Generic colour image segmentation via multi-stage region merging

We present a non-parametric unsupervised colour image segmentation system that is fast and retains significant perceptual correspondence with the input data. The method uses a region merging approach based on statistics of growing local structures. A two-stage algorithm is employed during which neighbouring regions of homogeneity are traced using feature gradients between groups of pixels, thus giving priority to topological relations. The system finds spatially cohesive and globally salient image regions usually without losing smaller localised areas of high saliency. Unoptimised implementations of the method work nearly in real-time, handling multiple frames a second. The system is successfully applied to problems such as object detection and tracking

Robust modelling and tracking of NonRigid objects using Active-GNG

This paper presents a robust approach to nonrigid modelling and tracking. The contour of the object is described by an active growing neural gas (A-GNG) network which allows the model to re-deform locally. The approach is novel in that the nodes of the network are described by their geometrical position, the underlying local feature structure of the image, and the distance vector between the modal image and any successive images. A second contribution is the correspondence of the nodes which is measured through the calculation of the topographic product, a topology preserving objective function which quantifies the neighbourhood preservation before and after the mapping. As a result, we can achieve the automatic modelling and tracking of objects without using any annotated training sets. Experimental results have shown the superiority of our proposed method over the original growing neural gas (GNG) network

Killing your device via your USB port

The USB killer is a testing device that has been marketed as having been designed to test the limits of the surge protection circuitry of electronics. The device can 'fry' an electronic device in a fraction of a second. The aim of this research is to identify to what extent the data that is stored on the device can be destroyed when utilising the USB Killer 2.0 since it could potentially become the weapon of a malicious user with access to a device with an active USB port. The authors conducted a series of experiments utilising the USB killer in different hardware configurations. The paper introduces the USB protocol and discusses the functionality of the USB killer before outlining the experiment and presenting the results of the study

Rhythmic cueing, dance, resistance training, and Parkinson's disease : a systematic review and meta-analysis

Objectives: The aim of the present systematic review and meta-analysis was to synthesize evidence associated with the functional and clinical effectiveness of rhythmic cueing, dance, or resistance training (RT) on motor and non-motor parameters in Parkinson's Disease patients, and to provide a comparative perspective not offered by existing systematic reviews. Methodology: Eligibility criteria for selecting studies retained no restrictions in methodological design and included interventions of rhythmic cueing, dance, RT, and measurements of motor and non-motor parameters. Animal studies, reviews, editorials, conferences, magazines, and gray literature articles were excluded. Two independent investigators searched Cochrane Library, Medline, PubMed, and SPORTDiscus from the date of their inception until 1 June 2021. The ROBINS-I tool was employed for the non-randomized controlled trials, and the updated for Risk of Bias 2 tool of Cochrane Library used for randomized controlled trials. For meta-analyses, the RevMan 5.4.13 software was used. For incompatible meta-analysis studies, a narrative data synthesis was conducted. Results: A total of 49 studies included in the systematic review involving 3767 PD participants. Meta-analyses revealed that rhythmic cueing training assists gait velocity (p = 0.01), stride length (p = 0.01), and motor symptoms (p = 0.03). Similarly, dance training benefits stride length (p = 0.05), lower extremity function-TUG (p = 0.01), and motor symptoms (p = 0.01), whilst RT improves lower extremity function-TUG (p = 0.01), quality of life (p = 0.01), knee flexion (p = 0.02), and leg press (p = 0.01). Subgroup analyses have shown non-significant differences in gait velocity (p = 0.26), stride length (p = 0.80), functional mobility-TUG (p = 0.74), motor symptoms-UPDRS-III (p = 0.46), and quality of life-PDQ39 (p = 0.44). Conclusion: Rhythmic cueing, dance, or RT positively affect the examined outcomes, with rhythmic cueing to be associated with three outcomes (Gait, Stride, and UPDRS-III), dance with three outcomes (TUG, Stride, and UPDRS-III), and RT with two outcomes (TUG and PDQ-39). Subgroup analyses confirmed the beneficial effects of these forms of exercise. Clinicians should entertain the idea of more holistic exercise protocols aiming at improving PD manifestations

A Hybrid Spam Detection Method Based on Unstructured Datasets

This document is the accepted manuscript version of the following article: Shao, Y., Trovati, M., Shi, Q. et al. Soft Comput (2017) 21: 233. The final publication is available at Springer via http://dx.doi.org/10.1007/s00500-015-1959-z. © Springer-Verlag Berlin Heidelberg 2015.The identification of non-genuine or malicious messages poses a variety of challenges due to the continuous changes in the techniques utilised by cyber-criminals. In this article, we propose a hybrid detection method based on a combination of image and text spam recognition techniques. In particular, the former is based on sparse representation-based classification, which focuses on the global and local image features, and a dictionary learning technique to achieve a spam and a ham sub-dictionary. On the other hand, the textual analysis is based on semantic properties of documents to assess the level of maliciousness. More specifically, we are able to distinguish between meta-spam and real spam. Experimental results show the accuracy and potential of our approach.Peer reviewedFinal Accepted Versio

Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction

An Experiential View to Children Learning in Museums with Augmented Reality

Museums facilitate schoolchildren’s experiential learning, and when combined with Augmented Reality (AR) applications, schoolchildren can benefit from interactive, engaging learning experiences. Experiential learning is therefore situated in a context relevant to schoolchildren’s learning experience with digital technologies such as AR in museums, hence, it seems appropriate to employ Kolb’s (1984) Experiential Learning Cycle as a theoretical base. A museum in the UK was used as a single case study, and experiments and three focus groups were conducted with 19 schoolchildren and data analysed using thematic analysis. This study revealed three new themes specific to schoolchildren’s experiential learning experiences with AR in museums including: (1) integrating AR could further enhance knowledge acquisition, (2) schoolchildren were able to identify their preferred learning style, and (3) schoolchildren are motivated to continue learning with AR in museums. Theoretical contributions and practical implications are presented, as well as suggestions for future research

Clinical significance of circulating anti-p53 antibodies in European patients with hepatocellular carcinoma

p53 alterations are considered to be predictive of poor prognosis in hepatocellular carcinoma (HCC) and may induce a humoral response. Anti-p53 serum antibodies were assessed by enzyme-linked immunosorbent assay (ELISA) using purified recombinant human p53 on 130 European HCC patients before treatment and during the clinical course of the disease. p53 immunohistochemistry was performed on tumours from the 52 patients who underwent surgery, and DNA sequencing analysis was initiated when circulating anti-p53 antibodies were detected. Nine (7%) HCC patients had anti-p53 serum antibodies before treatment. During a mean period of 30 months of follow-up, all the negative patients remained negative, even when recurrence was observed. Of the nine positive patients, eight were still positive 12–30 months after surgery. The presence of anti-p53 serum antibodies was correlated neither with mutation of the p53 gene nor the serum alpha-fetoprotein levels and clinicopathological characterics of the tumours. However, a greater incidence of vascular invasion and accumulation of p53 protein were observed in the tumours of these patients (P < 0.03 and P < 0.01 respectively) as well as a better survival rate without recurrence (P = 0.05). In conclusion, as was recently shown in pancreatic cancer, anti-p53 serum antibodies may constitute a marker of relative ‘good prognosis’ in a subgroup of patients exhibiting one or several markers traditionally thought to be of bad prognosis. © 1999 Cancer Research Campaig