An important capability of most smart, Internet-of-Things-enabled spaces (e.g., office, home, hospital, factory) is the ability to leverage context of use. Location is a key context element, particularly indoor location. Recent advances in radio ranging technologies, such as Wi-Fi RTT, promise the availability of low-cost, near-ubiquitous time-of-flight-based ranging estimates. In this paper, we build

Early perception of anomaly traffic patterns, both spatially and temporally, is of importance for emergency response in the smart cities. To capture the spatiotemporal correlations among traffic flows for city dynamics modeling in correspondence with normal states, we conduct sparse representation on taxi activity over spatially partitioned cells in a city. We can perceive the deviation from the normal

Microarray data analysis is a major challenging field of research in recent days. Machine learning–based automated gene data classification is an essential aspect for diagnosis of gene related any malfunctions and diseases. As the size of the data is very large, it is essential to design a suitable classifier that can process huge amount of data. Deep learning is one of the advanced machine learning

Many people with chronic obstructive pulmonary diseases (COPD) are subjected to emergencies triggered by breathing difficulties. Oxygen therapy, administered from medical oxygen cylinders, can be used to relieve respiratory airways, and restore the supply of oxygen to the body’s vital organs. In this paper, we present a location-based ubiquitous crowdsourcing solution to enable COPD patients to request

Fig. 7 and Fig. 8 have incorrect legends in the images and have been updated.

Recently, multimodal interfaces are incorporating smell as an additional means of interaction. Devices called olfactory displays have been designed to improve applications with various objectives, such as notifying or alerting through scents, increasing immersion in virtual or augmented reality applications, or learning and enhancement of mental functions. Based on the potential of olfactory memory

After almost a decade of relentless development, pervasive urban displays have fragmented into a diversity of approaches with radically distinct characteristics in terms of how mobile they are, as well as the materials they are made of. In this article, we investigate such a diversity in terms of the relationships between key conceptual entities of pervasive urban displays, namely the displayed content

The visual saliency models based on low-level features of an image have the problem of low accuracy and scalability, while the visual saliency models based on deep neural networks can effectively improve the prediction performance, but require a large amount of training data, e.g., eye tracking data, to achieve good results. However, the traditional eye tracking method is limited by high equipment

Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy

Automation in computing systems has always been considered a valuable solution to unburden the user. Internet of Things (IoT) technology best suits automation in different domains, such as home automation, retail, industry, and transportation, to name but a few. While these domains are strongly characterized by implicit user interaction, more recently, automation has been adopted also for the provision

Nowadays, automation not only permeates industry but also becomes a substantial part of our private, everyday lives. Driven by the idea of increased convenience and more time for the “important things in life,” automation relieves us from many daily chores—robots vacuum floors and automated coffee makers produce supposedly barista-quality coffee on the press of a button. In many cases, these offers

In this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area

Self-tracking technologies bring a new set of experiences into our lives. Through sensors and ubiquitous measurements of bodily performance, a new form of automation experience shapes our understanding of our body and our behavior. While for many individuals self-tracking has an important role in their daily lives, a theoretical understanding of the level and behavioral manifestations of commitment

There are multiple distinct sources through which numerous news contents that occur in digital medium tend to converge. Web contents constitute massive number of features. Complete coverage of all kinds of news is absolutely vital to retain customer confidence and to have a competitive edge over other news agencies. Aggregating such massive news content from different heterogeneous sources requires

COVID-19 has caused a serious impact on the global economy. Effectively stimulating consumption has become a momentous mission in responding to the impact of the epidemic. The popularity of mobile shopping makes shopping behavior no longer limited by time and space, so impulse purchase is more commonly seen nowadays; it can effectively promote residents’ consumption. However, consensus has not been

Deep learning has revolutionized many areas, including time series data mining. Multivariate time series classification (MTSC) remained to be a well-known problem in the time series data mining community, due to its availability in various practical applications such as healthcare, finance, geoscience, and bioinformatics. Recently, multivariate long short-term memory with fully convolutional network

Sleep behavior is traditionally monitored with polysomnography, and sleep stage patterns are a key marker for sleep quality used to detect anomalies and diagnose diseases. With the growing demand for personalized healthcare and the prevalence of the Internet of Things, there is a trend to use everyday technologies for sleep behavior analysis at home, having the potential to eliminate expensive in-hospital

Wireless underground sensor networks (WUSNs) consist of sensors that are buried in and communicate through soil medium, while the channel quality of WUSNs is greatly impacted by the underground environment, such as soil moisture and composition. Due to the precipitation and harsh weather, the underground environments change frequently, which make wireless communication in WUSNs much complicated than

The convergence of mobile edge computing (MEC) to the current Internet of Things (IoT) environment enables a great opportunity to enhance massive IoT data transmission. In the narrowband Internet of Things (NB-IoT), the huge number of IoT devices sends the information to the remote network. Simultaneously, the massive IoT devices from the heterogeneous locations access to the radio remote head (RRH)

Access to knowledge, information, and technology is a key element for the development of individuals and society as a whole. While computing systems play a fundamental role in this process, efforts aimed at diminishing the worldwide digital divide are still scarce. In this study, we propose a tailoring-based approach for personal systems design as a way to promote digital and social inclusion in contexts

With the advent of deep neural networks (DNNs) in the last two decades, tremendous developments have been made in many fields, such as image classification/recognition, voice recognition, and action recognition. These advanced DNNs require large amounts of labeled data, whose collection is costly and requires great effort. In this paper, we provide a convergence method for DNNs to solve some of these

When operating a conditionally automated vehicle, humans occasionally have to take over control. If the driver is out of the loop, a certain amount of time is necessary to gain situation awareness. This work evaluates the potential of stereoscopic 3D (S3D) dashboards for presenting smart S3D take-over-requests (TORs) to support situation assessment. In a driving simulator study with a 4 × 2 between-within

Traditional accident prediction models have been mostly designed with statistical analysis that finds and analyzes the causal relationships between traffic accidents and a variety of human, road geometry, and environmental factors. However, these statistical methods have limitations in that they are based on assumptions about data distribution and function type. Therefore, this study suggests an accident

The fast development of Internet of Things (IoT) has greatly driven the development of mobile crowdsensing vehicular sensor network (CVSN). A lot of fascinating big data–based applications have been developed such as traffic management, health monitoring, and smart city. How to effectively collect enough data while not increasing too much redundancy is still a challenging problem in the big data application

Bluetooth (BT) data has been extensively used for recognizing social patterns and inferring social networks, as BT is widely present in everyday technological devices. However, even though collecting BT data is subject to random noise and may result in substantial measurement errors, there is an absence of rigorous procedures for validating the quality of the inferred BT social networks. This paper

Opportunistic sensing has become an appealing mobile crowd sensing (MCS) paradigm due to the fact that it can reduce the energy consumption and cost of cellular network connections. However, its success rate and transmission speed depend on the social interaction and mobility patterns of nodes. In this paper, we provide a spatiotemporal opportunistic transmission method for MCS networks. Firstly, to

Large high-resolution displays (LHRDs) provide an enabling technology to achieve immersive, isometrically registered, virtual environments. It has been shown that LHRDs allow better size judgments, higher collaboration performance, and shorter task completion times. This paper presents novel insights into human size perception using large-scale floor displays, in particular in-depth evaluations of

This research presents an in-depth microscopic analysis of heterogeneous and undisciplined traffic at the signalized intersection. Traffic data extracted from the video recorded using an unmanned aerial vehicle (UAV) at an approach of a signalized intersection is analyzed to study the within green time dynamics of traffic flow. Various parameters of Wiedemann 74, Wiedemann 99, and lateral behavior

The actual Fig. 13 has been missing and in place of Fig. 13, Fig. 14 has been shown.

Internet of Things has developed quickly to share data from billions of physical devices. Completeness of data is important especially in crowd sensing Internet of Things. How to recover the lost data is a fundamental operation to utilize the coming of Internet of Things. Existing data recovery algorithms depend heavy on the accuracy distribution of environmental data and result in bad performance

The aim of this professional Doctor of Creative Industries (DCI) Research Project was to investigate music-making practice and self as a practitioner in the process of creating and producing a DIY music artefact, specifically to investigate why I as the practitioner felt an authentic connection with one form of music-making (acoustic instrument-based) and not a connection with another form of music-making

Similarly to the standardization effort initiated for the World Wide Web in the 1990s, the World Wide Web Consortium is currently working on the Web of Things (WoT) specification. This initiative aims to tackle current fragmentation in the so-called Internet of Things by using existing Web standards. The ultimate goal is to cope with the increasing number of devices that are being connected to the

Mobile crowd sensing is a novel large-scale sensing pattern, which applies users’ smart devices to analyze social context and human activity, and then learns the intelligent information serving for various innovative services. An augmented reality (AR) system can bring more authentic experience for human life, and more and more scholars are increasingly interested in this technology. However, there

Understanding how to take into account the breadth of considerations required by third-wave HCI remains a challenge when designing technologies. This requires an understanding of what embodiment means, and in particular, a collective notion of embodiment. Within the field of digital musical instrument (DMI) design, the need for a DMI to support musical agency has been recognised along with a view of

Collaborative virtual environments allow remote users to work together in a shared 3D space. To take advantage of the possibilities offered by such systems, their design must allow the users to interact and communicate efficiently. One open question in this field concerns the avatar fidelity of remote partners. This can impact communication between the remote users, more particularly when performing

This paper reports the results of a 2-year-long imagery analysis about microcephaly in Latin America from the perspective of caregivers. The content analysis included 500 images associated with the hashtag microcefalia posted on Instagram in 2017 and 2018. Microcephaly affected more than 4,000 babies in Latin America in a few years, and the quick outbreak of the epidemic made Instagram an accessible

Taxi passenger demand prediction is of great significance to perceive citywide human mobility and make a lot of urban sensing applications more convenient. There are two major challenges to develop accurate predictive models, i.e., the complexity of the spatial-temporal dependencies as well as the dynamicity caused by some unpredictable dependencies. Although existing work uses various methods such

We propose an environment for assisting in the transfer of motor skills by supporting nonverbal communication with wearable haptic technology. The proposed setting comprises a human expert and several learners, all endowed with wearable devices. Learners benefit from the automated feedback obtained via wearable technology regarding a desired move relevant to a specific motor skill. Similarly, the expert

In recent years, machine learning has developed rapidly, enabling the development of applications with high levels of recognition accuracy relating to the use of speech and images. However, other types of data to which these models can be applied have not yet been explored as thoroughly. Labelling is an indispensable stage of data pre-processing that can be particularly challenging, especially when

Social media platforms have been extensively used during natural disasters. However, most prior work has lacked focus on studying their usage during disasters in the Global South, where Internet access and social media usage differs from developing countries. In this paper, we study social media use in the aftermath of the 7.1-magnitude earthquake that hit Mexico on the 19th of September, 2017 (known

The pervasiveness of public displays is prompting an increased need for “fresh” content to be shown, that is highly engaging and useful to passerbys. As such, live or time-sensitive content is often shown in conjunction with “traditional” static content, which creates scheduling challenges. In this work, we propose a utility-based framework that can be used to represent the usefulness of a content

The timely delivery of life-saving products such as medicines from the pharmacy to the patient’s location requires availability of an adequate road infrastructure and reliable directions to the patient’s home. However, in many regions of low- and middle-income countries (LMIC), the road infrastructure is in poor state, and medicine delivery is affected by flooded roads, unsafe routes, congestion, traffic

As data collected and provided by Internet of Things (IoT) devices power an ever-growing number of applications and services, it is crucial that this data can be trusted. Data provenance solutions combined with blockchain technology are one way to make data more trustworthy by providing tamper-proof information about the origin and history of data records. However, current blockchain-based solutions

Face detection perceives great importance in surveillance paradigm and security paradigm areas. Face recognition is the technique to identify a person identity after face detection. Extensive research has been done on these topics. Another important research problem is to detect concealed faces, especially in high-security places like airports or crowded places like concerts and shopping centres, for

Two studies explore whether people’s digital filing behaviors are affected by emotional factors when engaging in personal information management (PIM). Cognitive science research shows that people’s information categorization behaviors are mood-dependent, so that positive moods induce larger, more inclusive organizational categories, whereas negative moods elicit more fine-grained organization. However

Poor adherence to prescribed drug treatments is one of the leading causes of illness and treatment failure, which increases re-hospitalizations. In Mexico, the factors that most contribute to the non-adherence problem are age, polypharmacy, and education. For instance, elderly patients are prescribed with an average of seven medications after they are discharged from hospitals, and 25% of them face

The advances in Internet of things lead to an increased number of devices generating and streaming data. These devices can be useful data sources for activity recognition by using machine learning. However, the set of available sensors may vary over time, e.g. due to mobility of the sensors and technical failures. Since the machine learning model uses the data streams from the sensors as input, it

This article presents a study concerning the evaluation of a smart home control system for elderly people with a sample of 10 users in a city in the interior of Brazil. The control system consisted of a prototype using a Web-based mobile application, developed considering requirements obtained from a previous study and recommendations from the literature. The test participants were over 60 years old

Recent W3C recommendations for the Web of Things (WoT) and the Social Web are turning hypermedia into a homogeneous information fabric that interconnects heterogeneous resources: devices, people, information resources, abstract concepts, etc. The integration of multi-agent systems with such hypermedia environments now provides a means to distribute autonomous behavior in worldwide pervasive systems

Recommender systems are expected to promote a student-centered teaching and learning environment. The age of information abundance has proven to need such systems. Recommender systems have been used to recommend learning items related to students’ research interests. Serendipity has also made its way into the academic environment, as systems recommend items that are useful and surprising to learners

The risk of falls among the elderly population is one that may lead to dire consequences. It can significantly affect the quality of life of the victims and even lead to their premature death. Many technological tools have been proposed in the literature to detect falls, but little effort has been done regarding their prevention. In this paper, our research team proposes an inexpensive open vibration

Identification of human activity considering social interactions and group dynamics non-intrusively has been one of the fundamental problems and a challenging area of research. In real life, it is required for designing human-centric applications like assisted living, health care, and creating a smart home environment. As human beings spend 90% of time indoors, such a system will be helpful to monitor

Periodic sensor networks (PSNs) represent one of the important networks which are used in Internet of Things (IoTs) future. It has gained a rapid increased interest due to its wide use in various fields of applications. However, it suffers from the drawback of a limited lifetime as its power supply cannot be replaced or renew due to the nature of its deployment. Much of the research in this area has

Wireless sensor network (WSN) consists of a number of nodes that are mostly distributed in a random way to monitor or control different phenomena, such as military operations, earthquake monitoring, environmental monitoring, factory automation and security. A sensor network is formed from a large number of tiny, low energy, limited processing capability and low-cost devices called sensor nodes (SNs)

Audio description is an accessibility service used by blind or visually impaired individuals. Often accompanying movies, television shows, and other visual art forms, the service provides spoken descriptions of visual content, allowing people with vision loss the ability to access information that sighted people obtain visually. At live theatrical events, audio description provides spoken descriptions

In this paper, we discuss how people’s user experience (UX) of autonomous driving (AD) cars can be understood as a shifting anticipatory experience, as people experience degrees of AD through evolving advanced driver assistance systems (ADAS) in their everyday context. We draw on our ethnographic studies of five families, who had access to AD research cars with evolving ADAS features in their everyday

Autonomous mobility is gaining increasing attraction in the international setting that results in a growing body of literature on various aspects. Preferences regarding the use of autonomous vehicles as an alternative transport mode are investigated in western and advanced countries as it provides the notion about what fraction of population will more inclined towards such technology and under what

The design of computer-supported caregiving technology has generally not acknowledged the complexity and heterogeneity of the informal care provision to older adults. For instance, most Latin American older adults have not been capable to broadly embrace digital technology to interact with their family members. This attitude, complemented with a high commitment derived from a strong filial obligation

Massive scientific and technical literature has recorded the developments of science and technology and contains plentiful knowledge. Researchers have to read scientific literature such as papers, patents, and reports to know the latest developments in time. However, it is difficult for researchers to read all the newly published and relevant literature. So there is an urgent need for scientific literature

This article presents the results of a study based on a group of participants’ interactions with an experimental sound installation at the National Science and Media Museum in Bradford, UK. The installation used audio augmented reality to attach virtual sound sources to a vintage radio receiver from the museum’s collection, with a view to understanding the potentials of this technology for promoting