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In this short response to the papers appearing in this special issue (Technology, AI Bots and Psychotherapy After Covid), psychotherapist and author Aaron Balick draws on the variety of themes that have arisen within the contributed papers to reflect on the wider issue of computer mediated human relations. In it he makes a distinction between the papers that focus on the therapeutic process mediated by technology and those that look more broadly at the paradigm of therapy practice in this context. Framing technology as a tool, the author pulls together both strands to explore what psychotherapy research may say about the broader issues of societies mediated by technology and how therapeutic research may contribute to these larger social issues. [ABSTRACT FROM AUTHOR]

The Coronavirus disease, i.e. (COVID-19) pandemic outbreaks has inevitably lead to a corresponding movement restrictive measure by the members of both central and state government leadership. The current limitation of healthcare systems cannot predict the COVID-19 outbreaks and vaccination drive. The need of the hour is to use Blockchain Technology for the construction of immutable ledger. In this paper a Blockchain and Artificial Intelligence (AI) enabled COVID-19 vaccine tracking system has been proposed. The proposed system is designed using the Ethereum Virtual Machine (EVM), decentralized storage by Inter Planetary File System (IPFS) and implemented using Truffle and Ganache Tool. The smart contract interaction among the entities is developed with Drizzle and the front-end using ReactJS. The performance of the proposed framework has been presented with Keccack 256 transaction hash, the total number of transactions, gas consumed by each contract. Additionally the performance parameters such as latency, throughput, traffic in, out, CPU, and memory utilization of the blockchain framework are also calculated. The artificial neural network (ANN) has been used to classify the vaccination group. Such an attempt is a worthwhile addition to the state of the art as evident from the results presented herein. The proposed framework provides a way for vaccinated members of the community to be tracked and indexed so as to avail the benefits, for example, less restrictive movements nationally, internationally, or simply in their own respective communities. This paper leverages the advantages of Blockchain Technology to obtain a reliable and accurate way to facilitate the entire tracking process. [ABSTRACT FROM AUTHOR]

The sudden outbreak of the novel Coronavirus (nCoV-19, COVID-19) and its rampant spread led to a significant number of people being infected worldwide and disrupted several businesses. With most of the countries imposing serious lockdowns due to the increasing number of fatalities, the social lives of millions of people were affected. Although the lockdown led to an increase in network activities, online shopping, and social network usage, it also raised questions On the mental wellness of society. Interestingly, excessive usage of social networks also witnessed humor traveling across the Internet in the form of Internet Memes during the lockdown period. Humor is known to affect our well-being, decision-making, and psychological systems. In this paper, we have analyzed the Internet Meme activity in Social Networks during the COVID-19 Lockdown period. As humor is known to relieve individuals from psychological stress, it is necessary to understand how human beings adopted Internet Memes for coping up with the lockdown stress and stress-relieving mechanism during the lockdown period. In this paper, we have considered thirty popular memes and the increase in the number of their captions within the period (September 2017 to August 2020). An increase in Internet Meme activity since the lockdown period (March 2020) depicts an increase in online social behavior. We analyze the internet meme activity in social networks during the COVID-19 lockdown period using random forest, multi-layer perceptron, and instance-based learning algorithms followed by data visualization using line graph and Heat Map (8 & 15 clustered). We also compared the performance of the models using evaluation parameters like mean absolute error, root-mean-squared error & Kappa statistics and observed that random forest and instance-based learning algorithms perform better than multi-layer perceptrons. The result indicates that random forest and instance-based learning classifiers are having near perfect classification tendencies whereas multi-layer perceptrons showed around 97% classification accuracy. [ABSTRACT FROM AUTHOR]

The spreading of Coronavirus (covid-19) is pushing the healthcare organizations under exceptional strain over the universe and increasing pressure concurring to the World Health Organization (WHO). With advancement of Artificial Intelligence, the discovery of this type of infection during the initial stage offers assistance in quick recuperation and in discharging the pressure on healthcare organizations. This paper presents the deep convolution network to detect the coronavirus in chest x-ray images. Due to the lack of benchmark datasets for covid-19 specifically in chest x-ray images, this work presents the framework that adopts the Generative Adversarial Network of Deep Convolution (DC-GAN). The proposed model of DC-GAN can generate a maximum of 30 different patterns for a single image and thus increases the size of dataset. It also offers assistance in overwhelming the overfitting issue and building the proposed framework more robust. The proposed model implements the transfer learning updated by the loss function using the ImageNet to attain the finest parameters from the pre-trained model. The publically available dataset is utilized to validate the proposed work. This work is validated by conducting the various experiments in various perspectives and also its performances are recorded using the measures namely accuracy, recall, precision and, F1score. These recorded results compared with the various existing methods.. [ABSTRACT FROM AUTHOR]

Researchers and scientists can use computational-based models to turn linked data into useful information, aiding in disease diagnosis, examination, and viral containment due to recent artificial intelligence and machine learning breakthroughs. In this paper, we extensively study the role of artificial intelligence and machine learning in delivering efficient responses to the COVID-19 pandemic almost four years after its start. In this regard, we examine a large number of critical studies conducted by various academic and research communities from multiple disciplines, as well as practical implementations of artificial intelligence algorithms that suggest potential solutions in investigating different COVID-19 decision-making scenarios. We identify numerous areas where artificial intelligence and machine learning can impact this context, including diagnosis (using chest X-ray imaging and CT imaging), severity, tracking, treatment, and the drug industry. Furthermore, we analyse the dilemma's limits, restrictions, and hazards. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19) pandemic has intensively damaged human socio-economic lives and the growth of countries around the world. Many efforts have been made in the direction of artificial intelligence (AI) techniques to detect the corona at an early stage and take necessary precautions to stop it from spreading or recovery from the infection. However, the situation and solutions are still challenging. In this paper, we proposed various technological aspects, solutions using a supervised/unsupervised manner and continuous health monitoring with physiological parameters. Finally, the performance of COVID-19 detection with Gaussian mixture model-universal background model (GMM-UBM) technique using the voice signal has been demonstrated. The developed system achieves the COVID-19 detection performance in terms of areas under receiver operating characteristic (ROC) curves in the range 60–67%. Moreover, the various lessons learned from the current COVID-19 crisis are presented for future directions. [ABSTRACT FROM AUTHOR]

A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values. [ABSTRACT FROM AUTHOR]

Aotearoa New Zealand's response to the COVID-19 pandemic has included the use of algorithms that could aid decision making. Te Pokapū Hātepe o Aotearoa, the New Zealand Algorithm Hub, was established to evaluate and host COVID-19 related models and algorithms, and provide a central and secure infrastructure to support the country's pandemic response. A critical aspect of the Hub was the formation of an appropriate governance group to ensure that algorithms being deployed underwent cross-disciplinary scrutiny prior to being made available for quick and safe implementation. This framework necessarily canvassed a broad range of perspectives, including from data science, clinical, Māori, consumer, ethical, public health, privacy, legal and governmental perspectives. To our knowledge, this is the first implementation of national algorithm governance of this type, building upon broad local and global discussion of guidelines in recent years. This paper describes the experiences and lessons learned through this process from the perspective of governance group members, emphasising the role of robust governance processes in building a high-trust platform that enables rapid translation of algorithms from research to practice. [ABSTRACT FROM AUTHOR]

In this study, we searched for the latest literature on the use of deep learning applications to combat COVID-19 and these were identified from several search engines including IEEE Xplore, Google Scholar, PubMed, and Scopus. This involved a comprehensive analysis of the studies to identify the challenges associated with the use of deep learning models with a view to highlight the possible future trends in the development of deep learning systems that are efficient and more reliable for the diagnosis of COVID-19 patients. This paper provides information related to the deep learning techniques used to detect COVID-19. This paper discusses the Convolutional Neural Networks’ (CNNs) structure, how to train CNNs, and highlights the different pre-trained models of CNNs that can be used for the detection of COVID-19. This paper explores the latest developments in the diagnosis of COVID-19 using deep learning applications that rely on the use of X-ray images taken from medical imaging samples. A review of the different models developed to facilitate effective diagnosis of COVID-19 provides information regarding the experimental data, the data splitting techniques used, as well as the proposed architecture for detecting COVID-19 and the different evaluation metrics for each model. This paper is a useful resource for medical and technical experts, as it helps them to develop a sound understanding of how deep learning techniques can be harnessed to stop the spread of COVID-19. [ABSTRACT FROM AUTHOR]

When combined with an appropriate level of human judgement, machine learning applications were crucial resources insupporting decision-making in the context of the Covid-19 crisis, resulting in more efficient and better-informed responses to ethicalissues. This paper focusses on four social dimensions (bioethical, political, psychological, and economic) from which the decisionstaken in the context of the Covid-19 crisis derived major ethical implications. On the one hand, I argue against the possibility ofaddressing these issues from a purely algorithmic approach, elaborating on two types of limitations for automated systems toaddress ethical issues. This leads me to discuss how different ethical situations call for different performance metrics with regards tothe 'contextual explicability and performance issue', as well as to enunciate a gold principle: 'legitimacy trumps accuracy'. On the otherhand, I present practical examples of machine learning applications which enhance, instead of dilute, human moral agency in betteraddressing these issues. I also suggest a 'moral perimeter' framework to ensure the responsibility of algorithms-assisted decisionmakersfor critical decisions. The unique potential of AI to 'solve' moral dilemmas by intervening on their conditions of possibility thenprompts me to discuss a new type of moral situation: AI-generated meta-dilemmas. [ABSTRACT FROM AUTHOR]

This paper explores the concept of mis/mal/dis-information (MMDI) and studies how information managers/professionals can manage the phenomenon of infodemic during the COVID-19 pandemic. We review existing literature to explore the current concepts, models, associations, and gaps in MMDI to highlight the critical role of information managers/professionals. The findings focus on defining MMDI/fake news and evaluating ongoing and emerging information literacy frameworks. Next, we highlight the existing initiatives and efforts made by Library and Information Science (LIS) professionals, LIS associations, and libraries to contradict the diffusion of MMDI and fake news and educate the public on how to navigate through an era of MMDI. Finally, the study summaries effective strategies designed by those within the LIS profession while suggesting recommendations as to how the information managers/LIS professionals can continue to improve their keys position in the digital age and contribute effectively to managing the phenomenon of infodemic. [ABSTRACT FROM AUTHOR]

An editorial is presented on the impacts of Covid-19 on workplaces. Topics include a range of papers with topical themes particularly that of gamification and virtual reality in online and blended learning; designing learning interventions and approaches for lower age groups including pre-schoolers; and Artificial Intelligence (AI) on the other hand emerging as a crucial technology for enhancing the learning experience online.