Collaborative-based tashrif lughowy in Qiroatul Kutub learning using the Reverso

The large number of grammatical explanations in learning Qiroatul Kutub made students increasingly slow in understanding Arabic texts, especially in Tashrif Lughowy, which demanded many changes. In order to speed up students' understanding in short meetings, this research was important to carry out. This study aimed to develop Tashrif Lughowy learning in the project-based learning Qiroatul Kutub course using the Reverso web application and tested its feasibility. It was designed with research and development research with the ADDIE approach. Expert and product tests were conducted to assess the feasibility of the product being developed. Respondents in the product test were 36 Islamic studies students at the Indonesian Islamic University. This research was developed with an emphasis on collaborative learning. Lessons were designed with projects in the analysis of the Arabic text provided. The results of the study indicated that this product development was very feasible to use, with an expert test score of 82.6% and a product test score of 89.4%. An increase in ability was also obtained by 24.3%. Collaborative learning in Tashrif Lughowy was very helpful in accelerating student understanding and maximizing lecturers in providing feedback, accelerating student understanding in a short time. In its application, lecturers checked student project work intensively every week to get maximum feedback. Digitalization of education could accelerate the transformation of quality education, so the results of this research really needed to be applied in teaching Tashrif Lughowy in learning Qiroatul Polar with very short meetings

Diversity Control in Evolutionary Computation using Asynchronous Dual-Populations with Search Space Partitioning

Diversity control is vital for effective global optimization using evolutionary computation (EC) techniques. This paper classifies the various diversity control policies in the EC literature. Many research works have attributed the high risk of premature convergence to sub-optimal solutions to the poor exploration capabilities resulting from diversity collapse. Also, excessive cost of convergence to optimal solution has been linked to the poor exploitation capabilities necessary to focus the search. To address this exploration-exploitation trade-off, this paper deploys diversity control policies that ensure sustained exploration of the search space without compromising effective exploitation of its promising regions. First, a dual-pool EC algorithm that facilitates a temporal evolution-diversification strategy is proposed. Then a quasi-random heuristic initialisation based on search space partitioning (SSP) is introduced to ensure uniform sampling of the initial search space. Second, for the diversity measurement, a robust convergence detection mechanism that combines a spatial diversity measure; and a population evolvability measure is utilised. It was found that the proposed algorithm needed a pool size of only 50 samples to converge to optimal solutions of a variety of global optimization benchmarks. Overall, the proposed algorithm yields a 33.34% reduction in the cost incurred by a standard EC algorithm. The outcome justifies the efficacy of effective diversity control on solving complex global optimization landscapes. Keywords: Diversity, exploration-exploitation tradeoff, evolutionary algorithms, heuristic initialisation, taxonomy

A Multiobjective Computation Offloading Algorithm for Mobile Edge Computing

In mobile edge computing (MEC), smart mobile devices (SMDs) with limited computation resources and battery lifetime can offload their computing-intensive tasks to MEC servers, thus to enhance the computing capability and reduce the energy consumption of SMDs. Nevertheless, offloading tasks to the edge incurs additional transmission time and thus higher execution delay. This paper studies the trade-off between the completion time of applications and the energy consumption of SMDs in MEC networks. The problem is formulated as a multiobjective computation offloading problem (MCOP), where the task precedence, i.e. ordering of tasks in SMD applications, is introduced as a new constraint in the MCOP. An improved multiobjective evolutionary algorithm based on decomposition (MOEA/D) with two performance enhancing schemes is proposed.1) The problem-specific population initialization scheme uses a latency-based execution location initialization method to initialize the execution location (i.e. either local SMD or MEC server) for each task. 2) The dynamic voltage and frequency scaling based energy conservation scheme helps to decrease the energy consumption without increasing the completion time of applications. The simulation results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art heuristics and meta-heuristics in terms of the convergence and diversity of the obtained nondominated solutions