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Yixuan Yang,Sony Peng,Doo-Soon Park,Hye-Jung Lee,Phonexay Vilakone Korea Information Processing Society 2024 Journal of information processing systems Vol.20 No.2
Amid the flood of data, social network analysis is beneficial in searching for its hidden context and verifying several pieces of information. This can be used for detecting the spread model of infectious diseases, methods of preventing infectious diseases, mining of small groups and so forth. In addition, community detection is the most studied topic in social network analysis using graph analysis methods. The objective of this study is to examine signed attributed social networks and identify the maximal balanced cliques that are both absolute and fair. In the same vein, the purpose is to ensure fairness in complex networks, overcome the "information cocoon" bottleneck, and reduce the occurrence of "group polarization" in social networks. Meanwhile, an empirical study is presented in the experimental section, which uses the personal information of 77 employees of a research company and the trust relationships at the professional level between employees to mine some small groups with the possibility of "group polarization." Finally, the study provides suggestions for managers of the company to align and group new work teams in an organization.
Detection of Maximal Balance Clique Using Three-way Concept Lattice
Yixuan Yang,박두순,Fei Hao,Sony Peng,이혜정,홍민표 한국정보처리학회 2023 Journal of information processing systems Vol.19 No.2
In the era marked by information inundation, social network analysis is the most important part of big dataanalysis, with clique detection being a key technology in social network mining. Also, detecting maximalbalance clique in signed networks with positive and negative relationships is essential. In this paper, we presenttwo algorithms. The first one is an algorithm, MCDA1, that detects the maximal balance clique using theimproved three-way concept lattice algorithm and object-induced three-way concept lattice (OE-concept). Thesecond one is an improved formal concept analysis algorithm, MCDA2, that improves the efficiency ofmemory. Additionally, we tested the execution time of our proposed method with four real-world datasets.
서명된 속성 소셜 네트워크에서의 Absolute-Fair Maximal Balanced Cliques 탐색
양예선 ( Yixuan Yang ),펭소니 ( Sony Peng ),박두순 ( Doo-soon Park ),이혜정 ( Hyejung Lee ) 한국정보처리학회 2022 한국정보처리학회 학술대회논문집 Vol.29 No.1
Community detection is a hot topic in social network analysis, and many existing studies use graph theory analysis methods to detect communities. This paper focuses on detecting absolute fair maximal balanced cliques in signed attributed social networks, which can satisfy ensuring the fairness of complex networks and break the bottleneck of the information cocoon .
클리크 마이닝에 기반한 새로운 커뮤니티 탐지 알고리즘 연구
양예선 ( Yixuan Yang ),펭소니 ( Sony Peng ),박두순 ( Doo-soon Park ),김석훈 ( Seok-hoon Kim ),이혜정 ( Hyejung Lee ),싯소포호트 ( Sophort Siet ) 한국정보처리학회 2022 한국정보처리학회 학술대회논문집 Vol.29 No.2
Community detection is meaningful research in social network analysis, and many existing studies use graph theory analysis methods to detect communities. This paper proposes a method to detect community by detecting maximal cliques and obtain the high influence cliques by high influence nodes, then merge the cliques with high similarity in social network.
Yixuan Xu,이재기,Ji-Jing Yan,Jung-Hwa Ryu,Songji Xu,Jaeseok Yang 대한진단검사의학회 2020 Annals of Laboratory Medicine Vol.40 No.1
Background: Anti-carbohydrate antibody responses, including those of anti-blood group ABO antibodies, are yet to be thoroughly studied in humans. Because anti-ABO antibody-mediated rejection is a key hurdle in ABO-incompatible transplantation, it is important to understand the cellular mechanism of anti-ABO responses. We aimed to identify the main human B cell subsets that produce anti-ABO antibodies by analyzing the correlation between B cell subsets and anti-ABO antibody titers. Methods: Blood group A-binding B cells were analyzed in peritoneal fluid and peripheral blood samples from 43 patients undergoing peritoneal dialysis and 18 healthy volunteers with blood group B or O. The correlation between each blood group A-specific B cell subset and anti-A antibody titer was then analyzed using Pearson’s correlation analysis. Results: Blood group A-binding B cells were enriched in CD27+CD43+CD1c− B1, CD5+ B1, CD11b+ B1, and CD27+CD43+CD1c+ marginal zone-B1 cells in peripheral blood. Blood group A-specific B1 cells (P=0.029 and R=0.356 for IgM; P=0.049 and R=0.325 for IgG) and marginal zone-B1 cells (P=0.011 and R=0.410 for IgM) were positively correlated with anti-A antibody titer. Further analysis of peritoneal B cells confirmed B1 cell enrichment in the peritoneal cavity but showed no difference in blood group A-specific B1 cell enrichment between the peritoneal cavity and peripheral blood. Conclusions: Human B1 cells are the key blood group A-specific B cells that have a moderate correlation with anti-A antibody titer and therefore constitute a potential therapeutic target for successful ABO-incompatible transplantation.
Centralized Machine Learning Versus Federated Averaging: A Comparison using MNIST Dataset
Sony Peng,Yixuan Yang,Makara Mao,Doo-Soon Park 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.2
A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment.
Sensitive parameters’ optimization of the permanent magnet supporting mechanism
Yongguang Liu,Xiaohui Gao,Yixuan Wang,Xiaowei Yang 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.7
The fast development of the ultra-high speed vertical rotor promotes the study and exploration for the supporting mechanism. It hasbecome the focus of research that how to improve the speed and overcome the vibration when the rotors pass through the low-order criticalfrequencies. This paper introduces a kind of permanent magnet (PM) supporting mechanism and describes an optimization method ofits sensitive parameters, which can make the vertical rotor system reach 80000 r/min smoothly. Firstly we find the sensitive parametersthrough analyzing the rotor’s features in the process of achieving high-speed, then, study these sensitive parameters and summarize theregularities with the method of combining the experiment and the finite element method (FEM), at last, achieve the optimization methodof these parameters. That will not only get a stable effect of raising speed and shorten the debugging time greatly, but also promote theextensive application of the PM supporting mechanism in the ultra-high speed vertical rotors.
펭소니 ( Sony Peng ),양예선 ( Yixuan Yang ),박두순 ( Doo-soon Park ),이혜정 ( Hyejung Lee ) 한국정보처리학회 2022 한국정보처리학회 학술대회논문집 Vol.29 No.1
With the tremendous rise in popularity of the Internet and technological advancements, many news keeps generating every day from multiple sources. As a result, the information (News) on the network has been highly increasing. The critical problem is that the volume of articles or news content can be overloaded for the readers. Therefore, the people interested in reading news might find it difficult to decide which content they should choose. Recommendation systems have been known as filtering systems that assist people and give a list of suggestions based on their preferences. This paper studies a personalized news recommendation system to help users find the right, relevant content and suggest news that readers might be interested in. The proposed system aims to build a hybrid system that combines collaborative filtering with content-based filtering to make a system more effective and solve a cold-start problem. Twitter social media data will analyze and build a user's profile. Based on users' tweets, we can know users' interests and recommend personalized news articles that users would share on Twitter.
Bi-directional Maximal Matching Algorithm to Segment Khmer Words in Sentence
Makara Mao,Sony Peng,Yixuan Yang,박두순 한국정보처리학회 2022 Journal of information processing systems Vol.18 No.4
In the Khmer writing system, the Khmer script is the official letter of Cambodia, written from left to rightwithout a space separator; it is complicated and requires more analysis studies. Without clear standardguidelines, a space separator in the Khmer language is used inconsistently and informally to separate words insentences. Therefore, a segmented method should be discussed with the combination of the future Khmernatural language processing (NLP) to define the appropriate rule for Khmer sentences. The critical process inNLP with the capability of extensive data language analysis necessitates applying in this scenario. One of theessential components in Khmer language processing is how to split the word into a series of sentences andcount the words used in the sentences. Currently, Microsoft Word cannot count Khmer words correctly. So,this study presents a systematic library to segment Khmer phrases using the bi-directional maximal matching(BiMM) method to address these problematic constraints. In the BiMM algorithm, the paper focuses on the Bidirectionalimplementation of forward maximal matching (FMM) and backward maximal matching (BMM) toimprove word segmentation accuracy. A digital or prefix tree of data structure algorithm, also known as a trie,enhances the segmentation accuracy procedure by finding the children of each word parent node. The accuracyof BiMM is higher than using FMM or BMM independently; moreover, the proposed approach improvesdictionary structures and reduces the number of errors. The result of this study can reduce the error by 8.57%compared to FMM and BFF algorithms with 94,807 Khmer words.