM method with deep learning approaches, namely Convolutional Neural Networks (CNN). Despite CNN having outperformed the proposed DFT-SVM compound method, the competitiveness of the results attained by DFT-SVM and the substantially reduced processing time make it appropriate to be implemented and embedded into Autopsy modules, by predicting the level of fakeness calculated for each analyzed multimedia file.