A Study on Efficient Design of A Multimedia Conversion Module in PESMS for Social Media Services

The main contribution of this paper is to present the Platform-as-a-Service(PaaS) Environment for Social Multimedia Service (PESMS), derived fromthe Social Media Cloud Computing Service Environment. The main role ofour PESMS is to support the development of social networking services thatinclude audio, image, and video formats. In this paper, we focus in particular on the design and implementation of PESMS, including the transcoding function for processing large amounts of social media in a parallel and distributed manner. PESMS is designed to improve the quality and speed of multimedia conversions by incorporating a multimedia conversion module based on Hadoop, consisting of Hadoop Distributed File System for storing large quantities of social data and MapReduce for distributed parallel processing of these data. In this way, our PESMS has the prospect of exponentially reducing the encoding time for transcoding large numbers of image files into specific formats. To test system performance for the transcoding function, we measured the image transcoding time under a variety of experimental conditions. Based on experiments performed on a 28-node cluster, we found that our system delivered excellent performance in the image transcoding function

MANAGING SPATIAL AFFINITY FOR EDGE APPLICATION DEVELOPMENT AND DEPLOYMENT

Current approaches for associating edge devices with the correct application instance as edge devices are on the move often require changing end device configuration/policy, which can be inefficient and burdensome. In order to address such issues, techniques proposed herein provide a library of tools, including an affinity manager, to enable scalable edge application development, deployment and management. The affinity manager facilitates communication between service instances and edge devices by registering service instances and providing edge device with information on the service instances. When an edge device moves to a new area, the proposed techniques leverage a local area translation function to quickly and easily translate the edge device’s location information, which can be used to query the affinity manager for new service instances in the new area. As a result, edge devices can be quickly connected to new service instances, thus decreasing communication latency

FEDERATED LEARNING-ENHANCED RETRIEVAL AUGMENTED GENERATION (RAG)

Techniques are provided to enhance the functions of a retrieval augmented generation (RAG) mechanism for a large language model (LLM). A Federated Learning (FL)-enhanced RAG (FLERAG) mechanism is provided that can account for relevant context-enhancing data from the retrieval process, as well as most recent data from the FL on which a large language model (LLM) may not have been trained. Using FLERAG, the output generation is determined through a scoring or ranking method that indicates whether the response from the LLM or the FL model is most accurate and relevant. This generated response is then provided back to a user

Three-dimensional photon counting optical encryption with enhanced visual quality and security level

In this paper, we propose three-dimensional (3D) photon counting double random phase encryption (DRPE) with enhanced visual quality and security level. Conventional 3D photon counting DRPE can quickly encrypt the data by using the 4f optical system and random phase masks with enhanced security because the 3D photon counting technique is used. 3D photon counting DRPE extracts photons from the encrypted data by using statistical methods such as the Poisson random process, and the visual quality of the decrypted data can be enhanced through the 3D reconstruction process. However, it still has a problem to visualize the data when we extract extremely a few photons. To improve the security and the visual quality of the decrypted data, we propose the random amplitude reconstruction process in the encryption stage. Our proposed method reconstructs the amplitude of the encrypted data at a random depth. Thus, the shifting pixel value and depth information can be another important key for decryption through the random process. Therefore, it can effectively decrypt data securely, and the visual quality of the decrypted data can be enhanced. Finally, through the random reconstruction process in the decryption stage, our proposed method can simultaneously enhance the security and visual quality. To verify our proposed method, we carry out the simulation and optical experiment

3D Visualization of objects in heavy scattering media by using wavelet peplography

In this paper, we propose three-dimensional(3D) visualization of objects in heavy scattering media by using peplography and wavelet transform. Conventional haze removal techniques can remove the light haze in the image by using various image processing algorithms or machine learning techniques. However, they may not provide a clear image under heavy scattering media. On the other hand, peplography can visualize the object by detecting ballistic object photons from heavy scattering media. Then, 3D image can be generated by integral imaging. However, it may not visualize 3D object information accurately because of the noise photons from scattering media. Therefore, the image quality for 3D object visualization may be degraded. To solve this problem, we use the discrete wavelet transform in peplography. It can detect the object photon signals from the scattering media and enhance 3D image contrast ratio by using a specific coefficient threshold technique. To prove our method, we carry out optical experiments and compare results with the conventional haze removal method and peplography by using various image quality metrics such as correlation, structural similarity, and peak signal-to-noise ratio