2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)最新文献_第3页

Neural Machine Based Mobile Applications Code Translation 基于神经机器的移动应用程序代码翻译

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257935

M. H. Hassan, Omar A. Mahmoud, O. A. Mohammed, Ammar Y. Baraka, Amira T. Mahmoud, A. Yousef

{"title":"Neural Machine Based Mobile Applications Code Translation","authors":"M. H. Hassan, Omar A. Mahmoud, O. A. Mohammed, Ammar Y. Baraka, Amira T. Mahmoud, A. Yousef","doi":"10.1109/NILES50944.2020.9257935","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257935","url":null,"abstract":"Although many cross platform mobile development software used a trans-compiler-based approach, it was very difficult to generalize it to work in both directions. For example, to convert between Java for Android Development and Swift for iOS development and vice versa. This is due to the need of writing a specific parser for each source language, and a specific code generator for each destination language. Neural network-based models are used successfully to translate between natural languages, including English, French, German any many others by providing enough datasets and without the need of adding language specific code for understanding and generation. In this paper, a source code converter based on the Neural Machine Translation Transformer Model that can translate from Java to Swift and vice versa is introduced. A synthesized dataset is used to train the model, the pipeline used for the translation as well as the code synthesis procedure throughout the work are illustrated. Initial results are promising and give motivation to further enhance the proposed tool.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123287386","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

Experimental Lane Keeping Assist for an Autonomous Vehicle Based on Optimal PID Controller 基于最优PID控制器的自动驾驶汽车车道保持辅助实验

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257969

M. K. Diab, Ammar N. Abbas, H. Ammar, R. Shalaby

引用次数: 5

Real-Time Lane Instance Segmentation Using SegNet and Image Processing 基于分段网和图像处理的实时车道实例分割

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257977

Gad Gad, Ahmed Mahmoud Annaby, N. Negied, M. Darweesh

引用次数: 8

IPXACT-Based RTL Generation Tool 基于ipxact的RTL生成工具

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257966

Ahmad El-Shiekh, Ahmad El-Alfy, A. Ammar, Mohamed Gamal, M. Dessouky, K. Salah, H. Mostafa

引用次数: 0

SoC loosely Coupled Navigation Algorithm Evaluation via 6-DOF Flight Simulation Model of Guided Bomb 基于制导炸弹六自由度飞行仿真模型的SoC松耦合导航算法评估

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257889

A. Hamdy, A. Ouda, A. Kamel, Y. Elhalwagy

引用次数: 0

Generalized Formula for Generating N-Scroll Chaotic Attractors 生成n涡旋混沌吸引子的广义公式

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257932

Ahmed N. Atiya, Hossam S. Hassan, Khaled E. Ibrahim, Omar M. ElGhandour, M. Tolba

引用次数: 0

Stochastic Modeling of Content-Dependent Scheduling in D2D Cache-Enabled Networks D2D缓存网络中内容相关调度的随机建模

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257951

Abdulmoneam A. Hassan, Laila H. Afify, A. El-Sherif, T. Elbatt

引用次数: 0

Improved Semantic Segmentation of Low-Resolution 3D Point Clouds Using Supervised Domain Adaptation 基于监督域自适应的低分辨率三维点云改进语义分割

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257903

Asmaa Elhadidy, Mohamed Afifi, Mohammed Hassoubah, Yara Ali, M. Elhelw

{"title":"Improved Semantic Segmentation of Low-Resolution 3D Point Clouds Using Supervised Domain Adaptation","authors":"Asmaa Elhadidy, Mohamed Afifi, Mohammed Hassoubah, Yara Ali, M. Elhelw","doi":"10.1109/NILES50944.2020.9257903","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257903","url":null,"abstract":"One of the key challenges in applying deep learning to solve real-life problems is the lack of large annotated datasets. Furthermore, for a deep learning model to perform well on the test set, all samples in the training and test sets should be independent and identically distributed (i.i.d.), which means that test samples should be similar to the samples that were used to train the model. In many cases, however, the underlying training and test set distributions are different. In such cases, it is common to adapt the test samples by transforming them to their equivalent counterparts in the domain of the training data before being processed by the deep learning model. In this paper, we perform domain adaptation of low-resolution 8, 16 and 32 channels LiDAR 3D point clouds projected on 2D spherical images in order to improve the quality of semantic segmentation tasks. To achieve this, the low-resolution 3D point clouds are transformed using an end-to-end supervised learning approach to spherical images that are very similar to those obtained by projecting high-resolution 64 channels LiDAR point clouds, without changing the underlying structure of the scene. The proposed framework is evaluated by training a semantic segmentation model on 64 channels LiDAR clouds from the Semantic KITTI dataset [1] and using this model to segment 8, 16 and 32 channel point clouds after adapting them using our framework. The results obtained from carried out experiments demonstrate the effectiveness of our framework where segmentation results surpassed those obtained with nearest neighbor interpolation methods.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125970120","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: 6

Three Dimension Angular Position Stabilization using LQR and Kalman Filter 基于LQR和卡尔曼滤波的三维角位置稳定

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257883

A. Sobh, A. Kamel, A. Farouk, Y. Elhalwagy

{"title":"Three Dimension Angular Position Stabilization using LQR and Kalman Filter","authors":"A. Sobh, A. Kamel, A. Farouk, Y. Elhalwagy","doi":"10.1109/NILES50944.2020.9257883","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257883","url":null,"abstract":"This paper presents a design and evaluation for controlling a coupled system using a robust Linear Quadratic Regulator (LQR) controller acting on the augmented integral state space matrix model of a coupled system. System under investigation consisted of dual fan module that is interlinked and its axis moving freely in the pitch plan. On the other hand, a counter weight was used to balance the two fans thrust to optimize the controller effort in the elevation plan. The counterweight axe was denoted as the elevation axis. If the fans are not on the same horizontal line, the rotation of the system around itself in clockwise or anti-clockwise direction was carried out around the travel axis. The LQR controller design parameters should be able to stabilize itself at any degree on the travel or elevation axes while maintaining hover level along the pitch axis. Such controller acts by defining the penalty of each type of error in controlling this system. The error was multiplied by relevant penalty, then fed-back to the controller that controls the fan speeds accordingly. The representing model had three axes, each have a proportional, derivative, and integral term for the travel and elevation axes but not the pitch axis, the reasons will be discussed later in the paper. Modeling started by design process through defining a non-linear model of the system, linearizing it, then was transferred to state space format, add integral part to the model, then finally design and testing of an LQR controller.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125188621","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

A Multi-Embeddings Approach Coupled with Deep Learning for Arabic Named Entity Recognition 结合深度学习的阿拉伯语命名实体识别多嵌入方法

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257975

Abeer Youssef, M. Elattar, S. El-Beltagy

{"title":"A Multi-Embeddings Approach Coupled with Deep Learning for Arabic Named Entity Recognition","authors":"Abeer Youssef, M. Elattar, S. El-Beltagy","doi":"10.1109/NILES50944.2020.9257975","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257975","url":null,"abstract":"Named Entity Recognition (NER) is an important task in many natural language processing applications. There are several studies that have focused on NER for the English language. However, there are some limitations when applying the current methodologies directly on the Arabic language text. Recent studies have shown the effectiveness of pooled contextual embedding representations and significant improvements in English NER tasks. This work investigates the performance of pooled contextual embeddings and bidirectional encoder representations from Transformers (BERT) model when used for NER on the Arabic language while addressing Arabic specific issues. The proposed method is an end-to-end deep learning model that utilizes a combination of pre-trained word embeddings, pooled contextual embeddings, and BERT model. Embeddings are then fed into bidirectional long-short term memory networks with a conditional random field. Different types of classical and contextual embeddings were experimented to pool for the best model. The proposed method achieves an F1 score of 77.62% on the AQMAR dataset, outperforming all previously published results of deep learning, and non-deep learning models on the same dataset. The presented results also surpass those of the wining system for the same task on the same data in the Topcoder website competition.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126722217","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