Phishing Attack: Raising Awareness and Protection Techniques

Phishing is a form of social engineering attack that can be used to steal sensitive and vital information and details from unsuspecting entities which could either be organizations or individuals. This paper gives a review on how phishing attacks are carried out and the protection techniques involved in defending against such attacks and how to raise awareness about such attacks in Bournemouth University using the MINDSPACE framework. The protection techniques would be classified into three layers namely; automated tools, training and knowledge, and multifactor authentication. The awareness would be raised using the MINDSPACE framework and it revealed that about 50% of the students approached were ignorant of the phishing attack and the tactics used to carry the attack out.

Syntactic complexity in Finnish-background EFL learners’ writing at CEFR levels A1–B2

The increasing importance of the Common European Framework of Reference (CEFR) has led to research on the linguistic characteristics of its levels, as this would help the application of the CEFR in the design of teaching materials, courses, and assessments. This study investigated whether CEFR levels can be distinguished with reference to syntactic complexity (SC). 14- and 17-year-old Finnish learners of English (N=397) wrote three writing tasks which were rated against the CEFR levels. The ratings were analysed with multi-facet Rasch analysis and the texts were analysed with automated tools. Findings suggest that the clearest separators at lower CEFR levels (A1–A2) were the mean sentence and T-unit length, variation in sentence length, infinitive density, clauses per sentence or T-unit, and verb phrases per T-unit. For higher levels (B1–B2) they were modifiers per noun phrase, mean clause length, complex nominals per clause, and left embeddedness. The results support previous findings that the length of and variation in the longer production units (sentences, T-units) are the SC indices that most clearly separate the lower CEFR levels, whereas the higher levels are best distinguished in terms of complexity at the clausal and phrasal levels.

Fatquant: An automated image analysis tool for identification of fat cells in heterogeneous hematoxylin and eosin tissue sections.

Hematoxylin and eosin (H and E) is one of the common histological staining techniques that provides information on the tissue cytoarchitecture. Adipose (fat) cells accumulation in pancreas has been shown to impact beta cell survival and its endocrine function. The current automated tools available for fat analysis are suited for white adipose tissue which is homogeneous and easier to segment unlike heterogeneous tissues such as pancreas where fat cells continue to play critical physiopathological functions. In the current study, we present an automated fat analysis tool, Fatquant, where mathematical formula to calculate diagonal of a square drawn inside circle is utilized for identification and analysis of fat cells in heterogeneous H and E tissue sections. Using histological images of pancreas from a publicly available database, we show an area accuracy overlap of 89-93% between manual versus automated algorithm based fat cell detection.

Deep Learning-Based Transfer Learning for Classification of Skin Cancer

One of the major health concerns for human society is skin cancer. When the pigments producing skin color turn carcinogenic, this disease gets contracted. A skin cancer diagnosis is a challenging process for dermatologists as many skin cancer pigments may appear similar in appearance. Hence, early detection of lesions (which form the base of skin cancer) is definitely critical and useful to completely cure the patients suffering from skin cancer. Significant progress has been made in developing automated tools for the diagnosis of skin cancer to assist dermatologists. The worldwide acceptance of artificial intelligence-supported tools has permitted usage of the enormous collection of images of lesions and benevolent sores approved by histopathology. This paper performs a comparative analysis of six different transfer learning nets for multi-class skin cancer classification by taking the HAM10000 dataset. We used replication of images of classes with low frequencies to counter the imbalance in the dataset. The transfer learning nets that were used in the analysis were VGG19, InceptionV3, InceptionResNetV2, ResNet50, Xception, and MobileNet. Results demonstrate that replication is suitable for this task, achieving high classification accuracies and F-measures with lower false negatives. It is inferred that Xception Net outperforms the rest of the transfer learning nets used for the study, with an accuracy of 90.48. It also has the highest recall, precision, and F-Measure values.

Automated landmarking of bends in vascular structures: a comparative study with application to the internal carotid artery

Automated tools for landmarking the internal carotid artery (ICA) bends have the potential for efficient and objective medical image-based morphometric analysis. The two existing algorithms rely on numerical approximations of curvature and torsion of the centerline. However, input parameters, original source code, comparability, and robustness of the algorithms remain unknown. To address the former two, we have re-implemented the algorithms, followed by sensitivity analyses. Of the input parameters, the centerline smoothing had the least impact resulting in 6–7 bends, which is anatomically realistic. In contrast, centerline resolution showed to completely over- and underestimated the number of bends varying from 3 to 33. Applying the algorithms to the same cohort revealed a variability that makes comparison of results between previous studies questionable. Assessment of robustness revealed how one algorithm is vulnerable to model smoothness and noise, but conceptually independent of application. In contrast, the other algorithm is robust and consistent, but with limited general applicability. In conclusion, both algorithms are equally valid albeit they produce vastly different results. We have provided a well-documented open-source implementation of the algorithms. Finally, we have successfully performed this study on the ICA, but application to other vascular regions should be performed with caution.

Bio-Inspired Computing-A Dive into Critical Problems, Potential Architecture and Techniques

The integration and development of electronics in the recent years have impacted a major development on the world and humans, one among that is nanotechnology. Nanotechnology has achieved a greater progress in biomedical engineering in diagnosis and treatment, leading to the introduction of nanomaterials for drug delivery, prostheses and implanting. This work describes the Bio-Nano-tools that are developed based on iron oxide properties, automated tools used in the tumor detection, satin bowerbird optimization (SBO) technique employed in diagnosis of skin cancer. This work also highlights the post introduction development of nanomaterials like combination of nanotechnology with Artificial Intelligence (AI) and its impact, advancement of nanomaterials based on their operations, shapes and characteristics that leading to the growth of nanostructures with operations control properties. The paper also highlights the improvement of silicon neuromorphic photonic processors and parallel simulators in the development of bio inspired computing. We are hopeful that this review article provides future directions in Bio-Inspired Computing. HIGHLIGHTS In processing of medical images, noise plays a challenging role. So, reduction of noise is important, with the data that is analyzed in our review, it is shown that noise reduction can be achieved using Gradient and Feature Adaptive Contour (GFAC) model, with effective results There are many algorithms that are used for skin cancer detection, as highlighted in our review. Amongst all the methods, the particle swarm optimization (PSO) algorithm shows impressive results when compared to other models in terms of feature extraction in dermoscopy images Satin bowerbird optimization (SBO) algorithm helps in improving the CNN efficiency. The optimal justification of the hyper parameter numbers in convolutional neural network (CNN) for skin cancer diagnosis can be achieved using an SBO algorithm

Bibliometrics of Machine Learning Research Using Homomorphic Encryption

Since the first fully homomorphic encryption scheme was published in 2009, many papers have been published on fully homomorphic encryption and its applications. Machine learning is one of the most interesting applications and has drawn a lot of attention from researchers. To better represent and understand the field of Homomorphic Encryption in Machine Learning (HEML), this paper utilizes automated citation and topic analysis to characterize the HEML research literature over the years and provide the bibliometrics assessments for this burgeoning field. This is conducted by using a bibliometric statistical analysis approach. We make use of web-based literature databases and automated tools to present the development of HEML. This allows us to target several popular topics for in-depth discussion. To achieve these goals, we have chosen the well-established Scopus literature database and analyzed them through keyword counts and Scopus relevance searches. The results show a relative increase in the number of papers published each year that involve both homomorphic cryptography and machine learning. Using text mining of articles titles, we have found that cloud computing is a popular topic in this field, which also includes neural networks, big data, and the Internet of Things. The analysis results show that China, the US, and India have generated almost half of all the research contributions in HEML. The citation statistics, keyword statistics, and topic analyses give us a quick overview of the development of the field, which can be of great help to new researchers. It is also possible to apply our methodology to other research areas, and we see great value in this approach.

Structural Models of Terminological Word Combinations for Marking up a Corpus of Scientific and Technical Texts

The article presents structural models of terminological phrases from the subject area “Welding” as the basis for creating automated tools to mark up the corpus of scientific and technical texts. The place of scientific and technical corpora in corpus linguistics and the prospects for their further research are outlined. The relevance of the research stems from the need to create corpora of scientific and technical texts in general and to provide tools for automatic detection of terms in particular. It is substantiated that the main problem in designing such corpora is the automatic markup of terminological phrases. The analysis of the current state of the term system of the subject area “Welding” has been carried out. The results of the analysis of two-, three-, four- and five-component terminological phrases of “Welding” and their structural models are presented and illustrated by examples. The necessity of listing all possible structural models of terminological combinations has been substantiated too. It has been established that the addition of a new component to the basic terminological combination most often occurs with introduction of one more postpositional at-tribute whose function is to add some specific feature to the basic meaning. The novelty of the study is seen in providing a theoretical approach for the formation of a database of structural models of terminological phrases which may be used as a core of a supersource database on the structure of the multicomponent scientific and technical terms. An approach to automatic markup of multicomponent terms is proposed too. It will be also helpful in future corpus research for identification of candidate word combinations as scientific and technical terms.