Nowadays, the rapid growth of Internet applications open the doors for people to communicate and do business around the world and thus saving time, efforts and money. However, the success of these applications is based on protecting the data from hackers. It is well known that preventing sensitive data from hackers while they are sent through the global network is a big challenge. There are many techniques used for securing data. Some of these techniques are based on encrypting a readable text into an unreadable text using mathematical operations, while other techniques are based on encrypting a readable text into an image (e.g., the Text-to-Image Encryption algorithm – TTIE) or into musical notes using an encryption key. The encryption key must be secure and should not be sent through the Internet. This paper proposes adding a new security level to the TTIE algorithm, and demonstrates how the encryption key produced by the TTIE algorithm is exchanged with the other party using the Diffie Hellman technique. Thus, this paper proposes a modified TTIE algorithm called the Diffie Hellman Text-to-Image Encryption algorithm (DHTTIE), and tests and analyses the proposed algorithm.

Purpose: Ethnic minority entrepreneurs (EMEs) are traditionally associated with lower growth industry sectors. The purpose of this paper is to draw on the theory of mixed embeddedness to determine if more recent EMEs have been able to break out of lower growth sectors and if break out varies across ethnic minority groups. It also compares entrepreneurial quality in terms of weekly hours worked, weekly earnings and job satisfaction. Design/methodology/approach: Quantitative inferential statistical analysis is undertaken on data drawn from the large scale, social sciences data set for the UK, Understanding Society. Findings: The study finds that break out is not associated with being a recent EME but does vary across ethnic minority groups. Break out is found to be associated with gender, education, English language proficiency and occupational status. Some variation in entrepreneurial quality is found for both recent EMEs and across ethnic minority groups. Practical implications: Understanding the nature and quality of ethnic minority entrepreneurship is important since it informs public debate about migration, informs policy and shapes activities of future EMEs. Originality/value: The study provides a theoretically grounded interpretation of the explanatory variables associated with EME break out and entrepreneurial quality. Second, it provides a large confirmatory study of break out and finally, it also finds an important empirical nuance to the concept of opportunity structure by identifying a variation over time in both external and socio-demographic factors.

This exploratory, qualitative study considers how online home-based businesses offer opportunities for ethnic entrepreneurs to ‘break out’ of t

With a large number of Local Binary Patterns (LBP) variants being currently used today, the significant and importance of visual descriptors in computer vision applications are prominent. This paper presents a novel visual descriptor, i.e., SIM-LBP. It employs a new matrix technique called the Symmetric Inline Matrix generator method, which acts as a new variant of LBP. The key feature that separates our variant from existing counterparts is that our variant is very efficient in extracting facial expression features like eyes, eye brows, nose and mouth in a wide range of lighting conditions. For testing our model, we applied SIM-LBP on the JAFFE dataset to convert all the images to its corresponding SIM-LBP transformed variant. These transformed images are then used to train a Convolution Neural Network (CNN) based deep learning model for facial expressions recognition (FER). Several performance evaluation metrics, i.e., recognition accuracy rate, precision, recall, and F1-score, were used to test mode efficiency in comparison with those using the traditional LBP descriptor and other LBP variants. Our model outperformed in all four matrices with the proposed SIM-LBP transformation on the input images against those of baseline methods. In comparison analysis with the other state-of-the-art methods, it shows the usefulness of the proposed SIM-LBP model. Our proposed SIM-LBP variant transformation can also be applied on facial images to identify a person’s mental states and predict mood variations.

The “problem of e-books” is defined as the difficulty improving the adoption rates of e-books by students. The adoption rates of e-books for academic use remain low, and research into the reasons for this have resulted in inconclusive findings. Factors such as student perception, and variations in experimental methodology and technology, contribute to difficulties in generalising findings and establishing conclusive causes for this problem. To better understand the causal factors for low adoption rates and the student's experience with ereaders and digital text, an investigation was conducted by the lead researcher as a student enrolled in a postgraduate course. The experiment was designed using e-book and digital text documents on an ereader for academic study and the results analysed with the framework of Activity Theory. This methodology allowed exploration of the problem within the authentic experience of a student to examine the effects of this social environment on ereader and e-book use.