Autonomous learning for face recognition in the wild via ambient wireless cues

Facial recognition is a key enabling component for emerging Internet of Things (IoT) services such as smart homes or responsive offices. Through the use of deep neural networks, facial recognition has achieved excellent performance. However, this is only possibly when trained with hundreds of images of each user in different viewing and lighting conditions. Clearly, this level of effort in enrolment and labelling is impossible for wide-spread deployment and adoption. Inspired by the fact that most people carry smart wireless devices with them, e.g. smartphones, we propose to use this wireless identifier as a supervisory label. This allows us to curate a dataset of facial images that are unique to a certain domain e.g. a set of people in a particular office. This custom corpus can then be used to finetune existing pre-trained models e.g. FaceNet. However, due to the vagaries of wireless propagation in buildings, the supervisory labels are noisy and weak. We propose a novel technique, AutoTune, which learns and refines the association between a face and wireless identifier over time, by increasing the inter-cluster separation and minimizing the intra-cluster distance. Through extensive experiments with multiple users on two sites, we demonstrate the ability of AutoTune to design an environment-specific, continually evolving facial recognition system with entirely no user effort

Developing autonomous learning in first year university students using perspectives from positive psychology

Autonomous learning is a commonly occurring learning outcome from university study, and it is argued that students require confidence in their own abilities to achieve this. Using approaches from positive psychology, this study aimed to develop confidence in first‐year university students to facilitate autonomous learning. Psychological character strengths were assessed in 214 students on day one at university. Two weeks later their top three strengths were given to them in study skills modules as part of a psycho‐educational intervention designed to increase their self‐efficacy and self‐esteem. The impact of the intervention was assessed against a control group of 40 students who had not received the intervention. The results suggested that students were more confident after the intervention, and that levels of autonomous learning increased significantly compared to the controls. Character strengths were found to be associated with self‐efficacy, self‐esteem and autonomous learning in ways that were theoretically meaningful

Acoustic Identification of Flat Spots On Wheels Using Different Machine Learning Techniques

BMBF, 01IS18049B, ALICE III - Autonomes Lernen in komplexen Umgebungen 3 (Autonomous Learning in Complex Environments 3

An architecture for an autonomous learning robot

An autonomous learning device must solve the example bounding problem, i.e., it must divide the continuous universe into discrete examples from which to learn. We describe an architecture which incorporates an example bounder for learning. The architecture is implemented in the GPAL program. An example run with a real mobile robot shows that the program learns and uses new causal, qualitative, and quantitative relationships

The organization of an autonomous learning system

The organization of systems that learn from experience is examined, human beings and animals being prime examples of such systems. How is their information processing organized. They build an internal model of the world and base their actions on the model. The model is dynamic and predictive, and it includes the systems' own actions and their effects. In modeling such systems, a large pattern of features represents a moment of the system's experience. Some of the features are provided by the system's senses, some control the system's motors, and the rest have no immediate external significance. A sequence of such patterns then represents the system's experience over time. By storing such sequences appropriately in memory, the system builds a world model based on experience. In addition to the essential function of memory, fundamental roles are played by a sensory system that makes raw information about the world suitable for memory storage and by a motor system that affects the world. The relation of sensory and motor systems to the memory is discussed, together with how favorable actions can be learned and unfavorable actions can be avoided. Results in classical learning theory are explained in terms of the model, more advanced forms of learning are discussed, and the relevance of the model to the frame problem of robotics is examined

Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions

A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from local Hebbian plasticity rules is not very well understood. The goal of this work is to discover interpretable local Hebbian learning rules that can provide autonomous global learning. To achieve this, we use a discrete representation to encode the learning rules in a finite search space. These rules are then used to perform synaptic changes, based on the local interactions of the neurons. We employ genetic algorithms to optimize these rules to allow learning on two separate tasks (a foraging and a prey-predator scenario) in online lifetime learning settings. The resulting evolved rules converged into a set of well-defined interpretable types, that are thoroughly discussed. Notably, the performance of these rules, while adapting the ANNs during the learning tasks, is comparable to that of offline learning methods such as hill climbing.Comment: Evolutionary Computation Journa

Autonomous Learning by Simple Dynamical Systems with Delayed Feedbacks

A general scheme for construction of dynamical systems able to learn generation of the desired kinds of dynamics through adjustment of their internal structure is proposed. The scheme involves intrinsic time-delayed feedback to steer the dynamics towards the target performance. As an example, a system of coupled phase oscillators, which can by changing the weights of connections between its elements evolve to a dynamical state with the prescribed (low or high) synchronization level, is considered and investigated

Investigation Into the Relationship Between Positive Interdependence and Autonomous Learning Ability of College Students in a Normal University

Positive interdependent, one of the key elements of cooperative learning, would be inspired when study members have quest for the same goal, whose relationship with autonomous learning has received more and more attention from research to explore whether the cultivation of positive interdependent can do benefit to students’ ability of autonomous learning in English learning. Meanwhile cultivating college students’ autonomous learning abilities has become one of the focuses of modern teaching reform, and a deeper concern of the advancement of quality education in China. Students who have been only finishing since transition to the autonomous learning ability, could further form initiative spirit and practice ability. But autonomous learning does not mean learning alone, students’ cooperation, the key element of which is positive interdependence, in autonomous learning plays an essential role in the cultivation of their learning ability, which can never be ignored.This study is carried out to investigate the overall condition of positive interdependence and autonomous learning ability of the non-English major college students in normal university, and to explore the relationship between college students’ positive interdependence and autonomous learning ability. The Positive Interdependence Questionnaire Scale and College Students’ Autonomous English Learning Ability Questionnaire Scale have been administered in order to elicit 120 college students’ responses. According to the results, non-English major college students perform well on positive interdependence and autonomous learning. The entire positive interdependence and autonomous learning ability are at a medium level. Positive goal interdependence is the more frequently perceived type of positive interdependence. Besides, there exists a significant positive relationship between English majors’ positive interdependence and autonomous learning ability

The development of a brief measure of learner autonomy in university students

A great deal of attention is paid to the requirement for university students to become autonomous learners. A review of the literature revealed a lack of relatively short psychometrically sound measures of autonomous learning despite its purported importance. This study aimed to develop a brief, psychometrically sound, measure of autonomous learning to facilitate empirical research in this area. Items for the scale were selected from reviewing the literature, and face validity was confirmed by experienced academics. In the first study, first‐year psychology students (n = 214) completed the measure. Principal components analysis produced a 12‐item measure with two subscales that appeared to be psychometrically sound. The factor structure was reproduced with a more diverse group of undergraduates (n =172) in a second study. The internal reliability and the concurrent validity of the scale were both found to be satisfactory, suggesting that this measure may prove useful to educational researchers