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<strong>Ozone and Carbon Monoxide Dataset Collected by the OpenSense Zurich Mobile Sensor Network</strong> This dataset contains ozone (O3) and carbon monoxide (CO) concentration measurements collected by the OpenSense (http://www.opensense.ethz.ch) mobile senor network over the course of 4.5 years (2012/02-2016/09). The sensors are mounted on top of 10 streetcars in the city of Zurich, Switzerland. <br> In particular, the dataset contains: Ozone (O3) data: 2012/02 - 2016/09 (19.9 Mio samples) Carbonmonoxide (CO) data: 2014/03 - 2016/09 (49.7Mio samples) <strong>Hardware:</strong><br> -------------- Ozone sensor: SGX (former e2V) MiCS-OZ-47 Ozone Sensing Head with Smart Transmitter PCB Carbon monoxide sensor: Alphasense CO-B4 GPS receiver: u-blox EVK-6p <strong>Data files format: </strong><br> -------------------------<br> co_data_*: Time of day: yyyy.mm.dd HH:MM Latitude WGS84 Longitude WGS84 HDOP: horizontal dilution of precision, uncertainty of the GPS position Tram ID WE_CHANNEL_SENSOR_1_MV: The voltage [in mV] at the working electrode of the electrochemical sensor (see Alphasense CO-B4 datasheet for more details) o3_data_*: Time of day: yyyy.mm.dd HH:MM Latitude WGS84 Longitude WGS84 HDOP: horizontal dilution of precision, uncertainty of the GPS position Tram ID Ozone [ppb]: On-device calibrated (according to manufacturer) ozone measurement [in parts-per-billion] Temperature [in °C] Relative Humidity [in %] <strong>Data quality:</strong><br> ------------------<br> The data has NOT been post-processed!<br> In order to achieve high data quality, the data needs to be cleaned (e.g. outlier filtering) and, most importantly, the sensors need to be individually calibrated.<br> Reference data can be obtained from www.ostluft.ch, the official air quality monitoring network in eastern Switzerland, which operates multiple monitoring stations in the city of Zurich. <strong>Plot Coverage Map (MATLAB):</strong><br> --------------------------------------------<br> The provided MATLAB script plot_data_coverage.m plots the locations of the collected samples onto the map of Zurich (map_zurich.png). <strong>References:</strong><br> -----------------<br> The dataset (and related aspects) has partly been used and is described in more detail in the following publications: Balz Maag et al. <strong>SCAN: Multi-Hop Calibration for Mobile Sensor Arrays</strong>. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol.1, No.2 (IMWUT), 2017. Olga Saukh et al. <strong>Reducing Multi-Hop Calibration Errors in Mobile Sensor Networks</strong>. In IEEE/ACM International Conference on Information Processing in Sensor Networks (IPSN), 2015. Best Paper Award! Olga Saukh et al. <strong>Route Selection for Mobile Sensor Nodes on Public Transport Networks</strong>. In Journal of Ambient Intelligence and Humanized Computing, 5(3), Springer, 2014. Olga Saukh et al. <strong>On Rendezvous in Mobile Sensing Networks</strong>. In Proceedings of the 5th Workshop on Real-World Wireless Sensor Networks (RealWSN), 2013. Jason Jingshi Li et al. <strong>Sensing the Air we Breathe – The OpenSense Zurich Dataset</strong>. In Proceedings of the 26th International Conference on Artificial Intelligence (AAAI), 2012. Olga Saukh et al. <strong>Route Selection for Mobile Sensors with Checkpointing Constraints</strong>. In Proceedings of the 8th International Workshop on Sensor Networks and Systems for Pervasive Computing (PerSeNS, in conjunction with IEEE PerCom), March 2012. David Hasenfratz et al. <strong>On-the-fly Calibration of Low-Cost Gas Sensors</strong>. In Proceedings of the 9th European Conference on Wireless Sensor Networks (EWSN), 2012. <br> For further information, visit: http://www.opensense.ethz.ch

BACKGROUND: Feelings of loneliness are associated with poor physical and mental health. Detection of loneliness through passive sensing on personal devices can lead to the development of interventions aimed at decreasing rates of loneliness. OBJECTIVE: The aim of this study was to explore the potential of using passive sensing to infer levels of loneliness and to identify the corresponding behavioral patterns. METHODS: Data were collected from smartphones and Fitbits (Flex 2) of 160 college students over a semester. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire at the beginning and end of the semester. For a classification purpose, the scores were categorized into high (questionnaire score>40) and low (≤40) levels of loneliness. Daily features were extracted from both devices to capture activity and mobility, communication and phone usage, and sleep behaviors. The features were then averaged to generate semester-level features. We used 3 analytic methods: (1) statistical analysis to provide an overview of loneliness in college students, (2) data mining using the Apriori algorithm to extract behavior patterns associated with loneliness, and (3) machine learning classification to infer the level of loneliness and the change in levels of loneliness using an ensemble of gradient boosting and logistic regression algorithms with feature selection in a leave-one-student-out cross-validation manner. RESULTS: The average loneliness score from the presurveys and postsurveys was above 43 (presurvey SD 9.4 and postsurvey SD 10.4), and the majority of participants fell into the high loneliness category (scores above 40) with 63.8% (102/160) in the presurvey and 58.8% (94/160) in the postsurvey. Scores greater than 1 standard deviation above the mean were observed in 12.5% (20/160) of the participants in both pre- and postsurvey scores. The majority of scores, however, fell between 1 standard deviation below and above the mean (pre=66.9% [107/160] and post=73.1% [117/160]). Our machine learning pipeline achieved an accuracy of 80.2% in detecting the binary level of loneliness and an 88.4% accuracy in detecting change in the loneliness level. The mining of associations between classifier-selected behavioral features and loneliness indicated that compared with students with low loneliness, students with high levels of loneliness were spending less time outside of campus during evening hours on weekends and spending less time in places for social events in the evening on weekdays (support=17% and confidence=92%). The analysis also indicated that more activity and less sedentary behavior, especially in the evening, was associated with a decrease in levels of loneliness from the beginning of the semester to the end of it (support=31% and confidence=92%). CONCLUSIONS: Passive sensing has the potential for detecting loneliness in college students and identifying the associated behavioral patterns. These findings highlight intervention opportunities through mobile technology to reduce the impact of loneliness on individuals' health and well-being.

The recent appearance of low cost virtual reality (VR) technologies - like the Oculus Rift, the HTC Vive and the Sony PlayStation VR - and Mixed Reality Interfaces (MRITF) - like the Hololens - is attracting the attention of users and researchers suggesting it may be the next largest stepping stone in technological innovation. However, the history of VR technology is longer than it may seem: the concept of VR was formulated in the 1960s and the first commercial VR tools appeared in the late 1980s. For this reason, during the last 20 years, 100s of researchers explored the processes, effects, and applications of this technology producing 1000s of scientific papers. What is the outcome of this significant research work? This paper wants to provide an answer to this question by exploring, using advanced scientometric techniques, the existing research corpus in the field. We collected all the existent articles about VR in the Web of Science Core Collection scientific database, and the resultant dataset contained 21,667 records for VR and 9,944 for augmented reality (AR). The bibliographic record contained various fields, such as author, title, abstract, country, and all the references (needed for the citation analysis). The network and cluster analysis of the literature showed a composite panorama characterized by changes and evolutions over the time. Indeed, whether until 5 years ago, the main publication media on VR concerned both conference proceeding and journals, more recently journals constitute the main medium of communication. Similarly, if at first computer science was the leading research field, nowadays clinical areas have increased, as well as the number of countries involved in VR research. The present work discusses the evolution and changes over the time of the use of VR in the main areas of application with an emphasis on the future expected VR's capacities, increases and challenges. We conclude considering the disruptive contribution that VR/AR/MRITF will be able to get in scientific fields, as well in human communication and interaction, as already happened with the advent of mobile phones by increasing the use and the development of scientific applications (e.g., in clinical areas) and by modifying the social communication and interaction among people.

BACKGROUND: The development of mobile computing technology has enabled the delivery of psychological interventions while people go about their everyday lives. The original visions of the potential of these "ecological momentary interventions" were presented over a decade ago, and the widespread adoption of smartphones in the intervening years has led to a variety of research studies exploring the feasibility of these aspirations. However, there is a dearth of research describing the different dimensions, characteristics, and features of these interventions, as constructed. OBJECTIVE: To provide an overview of the definitions given for "ecological momentary interventions" in the treatment of common mental health disorders, and describe the set of technological and interaction possibilities which have been used in the design of these interventions. METHODS: A systematic search identified relevant literature published between 2009 and 2020 in the PubMed, PsycInfo, and ACM Guide to the Computing Literature databases. Following screening, data were extracted from eligible articles using a standardized extraction worksheet. Selected articles were then thematically categorized. RESULTS: The search identified 583 articles of which 64 met the inclusion criteria. The interventions target a range of mental health problems, with diverse aims, intervention designs and evaluation approaches. The studies employed a variety of features for intervention delivery, but recent research is overwhelmingly comprised of studies based on smartphone apps (30 of 42 papers that described an intervention). Twenty two studies employed sensors for the collection of data in order to provide just-in-time support or predict psychological states. CONCLUSIONS: With the shift towards smartphone apps, the vision for EMIs has begun to be realised. Recent years have seen increased exploration of the use of sensors and machine learning, but the role of humans in the delivery of EMI is also varied. The variety of capabilities exhibited by EMIs motivates development of a more precise vocabulary for capturing both automatic and human tailoring of these interventions.

Summary Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real‐time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim , to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances.

The emergence of self-supervised learning in the field of wearables-based human activity recognition (HAR) has opened up opportunities to tackle the most pressing challenges in the field, namely to exploit unlabeled data to derive reliable recognition systems for scenarios where only small amounts of labeled training samples can be collected. As such, self-supervision, i.e., the paradigm of 'pretrain-then-finetune' has the potential to become a strong alternative to the predominant end-to-end training approaches, let alone hand-crafted features for the classic activity recognition chain. Recently a number of contributions have been made that introduced self-supervised learning into the field of HAR, including, Multi-task self-supervision, Masked Reconstruction, CPC, and SimCLR, to name but a few. With the initial success of these methods, the time has come for a systematic inventory and analysis of the potential self-supervised learning has for the field. This paper provides exactly that. We assess the progress of self-supervised HAR research by introducing a framework that performs a multi-faceted exploration of model performance. We organize the framework into three dimensions, each containing three constituent criteria, such that each dimension captures specific aspects of performance, including the robustness to differing source and target conditions, the influence of dataset characteristics, and the feature space characteristics. We utilize this framework to assess seven state-of-the-art self-supervised methods for HAR, leading to the formulation of insights into the properties of these techniques and to establish their value towards learning representations for diverse scenarios.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

This record serves an an index to the other dataset releases that are part of the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. We have chosen to split the dataset into several parts to meet Zenodo size requirements and make it easier to find specific pieces of data. In total, the following datasets exist: <strong>Raw data</strong><br> These datasets contain raw data, as collected directly from the devices doing the recording. It includes readings from several different sensors, as well as observed WiFi and BLE signals with their signal strength, and in one case, audio recordings. This raw data can be used to repeat our own experiments, or to apply different schemes to it to have a baseline for comparisons. Four datasets exist, mapped to the three scenarios discussed in the paper: Car Scenario Office Scenario Mobile Scenario + audio data in separate deposit (with access control) <strong>Processed Data</strong><br> The processed data is generated from the raw data using the processing code (which can be found in the code repository). The resulting data contains computed features from the five papers under investigation plus derived machine learning datasets, and can be used to see in detail how the schemes behave in specific situations. These datasets tend to be fairly large. Three datasets exist: Car Scenario Office Scenario Mobile Scenario <strong>Result Data</strong><br> Finally, the result datasets contain the results of the evaluation (i.e., the computed error rates and generated plots, plus associated caches). The code used to derive these results can once again be found in the source code repository. Here, five datasets exist, one for each investigated paper: Karapanos et al. Schürmann and Sigg Miettinen et al. Truong et al. Shrestha et al.

Abstract The adoption of artificial intelligence (AI) in healthcare is growing rapidly. Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in‐home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of RPM systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI‐enabled RPM. This review explores the benefits and challenges of patient‐centric RPM architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of AI in RPM ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that AI‐enabled RPM architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt AI to RPM systems and implementation issues. The future directions of AI in RPM applications are analyzed based on the challenges and trends. This article is categorized under: Application Areas &gt; Health Care Technologies &gt; Artificial Intelligence Technologies &gt; Internet of Things

Background Mainly due to an increase in stress-related health problems and driven by recent technological advances in biosensors, microelectronics, computing platform, and human-computer interaction, ubiquitous physiological information will potentially transform the role of biofeedback in clinical treatment. Such technology is also likely to provide a useful tool for stress management in everyday life. The aim of this systematic review is to: 1) Classify biofeedback systems for stress management, with a special focus on biosensing techniques, bio-data computing approaches, biofeedback protocol, and feedback modality. 2) Review ways of evaluating approaches to biofeedback applications in terms of their effectiveness in stress management. Method A systematic literature search was conducted using keywords for “Biofeedback” and “Stress” within the following databases: PubMed, IEEE Xplore, ACM, and Scopus. Two independent reviewers were involved in selecting articles. Results We identified 103 studies published between 1990 and 2016, 46 of which met our inclusion criteria and were further analyzed. Based on the evidence reviewed, HRV, multimodal biofeedback, RSP, HR, and GSR appear to be the most common techniques for alleviating stress. Traditional screen-based visual displays remain the most common devices used for biofeedback display. Biofeedback applications are usually assessed by making both physiological and psychological measurements. Conclusions This review reveals several challenges related to biofeedback for everyday stress management, such as the facilitating user’s perception and interpretating the biofeedback information, the demand of ubiquitous biosensing and display technologies, and field evaluation in order to understand the use of biofeedback in everyday environments. We expect that various emerging HCI technologies could be used to address these challenges. New interaction designs as well as biofeedback paradigms can be further explored in order to for improve the accessibility, usability, comfort, engagement with, and user experience of biofeedback in everyday use.

Background: The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual's changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual's state can change rapidly, unexpectedly, and in his/her natural environment. Purpose: Despite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap. Methods: Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration. Conclusions: As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention.

BACKGROUND: The number of patients with neurological diseases is currently increasing annually, which presents tremendous challenges for both patients and doctors. With the advent of advanced information technology, digital medical care is gradually changing the medical ecology. Numerous people are exploring new ways to receive a consultation, track their diseases, and receive rehabilitation training in more convenient and efficient ways. In this paper, we explore the use of facial expression recognition via artificial intelligence to diagnose a typical neurological system disease, Parkinson disease (PD). OBJECTIVE: This study proposes methods to diagnose PD through facial expression recognition. METHODS: We collected videos of facial expressions of people with PD and matched controls. We used relative coordinates and positional jitter to extract facial expression features (facial expression amplitude and shaking of small facial muscle groups) from the key points returned by Face++. Algorithms from traditional machine learning and advanced deep learning were utilized to diagnose PD. RESULTS: The experimental results showed our models can achieve outstanding facial expression recognition ability for PD diagnosis. Applying a long short-term model neural network to the positions of the key features, precision and F1 values of 86% and 75%, respectively, can be reached. Further, utilizing a support vector machine algorithm for the facial expression amplitude features and shaking of the small facial muscle groups, an F1 value of 99% can be achieved. CONCLUSIONS: This study contributes to the digital diagnosis of PD based on facial expression recognition. The disease diagnosis model was validated through our experiment. The results can help doctors understand the real-time dynamics of the disease and even conduct remote diagnosis.

The 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2021) took place from September 21–26 virtually, around the world; co-located with the International Symposium on Wearable Computers (ISWC). This article discusses some of the interesting research published in the Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies, as well as those accepted at ISWC this year—presented virtually with Live Q&A over Whova, Zoom, and Gather.Town. Furthermore, it covers some of the interesting plenary events, such as the Keynotes by Cynthia Breazeal (MIT Media Labs) and John LePore (Perception), as well as the panel discussion on the 30th anniversary of Mark Weiser’s paper on the Vision of a Computer for the 21st Century. Finally, some of the interesting highlights from the ISWC gadget-show and design-exhibition, alongside multiple virtual workshops as part of the adjunct program have been presented.

Will consumer wearable technology ever be adopted or accepted by the medical community? Patients and practitioners regularly use digital technology (e.g., thermometers and glucose monitors) to identify and discuss symptoms. In addition, a third of general practitioners in the United Kingdom report that patients arrive with suggestions for treatment based on online search results However, consumer health wearables are predicted to become the next "Dr Google." One in six (15%) consumers in the United States currently uses wearable technology, including smartwatches or fitness bands. While 19 million fitness devices are likely to be sold this year, that number is predicted to grow to 110 million in 2018 As the line between consumer health wearables and medical devices begins to blur, it is now possible for a single wearable device to monitor a range of medical risk factors (Fig Potentially, these devices could give patients direct access to personal analytics that can contribute to their health, facilitate preventive care, and aid in the management of ongoing illness. However, how this new wearable technology might best serve medicine remains unclear.

The rapid advancement of computing technologies has facilitated the implementation of AIED (Artificial Intelligence in Education) applications. AIED refers to the use of AI (Artificial Intelligence) technologies or application programs in educational settings to facilitate teaching, learning, or decision making. With the help of AI technologies, which simulate human intelligence to make inferences, judgments, or predictions, computer systems can provide personalized guidance, supports, or feedback to students as well as assisting teachers or policymakers in making decisions. Although AIED has been identified as the primary research focus in the field of computers and education, the interdisciplinary nature of AIED presents a unique challenge for researchers with different disciplinary backgrounds. In this paper, we present the definition and roles of AIED studies from the perspective of educational needs. We propose a framework to show the considerations of implementing AIED in different learning and teaching settings. The structure can help guide researchers with both computers and education backgrounds in conducting AIED studies. We outline 10 potential research topics in AIED that are of particular interest to this journal. Finally, we describe the type of articles we like to solicit and the management of the submissions.

Recently, the use of extended reality (XR) systems has been on the rise, to tackle various domains such as training, education, safety, etc. With the recent advances in augmented reality (AR), virtual reality (VR) and mixed reality (MR) technologies and ease of availability of high-end, commercially available hardware, the manufacturing industry has seen a rise in the use of advanced XR technologies to train its workforce. While several research publications exist on applications of XR in manufacturing training, a comprehensive review of recent works and applications is lacking to present a clear progress in using such advance technologies. To this end, we present a review of the current state-of-the-art of use of XR technologies in training personnel in the field of manufacturing. First, we put forth the need of XR in manufacturing. We then present several key application domains where XR is being currently applied, notably in maintenance training and in performing assembly task. We also reviewed the applications of XR in other vocational domains and how they can be leveraged in the manufacturing industry. We finally present some current barriers to XR adoption in manufacturing training and highlight the current limitations that should be considered when looking to develop and apply practical applications of XR.

Abstract Internet of Things (IoT) is a new paradigm that has changed the traditional way of living into a high tech life style. Smart city, smart homes, pollution control, energy saving, smart transportation, smart industries are such transformations due to IoT. A lot of crucial research studies and investigations have been done in order to enhance the technology through IoT. However, there are still a lot of challenges and issues that need to be addressed to achieve the full potential of IoT. These challenges and issues must be considered from various aspects of IoT such as applications, challenges, enabling technologies, social and environmental impacts etc. The main goal of this review article is to provide a detailed discussion from both technological and social perspective. The article discusses different challenges and key issues of IoT, architecture and important application domains. Also, the article bring into light the existing literature and illustrated their contribution in different aspects of IoT. Moreover, the importance of big data and its analysis with respect to IoT has been discussed. This article would help the readers and researcher to understand the IoT and its applicability to the real world.

This paper describes how mobile phone data can guide government and public\nhealth authorities in determining the best course of action to control the\nCOVID-19 pandemic and in assessing the effectiveness of control measures such\nas physical distancing. It identifies key gaps and reasons why this kind of\ndata is only scarcely used, although their value in similar epidemics has\nproven in a number of use cases. It presents ways to overcome these gaps and\nkey recommendations for urgent action, most notably the establishment of mixed\nexpert groups on national and regional level, and the inclusion and support of\ngovernments and public authorities early on. It is authored by a group of\nexperienced data scientists, epidemiologists, demographers and representatives\nof mobile network operators who jointly put their work at the service of the\nglobal effort to combat the COVID-19 pandemic.

For the past 20 years, researchers have investigated the use of eye tracking in security applications. We present a holistic view on gaze-based security applications. In particular, we canvassed the literature and classify the utility of gaze in security applications into a) authentication, b) privacy protection, and c) gaze monitoring during security critical tasks. This allows us to chart several research directions, most importantly 1) conducting field studies of implicit and explicit gaze-based authentication due to recent advances in eye tracking, 2) research on gaze-based privacy protection and gaze monitoring in security critical tasks which are under-investigated yet very promising areas, and 3) understanding the privacy implications of pervasive eye tracking. We discuss the most promising opportunities and most pressing challenges of eye tracking for security that will shape research in gaze-based security applications for the next decade.