The goal of this conference is to provide an international forum for presenting recent research results on multimedia for mobile devices, and to bring together experts from both academia and industry for a fruitful exchange of ideas and discussion on future challenges. The authors are encouraged to submit work-in-progress papers as well as updates on previously reported systems. Outstanding papers may be recommended for the publication in the Journal Electronic Imaging or the Journal of Imaging Science and Technology.
With the release of the Apple iPhone 12 pro in 2020, various features were integrated that make it attractive as a recording device for scene-related computer graphics pipelines. The captured Apple RAW images have a much higher dynamic range than the standard 8-bit images. Since a scene-based workflow naturally has an extended dynamic range (HDR), the Apple RAW recordings can be well integrated. Another feature is the Dolby Vision HDR recordings, which are primarily adapted to the respective display of the source device. However, these recordings can also be used in the CG workflow since at least the basic HLG transfer function is integrated. The iPhone12pro's two Laser scanners can produce complex 3D models and textures for the CG pipeline. On the one hand, there is a scanner on the back that is primarily intended for capturing the surroundings for AR purposes. On the other hand, there is another scanner on the front for facial recognition. In addition, external software can read out the scanning data for integration in 3D applications. To correctly integrate the iPhone12pro Apple RAW data into a scene-related workflow, two command-line-based software solutions can be used, among others: dcraw and rawtoaces. Dcraw offers the possibility to export RAW images directly to ACES2065-1. Unfortunately, the modifiers for the four RAW color channels to address the different white points are unavailable. Experimental test series are performed under controlled studio conditions to retrieve these modifier values. Subsequently, these RAW-derived images are imported into computer graphics pipelines of various CG software applications (SideFx Houdini, The Foundry Nuke, Autodesk Maya) with the help of OpenColorIO (OCIO) and ACES. Finally, it will be determined if they can improve the overall color quality. Dolby Vision content can be captured using the native Camera app on an iPhone 12. It captures HDR video using Dolby Vision Profile 8.4, which contains a cross-compatible HLG Rec.2020 base layer and Dolby Vision dynamic metadata. Only the HLG base layer is passed on when exporting the Dolby Vision iPhone video without the corresponding metadata. It is investigated whether the iPhone12 videos transferred this way can increase the quality of the computer graphics pipeline. The 3D Scanner App software controls the two integrated Laser Scanners. In addition, the software provides a large number of export formats. Therefore, integrating the OBJ-3D data into industry-standard software like Maya and Houdini is unproblematic. Unfortunately, the models and the corresponding UV map are more or less machine-readable. So, manually improving the 3D geometry (filling holes, refining the geometry, setting up new topology) is cumbersome and time-consuming. It is investigated if standard techniques like using the ZRemesher in ZBrush, applying Texture- and UV-Projection in Maya, and VEX-snippets in Houdini can assemble these models and textures for manual editing.
In recent years, we have seen significant progress in advanced image upscaling techniques, sometimes called super-resolution, ML-based, or AI-based upscaling. Such algorithms are now available not only in form of specialized software but also in drivers and SDKs supplied with modern graphics cards. Upscaling functions in NVIDIA Maxine SDK is one of the recent examples. However, to take advantage of this functionality in video streaming applications, one needs to (a) quantify the impacts of super-resolution techniques on the perceived visual quality, (b) implement video rendering incorporating super-resolution upscaling techniques, and (c) implement new bitrate+resolution adaptation algorithms in streaming players, enabling such players to deliver better quality of experience or better efficiency (e.g. reduce bandwidth usage) or both. Towards this end, in this paper, we propose several techniques that may be helpful to the implementation community. First, we offer a model quantifying the impacts of super resolution upscaling on the perceived quality. Our model is based on the Westerink-Roufs model connecting the true resolution of images/videos to perceived quality, with several additional parameters added, allowing its tuning to specific implementations of super-resolution techniques. We verify this model by using several recent datasets including MOS scores measured for several conventional up-scaling and super-resolution algorithms. Then, we propose an improved adaptation logic for video streaming players, considering video bitrates, encoded video resolutions, player size, and the upscaling method. This improved logic relies on our modified Westerink-Roufs model to predict perceived quality and suggests choices of renditions that would deliver the best quality for given display and upscaling method characteristics. Finally, we study the impacts of the proposed techniques and show that they can deliver practically appreciable results in terms of the expected QoE improvements and bandwidth savings.
To this day, most important documents are still issued on paper. The security is based on the fact that the cost of creating a counterfeit must be unattractive for counterfeiters in relation to the expected profit. This results typically in using expensive printing equipment and substrate. This work introduces an approach which evaluates paper documents using any internet enabled device with a camera and a web browser like smartphones and tablets. Optical character recognition (OCR) is used to make text machine readable after the document is recognized and rectified. Digital signatures are then used to verify the authenticity and integrity of the data. Beyond that, the requirements of privacy, robustness and usability are satisfied. By using JAB Code, a high-capacity matrix code, the data to be verified can be stored directly on the document without having to use a database. This brings key advantages compared to database-bound systems in terms of security and privacy. The use of OCR achieves high usability.
Open-source technologies (OSINT) and Social Media Intelligence (SOCMINT) are becoming increasingly popular with investigative and government agencies, intelligence services, media companies, and corporations. These OSINT and SOCMINT technologies use sophisticated techniques and special tools to efficiently analyze the continually growing sources of information. There is a great need for training and further education in the OSINT field worldwide. This report describes the importance of open source or social media intelligence for evaluating disaster management. It also gives an overview of the government work in Australia, Haiti, and Japan for disaster management using various OSINT tools and platforms. Thus, decision support for using OSINT and SOCMINT tools is given, and the necessary training needs for investigators can be better estimated.
Open-source intelligence is gaining popularity due to the rapid development of social networks. There is more and more information in the public domain. One of the most popular social networks is Twitter. It was chosen to analyze the dependence of changes in the number of likes, reposts, quotes and retweets on the aggressiveness of the post text for a separate profile, as this information can be important not only for the owner of the channel in the social network, but also for other studies that in some way influence user accounts and their behavior in the social network. Furthermore, this work includes a detailed analysis and evaluation of the Tweety library capabilities and situations in which it can be effectively applied. Lastly, this work includes the creation and description of a compiled neural network whose purpose is to predict changes in the number of likes, reposts, quotes, and retweets from the aggressiveness of the post text for a separate profile.
This paper presents a practical Open Source Intelligence (OSINT) use case for user similarity measurements with the use of open profile data from the Reddit social network. This PoC work combines the open data from Reddit and the part of the state-of-the-art BERT model. Using the PRAW Python library, the project fetches comments and posts of users. Then these texts are converted into a feature vector - representation of all user posts and comments. The main idea here is to create a comparable user's pair similarity score based on their comments and posts. For example, if we fix one user and calculate scores of all mutual pairs with other users, we will produce a total order on the set of all mutual pairs with that user. This total order can be described as a degree of written similarity with this chosen user. A set of "similar" users for one particular user can be used to recommend to the user interesting for him people. The similarity score also has a "transitive property": if $user_1$ is "similar" to $user_2$ and $user_2$ is similar to $user_3$ then inner properties of our model guarantees that $user_1$ and $user_3$ are pretty "similar" too. In this way, this score can be used to cluster a set of users into sets of "similar" users. It could be used in some recommendation algorithms or tune already existing algorithms to consider a cluster's peculiarities. Also, we can extend our model and calculate feature vectors for subreddits. In that way, we can find similar to the user's subreddits and recommend them to him.
Open Source Intelligence (OSINT) has come a long way, and it is still developing ideas, and lots of investigations are yet to happen in the near future. The main essential requirement for all the OSINT investigations is the information that is valuable data from a good source. This paper discusses various tools and methodologies related to Facebook data collection and analyzes part of the collected data. At the end of the paper, the reader will get a deep and clear insight into the available techniques, tools, and descriptions about tools that are present to scrape the data out of the Facebook platform and the types of investigations and analyses that the gathered data can do.
Incident Command Dashboard (ICD) plays an essential role in Emergency Support Functions (ESF). They are centralized with a massive amount of live data. In this project, we explore a decentralized mobile incident commanding dashboard (MIC-D) with an improved mobile augmented reality (AR) user interface (UI) that can access and display multimodal live IoT data streams in phones, tablets, and inexpensive HUDs on the first responder’s helmets. The new platform is designed to work in the field and to share live data streams among team members. It also enables users to view the 3D LiDAR scan data on the location, live thermal video data, and vital sign data on the 3D map. We have built a virtual medical helicopter communication center and tested the launchpad on fire and remote fire extinguishing scenarios. We have also tested the wildfire prevention scenario “Cold Trailing” in the outdoor environment.
The chatbot is designed to respond to users with automated responses with respect to the content provided by the user. But the question arises when the user provides a free text which is a synonym of required content or part of the content for the chatbot to understand. In this case most of the Chatbot models using huge libraries which has a large number of samples and require more computational time and storage. Keyword detection methods with a huge amount of data are suitable for most applications but chatbots were designed for specific tasks, for example, ordering food, customer support for the specific application, etc., so these types of chatbots don’t need huge training data. In this paper, we conducted a performance evaluation of different sets and sizes of samples based on certain keywords specifically used for the closed domain chatbot. In this research, we used Movielens 20M dataset which provides tag assignments between movies and unique tags. We used Deep Learning methods in this keyword extraction model.