Image recognition can be applied in many applications of Intelligent Transportation System. Through automated traffic flow counting, the traffic information can be presented effectively for a given area. After the existing image recognition model process the monitoring video, the coordinates of objects in each frame can be easily extracted. The extracted object coordinates are then filtered to obtain the required vehicle coordinates. To achieve the function of vehicle counting, it is necessary to identify the relationship of vehicles in different frames, i.e., whether or not they represent the same vehicle. Although the vehicle counting can be achieved by using the tracking algorithm, a short period of recognition failure may cause wrong tracking, which will lead to incorrect traffic counting. In this paper, we propose a system that utilizes the YOLO framework for traffic flow counting. The system architecture consists of three blocks, including the Detector that generates the bounding box of vehicles, the Buffer which stores coordinates of vehicles, and the Counter which is responsible for vehicle counting. The proposed system requires only to utilize simple distance calculations to achieve the purpose of vehicle counting. In addition, by adding checkpoints, the system is able to alleviate the consequence of false detection. The videos from different locations and angles are used to verify and analyze the correctness and overall efficiency of the proposed system, and the results indicate that our system achieves high counting accuracy under the environment with sufficient ambient light.
{"title":"A YOLO-Based Traffic Counting System","authors":"Jia-Ping Lin, Min-Te Sun","doi":"10.1109/TAAI.2018.00027","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00027","url":null,"abstract":"Image recognition can be applied in many applications of Intelligent Transportation System. Through automated traffic flow counting, the traffic information can be presented effectively for a given area. After the existing image recognition model process the monitoring video, the coordinates of objects in each frame can be easily extracted. The extracted object coordinates are then filtered to obtain the required vehicle coordinates. To achieve the function of vehicle counting, it is necessary to identify the relationship of vehicles in different frames, i.e., whether or not they represent the same vehicle. Although the vehicle counting can be achieved by using the tracking algorithm, a short period of recognition failure may cause wrong tracking, which will lead to incorrect traffic counting. In this paper, we propose a system that utilizes the YOLO framework for traffic flow counting. The system architecture consists of three blocks, including the Detector that generates the bounding box of vehicles, the Buffer which stores coordinates of vehicles, and the Counter which is responsible for vehicle counting. The proposed system requires only to utilize simple distance calculations to achieve the purpose of vehicle counting. In addition, by adding checkpoints, the system is able to alleviate the consequence of false detection. The videos from different locations and angles are used to verify and analyze the correctness and overall efficiency of the proposed system, and the results indicate that our system achieves high counting accuracy under the environment with sufficient ambient light.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121616031","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 propose a weighted voting method for a heterogeneous game system, which assigns the strength of engines and win probabilities of their positions to the weights for voting. Assigning the strength as the weight solves the problem of weaker engines entering the majority voting. The win probabilities are transformed from the evaluation values by a sigmoid function generated for each engine. Through the sigmoid functions, we can compare the win probabilities between the different engines and resolve the problem of optimistic voting in heterogeneous systems. Optimistic voting, which simply selects the highest-scoring move, may select a suboptimal random move when random players are involved in the game. Finally, we competed the proposed system and other voting systems against a single engine in shogi tournaments and compared the strengths of the systems in shogi. The experimental results confirmed the effectiveness of the proposed method.
{"title":"Weighted Majority Voting with a Heterogeneous System in the Game of Shogi","authors":"Shogo Takeuchi","doi":"10.1109/TAAI.2018.00035","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00035","url":null,"abstract":"In this paper, we propose a weighted voting method for a heterogeneous game system, which assigns the strength of engines and win probabilities of their positions to the weights for voting. Assigning the strength as the weight solves the problem of weaker engines entering the majority voting. The win probabilities are transformed from the evaluation values by a sigmoid function generated for each engine. Through the sigmoid functions, we can compare the win probabilities between the different engines and resolve the problem of optimistic voting in heterogeneous systems. Optimistic voting, which simply selects the highest-scoring move, may select a suboptimal random move when random players are involved in the game. Finally, we competed the proposed system and other voting systems against a single engine in shogi tournaments and compared the strengths of the systems in shogi. The experimental results confirmed the effectiveness of the proposed method.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116966057","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}
Game 2048 is a stochastic single-player game and development of strong computer players for 2048 has been based on N-tuple networks trained by reinforcement learning. In our previous study, we developed computer players for game 2048 based on convolutional neural networks (CNNs), and showed by experiments that networks with three or more convolution layers performed much better than that with two convolution layers. In this study, we analyze the inner working of our CNNs (i.e. white box approach) to identify the reasons of the performance. Our analyses include visualization of filters in the first layers and backward trace of the networks for some specific game states. We report several findings about inner working of our CNNs for game 2048.
Game 2048是一个随机的单人游戏，为2048开发强大的计算机玩家是基于强化学习训练的n元网络。在我们之前的研究中，我们基于卷积神经网络(cnn)开发了游戏2048的计算机播放器，并通过实验表明，具有三个或更多卷积层的网络比具有两个卷积层的网络性能要好得多。在本研究中，我们分析了我们的cnn的内部工作(即白盒方法)，以确定性能的原因。我们的分析包括对第一层过滤器的可视化和对某些特定游戏状态的网络的反向跟踪。我们报告了关于2048场比赛cnn内部工作的一些发现。
{"title":"Interpreting Neural-Network Players for Game 2048","authors":"Kiminori Matsuzaki, Madoka Teramura","doi":"10.1109/TAAI.2018.00038","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00038","url":null,"abstract":"Game 2048 is a stochastic single-player game and development of strong computer players for 2048 has been based on N-tuple networks trained by reinforcement learning. In our previous study, we developed computer players for game 2048 based on convolutional neural networks (CNNs), and showed by experiments that networks with three or more convolution layers performed much better than that with two convolution layers. In this study, we analyze the inner working of our CNNs (i.e. white box approach) to identify the reasons of the performance. Our analyses include visualization of filters in the first layers and backward trace of the networks for some specific game states. We report several findings about inner working of our CNNs for game 2048.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124582868","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 TAAI conference is an annual event that brings together researchers, engineers, and practitioners to present and exchange ideas, results, and experience in AI technologies and applications. This year, the conference received paper submissions from different countries. Every submission was assigned to at least two experts from the technical program committee to review. Due to the large amount of high-quality submissions, regular paper acceptance was very competitive. In addition, these proceedings feature high-quality papers. All of these papers provide novel ideas, new results, and state-of-the-art techniques in the field. We are honored to have several of the world’s leading experts in the field join us as distinguished keynote speakers. Altogether, we are proud to be able to present you a rich program that contains a variety of excellent researches.
{"title":"Message from the TAAI 2018 Program Co-Chairs","authors":"Chao-Tung Yang, Chao Chen","doi":"10.1109/taai.2018.00006","DOIUrl":"https://doi.org/10.1109/taai.2018.00006","url":null,"abstract":"The TAAI conference is an annual event that brings together researchers, engineers, and practitioners to present and exchange ideas, results, and experience in AI technologies and applications. This year, the conference received paper submissions from different countries. Every submission was assigned to at least two experts from the technical program committee to review. Due to the large amount of high-quality submissions, regular paper acceptance was very competitive. In addition, these proceedings feature high-quality papers. All of these papers provide novel ideas, new results, and state-of-the-art techniques in the field. We are honored to have several of the world’s leading experts in the field join us as distinguished keynote speakers. Altogether, we are proud to be able to present you a rich program that contains a variety of excellent researches.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131779009","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}
Tongue diagnosis is a unique practice in traditional Chinese medicine(TCM), which can be used to infer the health condition of a person. However, different TCM doctors may give different interpretations on the same tongue. If an artificial intelligence model can be developed based on a large number of doctor-interpreted tongue images, a more objective judgment will be obtained. Deep learning in artificial intelligence has excellent performance in image recognition, and feature extraction can be done automatically by deep learning without image processing experts. This study attempts to develop a deep learning model through a large number of tongue images, especially for tongue fissures. We also visualize the fissure regions with Gradient-weighted Class Activation Mapping(Grad-cam). Therefore, the model not only try to detect tongue fissures but also localize tongue fissure regions.
{"title":"Tongue Fissure Visualization with Deep Learning","authors":"Wen-Hsien Chang, H. Chu, Hen-Hong Chang","doi":"10.1109/TAAI.2018.00013","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00013","url":null,"abstract":"Tongue diagnosis is a unique practice in traditional Chinese medicine(TCM), which can be used to infer the health condition of a person. However, different TCM doctors may give different interpretations on the same tongue. If an artificial intelligence model can be developed based on a large number of doctor-interpreted tongue images, a more objective judgment will be obtained. Deep learning in artificial intelligence has excellent performance in image recognition, and feature extraction can be done automatically by deep learning without image processing experts. This study attempts to develop a deep learning model through a large number of tongue images, especially for tongue fissures. We also visualize the fissure regions with Gradient-weighted Class Activation Mapping(Grad-cam). Therefore, the model not only try to detect tongue fissures but also localize tongue fissure regions.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131644344","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}
{"title":"[Copyright notice]","authors":"","doi":"10.1109/taai.2018.00003","DOIUrl":"https://doi.org/10.1109/taai.2018.00003","url":null,"abstract":"","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124307492","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}
Werewolf, also known as Mafia, is a kind of game with imperfect information. Werewolf game agents must cope with two kinds of problems, "decision on who to trust or to kill", and "decision on information exchange". In this paper, we focus on the first problem. We apply techniques in Deep Q Network in building werewolf agents. We also improve representation of states and actions based on existing agents trained by Q learning method. Our proposed agents were compared with existing agents trained by Q learning method and with existing agents submitted to the AIWolf Contest, the most famous werewolf game agents contest in Japan. For every role, we prepared four agents with proposed method and investigated average win ratio of four agents in our experiments. Experimental results showed that when agents learned and played with the same group of players, our proposed agents have better player performances than existing agents trained by Q learning method and a part of agents submitted to the AIWolf Contest. We obtained promising results by using reinforcement learning method to solve "decision on who to trust or to kill" problem without using heuristic methods.
{"title":"Application of Deep Reinforcement Learning in Werewolf Game Agents","authors":"Tianhe Wang, Tomoyuki Kaneko","doi":"10.1109/TAAI.2018.00016","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00016","url":null,"abstract":"Werewolf, also known as Mafia, is a kind of game with imperfect information. Werewolf game agents must cope with two kinds of problems, \"decision on who to trust or to kill\", and \"decision on information exchange\". In this paper, we focus on the first problem. We apply techniques in Deep Q Network in building werewolf agents. We also improve representation of states and actions based on existing agents trained by Q learning method. Our proposed agents were compared with existing agents trained by Q learning method and with existing agents submitted to the AIWolf Contest, the most famous werewolf game agents contest in Japan. For every role, we prepared four agents with proposed method and investigated average win ratio of four agents in our experiments. Experimental results showed that when agents learned and played with the same group of players, our proposed agents have better player performances than existing agents trained by Q learning method and a part of agents submitted to the AIWolf Contest. We obtained promising results by using reinforcement learning method to solve \"decision on who to trust or to kill\" problem without using heuristic methods.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125448800","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}
{"title":"Message from the TAAI 2018 General Co-Chairs","authors":"","doi":"10.1109/taai.2018.00005","DOIUrl":"https://doi.org/10.1109/taai.2018.00005","url":null,"abstract":"","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116782467","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 structured electronic medical record is the basis for computers to process and achieve the target of precise diagnosis and treatment automatically using the knowledge and features of the techniques such as machine learning and artificial intelligence (AI). Because of the increasing demands on improving the efficiency and the flexibility during the step or phase of classification and extraction, providing the expansion mechanism for the automatic adaption of new NER (Named Entity Recognition, NER) model training during the NER model training stage anytime when the new entities/tags shall be learned and classified and hence the related knowledge database (DB) shall be expanded automatically. The proposed method includes a training stage involving the step of adaptive improved NER model training for the chest x-ray medical reports/files and a test stage involving the step of the dependency parsing and the relation extracting to be perform sequentially, and thus the goals of automatic information extraction and structured medical report generation using the machine learning technique, and the optimization and accuracy improvement of the doctor's work and performance through referring to the structured medical report for diagnosis and treatment can be achieved.
{"title":"Adaptive Generation of Structured Medical Report Using NER Regarding Deep Learning","authors":"Cheng-Tse Wu, Hsiao-ko Chang, Ji-Han Liu, J. Jang","doi":"10.1109/TAAI.2018.00012","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00012","url":null,"abstract":"The structured electronic medical record is the basis for computers to process and achieve the target of precise diagnosis and treatment automatically using the knowledge and features of the techniques such as machine learning and artificial intelligence (AI). Because of the increasing demands on improving the efficiency and the flexibility during the step or phase of classification and extraction, providing the expansion mechanism for the automatic adaption of new NER (Named Entity Recognition, NER) model training during the NER model training stage anytime when the new entities/tags shall be learned and classified and hence the related knowledge database (DB) shall be expanded automatically. The proposed method includes a training stage involving the step of adaptive improved NER model training for the chest x-ray medical reports/files and a test stage involving the step of the dependency parsing and the relation extracting to be perform sequentially, and thus the goals of automatic information extraction and structured medical report generation using the machine learning technique, and the optimization and accuracy improvement of the doctor's work and performance through referring to the structured medical report for diagnosis and treatment can be achieved.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130090923","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}
{"title":"[Title page i]","authors":"","doi":"10.1109/taai.2018.00001","DOIUrl":"https://doi.org/10.1109/taai.2018.00001","url":null,"abstract":"","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129994091","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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