Related papers: A Machine Learning Approach to Digital Contact Tra…

Contact tracing is an important measure to counter the COVID-19 pandemic. In the early phase, many countries employed manual contact tracing to contain the rate of disease spread, however it has many issues. The manual approach is…

Distance metric learning algorithms aim to appropriately measure similarities and distances between data points. In the context of clustering, metric learning is typically applied with the assist of side-information provided by experts,…

Research into, and design and construction of mobile systems and algorithms requires access to large-scale mobility data. Unfortunately, the wireless and mobile research community lacks such data. For instance, the largest available human…

Enhancing the energy efficiency of buildings significantly relies on monitoring indoor ambient temperature. The potential limitations of conventional temperature measurement techniques, together with the omnipresence of smartphones, have…

We consider the problem of learning a measure of distance among vectors in a feature space and propose a hybrid method that simultaneously learns from similarity ratings assigned to pairs of vectors and class labels assigned to individual…

We introduce a fundamentally different paradigm for contact tracing: for each positive case, do not only ask direct contacts to quarantine; instead, tell everyone how many relationships away the disease just struck (so, "2" is a close…

Deep learning-based fingerprinting is one of the current promising technologies for outdoor localization in cellular networks. However, deploying such localization systems for heterogeneous phones affects their accuracy as the cellular…

Contact tracing is the techno-choice of reference to address the COVID-19 pandemic. Many of the current approaches have severe privacy and security issues and fail to offer a sustainable contact tracing infrastructure. We address these…

With more than 1.7 million COVID-19 deaths, identifying effective measures to prevent COVID-19 is a top priority. We developed a mathematical model to simulate the COVID-19 pandemic with digital contact tracing and testing strategies. The…

A novel technique is proposed to optimize energy efficiency for wireless networks based on hierarchical mobile clustering. The new bi-level clustering technique minimizes mutual interference and energy consumption in large-scale tracking…

Contact tracing is an essential tool for public health officials and local communities to fight the spread of novel diseases, such as for the COVID-19 pandemic. The Singaporean government just released a mobile phone app, TraceTogether,…

Remote magnetic sensing can be used to monitor the position of objects in real-time, enabling ground transport monitoring, underground infrastructure mapping and hazardous detection. However, magnetic signals are typically weak and complex,…

Bluetooth is a promising wireless technology that enables portable devices to form short-range wireless ad hoc networks. Unlike wireless LAN, the communication of Bluetooth devices follow a strict master slave relationship, that is, it is…

A number of groups, from governments to non-profits, have quickly acted to innovate the contact-tracing process: they are designing, building, and launching contact-tracing apps in response to the COVID-19 crisis. A diverse range of…

In recent years WiFi became the primary source of information to locate a person or device indoor. Collecting RSSI values as reference measurements with known positions, known as WiFi fingerprinting, is commonly used in various positioning…

In the healthcare system, patients are required to use wearable devices for the remote data collection and real-time monitoring of health data and the status of health conditions. This adoption of wearables results in a significant increase…

Human activity recognition has grown in popularity with its increase of applications within daily lifestyles and medical environments. The goal of having efficient and reliable human activity recognition brings benefits such as accessible…

The goal of metric learning is to learn a function that maps samples to a lower-dimensional space where similar samples lie closer than dissimilar ones. Particularly, deep metric learning utilizes neural networks to learn such a mapping.…

This paper presents the design of deep learning architectures which allow to classify the social relationship existing between two people who are walking in a side-by-side formation into four possible categories --colleagues, couple, family…

Mobile phone metadata is increasingly used for humanitarian purposes in developing countries as traditional data is scarce. Basic demographic information is however often absent from mobile phone datasets, limiting the operational impact of…