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With the rapid development of the internet of things (IoT) and artificial intelligence (AI) technologies, human activity recognition (HAR) has been applied in a variety of domains such as security and surveillance, human-robot interaction, and entertainment. Even though a number of surveys and review papers have been published, there is a lack of HAR overview papers focusing on healthcare applications that use wearable sensors. Therefore, we fill in the gap by presenting this overview paper. In particular, we present our projects to illustrate the system design of HAR applications for healthcare. Our projects include early mobility identification of human activities for intensive care unit (ICU) patients and gait analysis of Duchenne muscular dystrophy (DMD) patients. We cover essential components of designing HAR systems including sensor factors (e.g., type, number, and placement location), AI model selection (e.g., classical machine learning models versus deep learning models), and feature engineering. In addition, we highlight the challenges of such healthcare-oriented HAR systems and propose several research opportunities for both the medical and the computer science community.

One of the most notable global transportation trends is the accelerated pace of development in vehicle automation technologies. Uncertainty surrounds the future of automated mobility as there is no clear consensus on potential adoption patterns, ownership versus shared use status and travel impacts. Adding to this uncertainty is the impact of the COVID-19 pandemic that has triggered profound changes in mobility behaviors as well as accelerated the adoption of new technologies at an unprecedented rate. This study examines the impact of the COVID-19 pandemic on willingness to adopt the emerging new technology of self-driving vehicles. Using data from a survey disseminated in June 2020 to 700 respondents in contiguous United States, we perform a difference-in-difference regression to analyze the shift in willingness to use autonomous vehicles as part of a shared fleet before and during the pandemic. The results reveal that the COVID-19 pandemic has a positive and highly significant impact on consideration of autonomous vehicles. This shift is present regardless of techsavviness, gender or political views. Individuals who are younger, left-leaning and frequent users of shared modes of travel are expected to become more likely to use autonomous vehicles once offered. Understanding the effects of these attributes on the increase in consideration of AVs is important for policy making, as these effects provide a guide to predicting adoption of autonomous vehicles - once available - and to identify segments of the population likely to be more resistant to adopting AVs.

This paper aims at providing the summary of the Global Data Science Project (GDSC) for COVID-19. as on May 31 2020. COVID-19 has largely impacted on our societies through both direct and indirect effects transmitted by the policy measures to counter the spread of viruses. We quantitatively analysed the multifaceted impacts of the COVID-19 pandemic on our societies including people's mobility, health, and social behaviour changes. People's mobility has changed significantly due to the implementation of travel restriction and quarantine measurements. Indeed, the physical distance has widened at international (cross-border), national and regional level. At international level, due to the travel restrictions, the number of international flights has plunged overall at around 88 percent during March. In particular, the number of flights connecting Europe dropped drastically in mid of March after the United States announced travel restrictions to Europe and the EU and participating countries agreed to close borders, at 84 percent decline compared to March 10th. Similarly, we examined the impacts of quarantine measures in the major city: Tokyo (Japan), New York City (the United States), and Barcelona (Spain). Within all three cities, we found the significant decline in traffic volume. We also identified the increased concern for mental health through the analysis of posts on social networking services such as Twitter and Instagram. Notably, in the beginning of April 2020, the number of post with #depression on Instagram doubled, which might reflect the rise in mental health awareness among Instagram users. Besides, we identified the changes in a wide range of people's social behaviors, as well as economic impacts through the analysis of Instagram data and primary survey data.

Technology has evolved over the years, making our lives easier. It has impacted the healthcare sector, increasing the average life expectancy of human beings. Still, there are gaps that remain unaddressed. There is a lack of transparency in the healthcare system, which results in inherent trust problems between patients and hospitals. In the present day, a patient does not know whether he or she will get the proper treatment from the hospital for the fee charged. A patient can claim reimbursement of the medical bill from any insurance company. However, today there is minimal scope for the Insurance Company to verify the validity of such bills or medical records. A patient can provide fake details to get financial benefits from the insurance company. Again, there are trust issues between the patient (i.e., the insurance claimer) and the insurance company. Blockchain integrated with the smart contract is a well-known disruptive technology that builds trust by providing transparency to the system. In this paper, we propose a blockchain-enabled \emph{Secure and Smart HealthCare System}. Fairness of all the entities: patient, hospital, or insurance company involved in the system is guaranteed with no one trusting each other. Privacy and security of patient's medical data are ensured as well. We also propose a method for privacy-preserving sharing of aggregated data with the research community for their own purpose. Shared data must not be personally identifiable, i.e, no one can link the acquired data to the identity of any patient or their medical history. We have implemented the prototype in the Ethereum platform and Ropsten test network, and have included the analysis as well.

Over the last 20 years, a very large number of startups have been launched, ranging from mobile application and game providers to enormous corporations that have started as tiny startups. Startups are an important topic for research and development. The fundamentals of success are the characteristics of individuals and teams, partner investors, the market, and the speed at which everything evolves. Startup's business environment is fraught with uncertainty, as actors tend to be young and inexperienced, technologies either new or rapidly evolving, and team-combined skills and knowledge either key or fatal. As over 90 per cent of software startups fail, having a capable and reliable team is crucial to survival and success. Many aspects of this topic have been extensively studied, and the results of the study on human capital are particularly important. Regarding human capital abilities, such as knowledge, experience, skills, and other cognitive abilities, this dissertation focuses on design skills and their deployment in startups. Design is widely studied in artistic and industrial contexts, but its application to startup culture and software startups follows its own method prison. In the method prison, old and conventional means are chosen instead of new techniques and demanding design studies. This means that when a software startup considers design as a foundation for creativity and generating better offerings, they can grab any industry with a disruptive agenda, making anything software-intensive.

Today's significant technological advancement allows early-stage software startups to build and launch innovative products quickly on the market. However, many of them die in the early years of their path due to market conditions, ignorance of customer needs, lack of resources, or focus, such as the misuse of well-established practices. The study's motivation is to analyze software engineering practices in startups from a practitioner's perspective. Our objective was to identify practices and tools the startups employ in their daily routines. We carried out an expert survey study with 140 software developers involved in software startups from different domains. The results show that startups in the initial and validation phases select practices and tools on an ad-hoc basis and based on the development team's prior knowledge. When they move into the growth phase, they recognize that they could have adopted better practices beforehand to support product scaling with a more mature team. The results also indicated that support tools are selected based on their integration with other tools and their ability to automate operational activities.

To build a robust secure solution for smart city IOT network from any Cyber attacks using Artificial Intelligence. In Smart City IOT network, data collected from different log collectors or direct sources from cloud or edge should harness the potential of AI. The smart city command and control center team will leverage these models and deploy it in different city IOT network to help on intrusion prediction, network packet surge, potential botnet attacks from external network. Some of the vital use cases considered based on the users of command-and-control center

Cyber-Physical Systems (CPSs) employed for Industrial Automation often require the adoption of a hybrid data processing approach mediating between cloud, edge, and fog computing paradigms. Nowadays, it is possible to shift data pre-processing capabilities closer to data sensing to collect environmental measurements locally on the edge or deep edge. In line with the emerging computing paradigms, this work proposes a solution that includes both software and hardware components and which simplifies the deployment of smart measurement systems. The solution stresses also the adoption of standards and open data paradigms for simplifying the integration and ensuring the interoperability of all the systems involved. The distributed smart measurement solution has been adopted in an Industry Automation use case included in the project Cyber-Physical Systems for Europe (CPS4EU). The use case attains with monitoring of an industrial trimming machine operating in the production process of a big part of a civil aircraft, where the sensing and processing capabilities of the distributed smart measurement system allow to collect different parameters of work parts to satisfy the expected quality of the production process.

We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reveals that such customer-centric design may lead to unfair distribution of exposure among the producers, which may adversely impact their well-being. On the other hand, a producer-centric design might become unfair to the customers. Thus, we consider fairness issues that span both customers and producers. Our approach involves a novel mapping of the fair recommendation problem to a constrained version of the problem of fairly allocating indivisible goods. Our proposed FairRec algorithm guarantees at least Maximin Share (MMS) of exposure for most of the producers and Envy-Free up to One item (EF1) fairness for every customer. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring a marginal loss in the overall recommendation quality.

There is a need for systems to dynamically interact with ageing populations to gather information, monitor health condition and provide support, especially after hospital discharge or at-home settings. Several smart devices have been delivered by digital health, bundled with telemedicine systems, smartphone and other digital services. While such solutions offer personalised data and suggestions, the real disruptive step comes from the interaction of new digital ecosystem, represented by chatbots. Chatbots will play a leading role by embodying the function of a virtual assistant and bridging the gap between patients and clinicians. Powered by AI and machine learning algorithms, chatbots are forecasted to save healthcare costs when used in place of a human or assist them as a preliminary step of helping to assess a condition and providing self-care recommendations. This paper describes integrating chatbots into telemedicine systems intended for elderly patient after their hospital discharge. The paper discusses possible ways to utilise chatbots to assist healthcare providers and support patients with their condition.