• 과학기술학연구
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• 제11권1호
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• pp.1-29
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• 2011

이 글에서는 신기술의 사회적 수용과정을 분석 평가하기 위한 틀을 개발하고 그에 입각하여 바이오가스 기술의 수용과정을 분석한다. 분석의 틀에서는 기술 조직 제도의 공진화론에 입각해서 신기술이 사회에 수용되기 위해서는 기술적 경제적 문제만이 아니라 신기술의 사회적 위험에 대한 관리가 이루어져야 한다는 점을 논의할 것이다. 이와 함께 기술 경제적 문제해결을 위한 기술학습활동과 기술의 정당성을 향상시키기 위한 기술정치활동이 필요하다는 점도 강조할 것이다. 다음으로 바이오가스 플랜트 기술의 특성과 개발 운영현황을 살펴본 후, 제시된 분석틀을 활용하여 바이오가스 플랜트 기술의 사회적 수용과정에서 나타나는 문제점을 검토하고 사회적 수용을 촉진하기 위한 방안을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3798-3814
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• 2022

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user's potential preferences, reduces algorithms' recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dot-product operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user's preferences, ignoring the direct impact on user's rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user's ratings preferences and user's preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset.

• 디지털융복합연구
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• 제17권5호
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• pp.137-143
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• 2019

가짜 뉴스는 소셜 미디어와 같이 사용자가 상호작용하는 미디어 플랫폼에서 정보가 빠른 속도로 확산되는 이점을 가지는 오류 정보(misinformation)의 한 형태이다. 최근 가짜 뉴스의 증가로 인해 사회적으로 많은 문제가 발생하고 있다. 본 논문에서는 이러한 가짜 뉴스를 탐지하는 방법을 제안한다. 이전의 가짜 뉴스 탐지는 텍스트 분석을 사용한 연구가 주로 수행되었다. 본 연구는 소셜 미디어의 뉴스가 확산되는 네트워크에 초점을 두고, 네트워크 임베딩 방법인 DeepWalk 로 자질을 생성하고 로지스틱 회귀분석을 사용하여 가짜 뉴스를 분류한다. 인터넷에 공개된 뉴스 211개와 120만개의 뉴스 확산 네트워크 데이터를 사용한 가짜 뉴스 탐지에 대한 실험을 수행하였다. 연구 결과 텍스트 분석에 비하여 네트워크 임베딩을 사용한 가짜 뉴스 탐지의 정확도가 최소 1.7%에서 최대 10.6% 더 높게 나타났다. 또한, 텍스트 분석과 네트워크 임베딩을 결합한 가짜 뉴스 탐지는 네트워크 임베딩에 비해 정확도의 상승이 나타나지 않았다. 본 연구의 결과는 기업이나 조직은 온라인 상에서 확산되는 가짜 뉴스 탐지에 효과적으로 활용될 수 있다.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• Journal of information and communication convergence engineering
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• 제21권1호
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• pp.9-16
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• 2023

The natural language on social network platforms has a certain front-to-back dependency in structure, and the direct conversion of Chinese text into a vector makes the dimensionality very high, thereby resulting in the low accuracy of existing text classification methods. To this end, this study establishes a deep learning model that combines a big data ultra-deep convolutional neural network (UDCNN) and long short-term memory network (LSTM). The deep structure of UDCNN is used to extract the features of text vector classification. The LSTM stores historical information to extract the context dependency of long texts, and word embedding is introduced to convert the text into low-dimensional vectors. Experiments are conducted on the social network platforms Sogou corpus and the University HowNet Chinese corpus. The research results show that compared with CNN + rand, LSTM, and other models, the neural network deep learning hybrid model can effectively improve the accuracy of text classification.

• 적정기술학회지
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• 제5권1호
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• pp.25-32
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• 2019

이 글에서는 대학에서 진행되는 사회문제 해결형 연구개발의 현황과 문제점을 검토하고 사회문제 해결형 연구개발을 활성화하기 위한 방안을 검토한다. 사회문제 해결형 연구개발은 사회적 목표 추구와 참여형 연구개발을 특성으로 하고 있어 기존 연구활동과는 추진방식이 다르다. 이 글에서는 포커스 그룹 인터뷰를 바탕으로 사회문제해결형 연구개발이 대학에 뿌리내리는 데 직면하는 문제점과 개선 방안을 정리하였다. 1) 주류 연구자와 시민사회의 참여를 활성화해 대학 내 핵심연구 활동으로 자리잡게 하는 방안, 2) 대학에 적합한 사회문제 해결형 연구개발 모델을 도출하고 확산하는 방안, 3) 장기 지속성을 갖는 사회문제 해결형 연구개발센터를 설립하여 대학 내에 사회문제 해결형 연구개발의 거점을 만드는 방안들이 활성화 방안으로 논의되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.4143-4159
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• 2015

We-the-media (aka wedia) is a concept where the users of social networking sites, such as Facebook, turn into the broadcasters. This study used the popular application Bitstrips as the experiment tool. Facebook was used as the Wedia platform for publishing designed comics, then used the three elements of Goffman's dramaturgy model-role, scene and dialog-to analyze 265 comics created by 3 researchers and observe the audience's responses within 9 months. The results showed that people want to see a good story with positive dialogue, and prefer scene is school more than work. As all these elements are controllable, Wedia communication has the potential for more applications. We also found that including the elements of news, gambling and gift-giving tended to trigger greater response. Furthermore, We suggesting that such embedding of product information in web episodes (webisodes) with caricature could be a successful marketing strategy.

• KIEE International Transactions on Power Engineering
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• 제3A권1호
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• pp.42-46
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• 2003

The economic operation of a utility in a deregulated environment brings about optimization problems different from those in vertically integrated one［1］. While each utility operates its own generation capacity to maximize profit, the market operator (or system operator) manages and allocates all the system resources and facilities to achieve the maximum social welfare. This paper presents a sequential application of non-cooperative and cooperative game theories in analyzing the entire power transaction process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.51-73
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• 2023

Social media play a significant role in communicating information across the globe, connecting with loved ones, getting the news, communicating ideas, etc. However, a group of people uses social media to spread fake information, which has a bad impact on society. Therefore, minimizing fake news and its detection are the two primary challenges that need to be addressed. This paper presents a multi-modal deep learning technique to address the above challenges. The proposed modal can use and process visual and textual features. Therefore, it has the ability to detect fake information from visual and textual data. We used EfficientNetB0 and a sentence transformer, respectively, for detecting counterfeit images and for textural learning. Feature embedding is performed at individual channels, whilst fusion is done at the last classification layer. The late fusion is applied intentionally to mitigate the noisy data that are generated by multi-modalities. Extensive experiments are conducted, and performance is evaluated against state-of-the-art methods. Three real-world benchmark datasets, such as MediaEval (Twitter), Weibo, and Fakeddit, are used for experimentation. Result reveals that the proposed modal outperformed the state-of-the-art methods and achieved an accuracy of 86.48%, 82.50%, and 88.80%, respectively, for MediaEval (Twitter), Weibo, and Fakeddit datasets.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2018년도 제30회 한글 및 한국어 정보처리 학술대회
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• pp.652-657
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• 2018

With the rapid development of e-commerce, many customers can now express their opinion on various kinds of product at discussion groups, merchant sites, social networks, etc. Discerning a consensus opinion about a product sold online is difficult due to more and more reviews become available on the internet. Opinion Mining, also known as Sentiment analysis, is the task of automatically detecting and understanding the sentimental expressions about a product from customer textual reviews. Recently, researchers have proposed various approaches for evaluation in sentiment mining by applying several techniques for document, sentence and aspect level. Aspect-based sentiment analysis is getting widely interesting of researchers; however, more complex algorithms are needed to address this issue precisely with larger corpora. This paper introduces an approach of knowledge representation for the task of analyzing product aspect rating. We focus on how to form the nature of sentiment representation from textual opinion by utilizing the representation learning methods which include word embedding and compositional vector models. Our experiment is performed on a dataset of reviews from electronic domain and the obtained result show that the proposed system achieved outstanding methods in previous studies.