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The malware can threaten personal privacy by opening backdoors for attackers to access user passwords, IP addresses, banking information, and other personal data, whilst some malware extracts personal data and sends them to people unknown to the users. In this chapter, the authors will present recent case studies and discuss the privacy and security threats associated with different types of malwares. The small medium enterprises (SMEs) have a unique working model forming the backbone of the UK economy and malware affects SMEs’ organizations. Also, the use of Artificial Intelligence (AI) as both an offense and defense mechanism, for the hacker, and the end user will be investigated further. In conclusion, finding a balance between IT expertise and the costs of products that are able to help SMEs protect and secure their data will benefit the SMEs by using a more intelligent controlled environment with applied machine learning techniques and not compromising on costs will be discussed.

Fusobacterium necrophorum causes a range of mild to life threatening infections and there is uncertainty in terms of diagnosis and treatment due to the lack of knowledge on their pathogenic mechanisms. This study characterised genomes of F. necrophorum to compare their virulence factors and investigate potential infection markers. 27 isolates of F. necrophorum from patients with pharyngotonsillitis were subjected to whole genome sequencing and compared with 42 genomes published in the NCBI database. Phylogenomics, pangemome, pan-GWAS and virulome were analysed to study strain variations with reference to virulence factors. Core genome based phylogenomic tree exhibited three clades of which Clade A belonged to F. necrophorum subsp necrophorum, clades B and C were F. necrophorum subsp funduliforme. Pan-GWAS and Pan-Virulome suggest some marker genes associated with clinical sources of isolation that needs further validation. Our study highlights some interesting features of the pathogenesis of F. necrophorum infections. Although the animal isolate genomes had some marker genes, the genomes of human isolates did not exhibit clear correlation to their clinical sources of isolation. This prompts to think of other mechanisms such as co-infections or host factors that can be involved in the pathogenesis.

BackgroundVarious complications have been reported in patients with COVID-19 including pneumomediastinum.MethodsThe primary objective of the study was to determine the incidence of pneumomediastinum in COVID-19 positive patients who underwent CT pulmonary angiography (CTPA). The secondary objectives were to analyse if the incidence of pneumomediastinum changed between March and May 2020 (peak of the first wave in the UK) and January 2021 (peak of the second wave in the UK) and to determine the mortality rate in patients with pneumomediastinum. We undertook an observational, retrospective, single-centre, cohort study of patients with COVID-19 admitted to Northwick Park Hospital.Results74 patients in the first wave and 220 patients in the second wave met the study criteria. Two patients during the first wave and eleven patients during the second wave developed pneumomediastinum.ConclusionsThe incidence of pneumomediastinum changed from 2.7% during the first wave to 5% during the second wave and this change was not statistically significant (p value 0.4057). The difference in mortality rates of patients with pneumomediastinum in both waves of COVID-19 (69.23%) versus patients without pneumomediastinum in both waves of COVID-19 (25.62%) was statistically significant (p value 0.0005). Many patients with pneumomediastinum were ventilated, which could be a confounding factor. When controlling for ventilation, there was no statistically significant difference in the mortality rates of ventilated patients with pneumomediastinum (81.81%) versus ventilated patients without pneumomediastinum (59.30%) (p value 0.14).

A growing trend over the last few years is storage outsourcing, where the concept of third-party data warehousing has become more popular. This trend prompts several interesting privacy and security issues. One of the biggest concerns with third-party data storage providers is accountability. This article, critically reviews two schemas/algorithms that allow users to check the integrity and availability of their outsourced data on untrusted data stores (i.e., third-party data storages). The reviewed schemas are provable data possession (PDP) and proofs of retrievability (POR). Both are cryptographic protocols designed to provide clients the assurance that their data are secure on the untrusted data storages. Furthermore, a conceptual framework is proposed to mitigate the weaknesses of the current storage solutions.

Brain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an essential step in diagnosis and treatment planning to maximize the likelihood of successful treatment. Magnetic resonance imaging (MRI) provides detailed information about brain tumor anatomy, making it an important tool for effective diagnosis which is requisite to replace the existing manual detection system where patients rely on the skills and expertise of a human. In order to solve this problem, a brain tumor segmentation &amp, detection system is proposed where experiments are tested on the collected BraTS 2018 dataset. This dataset contains four different MRI modalities for each patient as T1, T2, T1Gd, and FLAIR, and as an outcome, a segmented image and ground truth of tumor segmentation, i.e., class label, is provided. A fully automatic methodology to handle the task of segmentation of gliomas in pre-operative MRI scans is developed using a U-Net-based deep learning model. The first step is to transform input image data, which is further processed through various techniques—subset division, narrow object region, category brain slicing, watershed algorithm, and feature scaling was done. All these steps are implied before entering data into the U-Net Deep learning model. The U-Net Deep learning model is used to perform pixel label segmentation on the segment tumor region. The algorithm reached high-performance accuracy on the BraTS 2018 training, validation, as well as testing dataset. The proposed model achieved a dice coefficient of 0.9815, 0.9844, 0.9804, and 0.9954 on the testing dataset for sets HGG-1, HGG-2, HGG-3, and LGG-1, respectively.

This extended abstract contains the ?nding of two broad studies. Each study examines ideas of the challenges arising from aspects of cyber security in using Intrusion Detection mechanisms together with Arti?cial Intelligence (AI) and Machine Learning techniques in the protection of small and medium enterprises (SMEs) in the UK, for its operations and commercial environment. Both papers explored intrusion, detection, and prevention methods as a priority in the realisation of keeping their data secure and safe with the integration of real-world objects within the internet such as Machine Learning techniques and AI. Both papers go on to then establish the various methods of improving its cyber security.

This paper outlines a multidisciplinary collaboration between FIC (Cardiff School of Sport and Health Science), Welsh Centre for Tourism Research (WCTR) (Cardiff School of Management) and Cardiff School of Technologies, funded by the Global Academies R&I Development Fund on the potential application of Artificial Intelligence (AI) and Machine Learning (ML) for assessing hand hygiene standards in food manufacturing, hospitality and catering environments.

Management Information Systems and Cyber Security in Government, Public and Private Institutions Comparison of Developing and Developed Countries

Enhance The Understanding of IoT Threats on The Security and Privacy of End Users

Cyber security has made an impact and has challenged Small and Medium Enterprises (SMEs) in their approaches towards how they protect and secure data. With an increase in more wired and wireless connections and devices on SME networks, unpredictable malicious activities and interruptions have risen. Finding the harmony between the advancement of technology and costs has always been a balancing act particularly in convincing the finance directors of these SMEs to invest in capital towards their IT infrastructure. This paper looks at various devices that currently are in the market to detect intrusions and look at how these devices handle prevention strategies for SMEs in their working environment both at home and in the office, in terms of their credibility in handling zero-day attacks against the costs of achieving so. The experiment was set up during the 2020 pandemic referred to as COVID-19 when the world experienced an unprecedented event of large scale. The operational working environment of SMEs reflected the context when the UK went into lockdown. Pre-pandemic would have seen this experiment take full control within an operational office environment, however, COVID-19 times has pushed us into a corner to evaluate every aspect of cybersecurity from the office and keeping the data safe within the home environment. The devices chosen for this experiment were OpenSource such as SNORT and pfSense to detect activities within the home environment, and Cisco, a commercial device, set up within an SME network. All three devices operated in a live environment within the SME network structure with employees being both at home and in the office. All three devices were observed from the rules they displayed, their costs and machine learning techniques integrated within them. The results revealed these aspects to be important in how they identified zero-day attacks. The findings showed that OpenSource devices whilst free to download, required a high level of expertise in personnel to implement and embed machine learning rules into the business solution even for staff working from home. However, when using Cisco, the price reflected the buy-in into this expertise and Cisco’s mainframe network, to give up-to-date information on cyber-attacks. The requirements of the UK General Data Protection Regulations Act (GDPR) were also acknowledged as part of the broader framework of the study. Machine learning techniques such as anomaly-based intrusions did show better detection through a commercially subscription-based model for support from Cisco compared to that of the OpenSource model which required internal expertise in machine learning. A cost model was used to compare the outcome of SMEs’ decision making, in getting the right framework in place in securing their data. In conclusion, finding a balance between IT expertise and costs of products that are able to help SMEs protect and secure their data will benefit the SMEs from using a more intelligent controlled environment with applied machine learning techniques, and not compromising on costs.

E-Commerce applications provide an added advantage to customer to buy product with added suggestions in the form of reviews. Obviously, reviews are useful and impactful for customers those are going to a buy product. But these enormous amount of reviews create problem also for customers as they are not able to segregate useful ones. Therefore, there is a need for an approach which will showcase only relevant reviews to the customers. This same problem has been attempted in this research paper as this is a less explored area. Pairwise Review relevance ranking method is proposed in this research paper. This approach will sort reviews based on their relevance with the product and avoid showing irrelevant reviews. This work has been done in three phases- feature extraction, pairwise review ranking, and classification. The outcome is sorted list of reviews, review ranking accuracy and classification accuracy. Four classifiers- SVM, Random forest, Neural network, and logistic regression have been applied to validate ranking accuracy. Out of all four applied classification models, Random forest gives the best result. our proposed system is able to achieve 99.76% classification accuracy and 99.56% ranking accuracy for a complete dataset using random forest.

Pseudomonas aeruginosa ATCC 9027 was isolated in 1943 from a case of otitis externa and is commonly employed as a quality control strain for sterility, assessment of antibiofilm agents, and in vitro study of wound infection. Here, we present the 6.34-Mb draft genome sequence and highlight some pertinent genes that are associated with virulence.

Pseudomonas aeruginosa ATCC 9027 was isolated in 1943 from a case of otitis externa and is commonly employed as a quality control strain for sterility, assessment of antibiofilm agents, and in vitro study of wound infection. Here, we present the 6.34-Mb draft genome sequence and highlight some pertinent genes that are associated with virulence.

A challenging problem for e-assessment is automatic marking of diagrams. There are a number of difficulties not least that much of the meaning of a diagram resides in the labels and hence label matching is an important process in the e-assessment of diagrams. Previous research has shown that the labels used by the students in the diagrams can be diverse and imprecise which makes this problematic.In this paper we propose and evaluate a new method for label matching to support e-assessment of diagrams and address problems of synonyms, spelling errors and differing levels of decomposition.We have implemented the syntactic part of our method and evaluated it using 160 undergraduate assessments based upon a UML design task. We have found that our method performs better than the other syntax matching algorithms. This framework has significant implications for the ease in which we may develop future e-assessment systems. The results from this pilot study have been encouraging and motivate us to implement...

In this article we explore a problematic aspect of automated assessment of diagrams. Diagrams have partial and sometimes inconsistent semantics. Typically much of the meaning of a diagram resides in the labels; however, the choice of labeling is largely unrestricted. This means a correct solution may utilize differing yet semantically equivalent labels to the specimen solution. With human marking this problem can be easily overcome. Unfortunately with e-assessment this is challenging. We empirically explore the scale of the problem of synonyms by analyzing 160 student solutions to a UML task. From this we find that cumulative growth of synonyms only shows a limited tendency to reduce at the margin despite using a range of text processing algorithms such as stemming and auto-correction of spelling errors. This finding has significant implications for the ease in which we may develop future e-assessment systems of diagrams, in that the need for better algorithms for assessing label semantic similarity becomes inescapable.

This paper compares two different approaches of teaching introductory programming by quantitatively analysing the student assessments in a real classroom. The first approach is to emphasise the principles of object-oriented programming and design using Java from the very beginning. The second approach is to first teach the basic programming concepts (loops, branch, and use of libraries) using Python and then move on to oriented programming using Java. Each approach was adopted for one academic year (2008-09 and 2009-10) with first year undergraduate students. Quantitative analysis of the student assessments from the first semester of each year was then carried out. The results of this analysis are presented in this paper. These results suggest that the later approach leads to enhanced learning of introductory programming concepts by students.