Implementing Effective Learning with Ubiquitous Learning Technology During Coronavirus Pandemic

Ubiquitous computing supports U-learning to develop and implement a new educational environment that provides effective and interactive learning to students wherever they are.This study aims to present a qualitative evaluation for using U-learning instead of traditional education to avoid the spread of the Coronavirus pandemic.The authors introduce a UTAUT(Unified Theory of Acceptance and Use of Technology)model to assess the capability of the given factors that are expected to affect the learners’intention and behavior for accepting the U-learning technology for full E-learning.The research study shows a promising impact on the use of U-learning apps for implementing their learning activities in both the present and coming stage.The study also presents the descriptive statistics that affect the students’answers by using U-learning while implementing learning assignments including exams,projects,forums,essays,presentations,and laboratories.The research experiments demonstrate both the composite reliability(CR)and the average variance extracted(AVE)of the presented paradigm.The experimental results have shown that(1)the factor loadings≥0.75,CR≥0.9,and AVE≥0.75,which present appropriate proof for the validity and reliability of the proposed model,and(2)the total average of students that accept the U-learning is 82.99%,and who disagree is 8.13%,while who neither agree nor disagree is 6.44%.

Developing phoneme-based lip-reading sentences system for silent speech recognition

Lip-reading is a process of interpreting speech by visually analysing lip movements.Recent research in this area has shifted from simple word recognition to lip-reading sentences in the wild.This paper attempts to use phonemes as a classification schema for lip-reading sentences to explore an alternative schema and to enhance system performance.Different classification schemas have been investigated,including characterbased and visemes-based schemas.The visual front-end model of the system consists of a Spatial-Temporal(3D)convolution followed by a 2D ResNet.Transformers utilise multi-headed attention for phoneme recognition models.For the language model,a Recurrent Neural Network is used.The performance of the proposed system has been testified with the BBC Lip Reading Sentences 2(LRS2)benchmark dataset.Compared with the state-of-the-art approaches in lip-reading sentences,the proposed system has demonstrated an improved performance by a 10%lower word error rate on average under varying illumination ratios.

Code Smell Detection Using Whale Optimization Algorithm

Software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their capabilities.Code smells arise as one of the obstacles in the software industry.They are characteristics of software source code that indicate a deeper problem in design.These smells appear not only in the design but also in software implementation.Code smells introduce bugs,affect software maintainability,and lead to higher maintenance costs.Uncovering code smells can be formulated as an optimization problem of finding the best detection rules.Although researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be improved.Previous research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their types.Our research improves code smell detection by applying a search-based technique;we use the Whale Optimization Algorithm as a classifier to find ideal detection rules.Applying this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass variance.The proposed framework adopts if-then detection rules during the software development life cycle.Those rules identify the types for both medium and large projects.Experiments are conducted on five open-source software projects to discover nine smell types that mostly appear in codes.The proposed detection framework has an average of 94.24%precision and 93.4%recall.These accurate values are better than other search-based algorithms of the same field.The proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and cost.Additionally,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.