It has generally been well-established, by a number of studies, that the rapid advance and global diffusion of information and communication technology (ICT) is impacting the milieu of governance along with a socio-technical transformation of society at all levels. With a growing interest in digital government, the inquiry concerning a common digital model has been raised. To assist public administrators in thinking about new digital government for their organizations, a new developmental model framework is proposed in this article. This model is drawn from value-based analysis contained in developmental policies, and a subsequent policy development process is also proposed. In doing so, the new model would represent accelerated socio-technical transformation with intelligent technologies, escalating wicked problems appearing despite technological advances. The limitations of the current finite state model are also discussed.

In recent years, complex machine learning and deep learning algorithms have been used in prognostics and health management applications to monitor the state of industrial systems in real-time and predict failures in advance. However, the performance of these algorithms depends on the size of the collected labeled datasets. This study proposes a semi-weakly supervised learning method that creates label functions using a small amount of data and, consequently, combines weak supervision and semi-supervision methods to expand the labeled dataset for training. The proposed model training method creates labels automatically in the life prediction dataset of rolling element bearings, which were manually labeled by expert groups. The performance of the deep neural network is continuously improved through experiments, and a performance improvement similar to that of manually labeled training data is presented. Future research applications include meta-learner experiments (e.g., use of pre-trained word embedding or other model architectures) and more appropriate selection methods and definitions for the basic learner.

Recently the efficiency of neural information retrieval (IR) models has been significantly improved. However, there are technical challenges such as the data bottleneck problem. In real-world scenarios, only documents without related queries are available for training neural IR models. Existing studies propose synthetic queries derived from targeted passages using trained query generation models, which require q-d pair data from other domains for their training. Our research introduces the integrated keyword extraction-driven data augmentation method with weak supervised learning. We derived keywords from passages in a corpus to generate pseudo-queries. Using established weak supervised learning methods, we then generated relevance between these pseudo-queries and passages to produce pseudo-labels. Our approach demonstrates that keyword extraction techniques can efficiently formulate queries and train neural IR systems, outperforming the existing synthetic query generation method. Specifically, the performance of models utilizing pseudo-labels closely approximates that of models trained with ground truth data, underscoring the potential of pseudo-labeling approaches as effective alternatives in the absence of extensive ground truth data. Code and related materials are available on GitHub at https://github.com/guenjinahn/hoseo-cedr.

Since the 20th century, automobiles, which are the most common means of transportation, have been evolving as the use of electronic control devices and automotive semiconductors increases dramatically. Automotive semiconductors are a key component in automotive electronic control devices and are used to provide stability, efficiency of fuel use, and stability of operation to consumers. Since automotive semiconductors have a high data rate basically, a microprocessor unit is being used instead of a micro control unit. For example, semiconductors based on ARM processors are being used in telematics, audio/video multi-medias and navigation. Automotive semiconductors require characteristics such as high reliability, durability and long-term supply, considering the period of use of the automobile for more than 10 years. The reliability of automotive semiconductors is directly linked to the safety of automobiles. The semiconductor industry uses JEDEC and AEC standards to evaluate the reliability of automotive semiconductors. In addition, the life expectancy of the product is estimated at the early stage of development and at the early stage of mass production by using the reliability test method and results that are presented as standard in the automobile industry. However, there are limitations in predicting the failure rate caused by various parameters such as customer’s various conditions of use and usage time. To overcome these limitations, much research has been done in academia and industry. Among them, researches using data mining techniques have been carried out in many semiconductor fields, but application and research on automotive semiconductors have not yet been studied. In this regard, this study investigates the relationship between data generated during semiconductor assembly and package test process by using data mining technique, and uses data mining technique suitable for predicting potential failure rate using customer bad data.