• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.109-131
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• 2014

As the demand of nuclear power plant equipment is continuously growing worldwide, the importance of handling nuclear strategic materials is also increasing. While the number of cases submitted for the exports of nuclear-power commodity and technology is dramatically increasing, preadjudication (or prescreening to be simple) of strategic materials has been done so far by experts of a long-time experience and extensive field knowledge. However, there is severe shortage of experts in this domain, not to mention that it takes a long time to develop an expert. Because human experts must manually evaluate all the documents submitted for export permission, the current practice of nuclear material export is neither time-efficient nor cost-effective. Toward alleviating the problem of relying on costly human experts only, our research proposes a new system designed to help field experts make their decisions more effectively and efficiently. The proposed system is built upon case-based reasoning, which in essence extracts key features from the existing cases, compares the features with the features of a new case, and derives a solution for the new case by referencing similar cases and their solutions. Our research proposes a framework of case-based reasoning system, designs a case-based reasoning system for the control of nuclear material exports, and evaluates the performance of alternative keyword extraction methods (full automatic, full manual, and semi-automatic). A keyword extraction method is an essential component of the case-based reasoning system as it is used to extract key features of the cases. The full automatic method was conducted using TF-IDF, which is a widely used de facto standard method for representative keyword extraction in text mining. TF (Term Frequency) is based on the frequency count of the term within a document, showing how important the term is within a document while IDF (Inverted Document Frequency) is based on the infrequency of the term within a document set, showing how uniquely the term represents the document. The results show that the semi-automatic approach, which is based on the collaboration of machine and human, is the most effective solution regardless of whether the human is a field expert or a student who majors in nuclear engineering. Moreover, we propose a new approach of computing nuclear document similarity along with a new framework of document analysis. The proposed algorithm of nuclear document similarity considers both document-to-document similarity (${\alpha}$) and document-to-nuclear system similarity (${\beta}$), in order to derive the final score (${\gamma}$) for the decision of whether the presented case is of strategic material or not. The final score (${\gamma}$) represents a document similarity between the past cases and the new case. The score is induced by not only exploiting conventional TF-IDF, but utilizing a nuclear system similarity score, which takes the context of nuclear system domain into account. Finally, the system retrieves top-3 documents stored in the case base that are considered as the most similar cases with regard to the new case, and provides them with the degree of credibility. With this final score and the credibility score, it becomes easier for a user to see which documents in the case base are more worthy of looking up so that the user can make a proper decision with relatively lower cost. The evaluation of the system has been conducted by developing a prototype and testing with field data. The system workflows and outcomes have been verified by the field experts. This research is expected to contribute the growth of knowledge service industry by proposing a new system that can effectively reduce the burden of relying on costly human experts for the export control of nuclear materials and that can be considered as a meaningful example of knowledge service application.

• Cartoon and Animation Studies
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• s.33
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• pp.153-172
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• 2013

There are many cases that messages of boring contents of most contents with public interests appear on the surface. Audiences don't think these contents are interesting. It is true that animations cannot be generally boring when delivering messages of public interests. was produced to focus on making audiences experience that a global warming story, the boring and textbook contents is interesting. And it was composed by the multiform story to realize narration through audiences' participation by utilizing the characteristics of live windows, not just watching the animation. This paper examines the differences between theaters and live window through the case that was produced and examples which utilized interaction for audiences' participation based on this. It analyzes the differences between environments according to characteristics of places and audiences in the differences between the theaters and live window, examines the examples to utilize interaction focusing on the process that narration is gradually changed as response to user environment design and interaction for unspecified individuals, and suggests direction that animation should move forward as public art based on the results to show the animation in Millano Piazza. According to the characteristics of live windows, the audiences of are people in the streets who are heading for different destinations, not the ones who come to theaters to watch the animation. Showing the animation with narration to them was a new attempt. When it began to show it in Millano Piazza, the audiences were very satisfied with the experiences that the stories were changed as they participated in it by themselves and naturally thought of global warming problems. You cannot know how the message of change people's habits and thoughts for the present, but this attempt was an opportunity that animations play the social role. Many animations are being produced in the world. Most of them are being done to aim at theaters, TVs, and film festivals. They should meet audiences through more various methods. One of them is animations as public art. And can be the new attempt in this sense. And in the future, animations as public art should make efforts to show you interesting experiences that you can share thoughts to be able to live together. As art of various media is changing to the one which considers public interests, animations can be new types of public art by integrating them with various technologies.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.163-169
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• 2024

India is a developing nation and has come with comprehensive way in modernizing its reducing poverty, economy and rising living standards for an outsized fragment of its residents. The STEM (Science, Technology, Engineering, and Mathematics) education plays an important role in it. STEM is an educational curriculum that emphasis on the subjects of "science, technology, engineering, and mathematics". In traditional education scenario, these subjects are taught independently, but according to the educational philosophy of STEM that teaches these subjects together in project-based lessons. STEM helps the students in his holistic development. Youth unemployment is the biggest concern due to lack of adequate skills. There is a huge skill gap behind jobless engineers and the question arises how we can prepare engineers for a better tomorrow? Now a day's Industry 4.0 is a new fourth industrial revolution which is an intelligent networking of machines and processes for industry through ICT. It is based upon the usage of cyber-physical systems and Internet of Things (IoT). Industrial revolution does not influence only production but also educational system as well. IoT in academics is a new revolution to the Internet technology, which introduced "Smartness" in the entire IT infrastructure. To improve socio-economic status of the India students must equipped with 21st century digital skills and Universities, colleges must provide individual learning kits to their students which can help them in enhancing their productivity and learning outcomes. The major goal of this paper is to present a low cost, effective learning mechanism for STEM implementation using Raspberry Pi 3+ model (Single board computer) and Node Red open source visual programming tool which is developed by IBM for wiring hardware devices together. These tools are broadly used to provide hands on experience on IoT fundamentals during teaching and learning. This paper elaborates the appropriateness and the practicality of these concepts via an example by implementing a user interface (UI) and Dashboard in Node-RED where dashboard palette is used for demonstration with switch, slider, gauge and Raspberry pi palette is used to connect with GPIO pins present on Raspberry pi board. An LED light is connected with a GPIO pin as an output pin. In this experiment, it is shown that the Node-Red dashboard is accessing on Raspberry pi and via Smartphone as well. In the final step results are shown in an elaborate manner. Conversely, inadequate Programming skills in students are the biggest challenge because without good programming skills there would be no pioneers in engineering, robotics and other areas. Coding plays an important role to increase the level of knowledge on a wide scale and to encourage the interest of students in coding. Today Python language which is Open source and most demanding languages in the industry in order to know data science and algorithms, understanding computer science would not be possible without science, technology, engineering and math. In this paper a small experiment is also done with an LED light via writing source code in python. These tiny experiments are really helpful to encourage the students and give play way to learn these advance technologies. The cost estimation is presented in tabular form for per learning kit provided to the students for Hands on experiments. Some Popular In addition, some Open source tools for experimenting with IoT Technology are described. Students can enrich their knowledge by doing lots of experiments with these freely available software's and this low cost hardware in labs or learning kits provided to them.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.