In the rapid development of artificial intelligence(AI), explainability(XAI) in particular plays a critical role in ensuring the reliability and safety of AI systems. Existing XAI studies mainly focus on interpreting the process by which a model derives a specific conclusion, which is essential for providing transparency in AI decision-making in high-risk applications. However, most studies focus on how AI makes correct predictions, and there is a relative lack of research on how to analyze and effectively correct the root causes of errors that occur during the data collection process. Errors that occur during the data collection process can lead to biased model learning, misclassification, and decreased overall system reliability. In general, AI pipelines regard such errors as simple noise and respond by applying outlier detection or noise removal techniques to refine the data, but these approaches have limitations in resolving the root causes of errors. In this study, we propose a feedback-based error correction framework that goes beyond simply detecting data errors to explain why the errors occurred and quickly correct them through a more systematic approach. The proposed framework detects errors early in the data collection phase, classifies the types of errors, identifies the error occurrence mechanism through question-answering($\mathbf{Q A}$)-based cause analysis, and constructs a feedback loop to correct errors through automated correction or human intervention based on the results, thereby continuously improving data quality. In particular, it focuses on data error management in high-risk environments such as autonomous driving, and proposes an environment-adaptive data error detection and correction technique.

The revolutionary development of artificial intelligence is making significant advancements such as Gen AI, and safe autonomous driving technology systems are being researched to be applied to autonomous driving environments that require the application of advanced and reliable technologies. In order to establish a stable autonomous driving environment, object detection, motion prediction are being researched. Currently, road marking recognition is performed in the object detection field to acquire road driving environment information, however, the inference of possible situations and proactive response from road markings are not being conducted. To overcome the limitations, this paper proposes a Proactive Scenario-driven Adaptable Defensive Driving (PS-ADD) Architecture to infer Environment Adaptive Scenario from proactive scenarios and derive Environment Adaptive Preemptive Plan. To infer proactive scenarios, road markings and real-time object information is combined to generate Proactive Driving Approaches.Then, Environment Adaptive Defensive Decision Making (EADDM) algorithm enables Adaptable Defensive Decision Making by deriving realtime environmentally adaptive scenarios and preparing adaptive preemptive plans. PS-ADD Architecture enables to predict and handle potential scenarios in advance in complex driving situations, allowing for stable defensive driving in unanticipated situations.

As interactions between Big Data and AI increasingly raise concerns about bias and ethical errors in decision-making, establishing trust within the big data ecosystem has become essential. As data types diversify and the volume of information grows exponentially, environments where real-time information processing is crucial, such as autonomous driving and IoT, require fair and reliable system management. This study introduces a trust-centric big data ecosystem designed to address challenges through an ethical framework for autonomous driving. The core of the big data ecosystem is a trust management layer that ensures data integrity and transparency, and integrated with advanced algorithms for real-time bias detection and mitigation. The proposed framework incorporates a cultural context module that enables ethically sound and culturally appropriate decision-making, along with bias detection, self-inspection, and risk assessment. By embedding trust and ethics into every layer of data processing and decision-making, this system ensures fairness and transparency in big data processing, thus contributing to the advancement of the big data ecosystem.

Autonomous vehicles make decisions and controls based on various object recognition results. The driving environment is characterized by the coexistence of a multitude of objects of varying shapes and sizes. Therefore, the ability to accurately recognise fine-grained objects is essential for accurate object recognition in a variety of changing situations. For object detection, the autonomous vehicle performs bounding box and segmentation to provide the detected object information. However, bounding box and segmentation based object detection has difficulties in identifying objects with complex shapes, small or distant objects, and it is hard to distinguish and detect objects with similar colors to the background or similar colors and textures to surrounding objects. This has limitations for reliable object identification in autonomous driving environments containing a variety of objects, which is a challenge for clear criteria-based object avoidance and collision protection. To overcome these limitations, this paper proposes Edge-Adaptive Depth Estimation(EADE). EADE, the combination of edge extraction and depth estimation, enables detailed edge extraction and partial distance estimation of objects even in environments where object shape and size, surrounding objects, and backgrounds make it difficult to recognise distinct objects, which allows for reliable autonomous decision-making and control based on detailed object collision and avoidance criteria. To validate EADE, experiments were conducted with real-world driving environment image data. The results of EADE demonstrate that detailed object recognition is possible with clear edge recognition and estimation of object distance, even for complex shaped objects such as trees with branches in multiple directions, distant objects, and objects that are difficult to distinguish from the background such as curbs.

The Advanced and radical development of IT technology and artificial intelligence technology have made it possible to develop advanced services Digital Twin, Metaverse, Metatwin-verse, etc using Artificial Intelligence(AI). The results induced from AI present the correct solution when AI performs accurate study and analysis. Specifically, real situations reflecting complex relationships between objects, results from real situations have to be adaptive to convergence situations and then it should be possible to draw conclusions and make decisions that are not limited to specific situations. So, it is essential to conduct AI based study and analysis by considering these real world characteristics to provide digital twin services based on metaverse. Recently, there are many studies on Graph Neural Network(GNN) and services applied to GNN for learning the relationship between objects detected in real situations. Accordingly, this paper proposes a metaverse-based Digital Twin Convergence Service(DTCS) including spatial elements strategy that is possible to draw accurate conclusions in a changing convergence situation. DTCS is able to conduct causal reasoning and association learning between objects considering directions and distances change characteristics between objects and this is possible to make correct solution and decision making in the process of simulation and analysis of digital twin. In that DTCS proceeds by considering distance and changing angle between objects, this overcomes the limitation of existing GNN which only considers the degree of association or assigns the same parameters to connected objects. DTCS would be possible to expand the advanced services of Metatwinverse in that it is possible to have robust learning based conclusions in real-time changing convergence situations.

In spite of the development of IoT types and technologies, there are many difficulties in integrating IoT due to the individual technological advances of many companies and the sale of products. Although studies have conducted to integrate numerous protocols and routing technologies with IoT platform technologies, this problem continues. It is necessary to integrate IoT services at the user’s convenience based on smart home rather than separated by each brand. In effect, in order to solve these problems, this paper presents the concept of Super Things and the Cognitive Service algorithm technique to provide the necessary complex services. This aims to present practical solutions through service techniques on how to better utilize existing functions rather than network integration in the current state. In addition, in order to address the unintegrated limitations of existing technologies, they identify their function’s own roles and seek ways to combine them organically with each other, enabling objective analysis of IoT data in smart home environments and integrated operation services.

In Korea, a budget of tens of trillion won is put into the national R & D project every year [1]. Thanks to continuous technology and industrial development, Korea's ICT industry is gaining global attention. National R & D projects manage R & D projects in order of Planning - Evaluation - Management - Project results. In Korea, the R & D cycle process is diverse from the planning stage to the research results manager stage. In other words, we manage planning and evaluation by different departments and institutions. This type of management leads to problems such as redundant planning, insufficient sharing of research results, and duplicate submission. Currently, NTIS (National Technology Information Service) provides to address issues of redundancy and sharing. However, the NTIS's review of project redundancy is centered on keywords and does not provide information on the planning stage. This situation leads to redundant planning by ministries and closed management of research results. Therefore, we propose a platform that can easily share project planning information, review project redundancy, share research results, and check duplication of research results using block chain technology, which many experts and entrepreneurs refer to as future innovative technologies. In this paper, we propose a platform called Perfect Sharing Project Platform (PSPP). We describe that PSPP can achieve excellent research results through information sharing of project process. To support the perfect sharing, this platform uses a new notion of consensus algorithm, called POA (Proof of Atomicity). This platform is suitable for sharing information.

In this paper, we measure human physiological changes from different body parts to quantify human mental stress level by using multimodal bio-sensors. By integrating these physiological responses, we generate bio-index and rule for the prediction of mental status, such as tension, normal, and relax. We also develop an inspection service middleware for analyzing health parameters such as electroencephalography (EEG), electrocardiography (ECG), oxygen saturation (SpO2), blood pressure (BP), and respiration rate (RR). In this service middleware, we use the multi-level assessment model for mental stress level that consists of three steps as follows; classification, reasoning, and decision making. The classification of datasets from bio-sensors is enabled by fuzzy logic and SVM algorithm. The reasoning uses the decision-tree model and random forest algorithm to classify the mental stress level from the health parameters. Finally, we propose a prediction model to make a decision for the wellness contents by using Expectation Maximization (EM).

The recommendation system are widely adopted in today’s mainstream online sharing services, providing useful prediction of user’s rating or user’s preferences of sharing items (such as products, movies, books, and news articles). A key challenge of recommendation systems in sharing economy is to employ prediction algorithms to estimate the matching items with considering their interests and needs. The environment-context has been recognized as an important factor to consider in personalized recommender systems. Since dynamic information in environment-context describes the situation of items and users, the information affects the user’s decision process essentially to apply in recommender systems. However, most model-based collaborative filtering approaches such as Matrix Factorization do not provide an easy way of integrating context information into the model. In this paper, we introduce a Multidimensional Trust model based on Tensor Factorization. The generalization of Matrix Factorization allows for a flexible and generic integration of contextual information. According to the different types of context, the Multidimensional Trust model considers the additional dimensions for the representation of the data as a tensor. This is achieved by going through the collecting user’s behavior based on rating analysis and identification of users’ historical activity and viewing patterns. The benefits behavior solutions, which use the handle intelligently to meet the users’ needs, are the focus of this paper.

In recently, learners have always mobile devices including smart phones so that is collecting user's behavior by sensors mounted on the devices. This paper proposes a new notion for smart learning system by using the concept of elastic computing in cloud computing. The notion is Elastic 4S (Smart Pull, Smart Prospect, Smart Content and Smart Push) for the smart service. We focus on the benefits of smart computing for e-learning solution using handle intelligently to meet user's needs through collecting user's behaviors, prospecting, building, delivering, and rendering steps. The proposed smart-learning model will show the personalized and customized learning services to be possible in various fields.

An important requirement in geographic information systems is the ability to display multi-scale data. Existing spatial access methods do not access multi-scale data efficiently and do not deal with all types of multi-scale data. To resolve this, an efficient spatial access method in multi-scale GIS is described. A performance evaluation shows that our method outperforms existing spatial access methods.