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With almost one third of the world on a lockdown, the corporates and the offices have now rapidly shifted to working from home. Since no specific treatment has been suggested by any medical institution so far, World Health Organization has recommended that the only possible solution to be safe is to self-isolate and stay home. Due to this, the world has come to a screeching halt and the businesses have to be shifted to remote work. Work-from-Home is a very new experience for most of us and hence the perception of the people ranges from being very excited to very hopeless. This study aims to examine the sentiments of the people regarding Work-from-Home concept by analysing twitter activities posted on social media. Total 100,000 tweets were analysed for this study. Results indicate that Work-from-Home concept was taken positively by the people. The emotions associated with most of the tweets were of trust and anticipation indicating that this concept is being welcomed by the people.

With increasing numbers of patients with COVID-19 globally, China and the World Health Organization have been blamed by some for the spread of this disease. Consequently, instances of racism and hateful acts have been reported around the world. When US President Donald Trump used the term \"Chinese Virus,\" this issue gained momentum, and ethnic Asians are now being targeted. The online situation looks similar, with increases in hateful comments and posts. The aim of this paper is to analyze the increasing instances of cyber racism during the COVID-19 pandemic, by assessing emotions and sentiments associated with tweets on Twitter. In total, 16,000 tweets from April 11-16, 2020, were analyzed to determine their associated sentiments and emotions. Statistical analysis was carried out using R. Twitter API and the sentimentr package were used to collect tweets and then evaluate their sentiments, respectively. This research analyzed the emotions and sentiments associated with terms like \"Chinese Virus,\" \"Wuhan Virus,\" and \"Chinese Corona Virus.\" The results suggest that the majority of the analyzed tweets were of negative sentiment and carried emotions of fear, sadness, anger, and disgust. There was a high usage of slurs and profane words. In addition, terms like \"China Lied People Died,\" \"Wuhan Health Organization,\" \"Kung Flu,\" \"China Must Pay,\" and \"CCP is Terrorist\" were frequently used in these tweets. This study provides insight into the rise in cyber racism seen on Twitter. Based on the findings, it can be concluded that a substantial number of users are tweeting with mostly negative sentiments toward ethnic Asians, China, and the World Health Organization.

One of the major challenges in human-robot interaction is to determine the trustworthiness of the robot. In order to enhance and augment the human capabilities by establishing a human robot partnership, it is important to evaluate the reliability and dependability of the robots for the specific tasks. The trust relationship between the human and robot becomes critical especially in the cases where there is strong cohesion between humans and robots. In this article, a cognition based-trust model has been developed which measures the trust and other related cognitive parameters of the robot. This trust model has been applied on a customized robot which performs path planning tasks using three different algorithms. The simulation of the model has been done to evaluate the trust of the robot for the three algorithms. The results show that with each learning cycle of each method, the trust of the robot increases. An empirical evaluation has also been done to validate the model.

Since the last decade, one of the most noteworthy changes in our daily lives has been the efficacious invasion of Social Media and Social Networking Sites (SNS). Social media has affected the whole world in a rather contagious manner and the education sector is no exception. While there is no denying that social media and networking sites have affected us immensely, it is the need of the hour that their positive as well as negative effects must be analyzed. This research work concentrates on the students of the Fiji National University and scrutinizes the effects of social media and networking sites on their behavior as well as their education. For this study, personal communication as well as questionnaire analysis were done. According to this study, the students in Fiji have been affected both in a positive as well as in negative manner by the social media and social networking sites. The study also certain interventions that can be done to scale down the negative impacts on the students and help them in improving their performance in the colleges.

In this article, we have applied cognition on robot using Q-learning based situation operator model. The situation operator model takes the initial situation of the mobile robot and applies a set of operators in order to move the robot to the destination. The initial situation of the mobile robot is defined by a set of characteristics inferred by the sensor inputs. The Situation-Operator Model (SOM) model comprises of a planning and learning module which uses certain heuristics for learning through the mobile robot and a knowledge base which stored the experiences of the mobile robot. The control and learning of the robot is done using q-learning. A camera sensor and an ultrasonic sensor were used as the sensory inputs for the mobile robot. These sensory inputs are used to define the initial situation, which is then used in the learning module to apply the valid operator. The results obtained by the proposed method were compared to the result obtained by Reinforcement-Based Artificial Neural Network for path planning.