Biblio

The industrial revolution 4.0 has become a challenge for various sectors in mastering information technology, one of which is the arts and culture sector. Cultural arts that are quite widely spread and developed in Indonesia are traditional masks. Traditional masks are one of the oldest and most beautiful cultures in Indonesia. However, with the development of the era to the digital world in the era of the industrial revolution 4.0, this beloved culture is fading due to the entry of foreign cultures and technological developments. Many young people who succeed the nation do not understand this cultural art, namely traditional masks. So those cultural arts such as traditional masks can still keep up with the development of digital technology in industry 4.0, we conduct research to use technology to preserve this traditional mask culture. The research uses the ADDIE method starting with Analyze, Design, Develop, Implement, and Evaluate. We took some examples of traditional masks such as Malangan masks, Cirebon masks, and Panji masks from several regions in Indonesia. This research implements marker-based Augmented reality technology and makes a traditional mask book that can be a means of augmented reality.

ARtect is an Augmented Reality application developed with Unity 3D, which envisions an educational interactive and immersive tool for architects, designers, researchers, and artists. This digital instrument renders the competency to visualize custom-made 3D models and 2D graphics in interior and exterior environments. The user-friendly interface offers an accurate insight before the materialization of any architectural project, enabling evaluation of the design proposal. This practice could be integrated into learning architectural design process, saving resources of printed drawings, and 3D carton models during several stages of spatial conception.

Mobile and web application security, particularly the areas of data privacy, has raised much concerns from the public in recent years. Most applications, or apps for short, are installed without disclosing full information to users and clearly stating what the application has access to, which often raises concern when users become aware of unnecessary information being collected. Unfortunately, most users have little to no technical expertise in regards to what permissions should be turned on and can only rely on their intuition and past experiences to make relatively uninformed decisions. To solve this problem, we developed DroidNet, which is a crowd-sourced Android recommendation tool and framework. DroidNet alleviates privacy concerns and presents users with high confidence permission control recommendations based on the decision from expert users who are using the same apps. This paper explains the general framework, principles, and model behind DroidNet while also providing an experimental setup design which shows the effectiveness and necessity for such a tool.

Understanding how to group a set of binary files into the piece of software they belong to is highly desirable for software profiling, malware detection, or enterprise audits, among many other applications. Unfortunately, it is also extremely challenging: there is absolutely no uniformity in the ways different applications rely on different files, in how binaries are signed, or in the versioning schemes used across different pieces of software. In this paper, we show that, by combining information gleaned from a large number of endpoints (millions of computers), we can accomplish large-scale application identification automatically and reliably. Our approach relies on collecting metadata on billions of files every day, summarizing it into much smaller "sketches", and performing approximate k-nearest neighbor clustering on non-metric space representations derived from these sketches. We design and implement our proposed system using Apache Spark, show that it can process billions of files in a matter of hours, and thus could be used for daily processing. We further show our system manages to successfully identify which files belong to which application with very high precision, and adequate recall.