mHealth Technology: Towards a New Persuasive Mobile Application for Caregivers That Addresses Motivation and Usability

With the increasing use of mobile technologies and smartphones, new methods of promoting personal health have been developed. For example, there is now software for recording and tracking one\u27s exercise activity or blood pressure. Even though there are already many of these services, the mobile health field still presents many opportunities for new research. One apparent area of need would be software to support the efforts of caregivers for the elderly, especially those who suffer from multiple chronic conditions, such as cognitive impairment, chronic heart failure or diabetes. Very few mobile applications (apps) have been created that target caregivers of the elderly and most seem to be limited to a single condition or to creating generic to-do lists or tracking medications. None seem to address the complex tracking of multiple chronic conditions, nor one of the key difficulties found with written checklists for this population, namely that caregivers quit recording health information regularly as time passes. This dissertation will explore methods for improving the consistency of usage of health tracking software for the caregivers of the elderly with multiple chronic conditions by creating designs that explicitly address the context and motivations of caregivers. This work will assess a number of existing approaches and provide a design and a prototype for a new motivating application to help the caregivers of patients with multiple chronic conditions. It will assess how well the tool seems to address factors associated with intrinsic motivation (e.g. autonomy, competence, relatedness, and feedback). The overall usability of the software application will also be addressed, following guidelines from ISO standards and Nielsen’s theories

Intrusion Detection by Port Scan Using Snort

Network intrusion detection systems (NIDS) are an important part of any network security architecture. They provide a layer of defense which monitors network traffic for predefined suspicious activity or patterns, and alert system administrators when potential hostile traffic is detected. Network Intrusion Detection Systems (NIDS) perform deep packet inspection on packet payloads to identify, prevent, and inhibit malicious attacks over the Internet[l]. Snort is a lightweight intrusion detection system that can log packets coming across your network. This program can be used on smaller networks but on larger ones, with Gigabit Ethernet, snort can become unreliable. Snort doesn't require that you recompile your kernel or add any software or hardware to your existing distribution but it does require that you have root privileges

Traveling Waves for the Generalized Sinh-Gordon Equation with Variable Coefficients

The sinh-Gordon equation is simply the classical wave equation with a nonlinear sinh source term. It arises in diverse scientific applications including differential geometry theory, integrable quantum field theory, fluid dynamics, kink dynamics, and statistical mechanics. It can be used to describe generic properties of string dynamics for strings and multi-strings in constant curvature space. In the present paper, we study a generalized sinh-Gordon equation with variable coefficients with the goal of obtaining analytical traveling wave solutions. Our results show that the traveling waves of the variable coefficient sinh-Gordon equation can be derived from the known solutions of the standard sinh-Gordon equation under a specific selection of a choice of the variable coefficients. These solutions include some real single and multi-solitons, periodic waves, breaking kink waves, singular waves, periodic singular waves, and compactons. These solutions might be valuable when scientists model some real-life phenomena using the sinh-Gordon equation where the balance between dispersion and nonlinearity is perturbed

Traveling Waves for the Generalized Sinh-Gordon Equation with Variable Coefficients

The sinh-Gordon equation is simply the classical wave equation with a nonlinear sinh source term. It arises in diverse scientific applications including differential geometry theory, integrable quantum field theory, fluid dynamics, kink dynamics, and statistical mechanics. It can be used to describe generic properties of string dynamics for strings and multi-strings in constant curvature space. In the present paper, we study a generalized sinh-Gordon equation with variable coefficients with the goal of obtaining analytical traveling wave solutions. Our results show that the traveling waves of the variable coefficient sinh-Gordon equation can be derived from the known solutions of the standard sinh-Gordon equation under a specific selection of a choice of the variable coefficients. These solutions include some real single and multi-solitons, periodic waves, breaking kink waves, singular waves, periodic singular waves, and compactons. These solutions might be valuable when scientists model some real-life phenomena using the sinh-Gordon equation where the balance between dispersion and nonlinearity is perturbed

Cyberbullying detection framework for short and imbalanced Arabic datasets

Cyberbullying detection has attracted many researchers to detect negative comments deployed on communication platforms as cyberbullying can take many forms: verbal, implicit, explicit, or even nonverbal. The successful growth of social media in recent years has opened new perspectives on the detection of cyberbullying, although related research still encounters several challenges, such as data imbalance and expression implicitness. In this paper, we propose an automated cyberbullying detection framework designed to produce satisfactory results, especially when imbalanced short text and different dialects exist in the Arabic text data. In the proposed framework a new method to solve the imbalance problem is suggested, where the modified simulated annealing optimization algorithm is used to find the optimal set of samples from the majority class to balance the training set. This method has been evaluated using traditional machine learning algorithms including support vector machine, and deep learning algorithms including Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). To generate a framework that can detect Arabic written cyberbullying on communication platforms, the accuracy, recall, specificity, sensitivity and mean squared error are used as the main performance indicators. The results indicate that the proposed framework can improve the performance of the tested algorithms, and Bi-LSTM outperforms other methods for cyberbullying classification