Latent diffusion models (LDMs) have rapidly become the de facto backbone of web-scale generative systems, powering text-to-image platforms such as Stable Diffusion and their video, 3D, and domain-specific extensions. By performing the diffusion process in a compressed latent space rather than directly in pixel space, LDMs achieve a favorable trade-off between computational efficiency and generative fidelity, enabling deployment in interactive web applications and large-scale content pipelines. This paper presents a comprehensive survey of LDMs from the perspective of both foundational modeling and web engineering. We first review the background of diffusion models and latent representations, contrasting LDMs with classical VAEs, GANs, and pixel-space diffusion models. We then dissect the architectural design of LDMs, including autoencoder backbones, latent-space U-Nets and diffusion transformers, conditioning mechanisms, training objectives, and sampling accelerations. Building on recent general surveys of diffusion models in vision, temporal data, and inverse problems, we propose a taxonomy of LDM variants, covering 2D image models, video and 4D models, and domain-specific LDMs in medical imaging, watermarking, time series, and text. From a web engineering viewpoint, we analyze LDM-based services exposed via web APIs, hosted user interfaces, and developer platforms, and discuss system-level concerns such as scalability, latency, cost, safety, and governance. We review current evaluation methodologies (quality, diversity, downstream task performance, robustness, watermarking) and highlight open challenges in controllability, interpretability, resource efficiency, and regulatory compliance, especially in light of recent legal and societal developments around generative deepfakes and copyright. This survey aims to provide both a conceptual map of LDM research and practical guidance for designing, deploying, and governing LDM-driven web systems.

Recently, the demand for remote counseling has been on the rise using conversational agents with Large Language Models (LLM) in many areas. This LLM trend is expanding beyond generating simple words to constructing complex sentences, showing progress in various fields. The significant development of LLM is attributed to the progress in Natural Language Processing (NLP) technology, built upon extensive language data. This study introduces a client technology of Open API System based conversational agent using LLM. The target agent is a Chatbot which can generate health counseling messages including empathetic conversations for users with caring his chronic conditions requiring ongoing health management. The overall system can be linked with the community care system through user APPs and APIs for digital healthcare context management. The advantage with the client with REST API is that APIs make it easy to integrate new applications with existing software systems, allowing target systems to meet requirements across a variety of platforms.

With the full-fledged service of mobile carrier 5G networks, it is possible to use large-capacity, immersive content at high speed anytime, anywhere. It can be illegally distributed in web-hard and torrents through DRM dismantling and various transformation attacks; however, evaluation indicators that can objectively evaluate the filtering performance for copyright protection are required. Since applying existing 2D filtering techniques to immersive content directly is not possible, in this paper we propose a set of robustness indicators for immersive content. The proposed indicators modify and enlarge the existing 2D video robustness indicators to consider the projection and reproduction method, which are the characteristics of immersive content. A performance evaluation experiment has been carried out for a sample filtering system and it is verified that an excellent recognition rate of 95% or more is achieved in about 3 s of execution time.

In the realm of Generative AI, where various models are introduced, prompt engineering emerges as a significant technique within natural language processing-based Generative AI. Its primary function lies in effectively enhancing the results of sentence generation by large language models (LLMs). Notably, prompt engineering has gained attention as a method capable of improving LLM performance by modifying the structure of input prompts alone. In this study, we apply prompt engineering to Korean-based LLMs, presenting an efficient approach for generating specific conversational responses with less data. We achieve this through the utilization of the query transformation module (QTM). Our proposed QTM transforms input prompt sentences into three distinct query methods, breaking them down into objectives and key points, making them more comprehensible for LLMs. For performance validation, we employ Korean versions of LLMs, specifically SKT GPT-2 and Kakaobrain KoGPT-3. We compare four different query methods, including the original unmodified query, using Google SSA to assess the naturalness and specificity of generated sentences. The results demonstrate an average improvement of 11.46% when compared to the unmodified query, underscoring the efficacy of the proposed QTM in achieving enhanced performance.

This study examined ethical ways to use welfare technology in a situation where the demand for non-face-to-face welfare services using Cloud based healthcare systems had increased rapidly in caring for elderly people who live alone. Through focus group interviews with social workers related to the care of elderly people who live alone, in-depth interviews were conducted on the current situation, problems, ethical issues, and development directions arising in the implementation of welfare technology. The main areas of interest were focused on improving safety in caring them using IoT technology and enhancing emotional support in preventing lonely deaths using companion robot and AI speaker. Issues such as the need for individualization, client-centeredness, privacy, self-determination, competence, informed consent, right to know, convenience, and advocacy were identified as important ethical considerations related to use of welfare technology. The research results suggested that various stakeholders should participate in the development of ethical indicators and welfare technology for the ethical use of welfare technology.

The development of wireless communication technology and mobile devices has brought about the advent of an era of sharing text data that overflows on social media and the web. In particular, social media has become a major source of storing people’s sentiments in the form of opinions and views on specific issues in the form of unstructured information. Therefore, the importance of emotion analysis is increasing, especially with machine learning for both personal life and companies’ management environments. At this time, data reliability is an essential component for data classification. The accuracy of sentiment classification can be heavily determined according to the reliability of data, in which case noise data may also influence this classification. Although there is stopword that does not have meaning in such noise data, data that does not fit the purpose of analysis can also be referred to as noise data. This paper aims to provide an analysis of the impact of profanity data on deep learning-based sentiment classification. For this purpose, we used movie review data on the Web and simulated the changes in performance before and after the removal of the profanity data. The accuracy of the model trained with the data and the model trained with the data before removal were compared to determine whether the profanity is noise data that lowers the accuracy in sentiment analysis. The simulation results show that the accuracy dropped by about 2% when judging profanity as noise data in the sentiment classification for review data.

Various methods and models have recently been proposed and applied for evaluating IP Multimedia Subsystem (IMS) in terms of reliability, availability and performance. End-to-end availability is one of the key factors that can improve the quality of service (QoS) and increase the reliability of Next Generation Networks (NGNs). In this paper, an end-to-end availability model is proposed and evaluated using a combination of Reliability Block Diagrams (RBD) and a proposed five-state Markov model. The overall availability for intra and inter domain communication in IMS is analyzed, and the state-of-the-art models are compared with the proposed novel Markov-based reliability modeling for both simplex and redundant systems. The proposed model is proved to efficiently represent the system behaviors. The effect of the end-to-end availability model when applying redundancy in the IMS core system is investigated and numerical results are included.