University of Surrey

About: University of Surrey is a education organization based out in Guildford, Surrey, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 17976 authors who have published 44951 publications receiving 1249993 citations. The organization is also known as: Battersea Polytechnic Institute & Battersea College of Technology.

Enabling smart cities through a cognitive management framework for the internet of things

Abstract: The Internet of Things (IoT) is expected to substantially support sustainable development of future smart cities. This article identifies the main issues that may prevent IoT from playing this crucial role, such as the heterogeneity among connected objects and the unreliable nature of associated services. To solve these issues, a cognitive management framework for IoT is proposed, in which dynamically changing real-world objects are represented in a virtualized environment, and where cognition and proximity are used to select the most relevant objects for the purpose of an application in an intelligent and autonomic way. Part of the framework is instantiated in terms of building blocks and demonstrated through a smart city scenario that horizontally spans several application domains. This preliminary proof of concept reveals the high potential that self-reconfigurable IoT can achieve in the context of smart cities.

Annotation : Childhood bereavement following parental death

Abstract: Psychological outcomes in children who have experienced the death of a parent are heterogeneous. One child in five is likely to develop psychiatric disorder. In the year following bereavement, children commonly display grief, distress, and dysphoria. Nonspecific emotional and behavioural difficulties among children are often reported by surviving parents and the bereaved children themselves. The highest rates of reported difficulties are found in boys. This review identifies the moderating and mediating variables that lead to some children being more vulnerable to disturbance than others following parental death. Limitations and gaps in the recent bereavement literature are identified. Theoretical and methodological advances that are necessary for a coherent account of childhood bereavement are outlined.

When Intrusion Detection Meets Blockchain Technology: A Review

Abstract: With the purpose of identifying cyber threats and possible incidents, intrusion detection systems (IDSs) are widely deployed in various computer networks. In order to enhance the detection capability of a single IDS, collaborative intrusion detection networks (or collaborative IDSs) have been developed, which allow IDS nodes to exchange data with each other. However, data and trust management still remain two challenges for current detection architectures, which may degrade the effectiveness of such detection systems. In recent years, blockchain technology has shown its adaptability in many fields, such as supply chain management, international payment, interbanking, and so on. As blockchain can protect the integrity of data storage and ensure process transparency, it has a potential to be applied to intrusion detection domain. Motivated by this, this paper provides a review regarding the intersection of IDSs and blockchains. In particular, we introduce the background of intrusion detection and blockchain, discuss the applicability of blockchain to intrusion detection, and identify open challenges in this direction.

One is not enough: multiple views in a media space

Abstract: Media spaces support collaboration, but the limited access they provide to remote colleagues' activities can undermine their utility To address this limitation, we built an experimental system in which four switchable cameras were deployed in each of two remote offices, and observed participants using the system to collaborate on two tasks The new views allowed increased access to task-related artifacts; indeed, users preferred these views to more typical “face-to-face” ones However, problems of establishing a joint frame of reference were exacerbated by the additional complexity, leading us to speculate about more effective ways to expand access to remote sites

Omni-Scale Feature Learning for Person Re-Identification

Abstract: As an instance-level recognition problem, person re-identification (ReID) relies on discriminative features, which not only capture different spatial scales but also encapsulate an arbitrary combination of multiple scales. We call features of both homogeneous and heterogeneous scales omni-scale features. In this paper, a novel deep ReID CNN is designed, termed Omni-Scale Network (OSNet), for omni-scale feature learning. This is achieved by designing a residual block composed of multiple convolutional streams, each detecting features at a certain scale. Importantly, a novel unified aggregation gate is introduced to dynamically fuse multi-scale features with input-dependent channel-wise weights. To efficiently learn spatial-channel correlations and avoid overfitting, the building block uses pointwise and depthwise convolutions. By stacking such block layer-by-layer, our OSNet is extremely lightweight and can be trained from scratch on existing ReID benchmarks. Despite its small model size, OSNet achieves state-of-the-art performance on six person ReID datasets, outperforming most large-sized models, often by a clear margin. Code and models are available at: \url{this https URL}.