Human Faces Detection and Tracking for Crowd Management in Hajj and Umrah

Hajj and Umrah are two main religious duties for Muslims.To help faithfuls to perform their religious duties comfortably in overcrowded areas,a crowd management system is a must to control the entering and exiting for each place.Since the number of people is very high,an intelligent crowd management system can be developed to reduce human effort and accelerate the management process.In this work,we propose a crowd management process based on detecting,tracking,and counting human faces using Artificial Intelligence techniques.Human detection and counting will be performed to calculate the number of existing visitors and face detection and tracking will be used to identify all the humans for security purposes.The proposed crowd management system is composed form three main parts which are:(1)detecting human faces,(2)assigning each detected face with a numerical identifier,(3)storing the identity of each face in a database for further identification and tracking.The main contribution of this work focuses on the detection and tracking model which is based on an improved object detection model.The improved Yolo v4 was used for face detection and tracking.It has been very effective in detecting small objects in highresolution images.The novelty contained in thismethod was the integration of the adaptive attention mechanism to improve the performance of the model for the desired task.Channel wise attention mechanism was applied to the output layers while both channel wise and spatial attention was integrated in the building blocks.The main idea from the adaptive attention mechanisms is to make themodel focus more on the target and ignore false positive proposals.We demonstrated the efficiency of the proposed method through expensive experimentation on a publicly available dataset.The wider faces dataset was used for the train and the evaluation of the proposed detection and tracking model.The proposed model has achieved good results with 91.2%of mAP and a processing speed of 18 FPS on the Nvidia GTX 960 GPU.

New Approach of QoS Metric Modeling on Network on Chip

This paper presents a new NoC QoS metrics modeling shaped on mesh architecture. The new QoS model is based on the QoS parameters. The goal of this work is to quantify buffering requirements and packet switching techniques in the NoC nodes by analyzing some QoS metrics such as End-to-End delays (EEDs) and packet loss. This study is based on simulation approach of a 4 × 4 mesh NoC behavior under multimedia communication process. It proposes a study of NoC switching buffer size avoiding packet drop and minimizing EED. Mainly, we focus on percent flit losses due to buffer congestion for a network loading. This leads to identify the optimal buffer size for the switch design. The routing approach is based on the Wormhole Routing method.

Efficient Joint Key Authentication Model in E-Healthcare

Many patients have begun to use mobile applications to handle different health needs because they can better access high-speed Internet and smartphones.These devices and mobile applications are now increasingly used and integrated through the medical Internet of Things(mIoT).mIoT is an important part of the digital transformation of healthcare,because it can introduce new business models and allow efficiency improvements,cost control and improve patient experience.In the mIoT system,when migrating from traditional medical services to electronic medical services,patient protection and privacy are the priorities of each stakeholder.Therefore,it is recommended to use different user authentication and authorization methods to improve security and privacy.In this paper,our prosed model involves a shared identity verification process with different situations in the e-health system.We aim to reduce the strict and formal specification of the joint key authentication model.We use the AVISPA tool to verify through the wellknown HLPSL specification language to develop user authentication and smart card use cases in a user-friendly environment.Our model has economic and strategic advantages for healthcare organizations and healthcare workers.The medical staff can increase their knowledge and ability to analyze medical data more easily.Our model can continuously track health indicators to automatically manage treatments and monitor health data in real time.Further,it can help customers prevent chronic diseases with the enhanced cognitive functions support.The necessity for efficient identity verification in e-health care is even more crucial for cognitive mitigation because we increasingly rely on mIoT systems.

Common workflows for computing material properties using different quantum engines

The prediction of material properties based on density-functional theory has become routinely common,thanks,in part,to the steady increase in the number and robustness of available simulation packages.This plurality of codes and methods is both a boon and a burden.While providing great opportunities for cross-verification,these packages adopt different methods,algorithms,and paradigms,making it challenging to choose,master,and efficiently use them.We demonstrate how developing common interfaces for workflows that automatically compute material properties greatly simplifies interoperability and cross-verification.We introduce design rules for reusable,code-agnostic,workflow interfaces to compute well-defined material properties,which we implement for eleven quantum engines and use to compute various material properties.Each implementation encodes carefully selected simulation parameters and workflow logic,making the implementer’s expertise of the quantum engine directly available to nonexperts.All workflows are made available as open-source and full reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure.