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NSURL-2019 Task 7 focuses on Named Entity Recognition (NER) in Farsi. This task was chosen to compare different approaches to find phrases that specify Named Entities in Farsi texts, and to establish a standard testbed for future researches on this task in Farsi. This paper describes the process of making training and test data, a list of participating teams (6 teams), and evaluation results of their systems. The best system obtained 85.4% of F 1 score based on phrase-level evaluation on seven classes of NEs including person, organization, location, date, time, money and percent.

The issue of scheduling is one of the most important ones to be considered by providers of the cloud computing in the data center. Using a suitable solution lets the providers of cloud computing use the available resources more. Additionally, the satisfaction of clients is met through provision of service quality parameters. Most of the solutions for this problem aim at one of the service quality factors and in order to achieve this goal, variety of methods are used. Using the algorithm of modified black hole in this paper, a proper solution is presented to tackle the problem of scheduling the affairs in cloud environment. The proposed method reduces makespan, increases degree of load balancing, and improves the resource`s utilization by considering the capability of each virtual machine. We have compared the proposed algorithm with existing task scheduling algorithms. Simulation results indicate that the proposed algorithm makes a good improvement regarding the makespan and amount of resource utilization compared to schedulers based on Random assignment and particle swarm optimization Algorithms.

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