Design and Evaluation of Proactive Behavior in Conversational Assistants: Approach with the Eva Companion Robot

The importance of providing emotional support and assistance to older adults has been highlighted by the COVID-19 pandemic. An increasing number of older adults live alone, which promotes loneliness and depression risks. Also, the digital divide exacerbates these issues and other social difficulties, since older adults are not able to use technology to communicate. A socially assistive robot could help to address these loneliness and digital divide problems. However, it is critical to incorporate affectiveness and naturalness to promote the user acceptance of the robot. This project makes use of the existing EVA open-source robotics platform. The aim is to improve the quality of life of older adults by boosting their independence and alleviating loneliness or other emotional issues that can arise. To improve the user acceptance and to get a more natural, affective, non-passive behavior, this paper contributes to integrate several aspects to the EVA robot: a) assistiveness through conversations and a social messaging end-user skill to reduce the digital divide;b) proactivity by means of proactive interventions so EVA is able to start conversations;c) affectivity by means of showing emotions with eyes expressions, user recognition and emotion analysis in user input;and d) naturalness by blending all these characteristics with a low response time in the interaction and the novel wakeface activation method. Finally, a technical evaluation of the proposed solution provides evidence of its appropriate performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Ubiquitous and smart healthcare monitoring frameworks based on machine learning: A comprehensive review.

During the COVID-19 pandemic, the patient care delivery paradigm rapidly shifted to remote technological solutions. Rising rates of life expectancy of older people, and deaths due to chronic diseases (CDs) such as cancer, diabetes and respiratory disease pose many challenges to healthcare. While the feasibility of Remote Patient Monitoring (RPM) with a Smart Healthcare Monitoring (SHM) framework was somewhat questionable before the COVID-19 pandemic, it is now a proven commodity and is on its way to becoming ubiquitous. More health organizations are adopting RPM to enable CD management in the absence of individual monitoring. The current studies on SHM have reviewed the applications of IoT and/or Machine Learning (ML) in the domain, their architecture, security, privacy and other network related issues. However, no study has analyzed the AI and ubiquitous computing advances in SHM frameworks. The objective of this research is to identify and map key technical concepts in the SHM framework. In this context an interesting and meaningful classification of the research articles surveyed for this work is presented. The comprehensive and systematic review is based on the "Preferred Reporting Items for Systematic Review and Meta-Analysis" (PRISMA) approach. A total of 2540 papers were screened from leading research archives from 2016 to March 2021, and finally, 50 articles were selected for review. The major advantages, developments, distinctive architectural structure, components, technical challenges and possibilities in SHM are briefly discussed. A review of various recent cloud and fog computing based architectures, major ML implementation challenges, prospects and future trends is also presented. The survey primarily encourages the data driven predictive analytics aspects of healthcare and the development of ML models for health empowerment.

Toward a Cognitive-Inspired Hashtag Recommendation for Twitter Data Analysis

This research investigates hashtag suggestions in a heterogeneous and huge social network, as well as a cognitive-based deep learning solution based on distributed knowledge graphs. Community detection is first performed to find the connected communities in a vast and heterogeneous social network. The knowledge graph is subsequently generated for each discovered community, with an emphasis on expressing the semantic relationships among the Twitter platform's user communities. Each community is trained with the embedded deep learning model. To recommend hashtags for the new user in the social network, the correlation between the tweets of such user and the knowledge graph of each community is explored to set the relevant communities of such user. The models of the relevant communities are used to infer the hashtags of the tweets of such users. We conducted extensive testing to demonstrate the usefulness of our methods on a variety of tweet collections. Experimental results show that the proposed approach is more efficient than the baseline approaches in terms of both runtime and accuracy.

A Deep Analysis of Higher Education Cognitive and Psychological Learning Impact During Covid 19 Pandemic

Current scenario around the globe we can find that physical or face to face learning got a very big full stop for a long period of time. Virtual learning took its place, somewhat leaving behind both its positive and negative impact on the education sector. E-learning is playing a chief part in maintaining the decorum of education sector. The research and surveys found that young learners got many benefits through this type of education but also it is undeniable that it has negative aspects too, which needs to be solved. Mainly private higher education suffered less as compared to institutions in rural areas. This research proposes how to bring out the quality of output through e-learning for all the learners equally. It has become a challenge for private and government institutions to make this smart or virtual learning as the best integral part of educational system. © 2021 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).

Transcendent service management

PurposeThis vision article alerts service managers to the potential of cognitive computing to reframe their value propositions. Humans are bounded in three ways: perception, rationality and physicality. Cognitive computing, hardware or software that transcends these three limits, offers many opportunities to improve the performance of service systems, in particular those focused on customer engagement. The intangibility spectrum is presented as a mental model for service managers to consider how to use cognitive computing to support augmenting their value proposition by moving across the spectrum.Design/methodology/approachThree frameworks are integrated: a five systems framework, a system's impact classification of types of cognitive computing and a tangibility spectrum.FindingsThree examples illustrate the potential value of this integrative approach for service management.Originality/valueThis is the first integration of these frameworks, and two of them are the result of the first author's research.

Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment.

In the current pandemic, smart technologies such as cognitive computing, artificial intelligence, pattern recognition, chatbot, wearables, and blockchain can sufficiently support the collection, analysis, and processing of medical data for decision making. Particularly, to aid medical professionals in the disease diagnosis process, cognitive computing is helpful by processing massive quantities of data rapidly and generating customized smart recommendations. On the other hand, the present world is facing a pandemic of COVID-19 and an earlier detection process is essential to reduce the mortality rate. Deep learning (DL) models are useful in assisting radiologists to investigate the large quantity of chest X-ray images. However, they require a large amount of training data and it needs to be centralized for processing. Therefore, federated learning (FL) concept can be used to generate a shared model with no use of local data for DL-based COVID-19 detection. In this view, this paper presents a federated deep learning-based COVID-19 (FDL-COVID) detection model on an IoT-enabled edge computing environment. Primarily, the IoT devices capture the patient data, and then the DL model is designed using the SqueezeNet model. The IoT devices upload the encrypted variables into the cloud server which then performs FL on major variables using the SqueezeNet model to produce a global cloud model. Moreover, the glowworm swarm optimization algorithm is utilized to optimally tune the hyperparameters involved in the SqueezeNet architecture. A wide range of experiments were conducted on benchmark CXR dataset, and the outcomes are assessed with respect to different measures . The experimental outcomes pointed out the enhanced performance of the FDL-COVID technique over the other methods.