Scheduling the tasks of a workflow to the cloud resources is a well-known N-P hard problem. The stakeholders involved in a cloud environment have different interests in scheduling problem. In addition to the traditional objectives like makespan, budget, and deadline, optimized in workflow scheduling, considering the green aspect of cloud, (ie, energy consumption) increase the problem complexity. Moreover, the interests of a cloud's stakeholders are conflicting, and satisfying all these interests simultaneously is a big problem. In this paper, we proposed a new Multi-Objective Genetic Algorithm(MOGA) for workflow scheduling in a cloud environment. MOGA considered the conflicting interest of the cloud stakeholders for optimization and provided a solution, which not only minimizes the makespan under the budget and deadline constraints but also provided an energy efficient solution using the dynamic voltage frequency scaling. We provided a gap search algorithm in this paper, which is used to optimize the resource utilization of the cloud's resources.We compared our results with genetic algorithms considering the budget, deadline, and energy efficiency individually. We also compared the performance of MOGA with Multi-objective Particle Swarm Optimization (MOPSO) with the same objectives as those of MOGA. To the best of our knowledge, there is no solution presented in the literature that considers the diverse objectives considered in this work. The results show that our proposed algorithm MOGA has significantly improved not only in terms of budget, deadline, and energy but also improved the utilization of cloud's resources as compared to the competitive algorithms of this work.

Grid is a network of computational resources that may potentially span many continents. Maximization of the resource utilization hinges on the implementation of an efficient load balancing scheme, which provides (i) minimization of idle time, (ii) minimization of overloading, and (iii) minimization of control overhead. In this paper, we propose a dynamic and distributed load balancing scheme for grid networks. The distributed nature of the proposed scheme not only reduces the communication overhead of grid resources but also cuts down the idle time of the resources during the process of load balancing. We apply the proposed load balancing approach on Enhanced GridSim in order to gauge the effectiveness in terms of communication overhead and response time reduction. We show that significant savings are delivered by the proposed technique compared to other approaches such as centralized load balancing and no load balancing.

It is indeed a challenge for the existing machine learning approaches to segregate the hateful content from the one that is merely offensive. One prevalent reason for low accuracy of hate detection with the current methodologies is that these techniques treat hate classification as a multi-class problem. In this work, we present the hate identification on the social media as a multi-label problem. To this end, we propose a CNN-based service framework called "HateClassify" for labeling the social media contents as the hate speech, offensive, or non-offensive. Results demonstrate that the multi-class classification accuracy for the CNN based approaches particularly Sequential CNN (SCNN) is competitive and even higher than certain state-of-the-art classifiers. Moreover, in the multi-label classification problem, sufficiently high performance is exhibited by the SCNN among other CNN-based techniques. The results have shown that using multi-label classification instead of multi-class classification, hate speech detection is increased up to 20%.

Inductive Teaching Method (ITM) promotes effective learning in technological education (Felder & Silverman, 1988). Students prefer ITM more as it makes the subject easily understandable (Goltermann, 2011). The ITM motivates the students to actively participate in class activities and therefore could be considered a better approach to teach computer programming. There has been little research on implementing ITM in computer science courses despite its potential to improve effective learning. In this research, an existing computer programming lab course is taught using a traditional Deductive Teaching Method (DTM). The course is redesigned and taught by adopting the ITM instead. Furthermore, a comprehensive plan has been devised to deliver the course content in computer labs. The course was evaluated in an experiment consisting of 81 undergraduate students. The students in the Experimental Group (EG) (N = 45) were taught using the redesigned ITM course, whereas the students in the Control Group (CG) (N = 36) were taught using the DTM course. The performance of both groups was compared in terms of the marks obtained by them. A pre-test conducted to compare pre-course mathematical and analytical abilities showed that CG was better in analytical reasoning with no significant differences in mathematical abilities. Three post-tests were used to evaluate the groups theoretical and practical competence in programming and showed EG improved performance with large, medium, and small effect sizes as compared to CG. The results of this research could help computer programming educators to implement inductive strategies that could improve the learning of the computer programming.

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