Internationalisation: cui bono?

Many universities are striving to internationalise, each with its own rationale.  To benefit students, these efforts must go beyond the recruitment of international students and the development of transnational education, even though these bring their own fiscal and cultural rewards.  Here we examine the value of the other strands of the international agenda – student and staff exchange and internationalising the curriculum – as the aspects that most directly benefit the student experience

Deep learning model for cyber-attacks detection method in wireless sensor networks

Nowadays, electronic applications are being adopted instead of many traditional processes in data and information management that use Internet technology as a transmission medium. Therefore, these data and information suffer from different types of attacks that aim to destroy or steal them. One of these attacks is the cyber classification that can halt the whole system. In this paper, a cyber-attacks detector method is proposed based on deep learning technology for Wireless Sensor Network (WSN). This method adopts the behavior of the WSN's nodes as well as the data transmission that depends on the MQTT protocol. The use of the deep learning model in this method improves the detection accuracy compared to traditional machine learning methods. The results demonstrate the efficiency of using the combination of deep learning CNN-LSTM techniques to be 96.02% in training accuracy and 95.08% for validation accuracy depending on the dataset of [1]. The machine learning model in [1] obtains an accuracy between 87% and 91% for the augmented dataset processes

Dynamic Neural Network for Business and Market Analysis

© 2019, Springer Nature Switzerland AG. The problem of predicting nonlinear and nonstationary signals is complex since the physical law that controls them is unknown and it is complicated to be considered. In these cases, it is necessary to devise nonlinear models that imitate or learn the rules of behavior of the problem and can be developed based on historical data. For this reason, neural networks are useful tools to deal with this type of problem due to their nonlinearly and their capacity of generalizing. This paper aims at exploring various types of neural network architectures and study their performance with time series predictions. Predictions on two sets of data (of a very different nature) will be made using three neural networks including multilayer perceptrons, recurrent neural network and long-short term memory varying some important parameters: input neurons, epochs and the anticipation with which the predictions are made. Then, all results will be compared using standard metrics. As a conclusion, the influence of the type of series under study is more important than the parameters considered in what concerns the performance. The management of the memory in the networks is a key to its success in the prediction of S&P 500 and electrical power time series

Rapid Detection of Synthetic Cannabinoid Receptor Agonists Impregnated into Paper by Raman Spectroscopy

The last decade has witnessed the emergence of new psychoactive substances that are analogues of classical drugs of abuse in order to escape the regulations surrounding the latter drugs. These drugs were of both herbal and synthetic origin and were advertised initially as ‘legal highs’; thus, they were perceived as safe by users. Hence, upon their emergence, they were not controlled by the Misuse of Drugs Act 1971, which contributed to their popularity and increased sales online and within street markets. In 2016, the Psychoactive Substance Act introduced a blanket ban on all new psychoactive substances except for caffeine, alcohol, and nicotine. This in turn, contributed to the change in the sale of new psychoactive substances products that have been sold as concealed in different matrices, including herbal products, papers, fabrics, and textiles. Concealing drugs in paper has been very popular, especially since the drug product is of lightweight and can be sent via postal services. However, new psychoactive concealed in papers are toxic not only to the users; but also, to the person handling them (i.e. mail employees). One of the classes of new psychoactive substances that have been commonly concealed in papers and that have been linked to toxicity and hospitalization cases is synthetic cannabinoids. Therefore, there is a need to identify new psychoactive substances concealed in papers non-destructively and rapidly to prevent toxicity linked to them. Handheld Raman spectroscopy offers this advantage as it is of lightweight and carries the sample to the matrix. Therefore, this work used handheld Raman spectroscopy for identifying synthetic cannabinoids concealed in papers using Raman spectroscopy combined with machine learning analytics. Synthetic cannabinoid and paper samples were measured non-destructively using a handheld Raman spectrometer equipped with a 1064 nm laser wavelength. Spectral data was exported into Matlab 2020b where machine learning analytics including identification and prediction was. The results showed that Raman spectroscopy could identify specific synthetic cannabinoids in papers that were either deposited on the surface of the paper or diffused inside the paper substrate. When machine learning analytics were applied to the Raman spectra of the papers, quantitative information was obtained regarding the amount of synthetic cannabinoid deposited on the paper surface. In summary, handheld Raman spectroscopy could identify and quantify synthetic cannabinoids on paper rapidly and non-destructively. Future work involves testing other classes of new psychoactive substance and applying deep learning analytic

Smart Home Systems Security

© 2018 IEEE. Due to the increase of the Smart Home System market, it has become important to outline and understand the direction and progress needed to ensure that, as Smart Home Systems become more common, the security and functionality of these systems. This research sheds light on what has been done in the field and Smart Home System owners feel currently about the systems they already have, the reasons behind using it as well as what could be done differently to improve its security. The results are presented from feedback received from the questionnaire to provide knowledge and understanding of how a Smart Home System can be improved, and what the main paths of future progress in this area. The ultimate aims of this work are to identify the risks associated with Smart Home Systems and investigate how the risks can be mitigated

Gender Bias in Diagnosis, Prevention, and Treatment of Cardiovascular Diseases:A Systematic Review

Cardiovascular disease (CVDs) has been perceived as a ‘man’s disease’, and this impacted women’s referral to CVD diagnosis and treatment. This study systematically reviewed the evidence regarding gender bias in the diagnosis, prevention, and treatment of CVDs. Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were followed. We searched CINAHL, PubMed, Medline, Web of Science, British Nursing Index, Scopus, and Google Scholar. The included studies were assessed for quality using risk bias tools. Data extracted from the included studies were exported into Statistical Product and Service Solutions (SPSS, v26; IBM SPSS Statistics for Windows, Armonk, NY), where descriptive statistics were applied. A total of 19 studies were analysed. CVDs were less reported among women who either showed milder symptoms than men or had their symptoms misdiagnosed as gastrointestinal or anxiety-related symptoms. Hence, women had their risk factors under-considered by physicians (especially by male physicians). Subsequently, women were offered fewer diagnostic tests, such as coronary angiography, ergometry, electrocardiogram (ECG), and cardiac enzymes, and were referred to less to cardiologists and/or hospitalisation. Furthermore, if hospitalised, women were less likely to receive a coronary intervention. Similarly, women were prescribed cardiovascular medicines than men, with the exception of antihypertensive and anti-anginal medicines. When it comes to the perception of CVD, women considered themselves at lower risk of CVDs than men. This systematic review showed that women were offered fewer diagnostic tests for CVDs and medicines than men and that in turn influenced their disease outcomes. This could be attributed to the inadequate knowledge regarding the differences in manifestations among both genders

Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images.

Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex background, variations in lesion shape and color, which affect the accuracy of their segmentation and subsequent classification. This research proposes an automated system for detection and classification of ulcers in WCE images, based on state-of-the-art deep learning networks. Deep learning techniques, and in particular, convolutional neural networks (CNNs), have recently become popular in the analysis and recognition of medical images. The medical image datasets used in this study were obtained from WCE video frames. In this work, two milestone CNN architectures, namely the AlexNet and the GoogLeNet are extensively evaluated in object classification into ulcer or non-ulcer. Furthermore, we examine and analyze the images identified as containing ulcer objects to evaluate the efficiency of the utilized CNNs. Extensive experiments show that CNNs deliver superior performance, surpassing traditional machine learning methods by large margins, which supports their effectiveness as automated diagnosis tools

Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) downlink interference based on Artificial Neural Network Learning Models (ANN-LMS)

Fifth-generation (5G) networks have been deployed alongside fourth-generation networks in high-traffic areas. The most recent 5G mobile communication access technology includes mmWave and sub-6 GHz C-bands. However, 5G signals possibly interfere with existing radio systems because they are using adjacent and co-channel frequencies. Therefore, the minimisation of the interference of 5G with other signals already deployed for other services, such as fixed-satellite service Earth stations (FSS-Ess), is urgently needed. The novelty of this paper is that it addresses issues using measurements from 5G base stations (5G-BS) and FSS-ES, simulation analysis, and prediction modelling based on artificial neural network learning models (ANN-LMs). The ANN-LMs models are used to classify interference events into two classes, namely, adjacent and co-channel interference. In particular, ANN-LMs incorporating the radial basis function neural network (RBFNN) and general regression neural network (GRNN) are implemented. Numerical results considering real measurements carried out in Malaysia show that RBFNN evidences better accuracy with respect to its GRNN counterpart. The outcomes of this work can be exploited in the future as a baseline for coexistence and/or mitigation techniques.Agencia Estatal de Investigación | Ref. PID2020-115323RB-C33Agencia Estatal de Investigación | Ref. PID2020-113240RB-I0

Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks

Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques