Epidemic contact tracing with smartphone sensors

Contact tracing is widely considered as an effective procedure in the fight against epidemic diseases. However, one of the challenges for technology based contact tracing is the high number of false positives, questioning its trust-worthiness and efficiency amongst the wider population for mass adoption. To this end, this paper proposes a novel, yet practical smartphone-based contact tracing approach, employing WiFi and acoustic sound for relative distance estimate, in addition to the air pressure and the magnetic field for ambient environment matching. We present a model combining 6 smartphone sensors, prioritising some of them when certain conditions are met. We empirically verified our approach in various realistic environments to demonstrate an achievement of up to 95% fewer false positives, and 62% more accurate than Bluetooth-only system. To the best of our knowledge, this paper was one of the first work to propose a combination of smartphone sensors for contact tracing

Applications of Internet of Things

This book introduces the Special Issue entitled “Applications of Internet of Things”, of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITSs), (II) location-based services (LBSs), and (III) sensing techniques and applications. Three papers on ITSs are as follows: (1) “Vehicle positioning and speed estimation based on cellular network signals for urban roads,” by Lai and Kuo; (2) “A method for traffic congestion clustering judgment based on grey relational analysis,” by Zhang et al.; and (3) “Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road anomaly detection,” by Ishikawa and Fujinami. Three papers on LBSs are as follows: (1) “A high-efficiency method of mobile positioning based on commercial vehicle operation data,” by Chen et al.; (2) “Efficient location privacy-preserving k-anonymity method based on the credible chain,” by Wang et al.; and (3) “Proximity-based asynchronous messaging platform for location-based Internet of things service,” by Gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) “Detection of electronic anklet wearers’ groupings throughout telematics monitoring,” by Machado et al.; and (2) “Camera coverage estimation based on multistage grid subdivision,” by Wang et al

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Low-power neuromorphic sensor fusion for elderly care

Smart wearable systems have become a necessary part of our daily life with applications ranging from entertainment to healthcare. In the wearable healthcare domain, the development of wearable fall recognition bracelets based on embedded systems is getting considerable attention in the market. However, in embedded low-power scenarios, the sensor’s signal processing has propelled more challenges for the machine learning algorithm. Traditional machine learning method has a huge number of calculations on the data classification, and it is difficult to implement real-time signal processing in low-power embedded systems. In an embedded system, ensuring data classification in a low-power and real-time processing to fuse a variety of sensor signals is a huge challenge. This requires the introduction of neuromorphic computing with software and hardware co-design concept of the system. This thesis is aimed to review various neuromorphic computing algorithms, research hardware circuits feasibility, and then integrate captured sensor data to realise data classification applications. In addition, it has explored a human being benchmark dataset, which is following defined different levels to design the activities classification task. In this study, firstly the data classification algorithm is applied to human movement sensors to validate the neuromorphic computing on human activity recognition tasks. Secondly, a data fusion framework has been presented, it implements multiple-sensing signals to help neuromorphic computing achieve sensor fusion results and improve classification accuracy. Thirdly, an analog circuits module design to carry out a neural network algorithm to achieve low power and real-time processing hardware has been proposed. It shows a hardware/software co-design system to combine the above work. By adopting the multi-sensing signals on the embedded system, the designed software-based feature extraction method will help to fuse various sensors data as an input to help neuromorphic computing hardware. Finally, the results show that the classification accuracy of neuromorphic computing data fusion framework is higher than that of traditional machine learning and deep neural network, which can reach 98.9% accuracy. Moreover, this framework can flexibly combine acquisition hardware signals and is not limited to single sensor data, and can use multi-sensing information to help the algorithm obtain better stability

Enabling Deep Neural Network Inferences on Resource-constraint Devices

Department of Computer Science and EngineeringWhile deep neural networks (DNN) are widely used on various devices, including resource-constraint devices such as IoT, AR/VR, and mobile devices, running DNN from resource-constrained devices remains challenging. There exist three approaches for DNN inferences on resource-constraint devices: 1) lightweight DNN for on-device computing, 2) offloading DNN inferences to a cloud server, and 3) split computing to utilize computation and network resources efficiently. Designing a lightweight DNN without compromising the accuracy of DNN is challenging due to a trade-off between latency and accuracy, that more computation is required to achieve higher accuracy. One solution to overcome this challenge is pre-processing to extract and transfer helpful information to achieve high accuracy of DNN. We design the pre-processing, which consists of three processes. The first process of pre-processing is finding out the best input source. The second process is the input-processing which extracts and contains important information for DNN inferences among the whole information gained from the input source. The last process is choosing or designing a suitable lightweight DNN for processed input. As an instance of how to apply the pre-processing, in Sec 2, we present a new transportation mode recognition system for smartphones called DeepVehicleSense, which aims at achieving three performance objectives: high accuracy, low latency, and low power consumption at once by exploiting sound characteristics captured from the built-in microphone while being on candidate transportations. To achieve high accuracy and low latency, DeepVehicleSense makes use of non-linear filters that can best extract the transportation sound samples. For the recognition of five different transportation modes, we design a deep learning-based sound classifier using a novel deep neural network architecture with multiple branches. Our staged inference technique can significantly reduce runtime and energy consumption while maintaining high accuracy for the majority of samples. Offloading DNN inferences to a server is a solution for DNN inferences on resource-constraint devices, but there is one concern about latency caused by data transmission. To reduce transmission latency, recent studies have tried to make this offloading process more efficient by compressing data to be offloaded. However, conventional compression techniques are designed for human beings, so they compress data to be possible to restore data, which looks like the original from the perspective of human eyes. As a result, the compressed data through the compression technique contains redundancy beyond the necessary information for DNN inference. In other words, the most fundamental question on extracting and offloading the minimal amount of necessary information that does not degrade the inference accuracy has remained unanswered. To answer the question, in Sec 3, we call such an ideal offloading semantic offloading and propose N-epitomizer, a new offloading framework that enables semantic offloading, thus achieving more reliable and timely inferences in highly-fluctuated or even low-bandwidth wireless networks. To realize N-epitomizer, we design an autoencoder-based scalable encoder trained to extract the most informative data and scale its output size to meet the latency and accuracy requirements of inferences over a network. Even though our proposed lightweight DNN and offloading framework with the essential information extractor achieve low latency while preserving DNN performance, they alone cannot realize latency-guaranteed DNN inferences. To realize latency-guaranteed DNN inferences, the computational complexity of the lightweight DNN and the compression performance of the encoder for offloading should be adaptively selected according to current computation resources and network conditions by utilizing the DNN's trade-off between computational complexity and DNN performance and the encoder's trade-off between compression performance and DNN performance. To this end, we propose a new framework for latency-guaranteed DNN inferences called LG-DI, which predicts DNN performance degradation given a latency budget in advance and utilizes the better method between the lightweight DNN and offloading with compression. As a result, our proposed framework for DNN inferences can guarantee latency regardless of changes in computation and network resources while maintaining DNN performance as much as possible.ope

Across Space and Time. Papers from the 41st Conference on Computer Applications and Quantitative Methods in Archaeology, Perth, 25-28 March 2013

This volume presents a selection of the best papers presented at the forty-first annual Conference on Computer Applications and Quantitative Methods in Archaeology. The theme for the conference was "Across Space and Time", and the papers explore a multitude of topics related to that concept, including databases, the semantic Web, geographical information systems, data collection and management, and more

Audience-generated traces: audience experience in performance documentation

This thesis explores whether and how audience-generated content produced from and about audiences’ experience and during and as part of a live performance might become part of a theatre and performance work’s archive. It sets out to examine both the challenges as well as the documentational opportunities that this material might afford. The thesis is influenced by Gabriella Giannachi’s articulation of digital technologies as archival interfaces and Sarah Bay-Cheng’s convergence of live performance and documentation. It examines the function of audience-generated content during three case studies and postulates that audiences can be regarded as co-producers of performance documents. To do so, it analyses how Speak Bitterness by Forced Entertainment, Karen by Blast Theory, and Flatland by Extant request that their audiences activate the live performance or augment its experience by using a digital technology, and how by doing so they leave digital traces behind. Building upon this condition the thesis interrogates how the three company casestudies archive these works’ audience-generated traces. In addition, it investigates how digital traces are perceived by institutional theatre and performance collections. Through interviews with the case-study practitioners, the curator of the British Library Sound Archive and the archivists of the National Theatre and Victoria and Albert Museum the thesis reveals a set of technical and organisational challenges involved in this process. Although audience-generated traces are considered valuable marketing and research material they also unsettle established notions and structures of performance documentation and its archive. Rethinking the established notion of the performance document and the form of files through which it conveys knowledge, the thesis returns to Ricoeur’s theory of the trace so as to expand ideas of how performance documentation enables ways of knowing a past performance. It argues that, as direct remnants of the live performance moment originating in the participant, audiencegenerated content offers solutions to ‘presencing’ the audience in documentation and novel ways for revisiting a past performance work from within its unfolding

IKUWA6. Shared Heritage

Celebrating the theme ‘Shared heritage’, IKUWA6 (the 6th International Congress for Underwater Archaeology), was the first such major conference to be held in the Asia-Pacific region, and the first IKUWA meeting hosted outside Europe since the organisation’s inception in Germany in the 1990s. A primary objective of holding IKUWA6 in Australia was to give greater voice to practitioners and emerging researchers across the Asia and Pacific regions who are often not well represented in northern hemisphere scientific gatherings of this scale; and, to focus on the areas of overlap in our mutual heritage, techniques and technology. Drawing together peer-reviewed presentations by delegates from across the world who converged in Fremantle in 2016 to participate, this volume covers a stimulating diversity of themes and niche topics of value to maritime archaeology practitioners, researchers, students, historians and museum professionals across the world

Tools in and out of sight : an analysis informed by Cultural-Historical Activity Theory of audio-haptic activities involving people with visual impairments supported by technology

The main purpose of this thesis is to present a Cultural-Historical Activity Theory (CHAT) based analysis of the activities conducted by and with visually impaired users supported by audio-haptic technology.This thesis covers several studies conducted in two projects. The studies evaluate the use of audio-haptic technologies to support and/or mediate the activities of people with visual impairment. The focus is on the activities involving access to two-dimensional information, such as pictures or maps. People with visual impairments can use commercially available solutions to explore static information (raised lined maps and pictures, for example). Solu-tions for dynamic access, such as drawing a picture or using a map while moving around, are more scarce. Two distinct projects were initiated to remedy the scarcity of dynamic access solutions, specifically focusing on two separate activities.The first project, HaptiMap, focused on pedestrian outdoors navigation through audio feedback and gestures mediated by a GPS equipped mobile phone. The second project, HIPP, focused on drawing and learning about 2D representations in a school setting with the help of haptic and audio feedback. In both cases, visual feedback was also present in the technology, enabling people with vision to take advantage of that modality too.The research questions addressed are: How can audio and haptic interaction mediate activ-ities for people with visual impairment? Are there features of the programming that help or hinder this mediation? How can CHAT, and specifically the Activity Checklist, be used to shape the design process, when designing audio haptic technology together with persons with visual impairments?Results show the usefulness of the Activity Checklist as a tool in the design process, and provide practical application examples. A general conclusion emphasises the importance of modularity, standards, and libre software in rehabilitation technology to support the development of the activities over time and to let the code evolve with them, as a lifelong iterative development process. The research also provides specific design recommendations for the design of the type of audio haptic systems involved

Metafore mobilnih komunikacija ; Метафоры мобильной связи.

Mobilne komunikacije su polje informacione i komunikacione tehnologije koje karakteriše brzi razvoj i u kome se istraživanjem u analitičkim okvirima kognitivne lingvistike, zasnovanom na uzorku od 1005 odrednica, otkriva izrazito prisustvo metafore, metonimije, analogije i pojmovnog objedinjavanja. Analiza uzorka reči i izraza iz oblasti mobilnih medija, mobilnih operativnih sistema, dizajna korisničkih interfejsa, terminologije mobilnih mreža, kao i slenga i tekstizama koje upotrebljavaju korisnici mobilnih naprava ukazuje da pomenuti kognitivni mehanizmi imaju ključnu ulogu u olakšavanju interakcije između ljudi i širokog spektra mobilnih uređaja sa računarskim sposobnostima, od prenosivih računara i ličnih digitalnih asistenata (PDA), do mobilnih telefona, tableta i sprava koje se nose na telu. Ti mehanizmi predstavljaju temelj razumevanja i nalaze se u osnovi principa funkcionisanja grafičkih korisničkih interfejsa i direktne manipulacije u računarskim okruženjima. Takođe je analiziran i poseban uzorak od 660 emotikona i emođija koji pokazuju potencijal za proširenje značenja, imajući u vidu značaj piktograma za tekstualnu komunikaciju u vidu SMS poruka i razmenu tekstualnih sadržaja na društvenim mrežama kojima se redovno pristupa putem mobilnih uređaja...Mobile communications are a fast-developing field of information and communication technology whose exploration within the analytical framework of cognitive linguistics, based on a sample of 1005 entries, reveals the pervasive presence of metaphor, metonymy analogy and conceptual integration. The analysis of the sample consisting of words and phrases related to mobile media, mobile operating systems and interface design, the terminology of mobile networking, as well as the slang and textisms employed by mobile gadget users shows that the above cognitive mechanisms play a key role in facilitating interaction between people and a wide range of mobile computing devices from laptops and PDAs to mobile phones, tablets and wearables. They are the cornerstones of comprehension that are behind the principles of functioning of graphical user interfaces and direct manipulation in computing environments. A separate sample, featuring a selection of 660 emoticons and emoji, exhibiting the potential for semantic expansion was also analyzed, in view of the significance of pictograms for text-based communication in the form of text messages or exchanges on social media sites regularly accessed via mobile devices..