Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1最新文献

Exploring the Impact of Using Code Genie to Enhance the Programming Knowledge of Students and Across Genders: Experimental Study 探索使用代码精灵提高学生和性别编程知识的影响:实验研究

Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1 Pub Date : 2020-11-05 DOI: 10.1007/978-3-030-63128-4_69

Hadeel Mohammed Jawad, Samir Tout

引用次数: 0

A Functionally Separate Autoencoder 一个功能独立的自动编码器

Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1 Pub Date : 2020-03-30 DOI: 10.36227/techrxiv.12045534

Jinxin Wei

{"title":"A Functionally Separate Autoencoder","authors":"Jinxin Wei","doi":"10.36227/techrxiv.12045534","DOIUrl":"https://doi.org/10.36227/techrxiv.12045534","url":null,"abstract":"According to kids’ learning process, an auto-encoder which can be split into two parts is designed. The two parts can work well separately. The top half is an abstract network which is trained by supervised learning and can be used to classify and regress. The bottom half is a concrete network which is accomplished by inverse function and trained by self-supervised learning. It can generate the input of abstract network from concept or label. The network can achieve its intended functionality through testing by mnist dataset and convolution neural network. Round function is added between the abstract network and concrete network in order to get the representative generation of class. The generation ability can be increased by adding jump connection and negative feedback. At last, the characteristics of the network is discussed. The input can be changed to any form by encoder and then change it back by decoder through inverse function. The concrete network can be seen as the memory stored by the parameters. Lethe is that when new knowledge input, the training process makes the parameters change. At last, the application of the network is discussed. The network can be used for logic generation through deep reinforcement learning. The network can also be used for language translation, zip and unzip, encryption and decryption, compile and decompile, modulation and demodulation.","PeriodicalId":333895,"journal":{"name":"Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126291660","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

Multi-attribute Recognition,the key to Universal Neural Network 多属性识别，通用神经网络的关键

Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1 Pub Date : 2020-03-30 DOI: 10.36227/techrxiv.12045540.v1

Jinxin Wei

{"title":"Multi-attribute Recognition,the key to Universal Neural Network","authors":"Jinxin Wei","doi":"10.36227/techrxiv.12045540.v1","DOIUrl":"https://doi.org/10.36227/techrxiv.12045540.v1","url":null,"abstract":"To achieve the recognition of multi-attribute of object, I redesign the mnist dataset, change the color, size, location of the number. Meanwhile, I change the label accordingly. The deep neural network I use is the most common convolution neural network. Through test, we can conclude that we can use one neural network to recognize multi-attribute so long as the attribute difference of objects can be represented by functions. The Concrete network (generation network) can generate the output which the input rarely contained from the attributes the network learned. Its generalization ability is good because the network is a continuous function. Through one more test, we can conclude that one neural network can do image recognition, speech recognition, nature language processing and other things so long as the output node and the input node and more parameters add into the network. The network is universal so long as the network can process different inputs. By proof, fully connected network can do what convolution neural network and recurrent neural network do, so fully connected network is the universal network. The phenomenon of synesthesia is the result of multi-input and multi-output. Connection in mind can realize through the universal network and sending the output into input. Connection in mind is the key of creativity, synesthesia is the assistant.","PeriodicalId":333895,"journal":{"name":"Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128232811","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: 1

Correction to: Enabling Drones Collaboration in ITS Applications Using a BDI Architecture Based on a 5-Dimensional Social Model 更正:使用基于5维社会模型的BDI架构实现无人机在ITS应用中的协作

Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1 Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-63128-4_72

Hana Gharrad, N. Jabeur, A. Yasar, Khalid Ali Sulaiyam Al Abri, Youssef El-Hansali, Bruno Kochan

引用次数: 0