The Impact of Semi-Supervised Learning on the Performance of Intelligent Chatbot System

Artificial intelligent based dialog systems are getting attention from both business and academic communities.The key parts for such intelligent chatbot systems are domain classification,intent detection,and named entity recognition.Various supervised,unsupervised,and hybrid approaches are used to detect each field.Such intelligent systems,also called natural language understanding systems analyze user requests in sequential order:domain classification,intent,and entity recognition based on the semantic rules of the classified domain.This sequential approach propagates the downstream error;i.e.,if the domain classification model fails to classify the domain,intent and entity recognition fail.Furthermore,training such intelligent system necessitates a large number of user-annotated datasets for each domain.This study proposes a single joint predictive deep neural network framework based on long short-term memory using only a small user-annotated dataset to address these issues.It investigates value added by incorporating unlabeled data from user chatting logs into multi-domain spoken language understanding systems.Systematic experimental analysis of the proposed joint frameworks,along with the semi-supervised multi-domain model,using open-source annotated and unannotated utterances shows robust improvement in the predictive performance of the proposed multi-domain intelligent chatbot over a base joint model and joint model based on adversarial learning.

Effective Denoising Architecture for Handling Multiple Noises

Object detection,one of the core research topics in computer vision,is extensively used in various industrial activities.Although there have been many studies of daytime images where objects can be easily detected,there is relatively little research on nighttime images.In the case of nighttime,various types of noises,such as darkness,haze,and light blur,deteriorate image quality.Thus,an appropriate process for removing noise must precede to improve object detection performance.Although there are many studies on removing individual noise,only a few studies handle multiple noises simultaneously.In this paper,we pro-pose a convolutional denoising autoencoder(CDAE)-based architecture trained on various types of noises.We also present various composing modules for each noise to improve object detection performance for night images.Using the exclusively dark(ExDark)Image dataset,experimental results show that the Sequentialfiltering architecture showed superior mean average precision(mAP)compared to other architectures.

The Virtual Silk Road between China and the Arab States

In March 2015,China issued an action plan which described the main objectives of the Belt and Road Initiative(BRI).As part of the BRI,Beijing launched the Digital Silk Road(DSR)in 2015 with a loose mandate.It has since become a significant part of Beijing’s overall BRI strategy,under which China provides aid,political support,and other assistance to recipient states.In June 2014,Chinese President Xi Jinping attended the sixth session of the ministerial meeting of the China-Arab States Cooperation Forum.During the meeting,Xi put forward the strategic concept of China-Arab cooperation mode as‘1+2+3,’with the‘3’including nuclear energy,aerospace and satellite technology and new energy.The two sides have agreed on the establishment of the China-Arab Centre for Technology Transfer,the construction of training centres for Arabian countries peaceful use of nuclear energy,and research on the use of China’s Beidou Navigation Satellite system in the Arab states.in Tunisia in April 2018.This paper will review the collaboration of China and the Arab states to implement a positive model of technological development and digital connectivity.Further,the ICT development of the Arab states in comparison to China will be investigated to explain changes observed in the countries ICT Exports.