• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1223-1237
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• 2024

Autonomous vehicles use onboard sensors to sense the surrounding environment. In complex autonomous driving scenarios, the detection and recognition capabilities are constrained, which may result in serious accidents. An efficient way to enhance the detection and recognition capabilities is establishing collaborations with the neighbor vehicles. However, the collaborations introduce additional challenges in terms of the data heterogeneity, communication cost, and data privacy. In this paper, a novel personalized federated learning framework is proposed for addressing the challenges and enabling efficient collaborations in autonomous driving environment. For obtaining a global model, vehicles perform local training and transmit logits to a central unit instead of the entire model, and thus the communication cost is minimized, and the data privacy is protected. Then, the inference similarity is derived for capturing the characteristics of data heterogeneity. The vehicles are divided into clusters based on the inference similarity and a weighted aggregation is performed within a cluster. Finally, the vehicles download the corresponding aggregated global model and train a personalized model which is personalized for the cluster that has similar data distribution, so that accuracy is not affected by heterogeneous data. Experimental results demonstrate significant advantages of our proposed method in improving the efficiency of collaborative perception and reducing communication cost.

• Journal of Families and Better Life
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• v.24 no.2 s.80
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• pp.15-26
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• 2006

The purposes of this study were (1) to investigate children's personalized inferences of characters emotional reactions depending on character's personality trait, emotional situation, children's age and gender, (2) to investigate children's personalized inferences of character's behavioral reactions depending on character's personality trait, emotional situation, children's age and gender, (3) to investigate differences between children's personalized inferences of character's emotional reaction and that of character's behavioral reactions. The subjects were 103 children from three age groups (thirty-four 3-year-olds, thirty-three 5-year-olds and thirty-six 7-year-olds). The statistical methods adopted for the data analysis were frequency, percentile, mean, standard deviation, repeated measure ANOVA and paired t-test. The result showed that there were significant differences in children's personalized inferences of character's emotional reaction depending on character's personality trait, emotional situation and their age. There were significant differences in children's personalized inferences of character's behavioral reaction depending on children's age and gender. There were significant differences between personalized inferences of character's emotional reaction and behavioral reactions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2016-2034
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• 2011

Ubiquitous Healthcare (u-Healthcare) is the intelligent delivery of healthcare services to users anytime and anywhere. To provide robust healthcare services, recognition of patient daily life activities is required. Context information in combination with user real-time daily life activities can help in the provision of more personalized services, service suggestions, and changes in system behavior based on user profile for better healthcare services. In this paper, we focus on the intelligent manipulation of activities using the Context-aware Activity Manipulation Engine (CAME) core of the Human Activity Recognition Engine (HARE). The activities are recognized using video-based, wearable sensor-based, and location-based activity recognition engines. An ontology-based activity fusion with subject profile information for personalized system response is achieved. CAME receives real-time low level activities and infers higher level activities, situation analysis, personalized service suggestions, and makes appropriate decisions. A two-phase filtering technique is applied for intelligent processing of information (represented in ontology) and making appropriate decisions based on rules (incorporating expert knowledge). The experimental results for intelligent processing of activity information showed relatively better accuracy. Moreover, CAME is extended with activity filters and T-Box inference that resulted in better accuracy and response time in comparison to initial results of CAME.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.59-66
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• 2010

We present a personalized clothing recommender system - one that mines association rules from transaction described in ontologies and infers a recommendation from the rules. The recommender system can forecast frequently changing trends of clothing using the Onto-Apriori algorithm, and it makes appropriate recommendations for each users possible through the inference marked as meta nodes. We simulates the rule generator and the inferential search engine of the system with focus on accuracy and efficiency, and our results validate the system.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2009.05b
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• pp.175-179
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• 2009

Researches of current PMIS(Project Management Information System) information searching focus on providing personalized results as well as matching needed queries in an enormous amount of information. This paper aim at discovering hidden knowledge to provide personalized and inferred search results based on the ontology with categorized concepts and relations among construction data. The current PMIS searching occasionally presents too much redundant information or offers no matching results from large volumes of data. In this paper, we propose a service searching system, which becomes aware of users device using iPMIS(Intelligent Program Management Information System). And we design and plant the ontology-based iPMIS, which is aware of the context in its environment.

• Journal of Digital Convergence
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• v.10 no.2
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• pp.293-297
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• 2012

In order to provide with personalized recommendation service in context-awareness environment, the collected context data should be analyzed fast and the objective of user should be able to inferred effectively. But, the context collected from the mobile devices is not suitable for applying the existing inference algorithms as they are due to the omission or uncertainty of information and the efficient algorithms are required for mobile environment. In this paper, the behavior pattern was classified using naive bayes classification for minimize the loss caused by the omission or error of information. And pattern matching was used to effectively learn of the users inclination and infer the behavior purpose. The accuracy of the suggested inference model was evaluated by applying to the application recommendation service in the smart phones.

• Journal of Digital Convergence
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• v.10 no.2
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• pp.287-292
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• 2012

In this paper, user personalized services through the emotion perception required to support location-based sensing data preprocessing techniques and emotion data preprocessing techniques is studied for user's emotion data building and preprocessing in V-A emotion model. For this purpose the granular context tree and string matching based emotion pattern matching techniques are used. In addition, context-aware and personalized recommendation services technique using probabilistic reasoning is studied for personalized services based on context awareness.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1566-1574
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• 2008

Researches of current information searching focus on providing personalized results as well as matching needed queries in an enormous amount of information. This paper aims at discovering hidden knowledge to provide personalized and inferred search results based on the ontology with categorized concepts and relations among data. The current searching occasionally presents too much redundant information or offers no matching results from large volumes of data. To lessen this disadvantages in the information searching, we propose an inference algorithm that supports associated and inferred searching through the Jess engine based on the OWL ontology constraints and knowledge expressed by SWRL with association rules. After constructing the personalized preference ontology for domains such as restaurants, gas stations, bakeries, and so on, it shows that new knowledge information generated from the ontology and the rules is provided with an example of the domain of gas stations.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.37-45
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• 2013

In this paper proposes the latest network-assisted online telemedicine service to coincide with the point being discussed for health care providers to match patients, patients with personalized medical service support system. In order to design the system, to understand the requirements of the patient personalized medical support service system, the data were normalized and were designed architecture client server structure. Further, in order to implement the system that was designed to define the structure of server and client, ontology repository, we implement the system. In this paper, as a result of the test by creating a scenario and prerequisites for testing patient personalized medical service support system that is design and implementation, selecting a patient's condition, department of symptoms by the selected but it was confirmed that the inference is, inference medical institutions that fits department inferred one following upon the items medical patient has the required.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1111-1123
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• 2023

In recent years, as generative models have developed, research that threatens them has also been actively conducted. We propose a new membership inference attack against text-to-image model. Existing membership inference attacks on Text-to-Image models produced a single image as captions of query images. On the other hand, this paper uses personalized embedding in query images through Textual Inversion. And we propose a membership inference attack that effectively generates multiple images as a method of generating Adversarial Prompt. In addition, the membership inference attack is tested for the first time on the Stable Diffusion model, which is attracting attention among the Text-to-Image models, and achieve an accuracy of up to 1.00.