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Active Learning (AL) refers to the setting where the learner has the ability to perform queries to an oracle to acquire the true label of an instance or, sometimes, a set of instances. Even though Active Learning has been studied extensively, the setting is usually restricted to assume that the oracle is trustworthy and will provide the actual label. We argue that, while common, this approach can be made more flexible to account for different forms of supervision. In this paper, we propose a new framework that allows the algorithm to request the label for a bag of samples at a time. Although this label will come in the form of proportions of class labels in the bags and therefore encode less information, we demonstrate that we can still learn effectively.

Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags. This paradigm is particularly suitable in contexts where providing individual labels is expensive and label aggregates are more easily obtained. In the healthcare domain, it is a burden for a patient to keep a detailed diary of their daily routines, but often they will be amenable to provide higher level summaries of daily behavior. We present a novel and efficient graph-based algorithm that encourages local smoothness and exploits the global structure of the data, while preserving the `mass' of each bag., Comment: Accepted to MLSP 2018

In residential environments, floor maps, often required by location-based services, cannot be trivially acquired. Researchers have addressed the problem of automatic floor map construction in indoor environments using various modalities, such as inertial sensors, Radio Frequency (RF) fingerprinting and video cameras. Considering that some of these techniques are unavailable or impractical to implement in residential environments, in this paper, we focus on using RF signals to predict the number of walls between a wearable device and an access point. Using both supervised and unsupervised learning techniques on two data sets; a system-level data set of Bluetooth packets, and measurements on the signal attenuation, we construct wall prediction models that yield up to 91% identification rate. As a proof-of-concept, we also use the wall prediction models to infer the floor plan of a smart home deployment in a real residential environment.

In this paper, we tackle the problem of the food allergic detection in children, based on the analysis of the ECG signal. Through the detection of some changes of this signal, it is possible to detect any reaction before the tested subject experiments any physical reaction or any reaction that could be harmful to his/her life. To be able to realize this process in real-time and with portable devices, it is necessary to reduce the computational cost of the full process, from the ECG analysis to the allergy detection process.

This paper details the process of oral food challenges (‘allergy tests’) and steps followed to investigate whether automatic classification of the tests is possible. It has been observed by trained staff that the mood and physiological signals of a subject being tested for allergies can change during the test if they are sensitive to the allergen they are being tested against. Data from thirteen subjects was recorded, and thirteen features were extracted from each of these datasets. The changes in the features were then analysed over the course of each test. It was noted that when a subject failed the challenge, some of the features extracted from the ECG trace changed suddenly near the time that the test was stopped. Threshold classification was employed, and ROC curves were generated. Some features gave rise to ROC areas of over 0.97 on certain subjects. An average ROC area of 0.57 was computed over all subjects and all features due to wide inter subject variability.

Many accelerometer-based energy expenditure estimation algorithms and platforms have been established in recent topical literature, and each boasts a high correlation against the gold standard in energy expenditure measurement, i.e. indirect calorimetry. The aim of this study was to implement a set of these algorithms, run them all over a common dataset and investigate the strengths and weaknesses associated with each. The algorithms were then ported to a SHIMMER device for a real time, mobile and non-invasive energy expenditure estimation solution. High correlations were found between the accelerometer-regressed energy expenditure estimates and the reference dataset both on a computer and SHIMMER-implementation of the algorithms.