Employing Information and Communications Technologies in Homes and Cities for the Health and Well-Being of Older People

YesHe X and Sheriff RE (Eds.) Employing ICT in Homes and Cities for the Health and Well-Being of Older People. Workshop Proceedings of ICT4HOP’16. 15-17 Aug 2016. Sichuan University, Chengdu, China.British Council, Researcher Links, Newton Fund, NSF

URSS Event Program Booklet 2016

This event program booklet contains a schedule of the event, list of awards, and student abstracts

Deep Feature Learning and Adaptation for Computer Vision

We are living in times when a revolution of deep learning is taking place. In general, deep learning models have a backbone that extracts features from the input data followed by task-specific layers, e.g. for classification. This dissertation proposes various deep feature extraction and adaptation methods to improve task-specific learning, such as visual re-identification, tracking, and domain adaptation. The vehicle re-identification (VRID) task requires identifying a given vehicle among a set of vehicles under variations in viewpoint, illumination, partial occlusion, and background clutter. We propose a novel local graph aggregation module for feature extraction to improve VRID performance. We also utilize a class-balanced loss to compensate for the unbalanced class distribution in the training dataset. Overall, our framework achieves state-of-the-art (SOTA) performance in multiple VRID benchmarks. We further extend our VRID method for visual object tracking under occlusion conditions. We motivate visual object tracking from aerial platforms by conducting a benchmarking of tracking methods on aerial datasets. Our study reveals that the current techniques have limited capabilities to re-identify objects when fully occluded or out of view. The Siamese network based trackers perform well compared to others in overall tracking performance. We utilize our VRID work in visual object tracking and propose Siam-ReID, a novel tracking method using a Siamese network and VRID technique. In another approach, we propose SiamGauss, a novel Siamese network with a Gaussian Head for improved confuser suppression and real time performance. Our approach achieves SOTA performance on aerial visual object tracking datasets. A related area of research is developing deep learning based domain adaptation techniques. We propose continual unsupervised domain adaptation, a novel paradigm for domain adaptation in data constrained environments. We show that existing works fail to generalize when the target domain data are acquired in small batches. We propose to use a buffer to store samples that are previously seen by the network and a novel loss function to improve the performance of continual domain adaptation. We further extend our continual unsupervised domain adaptation research for gradually varying domains. Our method outperforms several SOTA methods even though they have the entire domain data available during adaptation

Harnessing the Power of Collective Intelligence: the Case Study of Voxel-based Soft Robots

The field of Evolutionary Robotics (ER) is concerned with the evolution of artificial agents---robots. Albeit groundbreaking, progress in the field has recently stagnated. In the research community, there is a strong feeling that a paradigm change has become necessary to disentangle ER. In particular, a solution has emerged from ideas from Collective Intelligence (CI). In CI---which has many relevant examples in nature---behavior emerges from the interaction between several components. In the absence of central intelligence, collective systems are usually more adaptable. In this thesis, we set out to harness the power of CI, focusing on the case study of simulated Voxel-based Soft Robots (VSRs): they are aggregations of homogeneous and soft cubic blocks that actuate by altering their volume. We investigate two axes. First, the morphologies of VSRs are intrinsically modular and an ideal substrate for CI; nevertheless, controllers employed until now do not take advantage of such modularity. Our results prove that VSRs can truly be controlled by the CI of their modules. Second, we investigate the spatial and time scales of CI. In particular, we evolve a robot to detect its global body properties given only local information processing, and, in a different study, generalize better to unseen environmental conditions through Hebbian learning. We also consider how evolution and learning interact in VSRs. Looking beyond VSRs, we propose a novel soft robot formalism that more closely resembles natural tissues and blends local with global actuation.The field of Evolutionary Robotics (ER) is concerned with the evolution of artificial agents---robots. Albeit groundbreaking, progress in the field has recently stagnated. In the research community, there is a strong feeling that a paradigm change has become necessary to disentangle ER. In particular, a solution has emerged from ideas from Collective Intelligence (CI). In CI---which has many relevant examples in nature---behavior emerges from the interaction between several components. In the absence of central intelligence, collective systems are usually more adaptable. In this thesis, we set out to harness the power of CI, focusing on the case study of simulated Voxel-based Soft Robots (VSRs): they are aggregations of homogeneous and soft cubic blocks that actuate by altering their volume. We investigate two axes. First, the morphologies of VSRs are intrinsically modular and an ideal substrate for CI; nevertheless, controllers employed until now do not take advantage of such modularity. Our results prove that VSRs can truly be controlled by the CI of their modules. Second, we investigate the spatial and time scales of CI. In particular, we evolve a robot to detect its global body properties given only local information processing, and, in a different study, generalize better to unseen environmental conditions through Hebbian learning. We also consider how evolution and learning interact in VSRs. Looking beyond VSRs, we propose a novel soft robot formalism that more closely resembles natural tissues and blends local with global actuation

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

CORPORATE SOCIAL RESPONSIBILITY IN ROMANIA

The purpose of this paper is to identify the main opportunities and limitations of corporate social responsibility (CSR). The survey was defined with the aim to involve the highest possible number of relevant CSR topics and give the issue a more wholesome perspective. It provides a basis for further comprehension and deeper analyses of specific CSR areas. The conditions determining the success of CSR in Romania have been defined in the paper on the basis of the previously cumulative knowledge as well as the results of various researches. This paper provides knowledge which may be useful in the programs promoting CSR.Corporate social responsibility, Supportive policies, Romania