In recent years, the usage model of the Internet has changed, pushing researchers towards the design of the ICN paradigm as a possible replacement of the existing architecture. Even though both Academia and Industry have investigated the feasibility and effectiveness of ICN, achieving the complete replacement of the IP is a challenging task: (i) the process involves multiple parties, such as ISPs, that need to coordinate among each other; (ii) it requires an indefinite amount of time to update hardware and software of network components; and (iii) it is a high risk goal that might introduce unexpected complications. Thus, the process of replacing the current Internet will inevitably lead towards a period of coexistence between the old and the new architectures. Given the urgency of the problem, this transition phase will happen very soon and people should address it in a smooth way. Some research groups have already addressed the coexistence by designing their own architectures, but none of those is the final solution to move towards the future Internet considering the unaltered state of the networking. To design such architecture, the research community needs now a comprehensive overview of the existing solutions that have so far addressed the coexistence. The purpose of this paper is to reach this goal by providing the first comprehensive survey and classification of the coexistence architectures according to their features (i.e., deployment approach, deployment scenarios, addressed coexistence requirements and additional architecture or technology used) and evaluation parameters (i.e., challenges emerging during the deployment and the runtime behaviour of an architecture). We believe that this paper will finally fill the gap required for moving towards the design of the final coexistence architecture.

Face recognition (FR) has received considerable attention in the field of security, especially in the use of closed-circuit television (CCTV) cameras in security monitoring. Although significant advances in the field of computer vision are made, advanced face recognition systems provide satisfactory performance only in controlled conditions. They deteriorate significantly in the face of real-world scenarios such as lighting conditions, motion blur, camera resolution, etc. This article shows how we design, implement, and conduct the empirical comparisons of machine learning open libraries in building attendance taking (AT) support systems using indoor security cameras called ATSS. Our trial system was deployed to record the appearances of 120 students in five classes who study on the third floor of FPT Polytechnic College building. Our design allows for flexible system scaling, and it is not only usable for a school but a generic attendance system with CCTV. The measurement results show that the accuracy is suitable for many different environments.

Rust detection is an essential topic in many areas, especially in telecommunication, which needs effective systems to segment and recognize rust on power electric towers, antenna. Our exclusive architecture use is based on a fully convolutional neural network for semantic segmentation and composed of Densenet encoder PSP intermediate layers and two skip connections upsample layers. The code written in Python used Pytorch libraries to compute and categorize the images. Comparing between models such as E-Net, U-Net, FCN, we have received our highest FCN (Fully Convolutional Neural) model for the most stable ratio of IoU (Intersection over Union) in 3 models stated with mean scores are 58.1 for origin images and 61.8 for background removal. With the results, we will contribute to detect rust on electric poles in time to avoid rust-causing serious consequences.