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On the backdrop of COVID-19 pandemic lockdown in India that was implemented suddenly since 24th March 2020, the educational institutes faced the challenge of course delivery and academic engagement of the students. Many institutes responded through online teaching-learning mode. However, the transition from physical classes to virtual mode course delivery was all of a sudden for both; student as well as faculty community. Hence, it is important to develop an understanding of perception of students and faculty members to this experience of online teaching. This work reports preliminary findings on an online course delivery initiative at an autonomous institute during the lockdown period and efficacy of the virtual model in order to validate the hypothesis that virtual course delivery is well received by the students and proves beneficial. Also, an effort is made to investigate whether the students' acceptance to online teaching is influenced by the stream they belong to and if there is any correlation between the academic achievements of students and their acceptance to online mode. In view of growing trend towards online learning post COVID-19 scenario, the findings offer valuable insights. From the view point of overall experience and benefits from online teaching-learning, a clear division is found among the students' opinions. Nearly half of the students (48.08%) have found to be benefitted through online classes while the remaining don't seem to have satisfied and thus benefitted from the initiative. The authors propose a hybrid approach combining physical and virtual experiences for future purposes, owing to inherent limitations of online teaching being reflected through the students' responses. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The teaching-learning based optimization (TLBO) algorithm is a recently developed algorithm. The proposed work presents a design of a master-slave TLBO algorithm. OBJECTIVES: This research aims to design a master-slave TLBO algorithm to improve its performance and system utilization for CEC2006 single-objective benchmark functions. METHODS: The proposed approach implemented using OpenMP and CUDA C, a hybrid programming approach to enhance the utilization of the system's computational resources. The device utilization and performance of the proposed approach evaluated using CEC2006 benchmark functions. RESULTS: The proposed approach obtains best results in significantly reduced time for CEC2006 benchmark functions. The maximum speed-up achieved is 30.14X. The average GPGPU utilization is 90% and the average utilization of logical processors is more than 90%. CONCLUSION: The master-slave TLBO algorithm improves the utilization of computational resources significantly and obtains the best results for CEC2006 benchmark functions. [ABSTRACT FROM AUTHOR]

Outcome-based professional education processes are a new standard for all stakeholders and knowledge economy. Majority of the time the IT services/consulting industry demands graduates who shall be billable from day one of joining the enterprise. According to the National Employability Report Engineers 2019, only 3% of engineers possess new-age skills in areas such as AI, data analytics and mobile technologies. Academic project works shall be seen in the light of aforesaid observations and therefore suggests treating this as an opportunity to initiate undergraduates to more hands-on experiential learning considering the advancements in technology and market need. The objective of this paper is to identify the gap between the needs of the market/job trends and undergraduate CS/IT student projects areas. The second objective is to identify the factors affecting the project selection process. Our empirical study in general spanning over seven Engineering Institutes in western Maharashtra. The statistical data is collected from a controlled group of 4000 students. The data consists of project topics completed by students in the last nine years (from the year 2011 to the year 2019). Two indicators are used to understand the IT market. First, Gartner strategic technologies present the worldwide trend of technologies. Second, student project areas are compared with job trends in India. The student's feedback on technology adoption theory presents the challenges in adoption of recent IT technologies in academic projects. 62% of students have reported unavailability of resources at the institute to encourage the adoption of the recent technologies. The student's perception about the adoption of recent technologies needs to change. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The artificial bee colony (ABC) algorithm is a nature-inspired technique used for solving different optimization problems. This paper presents a multi-hive ABC algorithm for solving constrained benchmark functions of CEC2006. The CEC2006 data set contains the global benchmark functions with different design variables, number and type of constraints. OBJECTIVES: The objective of the proposed work is to design and apply the GPGPU based multi-hive ABC algorithm to solve constrained optimization problems. METHODS: The proposed approach is a multi-population coarse-grained system implemented using General Purpose Graphics Processing Unit (GPGPU). The performance of the proposed approach is compared with the serial ABC algorithm for eleven benchmark functions and results in the literature. The multi-hive ABC algorithm has multiple hives, each running separate ABC algorithm on different cores of GPGPU. RESULTS: The proposed approach provides global best solutions in significantly reduced time for all benchmark functions. The speed-up obtained is approximately 7X to 9X. The GPGPU device utilization is approximately 57% to 91%. CONCLUSION: The GPGPU based multi-hive ABC algorithm is a found good with respect to best results, speed up factor and GPU utilization to solve constrained optimization problems. [ABSTRACT FROM AUTHOR]