Intelligent Virtual Assistant System(IVAS) for Air Traffic Controllers

Air traffic control is an essential obligation in the aviation industry to have safe and efficient air transportation.Year by year,the workload and on-job-stress of the air traffic controllers are rapidly increasing due to the rapid growth of air traveling.Controllers are usually dealing with multiple aircrafts at a time and must make quick and accurate decisions to ensure the safety of aircrafts.Heavy workload and high responsibilities create air traffic control a stressful job that sometimes could be error-prone and time-consuming,since controlling and decision-making are solely dependent on human intelligence.To provide effective solutions for the mentioned on the job challenges of the controllers,this study proposed an intelligent virtual assistant system(IVAS)to assist the controllers thereby to reduce the controllers’workload.Consisting of four main parts,which are voice recognition,display conversation on screen,task execution,and text to speech,the proposed system is developed with the aid of artificial intelligence(AI)techniques to make speedy decisions and be free of human interventions.IVAS is a computer-based system that can be activated by the voice of the air traffic controller and then appropriately assist to control the flight.IVAS identifies the words spoken by the controller and then a virtual assistant navigates to collect the data requested from the controllers,which allows additional or free time to the controllers to contemplate more on the work or could assist to another aircraft.The Google speech application programming interface(API)converts audio to text to recognize keywords.AI agent is trained using the Hidden marko model(HMM)algorithm such that it could learn the characteristics of the distinct voices of the controllers.At this stage,the proposed IVAS can be used to provide training for novice air traffic controllers effectively.The system is to be developed as a real-time system which could be used at the air traffic controlling base for actual traffic controlling purposes and the system is to be further upgraded to perform the task by recognizing keywords directly from the pilot voice command.

CVTD: A Robust Car-Mounted Video Text Detector

Text perception is crucial for understanding the semantics of outdoor scenes,making it a key requirement for building intelligent systems for driver assistance or autonomous driving.Text information in car-mounted videos can assist drivers in making decisions.However,Car-mounted video text images pose challenges such as complex backgrounds,small fonts,and the need for real-time detection.We proposed a robust Car-mounted Video Text Detector(CVTD).It is a lightweight text detection model based on ResNet18 for feature extraction,capable of detecting text in arbitrary shapes.Our model efficiently extracted global text positions through the Coordinate Attention Threshold Activation(CATA)and enhanced the representation capability through stacking two Feature Pyramid Enhancement Fusion Modules(FPEFM),strengthening feature representation,and integrating text local features and global position information,reinforcing the representation capability of the CVTD model.The enhanced feature maps,when acted upon by Text Activation Maps(TAM),effectively distinguished text foreground from non-text regions.Additionally,we collected and annotated a dataset containing 2200 images of Car-mounted Video Text(CVT)under various road conditions for training and evaluating our model’s performance.We further tested our model on four other challenging public natural scene text detection benchmark datasets,demonstrating its strong generalization ability and real-time detection speed.This model holds potential for practical applications in real-world scenarios.

A survey on security analysis of Amazon echo devices

Since its launch in 2014,Amazon Echo family of devices has seen a considerable increase in adaptation in consumer homes and offices.With a market worth millions of dollars,Echo is used for diverse tasks such as accessing online information,making phone calls,purchasing items,and controlling the smart home.Echo offers user-friendly voice interaction to automate everyday tasks making it a massive success.Though many people view Amazon Echo as a helpful assistant at home or office,few know its underlying security and privacy implications.In this paper,we present the findings of our research on Amazon Echo’s security and privacy concerns.The findings are divided into different categories by vulnerability or attacks.The proposed mitigation(s)to the vulnerabilities are also presented in the paper.We conclude that though numerous privacy concerns and security vulnerabilities associated with the device are mitigated,many vulnerabilities still need to be addressed.