Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems最新文献_第2页

Machine Computing Function Designing for Creative Thinking 创造性思维下的机器计算功能设计

Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2020-11-13 DOI: 10.1145/3440840.3440851

Zhu Ping

引用次数: 0

Context-based Trajectory Prediction with LSTM Networks 基于上下文的LSTM网络轨迹预测

Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2020-11-13 DOI: 10.1145/3440840.3440842

Xin Xu

{"title":"Context-based Trajectory Prediction with LSTM Networks","authors":"Xin Xu","doi":"10.1145/3440840.3440842","DOIUrl":"https://doi.org/10.1145/3440840.3440842","url":null,"abstract":"Traditional target trajectory prediction model is generally trained on the previous trajectories purely while the context information of the trajectory is simply ignored. We assume that the trajectory pattern generally associates with a certain set of positions. For instance, the travelling trajectories of people of similar interest may be highly correlated. Such kind of context information provides more clues for trajectory prediction. As a result, context information should be utilized during trajectory predictions. Inspired by the above issue, we have designed an effective context-based trajectory prediction method with two types of LSTMs. The first type of LSTM model is specially built to predict the distinctive pattern that the trajectory follows while the other type of LSTM models are designed to predict the future positions of the trajectory given the context of the pattern it follows. First, we convert the real-valued target trajectories into discrete path sets with grids. And then we discover the distinctive patterns with hierarchical clustering. The context of the trajectory is modeled as the closest grid of the associated pattern. Later, we train the two types of LSTM models with the corresponding samples. Lastly, we apply the LSTM models for trajectory prediction. Experimental results indicate that our method outperforms the traditional LSTM neural networks significantly by making use of the context information of the trajectory.","PeriodicalId":273859,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123843824","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}

引用次数: 3

Automatic Identification of Braille Blocks by Neural Network Using Multi-Channel Pressure Sensor Array 基于多通道压力传感器阵列的神经网络盲文块自动识别

Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2020-11-13 DOI: 10.1145/3440840.3440858

K. Kuzume, Haruko Masuda, Yudai Murakami

{"title":"Automatic Identification of Braille Blocks by Neural Network Using Multi-Channel Pressure Sensor Array","authors":"K. Kuzume, Haruko Masuda, Yudai Murakami","doi":"10.1145/3440840.3440858","DOIUrl":"https://doi.org/10.1145/3440840.3440858","url":null,"abstract":"In recent years, the number of visually impaired people in Japan has exceeded 300,000 including those with low vision, and accidental falls on the station platform involving them have not been eliminated. Persons having acquired visual impairment make up one third of all cases of blindness in Japan. It is known that they often cannot walk alone with only a white cane or guide dog. The main cause of platform accidents was misidentification of braille blocks. Therefore, it was necessary to develop an auxiliary device for accurately identifying braille blocks that the acquired visually impaired could also use easily. In this research, we developed an automatic identification system for braille blocks using foot pressure data acquired by a multi-channel pressure sensor array. First, we devised a new foot pressure data acquisition device using a multi-channel pressure sensor array. Our proposed device had excellent features such as being light weight, low cost, and easy to extend to multi-channel. Second, in order to accurately identify the braille blocks, the foot pressure data acquired under various conditions was learned by neural network, and identification performance evaluated. As a result of the experiment, the braille blocks could be identified with a high rate of at least 98% accuracy by neural network, with a very simple structure of an input layer (16 neurons), a hidden layer (5 neurons), and an output layer (4 neurons).","PeriodicalId":273859,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems","volume":"2008 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125588297","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}

引用次数: 2

Part-Based Pedestrian Attribute Analysis 基于部分的行人属性分析

Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2020-11-13 DOI: 10.1145/3440840.3440846

Xue Chen, Jianwen Cao

引用次数: 0

Efficient Low-Latency Dynamic Licensing for Deep Neural Network Deployment on Edge Devices 边缘设备上深度神经网络部署的高效低延迟动态许可

Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2020-11-13 DOI: 10.1145/3440840.3440860

Toan Pham Van, Ngoc N. Tran, Hoang Pham Minh, T. N. Minh, Thanh Ta Minh

{"title":"Efficient Low-Latency Dynamic Licensing for Deep Neural Network Deployment on Edge Devices","authors":"Toan Pham Van, Ngoc N. Tran, Hoang Pham Minh, T. N. Minh, Thanh Ta Minh","doi":"10.1145/3440840.3440860","DOIUrl":"https://doi.org/10.1145/3440840.3440860","url":null,"abstract":"Along with the rapid development in the field of artificial intelligence (AI), especially deep learning, deep neural network (DNN) applications are becoming more and more popular in reality. To be able to withstand the heavy load from mainstream users, deployment techniques are essential in bringing neural network models from research to production. Among the two popular computing topologies for deploying neural network models in production are cloud-computing and edge-computing. Recent advances in communication technologies, along with the great increase in the number of mobile devices, has made edge-computing gradually become an inevitable trend. In this paper, we propose an architecture to solve deploying and processing deep neural networks on edge-devices by leveraging their synergy with the cloud and the access-control mechanisms of the database. Adopting this architecture allows low-latency DNN model updates on devices. At the same time, with only one model deployed, we can easily make different versions of it by setting access permissions on the model weights. This method allows for dynamic model licensing, which benefits commercial applications.","PeriodicalId":273859,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121798569","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}

引用次数: 2

Dynamic Portfolio Management Based on Pair Trading and Deep Reinforcement Learning 基于配对交易和深度强化学习的动态投资组合管理

Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2020-11-13 DOI: 10.1145/3440840.3440861

F. Xu, S. Tan

引用次数: 4

Improved Rehabilitation Robot Trajectory Regeneration by Learning from the Healthy Ankle Demonstration 借鉴健康踝关节示范改进康复机器人轨迹再生

Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2020-11-13 DOI: 10.1145/3440840.3440852

Yunkai Wang, Qingsong Ai, Ling Ai, Quan Liu, Jiwei Hu, Zude Zhou

{"title":"Improved Rehabilitation Robot Trajectory Regeneration by Learning from the Healthy Ankle Demonstration","authors":"Yunkai Wang, Qingsong Ai, Ling Ai, Quan Liu, Jiwei Hu, Zude Zhou","doi":"10.1145/3440840.3440852","DOIUrl":"https://doi.org/10.1145/3440840.3440852","url":null,"abstract":"The prevalence of ankle injuries in daily life has prompted the widespread application of rehabilitation robots. One of the important factors affecting robot-assisted ankle rehabilitation is the training trajectory which is usually regenerated from ankle movements. The traditional trajectory regeneration method is not suitable for the clinically recommended periodic ankle movements. In this paper, an improved robot trajectory regeneration method based on the individual characteristics is proposed to provide training reference trajectory for rehabilitation robots. This method extracts sample characteristics from the demonstration of the healthy ankle and reconstructs the sample space. Based on Learning from Demonstration (LfD) technology, the reference trajectory is regenerated for the rehabilitation of the injured ankle. The analysis of statistics and the regeneration of spatial features are performed to prove that this proposed method can regenerate the rehabilitation reference trajectory by learning from the healthy ankle demonstration.","PeriodicalId":273859,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124229009","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}

引用次数: 0

Hyperbolic Attributed Network Embedding with self-adaptive Random Walks 具有自适应随机游走的双曲属性网络嵌入

Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2020-11-13 DOI: 10.1145/3440840.3440859

Bin Wu, Yijia Zhang, Yuxin Wang

{"title":"Hyperbolic Attributed Network Embedding with self-adaptive Random Walks","authors":"Bin Wu, Yijia Zhang, Yuxin Wang","doi":"10.1145/3440840.3440859","DOIUrl":"https://doi.org/10.1145/3440840.3440859","url":null,"abstract":"Network embedding aims to learn low-dimensional vectors for vertices in complex networks. In real-world systems, nodes in networks are commonly associated with diverse attributes. However, classic approaches generally ignored the implicit relations and hierarchical information introduced by attributes. Motivated by this, we propose a new method named AHANE, short for Adaptive Hyperbolic Attributed Network Embedding, to learn the vertex representations of attributed networks. We perform a biased self-adaptive random walk, generating vertices sequences that can well retain the distribution of vertices in attributed networks. And then propose a novel framework to optimize both the explicit relations (i.e., observed directly connected links between nodes) and implicit relations (i.e., unobserved but transitive links through attributes) by using hyperbolic skip-gram model. We conducted extensive experiments on real datasets related to vertex classification, link prediction and nearest nodes searching. Experimental results on real-world datasets demonstrate the efficiency and effectiveness of AHANE.","PeriodicalId":273859,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127581942","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}

引用次数: 0

Memory network based Knowledge Driven Model for Response Generation in Dialog System 基于记忆网络的对话系统响应生成知识驱动模型

Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2020-11-13 DOI: 10.1145/3440840.3440844

Wansen Wu, Xinmeng Li, Quanjun Yin

引用次数: 0

Cheat Detection in a Multiplayer First-Person Shooter Using Artificial Intelligence Tools 在多人第一人称射击游戏中使用人工智能工具进行作弊检测

Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2020-11-13 DOI: 10.1145/3440840.3440857

Ruan Spijkerman, E. M. Ehlers

引用次数: 2