Digital Library[ Search Result ]
Analyzing Model Hubs for Effective Composition of Pre-Trained Machine Learning Models
http://doi.org/10.5626/JOK.2025.52.1.42
Deep Neural Network (DNN) models have become prevalent. They are increasingly adopted as components in software systems. Designing and training these DNNs from scratch is not trivial. Designing requires domain expertise and familiarity with DNN frameworks while training necessitates substantial computational resources and large training datasets. Following the philosophy of traditional software engineering, developers often reuse Pre-Trained Models (PTMs) organized in model hubs. However, challenges arise when PTMs that match a developer’s specific requirements are lacking. In this paper, we explored the concept of PTM composition and investigated whether a combination of PTMs could fulfill application requirements without needing fine-tuning or creating a new DNN. We present current challenges in PTM composition through our case study and identified shortcomings of existing model hubs. By drawing parallels between PTM composition and web service composition, we highlighted essential technologies required for successful PTM composition and discussed potential solutions to these issues.
A User-Centric Conversational Service Mashup Model and Engine
http://doi.org/10.5626/JOK.2021.48.5.584
In Internet of Things (IoT) environments, users not only consume services that are provided by IoT devices, but also create their own service mashup applications. Several visual-based approaches have been proposed to support users in creating IoT service mashups. However, as it is not easy for users to understand the visually-represented execution flow of a service mashup, they often find it difficult to create them. This study proposes a conversational service mashup model and an engine, which end-users without programming experience can use to create IoT service mashups through a natural language. The conversational service mashup model comprises four types of keywords to identify user commands. The service mashup engine comprises an interaction manager, a sematic matching module, and a service mashup module. To evaluate the proposed model, we conduct a case study based on a smart home IoT environment scenario. The study results confirm that end-users can easily use the conversational service mashup model and the engine to create required IoT service mashups.
An Optimization Method for Perceived Quality of Services in Service-oriented V2X Software Environments
HyeongCheol Moon, KyeongDeok Baek, In-Young Ko
http://doi.org/10.5626/JOK.2019.46.7.664
Recent advancements in computing technologies have led to researches on large-scale Cyber Physical System (CPS) applications such as Vehicle-to-Everything (V2X) applications. Numerous researchers have attempted to apply service computing technologies to V2X environments to make them more flexible and reliable. In a service-oriented V2X environment, users receive services through the Internet of Things (IoT) devices. Most of the existing V2X environments estimate or evaluate service quality from the network point of view. Apparently, the users" perceived Quality of Service (QoS), which is affected by various factors in V2X environments, especially by the users’ mobility, cannot be guaranteed. In the present work, we investigate the mobility-related factors that affect the users’ perceived QoS with an aim to optimize the users’ perceived QoS in V2X environments and propose an algorithm that considers the effectiveness of delivering service effects and the overhead of service handover among different IoT devices. We conducted a series of experiments, and it was observed that our QoS optimization approach outperforms the existing methods that consider only the quality factors in the network"s perspective.
Analysis of the Cost-effectiveness of Regression Testing Techniques in Continuous Integration Environments based on Failure to Pay Attention
http://doi.org/10.5626/JOK.2018.45.10.1029
In continuous integration (CI) environments, it is possible to provide fast feedback on test failures by applying cost-effective regression-testing techniques. In this study, we analyze the cost-effectiveness of two test-case prioritization techniques based on the test history of three industrial projects. In addition, because test failures may have different degrees of attention paid to them by different developers in CI environments, we consider this characteristic in the experiment. As a result, we discovered that the cost-effectiveness of applying the TCP techniques can be similar to that of not applying any of the TCP techniques when failure of the developers to pay attention is considered. The experiment shows that it is necessary to improve the state-of-the-art test-case prioritization techniques for CI environments by considering such characteristics.
Member Organization-based Service Recommendation for User Groups in Internet of Things Environments
Recommender systems can be used to assist users in selecting required services for their tasks in Internet of Things (IoT) environments in which diverse services can be provided by utilizing IoT devices. Traditional research on recommendation mainly focuses on predicting preferences of individual users. However, in IoT environments, not only individual users but also groups of users can access services in the environments. In this study, we analyzed user groups" preferences on services and developed service recommendation approach for new groups that do not have a history of accessing IoT-services in a certain place. Our approach extends the traditional user-based collaborative filtering by considering the similarity between user groups based on their member organization. We conducted experiments with a real-world dataset collected from IoT testbed environments. The results demonstrate that the proposed approach is effective to recommend services to new user groups in IoT environments.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr