The fashion industry operates in a fast moving and dynamic environment which requires fashion designers to respond to market trends continuously. This study investigates potential for application of Internet of Things (IoT) in fashion retail. Customer in-store behaviors may reflect their hidden preferences. This study is based on use of IoT as a framework of data collection tools to capture customer behaviors in-store. Artificial intelligence (AI) such Fuzzy logic and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used to analyze customer purchasing intentions and simulation will be used to illustrate the model [1]. This study shows that IoT can obtain the required data of customer behaviors and use AI to analyze the preferences. It can be used in-store to help salespersons to respond to customer needs faster and accurately. The data obtained after analyzing can be used in supply chain planning.
{"title":"Implementing IoT-Adaptive Fuzzy Neural Network Model Enabling Service for Supporting Fashion Retail","authors":"C. Chan, H. Lau, Youqing Fan","doi":"10.1145/3380688.3380692","DOIUrl":"https://doi.org/10.1145/3380688.3380692","url":null,"abstract":"The fashion industry operates in a fast moving and dynamic environment which requires fashion designers to respond to market trends continuously. This study investigates potential for application of Internet of Things (IoT) in fashion retail. Customer in-store behaviors may reflect their hidden preferences. This study is based on use of IoT as a framework of data collection tools to capture customer behaviors in-store. Artificial intelligence (AI) such Fuzzy logic and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used to analyze customer purchasing intentions and simulation will be used to illustrate the model [1]. This study shows that IoT can obtain the required data of customer behaviors and use AI to analyze the preferences. It can be used in-store to help salespersons to respond to customer needs faster and accurately. The data obtained after analyzing can be used in supply chain planning.","PeriodicalId":414793,"journal":{"name":"Proceedings of the 4th International Conference on Machine Learning and Soft Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129559060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. C. Lim, Swee Heng Sin, C. Lee, Weng Khin Chin, Junliang Lin, Khang Nguyen, Quang H. Nguyen, Binh P. Nguyen, M. Chua
Hand gesture recognition is a hot topic and a central key for different types of application. As applications of computers and intelligent systems are growing in our daily life, facilitating natural human computer interaction becomes more important. In this paper, we focus on video-based approach on hand gesture recognition integrated with 3-D hand skeletal features to construct the raw video sequences, retaining the key video frames, extracting spatial temporal data and feeding them into a Support Vector Machine model for 2-D hand sign classification. Our novel method integrates hand skeletal descriptor into video sequence to retain the spatial temporal information which will be extracted as vectors for classification task. As oppose to conventional method of requiring a well placed pair of cameras or depth detection hardware, our method only require only one camera. The proposed approach outperforms state-of-the-art static hand gesture recognition methods, achieving almost 100% accuracy among 24 classes.
{"title":"Video-based Skeletal Feature Extraction for Hand Gesture Recognition","authors":"K. C. Lim, Swee Heng Sin, C. Lee, Weng Khin Chin, Junliang Lin, Khang Nguyen, Quang H. Nguyen, Binh P. Nguyen, M. Chua","doi":"10.1145/3380688.3380711","DOIUrl":"https://doi.org/10.1145/3380688.3380711","url":null,"abstract":"Hand gesture recognition is a hot topic and a central key for different types of application. As applications of computers and intelligent systems are growing in our daily life, facilitating natural human computer interaction becomes more important. In this paper, we focus on video-based approach on hand gesture recognition integrated with 3-D hand skeletal features to construct the raw video sequences, retaining the key video frames, extracting spatial temporal data and feeding them into a Support Vector Machine model for 2-D hand sign classification. Our novel method integrates hand skeletal descriptor into video sequence to retain the spatial temporal information which will be extracted as vectors for classification task. As oppose to conventional method of requiring a well placed pair of cameras or depth detection hardware, our method only require only one camera. The proposed approach outperforms state-of-the-art static hand gesture recognition methods, achieving almost 100% accuracy among 24 classes.","PeriodicalId":414793,"journal":{"name":"Proceedings of the 4th International Conference on Machine Learning and Soft Computing","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114320066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Huynh, Ky Minh Nguyen, Khoa Duc Nguyen, H. H. Luong, N. C. Tran, L. Nguyen, T. Tran, Phuong Truc Thi Pham, S. Niculescu
Although agricultural production contributed a significant share of Vietnam's total production, the advancement and proficiency of remote sensing are still narrowly applied in this sector. Recent years, by the open access to satellite products of sufficient characteristics, the agriculture with satellite images supporting is being boosted. This paper focuses on identifying land use and its long-term changes in the selected regions of the Mekong Delta.
{"title":"Discovered changes in rice occupation with satellite images based on random forest approach","authors":"H. Huynh, Ky Minh Nguyen, Khoa Duc Nguyen, H. H. Luong, N. C. Tran, L. Nguyen, T. Tran, Phuong Truc Thi Pham, S. Niculescu","doi":"10.1145/3380688.3380699","DOIUrl":"https://doi.org/10.1145/3380688.3380699","url":null,"abstract":"Although agricultural production contributed a significant share of Vietnam's total production, the advancement and proficiency of remote sensing are still narrowly applied in this sector. Recent years, by the open access to satellite products of sufficient characteristics, the agriculture with satellite images supporting is being boosted. This paper focuses on identifying land use and its long-term changes in the selected regions of the Mekong Delta.","PeriodicalId":414793,"journal":{"name":"Proceedings of the 4th International Conference on Machine Learning and Soft Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125381193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, the authors propose an ensemble multi-objective particle swarm optimisation approach (named EMPSO) for forecasting the currency exchange rate chain. The proposed algorithm consists of two main phases. The first phase uses a multi-objective particle swarm optimisation algorithm to find a set of the best optimal particles (named leaders). The second phase then uses these leaders to jointly calculate the final results by using the soft voting ensemble method. The two objective functions used here are predictive error and particle diversity. The empirical data used in this study are six different sets of currency exchange rates. Through comparison results with other evolutionary algorithms and other multi-objective PSO algorithms, the proposed algorithm shows that it can achieve better as well as more stability results on experimental data sets.
{"title":"An ensemble multi-objective particle swarm optimization approach for exchange rates forecasting problem","authors":"T. Dinh, V. Vu, L. Bui","doi":"10.1145/3380688.3380717","DOIUrl":"https://doi.org/10.1145/3380688.3380717","url":null,"abstract":"In this paper, the authors propose an ensemble multi-objective particle swarm optimisation approach (named EMPSO) for forecasting the currency exchange rate chain. The proposed algorithm consists of two main phases. The first phase uses a multi-objective particle swarm optimisation algorithm to find a set of the best optimal particles (named leaders). The second phase then uses these leaders to jointly calculate the final results by using the soft voting ensemble method. The two objective functions used here are predictive error and particle diversity. The empirical data used in this study are six different sets of currency exchange rates. Through comparison results with other evolutionary algorithms and other multi-objective PSO algorithms, the proposed algorithm shows that it can achieve better as well as more stability results on experimental data sets.","PeriodicalId":414793,"journal":{"name":"Proceedings of the 4th International Conference on Machine Learning and Soft Computing","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131850814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quynh N. Nguyen, Nghia Duong-Trung, Dung Ngoc Le Ha, H. Son, T. Phan, Hien Xuan Pham, H. Huynh
20 years of MovieLens datasets have witnessed a blossom of research that is garnering a remarkable significance with the advent of e-commerce and the whole industry. Four variations of the dataset have been downloaded hundreds of thousands of times, reflecting their popularity and distinctive contribution in the field of recommendation systems and connected subjects. This paper exploits the movie recommender system based on movies' genres and actors/actresses themselves as the input tags, or tag interpolation. The problem has not been addressed in the literature, especially for the 100K variations of the MovieLens datasets. We apply tag-based filtering and collaborative filtering that can effectively predict a list of movies that is similar to the movie that a user has been watched. Due to not depending on users' profiles, our model has eliminated the e.ect of the cold-start problem. The experimental results provide us much better recommendations to users because it utilizes the underlying relation between movies based on their similar genres and actors/actresses. A movie recommender system has been deployed to demonstrate our work.
{"title":"Movie Recommender Systems Made Through Tag Interpolation","authors":"Quynh N. Nguyen, Nghia Duong-Trung, Dung Ngoc Le Ha, H. Son, T. Phan, Hien Xuan Pham, H. Huynh","doi":"10.1145/3380688.3380712","DOIUrl":"https://doi.org/10.1145/3380688.3380712","url":null,"abstract":"20 years of MovieLens datasets have witnessed a blossom of research that is garnering a remarkable significance with the advent of e-commerce and the whole industry. Four variations of the dataset have been downloaded hundreds of thousands of times, reflecting their popularity and distinctive contribution in the field of recommendation systems and connected subjects. This paper exploits the movie recommender system based on movies' genres and actors/actresses themselves as the input tags, or tag interpolation. The problem has not been addressed in the literature, especially for the 100K variations of the MovieLens datasets. We apply tag-based filtering and collaborative filtering that can effectively predict a list of movies that is similar to the movie that a user has been watched. Due to not depending on users' profiles, our model has eliminated the e.ect of the cold-start problem. The experimental results provide us much better recommendations to users because it utilizes the underlying relation between movies based on their similar genres and actors/actresses. A movie recommender system has been deployed to demonstrate our work.","PeriodicalId":414793,"journal":{"name":"Proceedings of the 4th International Conference on Machine Learning and Soft Computing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133071880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays, the agricultural data can be generated through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, agricultural laboratories, farmers, government agencies and agribusinesses. The analysis of this big data enables farmers, companies and agronomists to extract high business and scientific knowledge, improving their operational processes and product quality. However, before analysing this data, different data sources need to be normalised, homogenised and integrated into a unified data representation. In this paper, we propose an agricultural data integration method using a constellation schema which is designed to be flexible enough to incorporate other datasets and big data models. We also apply some methods to extract knowledge with the view to improve crop yield; these include finding suitable quantities of soil properties, herbicides and insecticides for both increasing crop yield and protecting the environment.
{"title":"Crop Knowledge Discovery Based on Agricultural Big Data Integration","authors":"V. M. Ngo, Mohand Tahar Kechadi","doi":"10.1145/3380688.3380705","DOIUrl":"https://doi.org/10.1145/3380688.3380705","url":null,"abstract":"Nowadays, the agricultural data can be generated through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, agricultural laboratories, farmers, government agencies and agribusinesses. The analysis of this big data enables farmers, companies and agronomists to extract high business and scientific knowledge, improving their operational processes and product quality. However, before analysing this data, different data sources need to be normalised, homogenised and integrated into a unified data representation. In this paper, we propose an agricultural data integration method using a constellation schema which is designed to be flexible enough to incorporate other datasets and big data models. We also apply some methods to extract knowledge with the view to improve crop yield; these include finding suitable quantities of soil properties, herbicides and insecticides for both increasing crop yield and protecting the environment.","PeriodicalId":414793,"journal":{"name":"Proceedings of the 4th International Conference on Machine Learning and Soft Computing","volume":"38 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120891322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we study model-based reinforcement learning in an unknown constrained Markov Decision Processes (CMDPs) with reset action. We propose an algorithm, Constrained-UCRL, which uses confidence interval like UCRL2, and solves linear programming problem to compute policy at the start of each episode. We show that Constrained-UCRL achieves sublinear regret bounds Õ(SA1/2T3/4) up to logarithmic factors with high probability for both the gain and the constraint violations.
{"title":"A Sublinear-Regret Reinforcement Learning Algorithm on Constrained Markov Decision Processes with reset action","authors":"Takashi Watanabe, T. Sakuragawa","doi":"10.1145/3380688.3380706","DOIUrl":"https://doi.org/10.1145/3380688.3380706","url":null,"abstract":"In this paper, we study model-based reinforcement learning in an unknown constrained Markov Decision Processes (CMDPs) with reset action. We propose an algorithm, Constrained-UCRL, which uses confidence interval like UCRL2, and solves linear programming problem to compute policy at the start of each episode. We show that Constrained-UCRL achieves sublinear regret bounds Õ(SA1/2T3/4) up to logarithmic factors with high probability for both the gain and the constraint violations.","PeriodicalId":414793,"journal":{"name":"Proceedings of the 4th International Conference on Machine Learning and Soft Computing","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123598273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Linh My Thi Ong, N. T. Nguyen, H. H. Luong, N. C. Tran, H. Huynh
Cyber-Physical System (CPS) is a new generation of physical, biological and engineered systems which use the computing and communication core to monitor, coordinate, control and integrate their operations. This plays a key role for developing the smart plants in industry 4.0. For more clarification on it, the paper will have a review on its architecture, applications and challenges.
{"title":"Cyber Physical System: Achievements and challenges","authors":"Linh My Thi Ong, N. T. Nguyen, H. H. Luong, N. C. Tran, H. Huynh","doi":"10.1145/3380688.3380695","DOIUrl":"https://doi.org/10.1145/3380688.3380695","url":null,"abstract":"Cyber-Physical System (CPS) is a new generation of physical, biological and engineered systems which use the computing and communication core to monitor, coordinate, control and integrate their operations. This plays a key role for developing the smart plants in industry 4.0. For more clarification on it, the paper will have a review on its architecture, applications and challenges.","PeriodicalId":414793,"journal":{"name":"Proceedings of the 4th International Conference on Machine Learning and Soft Computing","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126838885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents the method for tackling the challenge of fully automatically image colorization. We improve U-net by fusion multi-level feature from the pre-trained ImageNet to enhance the model under the small datasets. Furthermore, we reduce the unbalance colors by the enhancement distribution over quantized colors based on the smoothness of the prior distribution. The experiments in the DIV2K dataset show that our results are very encouraging. Our method improves PSNR as well as colorizes the images under complex textures.
{"title":"MLEU","authors":"Tram-Tran Nguyen-Quynh, Nhu-Tai Do, Soohyung Kim","doi":"10.1145/3380688.3380720","DOIUrl":"https://doi.org/10.1145/3380688.3380720","url":null,"abstract":"This paper presents the method for tackling the challenge of fully automatically image colorization. We improve U-net by fusion multi-level feature from the pre-trained ImageNet to enhance the model under the small datasets. Furthermore, we reduce the unbalance colors by the enhancement distribution over quantized colors based on the smoothness of the prior distribution. The experiments in the DIV2K dataset show that our results are very encouraging. Our method improves PSNR as well as colorizes the images under complex textures.","PeriodicalId":414793,"journal":{"name":"Proceedings of the 4th International Conference on Machine Learning and Soft Computing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126601356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The recent boom in e-commerce has created active electronic communities where consumers share their thoughts about the product and the company. These reviews play a very important part in building customer opinion about the said item. For a popular product or service, there might be thousands of reviews, making it difficult for the customer to make an informed decision about the product. In this paper, we present a way to surface only those reviews that contain information relevant to the user. To address this problem, we try to surface out the reviews that are outliers to the general cluster of reviews during a particular time period.We are leveraging anomaly detection algorithms to achieve this.
{"title":"Cerebro","authors":"Ankita Sinha, Vignesh Subrahmaniam","doi":"10.1145/3380688.3380701","DOIUrl":"https://doi.org/10.1145/3380688.3380701","url":null,"abstract":"The recent boom in e-commerce has created active electronic communities where consumers share their thoughts about the product and the company. These reviews play a very important part in building customer opinion about the said item. For a popular product or service, there might be thousands of reviews, making it difficult for the customer to make an informed decision about the product. In this paper, we present a way to surface only those reviews that contain information relevant to the user. To address this problem, we try to surface out the reviews that are outliers to the general cluster of reviews during a particular time period.We are leveraging anomaly detection algorithms to achieve this.","PeriodicalId":414793,"journal":{"name":"Proceedings of the 4th International Conference on Machine Learning and Soft Computing","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116868001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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