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With a large of time series dataset from the Internet of Things in Ambient Intelligence-enabled smart environments, many supervised learning-based anomaly detection methods have been investigated but ignored the correlation among the time series. To address this issue, we present a new idea for anomaly detection based on dynamic graph embedding, in which the dynamic graph comprises the multiple time series and their correlation in each time interval. We propose an entropy for measuring a graph’s information injunction with a correlation matrix to define similarity between graphs. A dynamic graph embedding model based on the graph similarity is proposed to cluster the graphs for anomaly detection. We implement the proposed model in vehicular edge computing for traffic incident detection. The experiments are carried out using traffic data produced by the Simulation of Urban Mobility framework. The experimental findings reveal that the proposed method achieves better results than the baselines by 14.5% and 18.1% on average with respect to F1-score and accuracy, respectively.
Gen Li; Tri-Hai Nguyen; Jason Jung. Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing. Applied Sciences 2021, 11, 5861 .
AMA StyleGen Li, Tri-Hai Nguyen, Jason Jung. Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing. Applied Sciences. 2021; 11 (13):5861.
Chicago/Turabian StyleGen Li; Tri-Hai Nguyen; Jason Jung. 2021. "Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing." Applied Sciences 11, no. 13: 5861.
Traffic congestion is one of the most critical issues in developing sustainable transportation in smart cities. As the Internet of Things evolves, connected vehicle technology has arisen as an essential research topic in smart, sustainable transportation. This study investigates a decentralized green traffic optimization framework by pushing swarm intelligence into connected vehicles to mitigate traffic congestion. We present a dynamic traffic routing method based on ant species’ swarm intelligence for connected vehicles so that they can communicate with each other and their surrounding environment via digital pheromones to perform routing decision-making in a decentralized manner. Traditional pheromones attract other vehicles to move to the optimal path, which will soon be congested if many vehicles travel on that path concurrently. To overcome this limitation, we propose the concept of repelling pheromone, which generates a repulsive force among vehicles so that their travel paths are distributed throughout a road network, resulting in a congestion-free road network. The proposed method is validated in the Simulation of Urban Mobility platform. Simulation findings reveal that the proposed method outperforms baseline methods in mitigating traffic congestion, reducing average fuel consumption and emissions by 13–19% and the average trip duration by 19–28%.
Tri-Hai Nguyen; Jason J. Jung. Swarm intelligence-based green optimization framework for sustainable transportation. Sustainable Cities and Society 2021, 71, 102947 .
AMA StyleTri-Hai Nguyen, Jason J. Jung. Swarm intelligence-based green optimization framework for sustainable transportation. Sustainable Cities and Society. 2021; 71 ():102947.
Chicago/Turabian StyleTri-Hai Nguyen; Jason J. Jung. 2021. "Swarm intelligence-based green optimization framework for sustainable transportation." Sustainable Cities and Society 71, no. : 102947.
In the recommender system field, diversity as the measure of recommendation quality has gained much attention recently. However, many pieces of research have shown that it has a trade-off relation with predictive performance. To improve recommendation diversity and predictive performance in multi-criteria recommender systems, we propose a clustering-based parallel tensor factorization (ClustPTF). In the ClustPTF, sentiment analysis alleviates model sparsity, and the K-means clustering considering rating behaviors groups similar user preferences into sub-models and leads to improve recommendation diversity. The sub-models are then factorized in parallel to predict ratings in near real-time. With one dataset gathered from TripAdvisor, experiments showed that the ClustPTF considerably improve recommendation diversity (13.44x of a conventional tensor factorization (TF0)) and response time (23.13x of the TF0). Even its predictive performance is superior to the TF0 (41.06% improvement in MAE). Furthermore, the ClustPTF outperformed recent techniques in recommendation diversity and predictive performance (i.e., MAE and precision).
Minsung Hong; Jason J. Jung. ClustPTF: Clustering-based parallel tensor factorization for the diverse multi-criteria recommendation. Electronic Commerce Research and Applications 2021, 47, 101041 .
AMA StyleMinsung Hong, Jason J. Jung. ClustPTF: Clustering-based parallel tensor factorization for the diverse multi-criteria recommendation. Electronic Commerce Research and Applications. 2021; 47 ():101041.
Chicago/Turabian StyleMinsung Hong; Jason J. Jung. 2021. "ClustPTF: Clustering-based parallel tensor factorization for the diverse multi-criteria recommendation." Electronic Commerce Research and Applications 47, no. : 101041.
Collaborative filtering recommendation systems, which analyze sets of user ratings, have been applied to various domains and have resulted in considerable improvements in the traditional recommendation system. However, they still have problems with data sparsity and cold‐start of the user ratings. To solve these problems, we present a hybrid recommendation approach by combining collaborative filtering methods and word embedding‐based content analysis. This study focuses on the movie domain, and therefore, the contents of the items are represented as a set of features such as titles, genres, directors, actors, and plots. The main aim of this paper is to understand the content of the movie plot using a word embedding to improve the measurement of similarity of each plot content to other plot content (called plot embedding). To enhance the accuracy in measuring the similarity between movies, we also consider other features such as titles, genres, directors, and actors extracted from movies. In the experiments, the movie dataset was collected by our crowdsourcing platform, which is the OMS platform. The experimental findings indicate that the proposed approach can enhance the efficiency of applied collaborative filtering recommendation systems.
Luong Vuong Nguyen; Tri‐Hai Nguyen; Jason J. Jung; David Camacho. Extending collaborative filtering recommendation using word embedding: A hybrid approach. Concurrency and Computation: Practice and Experience 2021, e6232 .
AMA StyleLuong Vuong Nguyen, Tri‐Hai Nguyen, Jason J. Jung, David Camacho. Extending collaborative filtering recommendation using word embedding: A hybrid approach. Concurrency and Computation: Practice and Experience. 2021; ():e6232.
Chicago/Turabian StyleLuong Vuong Nguyen; Tri‐Hai Nguyen; Jason J. Jung; David Camacho. 2021. "Extending collaborative filtering recommendation using word embedding: A hybrid approach." Concurrency and Computation: Practice and Experience , no. : e6232.
Existing dynamic graph embedding-based outlier detection methods mainly focus on the evolution of graphs and ignore the similarities among them. To overcome this limitation for the effective detection of abnormal climatic events from meteorological time series, we proposed a dynamic graph embedding model based on graph proximity, called DynGPE. Climatic events are represented as a graph where each vertex indicates meteorological data and each edge indicates a spurious relationship between two meteorological time series that are not causally related. The graph proximity is described as the distance between two graphs. DynGPE can cluster similar climatic events in the embedding space. Abnormal climatic events are distant from most of the other events and can be detected using outlier detection methods. We conducted experiments by applying three outlier detection methods (i.e., isolation forest, local outlier factor, and box plot) to real meteorological data. The results showed that DynGPE achieves better results than the baseline by 44.3% on average in terms of the F-measure. Isolation forest provides the best performance and stability. It achieved higher results than the local outlier factor and box plot methods, namely, by 15.4% and 78.9% on average, respectively.
Gen Li; Jason J. Jung. Dynamic graph embedding for outlier detection on multiple meteorological time series. PLOS ONE 2021, 16, e0247119 .
AMA StyleGen Li, Jason J. Jung. Dynamic graph embedding for outlier detection on multiple meteorological time series. PLOS ONE. 2021; 16 (2):e0247119.
Chicago/Turabian StyleGen Li; Jason J. Jung. 2021. "Dynamic graph embedding for outlier detection on multiple meteorological time series." PLOS ONE 16, no. 2: e0247119.
In this paper, we propose a novel strategy that identifies the dynamic relationship pattern for abnormality detection on financial time series. In particular, we select the basis indices that affect financial time series to discover the spurious relationships and construct a dynamic relationship matrix to model these. Then, we propose a graph embedding model by modifying the structural deep network embedding model to map these relationships into an embedding space. The abnormality is detected by using the outlier detection methods. To evaluate the proposed model, we have conducted the experiments by using the real financial time series (e.g., Apple, Amazon, Coke, Starbucks, and McDonald’s). The results showed that the proposed model achieved higher accuracy than the baselines by 4%.
Gen Li; Jason J. Jung. Dynamic relationship identification for abnormality detection on financial time series. Pattern Recognition Letters 2021, 145, 194 -199.
AMA StyleGen Li, Jason J. Jung. Dynamic relationship identification for abnormality detection on financial time series. Pattern Recognition Letters. 2021; 145 ():194-199.
Chicago/Turabian StyleGen Li; Jason J. Jung. 2021. "Dynamic relationship identification for abnormality detection on financial time series." Pattern Recognition Letters 145, no. : 194-199.
Managers make decisions on team tactics, formations, and player selection based on their own experiences. The managers have limitations in understanding the team's situation and sometimes they can think wrong. The purpose of this study is to make decisions on player selection and tactical formation according to the level of the opponent based on the data, not on the intuition of the manager. In our previous study, the Boruta algorithm was used to extract important features from 69 features in soccer player data by position. The detailed roles of each position were defined by using K‐means algorithm. For example, the detailed roles of each position were defined as Mezzala, Shadow Striker, Deep‐lying playmaker, and so on. That is, forward positions are classified as Target Man (TM) and Shadow Striker (SS). TM is a high‐goal, high‐competitive forward, and SS is a high‐dribble, high‐pass forward. In this study, we analyze a clustering dataset and the game appearance dataset. The game appearance dataset are divided into CL (Champions league Level), EL (Europa league Level), ML (Middle Level), and RL (Relegation Level). Association rule mining algorithm analyzes the synergy between positions, and selects a position with high synergy. Weighted association rule mining algorithm establishes player selection and tactical formation with the weight, which is the player's rating data. Finally, using the obtained results, we visualize the synergy between positions, tactical formation, and player characteristics depending on the level of the opponent.
Geon Ju Lee; Jason J. Jung; David Camacho. Exploiting weighted association rule mining for indicating synergic formation tactics in soccer teams. Concurrency and Computation: Practice and Experience 2021, e6221 .
AMA StyleGeon Ju Lee, Jason J. Jung, David Camacho. Exploiting weighted association rule mining for indicating synergic formation tactics in soccer teams. Concurrency and Computation: Practice and Experience. 2021; ():e6221.
Chicago/Turabian StyleGeon Ju Lee; Jason J. Jung; David Camacho. 2021. "Exploiting weighted association rule mining for indicating synergic formation tactics in soccer teams." Concurrency and Computation: Practice and Experience , no. : e6221.
This study aims at representing research patterns of bibliographic entities (e.g., scholars, papers, and venues) with a fixed-length vector. Bibliographic network structures rooted in the entities are incredibly diverse, and this diversity increases in the outstanding entities. Thus, despite their significant volume, the outstanding entities obtain minimal learning opportunities, whereas low-performance entities are over-represented. This study solves the problem by representing the patterns of the entities rather than depicting individual entities in a precise manner. First, we describe structures rooted in the entities using the Weisfeiler–Lehman (WL) relabeling process. Each subgraph generated by the relabeling process provides information on the scholars, kinds of papers they published, standards of venues in which the papers were published, and types of their collaborators. We assume that a subgraph depicts the research patterns of bibliographic entities, such as the preference of a scholar in choosing either a few highly impactful papers or numerous papers of moderate impact. Then, we simplify the subgraphs according to multiple levels of detailedness. Original subgraphs represent the individuality of the entities, and simplified subgraphs represent the entities sharing the same research patterns. In addition, simplified subgraphs balance the learning opportunities of high- and low-performance entities by co-occurring with both types of entities. We embed the subgraphs using the Skip-Gram method. If the results of the embedding represent the research patterns of the entities, the obtained vectors should be able to represent various aspects of the research performance in both the short-term and long-term durations regardless of the performances of the entities. Therefore, we conducted experiments for predicting 23 performance indicators during four time periods for four performance groups (top 1%, 5%, 10%, and all entities) using only the vector representations. The proposed model outperformed the existing network embedding methods in terms of both accuracy and variance.
O-Joun Lee; Hyeon-Ju Jeon; Jason J. Jung. Learning multi-resolution representations of research patterns in bibliographic networks. Journal of Informetrics 2021, 15, 101126 .
AMA StyleO-Joun Lee, Hyeon-Ju Jeon, Jason J. Jung. Learning multi-resolution representations of research patterns in bibliographic networks. Journal of Informetrics. 2021; 15 (1):101126.
Chicago/Turabian StyleO-Joun Lee; Hyeon-Ju Jeon; Jason J. Jung. 2021. "Learning multi-resolution representations of research patterns in bibliographic networks." Journal of Informetrics 15, no. 1: 101126.
Many tourism recommender systems have been studied to offer users the items meeting their interests. However, it is a non-trivial task to reflect the multi-criteria ratings and the cultural differences, which significantly influence users’ reviews of tourism facilities, into recommendation services. This paper proposes two “single tensor” models, consisting of users (or countries), items, multi-criteria ratings, and cultural groups, in order to consider simultaneously an inherent structure and interrelations of these factors into recommendation processes. With one Tripadvisor dataset, including 13 K users from 120 countries, experiments demonstrated that, in terms of MAE, the two proposed models for user and country give an improvement of 21.31% and 7.11% than other collaborative filtering and multi-criteria recommendation techniques. Besides, there were the positive influences of multiple-criteria ratings and cultural group factors on recommendation performances. The comparative analysis of several variants of the proposed models showed that considering Western and Eastern cultures is appropriate for improving predictive performances and their stability.
Minsung Hong; Jason J. Jung. Multi-criteria tensor model for tourism recommender systems. Expert Systems with Applications 2020, 170, 114537 .
AMA StyleMinsung Hong, Jason J. Jung. Multi-criteria tensor model for tourism recommender systems. Expert Systems with Applications. 2020; 170 ():114537.
Chicago/Turabian StyleMinsung Hong; Jason J. Jung. 2020. "Multi-criteria tensor model for tourism recommender systems." Expert Systems with Applications 170, no. : 114537.
This paper presents a cross-cultural crowdsourcing platform, called OurPlaces, where people from different cultures can share their spatial experiences. We built a three-layered architecture composed of: (i) places (locations where people have visited); (ii) cognition (how people have experienced these places); and (iii) users (those who have visited these places). Notably, cognition is represented as a paring of two similar places from different cultures (e.g., Versailles and Gyeongbokgung in France and Korea, respectively). As a case study, we applied the OurPlaces platform to a cross-cultural tourism recommendation system and conducted a simulation using a dataset collected from TripAdvisor. The tourist places were classified into four types (i.e., hotels, restaurants, shopping malls, and attractions). In addition, user feedback (e.g., ratings, rankings, and reviews) from various nationalities (assumed to be equivalent to cultures) was exploited to measure the similarities between tourism places and to generate a cognition layer on the platform. To demonstrate the effectiveness of the OurPlaces-based system, we compared it with a Pearson correlation-based system as a baseline. The experimental results show that the proposed system outperforms the baseline by 2.5% and 4.1% in the best case in terms of MAE and RMSE, respectively.
Luong Vuong Nguyen; Jason J. Jung; Myunggwon Hwang. OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services. ISPRS International Journal of Geo-Information 2020, 9, 711 .
AMA StyleLuong Vuong Nguyen, Jason J. Jung, Myunggwon Hwang. OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services. ISPRS International Journal of Geo-Information. 2020; 9 (12):711.
Chicago/Turabian StyleLuong Vuong Nguyen; Jason J. Jung; Myunggwon Hwang. 2020. "OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services." ISPRS International Journal of Geo-Information 9, no. 12: 711.
Emotion detection is an important research issue in electroencephalogram (EEG). Signal preprocessing and feature selection are parts of feature engineering, which determines the performance of emotion detection and reduces the training time of the deep learning models. To select the efficient features for emotion detection, we propose a maximum marginal approach on EEG signal preprocessing. The approach selects the least similar segments between two EEG signals as features that can represent the difference between EEG signals caused by emotions. The method defines a signal similarity described as the distance between two EEG signals to find the features. The frequency domain of EEG is calculated by using a wavelet transform that exploits a wavelet to calculate EEG components in a different frequency. We have conducted experiments by using the selected feature from real EEG data recorded from 10 college students. The experimental results show that the proposed approach performs better than other feature selection methods by 17.9% on average in terms of accuracy. The maximum marginal approach-based models achieve better performance than the models without feature selection by 21% on average in terms of accuracy.
Gen Li; Jason J. Jung. Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection. Applied Sciences 2020, 10, 7677 .
AMA StyleGen Li, Jason J. Jung. Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection. Applied Sciences. 2020; 10 (21):7677.
Chicago/Turabian StyleGen Li; Jason J. Jung. 2020. "Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection." Applied Sciences 10, no. 21: 7677.
Sustainable energy development consists of design, planning, and control optimization problems that are typically complex and computationally challenging for traditional optimization approaches. However, with developments in artificial intelligence, bio-inspired algorithms mimicking the concepts of biological evolution in nature and collective behaviors in societies of agents have recently become popular and shown potential success for these issues. Therefore, we investigate the latest research on bio-inspired approaches for smart energy management systems in smart homes, smart buildings, and smart grids in this paper. In particular, we give an overview of the well-known and emerging bio-inspired algorithms, including evolutionary-based and swarm-based optimization methods. Then, state-of-the-art studies using bio-inspired techniques for smart energy management systems are presented. Lastly, open challenges and future directions are also addressed to improve research in this field.
Tri-Hai Nguyen; Luong Nguyen; Jason Jung; Israel Agbehadji; Samuel Frimpong; Richard Millham. Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges. Sustainability 2020, 12, 8495 .
AMA StyleTri-Hai Nguyen, Luong Nguyen, Jason Jung, Israel Agbehadji, Samuel Frimpong, Richard Millham. Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges. Sustainability. 2020; 12 (20):8495.
Chicago/Turabian StyleTri-Hai Nguyen; Luong Nguyen; Jason Jung; Israel Agbehadji; Samuel Frimpong; Richard Millham. 2020. "Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges." Sustainability 12, no. 20: 8495.
In this study, we focus on dynamic traffic routing of connected vehicles with various origins and destinations; this is referred to as a multi-source multi-destination traffic routing problem. Ant colony optimization (ACO)-based routing method, together with the idea of coloring ants, is proposed to solve the defined problem in a distributed manner. Using the concept of coloring ants, traffic flows of connected vehicles to different destinations can be distinguished. To evaluate the performance of the proposed method, we perform simulations on the multi-agent NetLogo platform. The simulation results indicate that the ACO-based routing method outperforms the shortest path-based routing method (i.e., given the same simulation period, the average travel time decreases by 8% on average and by 11% in the best case, whereas the total number of arrived vehicles increases by 13% on average and by 23% in the best case).
Tri-Hai Nguyen; Jason J. Jung. Multiple ACO-based method for solving dynamic MSMD traffic routing problem in connected vehicles. Neural Computing and Applications 2020, 33, 6405 -6414.
AMA StyleTri-Hai Nguyen, Jason J. Jung. Multiple ACO-based method for solving dynamic MSMD traffic routing problem in connected vehicles. Neural Computing and Applications. 2020; 33 (12):6405-6414.
Chicago/Turabian StyleTri-Hai Nguyen; Jason J. Jung. 2020. "Multiple ACO-based method for solving dynamic MSMD traffic routing problem in connected vehicles." Neural Computing and Applications 33, no. 12: 6405-6414.
This paper provides a new approach that improves collaborative filtering results in recommendation systems. In particular, we aim to ensure the reliability of the data set collected which is to collect the cognition about the item similarity from the users. Hence, in this work, we collect the cognitive similarity of the user about similar movies. Besides, we introduce a three-layered architecture that consists of the network between the items (item layer), the network between the cognitive similarity of users (cognition layer) and the network between users occurring in their cognitive similarity (user layer). For instance, the similarity in the cognitive network can be extracted from a similarity measure on the item network. In order to evaluate our method, we conducted experiments in the movie domain. In addition, for better performance evaluation, we use the F-measure that is a combination of two criteria P r e c i s i o n and R e c a l l . Compared with the Pearson Correlation, our method more accurate and achieves improvement over the baseline 11.1% in the best case. The result shows that our method achieved consistent improvement of 1.8% to 3.2% for various neighborhood sizes in MAE calculation, and from 2.0% to 4.1% in RMSE calculation. This indicates that our method improves recommendation performance.
Luong Vuong Nguyen; Min-Sung Hong; Jason J. Jung; Bong-Soo Sohn. Cognitive Similarity-Based Collaborative Filtering Recommendation System. Applied Sciences 2020, 10, 4183 .
AMA StyleLuong Vuong Nguyen, Min-Sung Hong, Jason J. Jung, Bong-Soo Sohn. Cognitive Similarity-Based Collaborative Filtering Recommendation System. Applied Sciences. 2020; 10 (12):4183.
Chicago/Turabian StyleLuong Vuong Nguyen; Min-Sung Hong; Jason J. Jung; Bong-Soo Sohn. 2020. "Cognitive Similarity-Based Collaborative Filtering Recommendation System." Applied Sciences 10, no. 12: 4183.
The previous recommendation system applied the matrix factorization collaborative filtering (MFCF) technique to only single domains. Due to data sparsity, this approach has a limitation in overcoming the cold-start problem. Thus, in this study, we focus on discovering latent features from domains to understand the relationships between domains (called domain coherence). This approach uses potential knowledge of the source domain to improve the quality of the target domain recommendation. In this paper, we consider applying MFCF to multiple domains. Mainly, by adopting the implicit stochastic gradient descent algorithm to optimize the objective function for prediction, multiple matrices from different domains are consolidated inside the cross-domain recommendation system (CDRS). Additionally, we design a conceptual framework for CDRS, which applies to different industrial scenarios for recommenders across domains. Moreover, an experiment is devised to validate the proposed method. By using a real-world dataset gathered from Amazon Food and MovieLens, experimental results show that the proposed method improves 15.2% and 19.7% in terms of computation time and MSE over other methods on a utility matrix. Notably, a much lower convergence value of the loss function has been obtained from the experiment. Furthermore, a critical analysis of the obtained results shows that there is a dynamic balance between prediction accuracy and computational complexity.
Nam D. Vo; Minsung Hong; Jason J. Jung. Implicit Stochastic Gradient Descent Method for Cross-Domain Recommendation System. Sensors 2020, 20, 2510 .
AMA StyleNam D. Vo, Minsung Hong, Jason J. Jung. Implicit Stochastic Gradient Descent Method for Cross-Domain Recommendation System. Sensors. 2020; 20 (9):2510.
Chicago/Turabian StyleNam D. Vo; Minsung Hong; Jason J. Jung. 2020. "Implicit Stochastic Gradient Descent Method for Cross-Domain Recommendation System." Sensors 20, no. 9: 2510.
Ciyuan Peng; Jason J Jung. Interpretation of metaphors in Chinese poetry: Where did Li Bai place his emotions? Digital Scholarship in the Humanities 2020, 1 .
AMA StyleCiyuan Peng, Jason J Jung. Interpretation of metaphors in Chinese poetry: Where did Li Bai place his emotions? Digital Scholarship in the Humanities. 2020; ():1.
Chicago/Turabian StyleCiyuan Peng; Jason J Jung. 2020. "Interpretation of metaphors in Chinese poetry: Where did Li Bai place his emotions?" Digital Scholarship in the Humanities , no. : 1.
Everyone, knowingly or not, is in a position to generate data at all times in our lives. Mainly in the professional life, where the production of data is addressed and aimed at achieving a series of objectives, and in social and personal life where the production of data, direct and indirect, is equally important although not always clearly identifiable. Always and in any case, the representation of our actions in digital contents or the analysis of our behaviour as consumers, just to give two examples, are a common denominator of our relationship with the digital framework which has a direct impact at the level of relationship with companies or with public administrations. In the business, the most appropriate example today is that of Industry 4.0 which with the virtualization of the processes and products themselves is allowing companies to explore novel paradigms of innovation that were once unthinkable.
Francesco Piccialli; Nik Bessis; Jason J. Jung. Guest Editorial: Data Science Challenges in Industry 4.0. IEEE Transactions on Industrial Informatics 2020, 16, 5924 -5928.
AMA StyleFrancesco Piccialli, Nik Bessis, Jason J. Jung. Guest Editorial: Data Science Challenges in Industry 4.0. IEEE Transactions on Industrial Informatics. 2020; 16 (9):5924-5928.
Chicago/Turabian StyleFrancesco Piccialli; Nik Bessis; Jason J. Jung. 2020. "Guest Editorial: Data Science Challenges in Industry 4.0." IEEE Transactions on Industrial Informatics 16, no. 9: 5924-5928.
Narrative works (e.g., novels and movies) consist of various utterances (e.g., scenes and episodes) with multi-layered structures. However, the existing studies aimed to embed only stories in a narrative work. By covering other granularity levels, we can easily compare narrative utterances that are coarser (e.g., movie series) or finer (e.g., scenes) than a narrative work. We apply the multi-layered structures on learning hierarchical representations of the narrative utterances. To represent coarser utterances, we consider adjacency and appearance of finer utterances in the coarser ones. For the movies, we suppose a four-layered structure (character roles ∈ characters ∈ scenes ∈ movies) and propose three learning methods bridging the layers: Char2Vec, Scene2Vec, and Hierarchical Story2Vec. Char2Vec represents a character by using dynamic changes in the character’s roles. To find the character roles, we use substructures of character networks (i.e., dynamic social networks of characters). A scene describes an event. Interactions between characters in the scene are designed to describe the event. Scene2Vec learns representations of a scene from interactions between characters in the scene. A story is a series of events. Meanings of the story are affected by order of the events as well as their content. Hierarchical Story2Vec uses sequential order of scenes to represent stories. The proposed model has been evaluated by estimating the similarity between narrative utterances in real movies.
O-Joun Lee; Jason J. Jung; Jin-Taek Kim. Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia. Sensors 2020, 20, 1978 .
AMA StyleO-Joun Lee, Jason J. Jung, Jin-Taek Kim. Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia. Sensors. 2020; 20 (7):1978.
Chicago/Turabian StyleO-Joun Lee; Jason J. Jung; Jin-Taek Kim. 2020. "Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia." Sensors 20, no. 7: 1978.
The emergence of various disruptive technologies such as big data, Internet of Things, and artificial intelligence have instigated our society to generate enormous volumes of data. The effective, efficient, and transparent capture and fusion of knowledge from a massive amount data is becoming an increasingly popular and crucial topic. In this study, we aim to provide a broad, complete, and systematic overview of the definitions and challenges of the knowledge graph fusion, which represents a holistic approach for integrating, enhancing, and unifying knowledge graphs. Further, advanced techniques for handling knowledge graph fusion along with the pragmatic smart systems leveraging it are discussed as a part of multiple perspectives. We believe that this survey study can be used as a potential reference for system practitioners and researchers in surpassing current obstacles as well as shaping their future direction.
Hoang Long Nguyen; Dang Thinh Vu; Jason J. Jung. Knowledge graph fusion for smart systems: A Survey. Information Fusion 2020, 61, 56 -70.
AMA StyleHoang Long Nguyen, Dang Thinh Vu, Jason J. Jung. Knowledge graph fusion for smart systems: A Survey. Information Fusion. 2020; 61 ():56-70.
Chicago/Turabian StyleHoang Long Nguyen; Dang Thinh Vu; Jason J. Jung. 2020. "Knowledge graph fusion for smart systems: A Survey." Information Fusion 61, no. : 56-70.
The aim of this research is to present a crowdsourcing-based recommendation platform called OurMovieSimilarity (OMS), which can collect and sharecognitive feedbacks from users. In particular, we focus on the user’s cognition patterns on the similarity between the two movies. OMS also analyzes the collected data of the user to classify the user group and dynamic changes movie recommendations for each different user. The purpose of this is to make OMS interact intelligently and the data collected faster and more accurately. We received more than a thousand feedbacks from 50 users and did the analyzes this data to group the user. A group of the users can be dynamically changed, with respect to the selection of each user. OMS now still online and collecting data. We have been trying to enrich the cognitive feedback dataset including more than 20,000 feedbacks from 5000 users, so that the recommendation system can make more accurate analysis of user cognitive in choosing the movie similarity.
Nguyen Luong Vuong; Jason J. Jung. Crowdsourcing Platform for Collecting Cognitive Feedbacks from Users: A Case Study on Movie Recommender System. Springer Series in Reliability Engineering 2020, 139 -150.
AMA StyleNguyen Luong Vuong, Jason J. Jung. Crowdsourcing Platform for Collecting Cognitive Feedbacks from Users: A Case Study on Movie Recommender System. Springer Series in Reliability Engineering. 2020; ():139-150.
Chicago/Turabian StyleNguyen Luong Vuong; Jason J. Jung. 2020. "Crowdsourcing Platform for Collecting Cognitive Feedbacks from Users: A Case Study on Movie Recommender System." Springer Series in Reliability Engineering , no. : 139-150.