Personalized Mobile System Application: A Case Study

This paper presents an overview of the components, approaches and techniques which are used to build a mobile phone- application that uses short messaging service (SMS) text messages to improve interaction, information distribution and communication of stakeholders in a university setting. The proposed application is built upon a multiple compatible mobile phone menu-based subscription management application that is also customizable.  Since SMS has the potential to fill significant connectivity and service gaps, this application can provide support for them to become more ubiquitous. Event-based approach towards context-aware personalized notification service is adopted, i.e. user will receive relevant immediate SMS to his/her mobile phone based on his/her subscription for preferred notifications. A trigger enables event management system to send out (semi-) automated personalized notification. Notification services that understand the context within which their users operate, i.e. identity, activity and time are derived based on a set of predetermined rules. This will benefit the stakeholders in terms of getting up-to-date notifications via the delivery system which implements SysMan TellMe V8.04 as the delivery protocol and SMS server

The HARX-GJR-GARCH skewed-t multipower realized volatility modelling for S&P 500

The heterogeneous autoregressive (HAR) models are used in modeling high frequency multipower realized volatility of the S&P 500 index. Extended from the standard realized volatility, the multipower realized volatility representations have the advantage of handling the possible abrupt jumps by smoothing the consecutive volatility. In order to accommodate clustering volatility and asymmetric of multipower realized volatility, the HAR model is extended by the threshold autoregressive conditional heteroscedastic (GJR-GARCH) component. In addition, the innovations of the multipower realized volatility are characterized by the skewed student-t distributions. The extended model provides the best performing in-sample and out-of-sample forecast evaluations

A Color Based Touchless Finger Mouse

People work with computers almost anytime, everywhere  in the current trend. However, continuously controlling a computer with mouse for a long time might cause much strains to people’s wrist. This work proposes a touchless finger mouse using webcam. A marker with different colours representing different actions is used. The webcam will capture the information on the marker and trigger the associated actions. This prototype is proven to be able to perform most of the actions a normal mouser can perform

Pulse wave velocity is associated with increased plasma oxLDL in ageing but not with FGF21 and habitual exercise

Fibroblast growth factor 21 (FGF21) and adiponectin increase expression of genes involved in antioxidant pathways, but their roles in mediating oxidative stress and arterial stiffness with ageing and habitual exercise remain unknown. We explored the role of the FGF21–adiponectin axis in mediating oxidative stress and arterial stiffness with ageing and habitual exercise. Eighty age- and sex-matched healthy individuals were assigned to younger sedentary or active (18–36 years old,n=20 each) and older sedentary or active (45–80 years old,n=20 each) groups. Arterial stiffness was measured indirectly using pulse wave velocity (PWV). Fasted plasma concentrations of FGF21, adiponectin and oxidized low-density lipoprotein (oxLDL) were measured. PWV was 0.2-fold higher and oxLDL concentration was 25.6% higher (both p<0.001) in older than younger adults, despite no difference in FGF21 concentration (p=0.097) between age groups. PWV (p=0.09) and oxLDL concentration (p=0.275) did not differ between activity groups but FGF21 concentration was 9% lower in active than sedentary individuals (p=0.011). Adiponectin concentration did not differ by age (p=0.642) or exercise habits (p=0.821). In conclusion, age, but not habitual exercise, was associated with higher oxidative stress and arterial stiffness. FGF21 and adiponectin did not differ between younger and older adults, unlikely mediating oxidative stress and arterial stiffness in healthy adults. <br

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Model-Free Representations for Gait Recognition

Gait is the manner of walking, and gait recognition concerns the identification of people in video sequences by the way they walk. There is a number of advantages that makes gait valuable as a biometric. For instance, it is possible to detect and measure gait even in low resolution video, where it is often difficult to get other modalities, e.g., face or iris information at high enough resolution for recognition purposes. In addition, gait is difficult to disguise or conceal. Psychophysical studies indicate that humans have the capability for recognising people from even impoverished displays of gait, revealing the presence of identity information in the gait. Henceforth, it is interesting to study the utility of gait as a biometric. The goal of this thesis is to extract the motion information contained in the video sequences of the human gait and to exploit these information in means that facilitate individual recognition. To that end, four model-free methods are proposed. The proliferation of Fourier descriptors in shape analysis inspires the creation of the gait representation incorporating Fourier descriptors. The second is a method that captures the recency of gait using motion history, described by the histograms of oriented gradients. Since gait is a spatiotemporal phenomenon, it is also intuitive to explore the possibilities of characterising these spatiotemporal patterns using temporal motion patterns and statistical distribution. This notion led to the third and fourth methods; the former encodes the transient binary patterns and the latter exploits the statistical mean and variance of the silhouette deformation in the gait cycle

Mfrd-80k: A dataset and benchmark for masked face recognition

Wearing face masks in public spaces has become an essential step to prevent the spread of COVID-19. This step poses some challenges to conventional face recognition due to several reasons: 1) the absence of large real-world masked face recognition dataset, and 2) the loss of some visual cues due to the occlusion by the face masks. To address these challenges, this paper presents a real-world masked face recognition dataset that consists of 80500 masked face images of 161 subjects, referred to as MFRD-80K dataset. Every subject contributes 500 masked face images, which are then partitioned into 60:20:20 for train, validation and test. Subsequently, we conduct some benchmark studies to evaluate the performance of the existing face recognition and classification methods on the MFRD-80K dataset. The methods include k-Nearest Neighbour, Multinomial Logistic Regression, Support Vector Machines, Random Forest, Multilayer Perceptron and Convolutional Neural Networks. Since the parameter settings affect the performance of each method, a grid search is performed to determine the optimal parameter settings. The empirical results demonstrate that Convolutional Neural Network achieves the highest test accuracy of 97.16% on MFRD-80K dataset

COVID-19 Diagnosis on Chest Radiographs with Enhanced Deep Neural Networks

The COVID-19 pandemic has caused a devastating impact on the social activity, economy and politics worldwide. Techniques to diagnose COVID-19 cases by examining anomalies in chest X-ray images are urgently needed. Inspired by the success of deep learning in various tasks, this paper evaluates the performance of four deep neural networks in detecting COVID-19 patients from their chest radiographs. The deep neural networks studied include VGG16, MobileNet, ResNet50 and DenseNet201. Preliminary experiments show that all deep neural networks perform promisingly, while DenseNet201 outshines other models. Nevertheless, the sensitivity rates of the models are below expectations, which can be attributed to several factors: limited publicly available COVID-19 images, imbalanced sample size for the COVID-19 class and non-COVID-19 class, overfitting or underfitting of the deep neural networks and that the feature extraction of pre-trained models does not adapt well to the COVID-19 detection task. To address these factors, several enhancements are proposed, including data augmentation, adjusted class weights, early stopping and fine-tuning, to improve the performance. Empirical results on DenseNet201 with these enhancements demonstrate outstanding performance with an accuracy of 0.999%, precision of 0.9899%, sensitivity of 0.98%, specificity of 0.9997% and F1-score of 0.9849% on the COVID-Xray-5k dataset

Minimal Redundancy Maximal Relevance Criterion-based Multi-biometric Feature Selection

Multimodal biometrics are always adopted to improve the recognition performance of single modality biometric systems. Besides introducing more discriminating power to the biometric system, integrating multiple modalities also leads to the curse of dimensionality problem. In this paper, we engage the minimal redundancy maximal relevance criterion to reduce the dimensionality of the feature vector. The minimal redundancy maximal relevance criterion is a feature selection criterion that aims to retain the most relevant elements while discarding the other redundant elements. Our experiments show that, with only 15% of the original feature length, minimal redundancy maximal relevance criterion-based features are able to perform similarly well or even better than the baseline results