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In automotive domain, operation of secondary tasks like accessing infotainment system, adjusting air conditioning vents, and side mirrors distract drivers from driving. Though existing modalities like gesture and speech recognition systems facilitate undertaking secondary tasks by reducing duration of eyes off the road, those often require remembering a set of gestures or screen sequences. In this paper, we have proposed two different modalities for drivers to virtually touch the dashboard display using a laser tracker with a mechanical switch and an eye gaze switch. We compared performances of our proposed modalities against conventional touch modality in automotive environment by comparing pointing and selection times of representative secondary task and also analysed effect on driving performance in terms of deviation from lane, average speed, variation in perceived workload and system usability. We did not find significant difference in driving and pointing performance between laser tracking system and existing touchscreen system. Our result also showed that the driving and pointing performance of the virtual touch system with eye gaze switch was significantly better than the same with mechanical switch. We evaluated the efficacy of the proposed virtual touch system with eye gaze switch inside a real car and investigated acceptance of the system by professional drivers using qualitative research. The quantitative and qualitative studies indicated importance of using multimodal system inside car and highlighted several criteria for acceptance of new automotive user interface.

The rapid spread of COVID-19 infections on a global level has highlighted the need for accurate, transparent and timely information regarding collective mobility patterns to inform de-escalation strategies as well as to provide forecasting capacity for re-escalation policies aiming at addressing further waves of the virus. Such information can be extracted using aggregate anonymised data from innovative sources such as mobile positioning data. This paper presents lessons learnt and results of a unique Business-to-Government (B2G) initiative between several Mobile Network Operators in Europe and the European Commission. Mobile positioning data have supported policy makers and practitioners with evidence and data-driven knowledge to understand and predict the spread of the disease, the effectiveness of the containment measures, their socio-economic impacts while feeding scenarios at EU scale and in a comparable way across countries. The challenges of this data sharing initiative are not limited to data quality, harmonisation, and comparability across countries, however important they are. Equally essential aspects that need to be addressed from the onset are related to data privacy, security, fundamental rights and commercial sensitivity.

Unlike most bibliometric studies focusing on publications, taking Big Data research as a case study, we introduce a novel bibliometric approach to unfold the status of a given scientific community from an individual level perspective. We study the academic age, production, and research focus of the community of authors active in Big Data research. Artificial Intelligence (AI) is selected as a reference area for comparative purposes. Results show that the academic realm of "Big Data" is a growing topic with an expanding community of authors, particularly of new authors every year. Compared to AI, Big Data attracts authors with a longer academic age, who can be regarded to have accumulated some publishing experience before entering the community. Despite the highly skewed distribution of productivity amongst researchers in both communities, Big Data authors have higher values of both research focus and production than those of AI. Considering the community size, overall academic age, and persistence of publishing on the topic, our results support the idea of Big Data as a research topic with attractiveness for researchers. We argue that the community-focused indicators proposed in this study could be generalized to investigate the development and dynamics of other research fields and topics.

Updating observations of a signal due to the delays in the measurement process is a common problem in signal processing, with prominent examples in a wide range of fields. An important example of this problem is the nowcasting of COVID-19 mortality: given a stream of reported counts of daily deaths, can we correct for the delays in reporting to paint an accurate picture of the present, with uncertainty? Without this correction, raw data will often mislead by suggesting an improving situation. We present a flexible approach using a latent Gaussian process that is capable of describing the changing auto-correlation structure present in the reporting time-delay surface. This approach also yields robust estimates of uncertainty for the estimated nowcasted numbers of deaths. We test assumptions in model specification such as the choice of kernel or hyper priors, and evaluate model performance on a challenging real dataset from Brazil. Our experiments show that Gaussian process nowcasting performs favourably against both comparable methods, and against a small sample of expert human predictions. Our approach has substantial practical utility in disease modelling -- by applying our approach to COVID-19 mortality data from Brazil, where reporting delays are large, we can make informative predictions on important epidemiological quantities such as the current effective reproduction number.

Since December 2019, the COVID-19 pandemic has caused people around the world to exercise social distancing, which has led to an abrupt rise in the adoption of remote communications for working, socializing, and learning from home. As remote communications will outlast the pandemic, it is crucial to protect users' security and respect their privacy in this unprecedented setting, and that requires a thorough understanding of their behaviors, attitudes, and concerns toward various aspects of remote communications. To this end, we conducted an online study with 220 worldwide Prolific participants. We found that privacy and security are among the most frequently mentioned factors impacting participants' attitude and comfort level with conferencing tools and meeting locations. Open-ended responses revealed that most participants lacked autonomy when choosing conferencing tools or using microphone/webcam in their remote meetings, which in several cases contradicted their personal privacy and security preferences. Based on our findings, we distill several recommendations on how employers, educators, and tool developers can inform and empower users to make privacy-protective decisions when engaging in remote communications.

Mobile and IoT applications have greatly enriched our daily life by providing convenient and intelligent services. However, these smart applications have been a prime target of adversaries for stealing sensitive data. It poses a crucial threat to users' identity security, financial security, or even life security. Research communities and industries have proposed many Information Flow Control (IFC) techniques for data leakage detection and prevention, including secure modeling, type system, static analysis, dynamic analysis, \textit{etc}. According to the application's development life cycle, although most attacks are conducted during the application's execution phase, data leakage vulnerabilities have been introduced since the design phase. With a focus on lifecycle protection, this survey reviews the recent representative works adopted in different phases. We propose an information flow based defensive chain, which provides a new framework to systematically understand various IFC techniques for data leakage detection and prevention in Mobile and IoT applications. In line with the phases of the application life cycle, each reviewed work is comprehensively studied in terms of technique, performance, and limitation. Research challenges and future directions are also pointed out by consideration of the integrity of the defensive chain.

Coronavirus disease (COVID-19) pandemic has changed various aspects of people's lives and behaviors. At this stage, there are no other ways to control the natural progression of the disease than adopting mitigation strategies such as wearing masks, watching distance, and washing hands. Moreover, at this time of social distancing, social media plays a key role in connecting people and providing a platform for expressing their feelings. In this study, we tap into social media to surveil the uptake of mitigation and detection strategies, and capture issues and concerns about the pandemic. In particular, we explore the research question, "how much can be learned regarding the public uptake of mitigation strategies and concerns about COVID-19 pandemic by using natural language processing on Reddit posts?" After extracting COVID-related posts from the four largest subreddit communities of North Carolina over six months, we performed NLP-based preprocessing to clean the noisy data. We employed a custom Named-entity Recognition (NER) system and a Latent Dirichlet Allocation (LDA) method for topic modeling on a Reddit corpus. We observed that 'mask', 'flu', and 'testing' are the most prevalent named-entities for "Personal Protective Equipment", "symptoms", and "testing" categories, respectively. We also observed that the most discussed topics are related to testing, masks, and employment. The mitigation measures are the most prevalent theme of discussion across all subreddits.

COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic's potential impact on the adoption of the Internet of Things (IoT) in various broad sectors namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption.

In 2020, due to the COVID-19 pandemic, educational activities had to be done remotely as a way to avoid the spread of the disease. What happened was not exactly a shift to an online learning model but a transition to a new approach called Emergency Remote Teaching. It is a temporary strategy to keep activities going on until it is safe again to return to the physical facilities of universities. This new setting became a challenge to both teachers and students. The lack of interaction and classroom socialization became obstacles for students to continue engaged. Before the pandemic, hackathons -- short-lived events (1 to 3 days) where participants intensively collaboration to develop software prototypes -- were starting to be explored as an alternative venue to engage students in acquiring and practicing technical skills. In this paper, we present an experience report on the usage of an online hackathon as a resource to engage students in the development of their semester project in a distributed applications course during this emergency remote teaching period. We describe details of the intervention and present an analysis of the students' perspective of the approach. One of the important findings was the efficient usage of the Discord communication tool -- already used by all students while playing games -- which helped them socialize and keep them continuously engaged in synchronous group work, "virtually collocated".

In recent years, disinformation including fake news, has became a global phenomenon due to its explosive growth, particularly on social media. The wide spread of disinformation and fake news can cause detrimental societal effects. Despite the recent progress in detecting disinformation and fake news, it is still non-trivial due to its complexity, diversity, multi-modality, and costs of fact-checking or annotation. The goal of this chapter is to pave the way for appreciating the challenges and advancements via: (1) introducing the types of information disorder on social media and examine their differences and connections; (2) describing important and emerging tasks to combat disinformation for characterization, detection and attribution; and (3) discussing a weak supervision approach to detect disinformation with limited labeled data. We then provide an overview of the chapters in this book that represent the recent advancements in three related parts: (1) user engagements in the dissemination of information disorder; (2) techniques on detecting and mitigating disinformation; and (3) trending issues such as ethics, blockchain, clickbaits, etc. We hope this book to be a convenient entry point for researchers, practitioners, and students to understand the problems and challenges, learn state-of-the-art solutions for their specific needs, and quickly identify new research problems in their domains.