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Recognizing named entities like Person, Organization, Locations and Date are very useful for web mining. Named Entity Recognition (NER) is an emerging research area which aims to address problems such as Machine Translation, Question Answering Systems and Semantic Web Search. The study focuses on proposing a methodology based on the integration of an NER system and Text Analytics to provide information necessary for business in Additive Manufacturing. The study proposes a foundation of utilizing the Stanford NER system for tagging news data related to the keywords “Additive Manufacturing”. The objective is to first derive the organization names from news data. This information is useful to define the digital footprints of an organization in the Additive Manufacturing sector. The existence of an organization derived using the NER approach is validated by matching their names with companies listed on the Companies House portal. The organization names will be matched using a Fuzzy-based text matching algorithm. Further information on company profile, officers and key financial data is extracted to provide information about companies interested and working within the Additive Manufacturing sector. This data gives an insight into which companies have digital footprints in the Additive Manufacturing sector within the UK.

Ransomware is a continuing threat and has resulted in the battle between the development and detection of new techniques. Detection and mitigation systems have been developed and are in wide-scale use; however, their reactive nature has resulted in a continuing evolution and updating process. This is largely because detection mechanisms can often be circumvented by introducing changes in the malicious code and its behaviour. In this paper, we demonstrate a classification technique of integrating both static and dynamic features to increase the accuracy of detection and classification of ransomware. We train supervised machine learning algorithms using a test set and use a confusion matrix to observe accuracy, enabling a systematic comparison of each algorithm. In this work, supervised algorithms such as the Naive Bayes algorithm resulted in an accuracy of 96% with the test set result, SVM 99.5%, random forest 99.5%, and 96%. We also use Youden’s index to determine sensitivity and specificity.

In this chapter, we explore the meaning of the term “global graduate” and discuss the multifaceted approach taken by the School of Computing and Engineering at the University of Huddersfield, UK, to expose students to a range of experiences and opportunities in order to facilitate their development. Specifically, we focus on both the opportunities available and the challenges faced by examining the different ways that the School works with its students. We highlight some of the key areas in which students are exposed to globalisation, through its award-winning placements unit, and cross-curriculum team challenges led by global organisations. Our discussion draws on current literature and the experience and expertise of academic and professional services staff working with the students. In order to capture the students’ voice, we have undertaken a focus group of both national and international students. The approach taken in gathering student opinion on how well the University encourages and supports students to be global graduates is presented together with a summary of its findings.

With organisations and governments significantly investing in cyber de-fenses, there is a significant need to develop tools and technologies to help analystsunderstand cyber security within their domain of application. A critical aspect ofthis is to develop and maintain Situation Awareness of security aspects within cyberinfrastructures. Visual analytics provides much support to analysts to help under-stand evolving situations and overall status of systems and situations, particularlywhen dealing with the challenge of dealing with massive volumes of data. Thischapter explores situation awareness in cyber security in more details, aligning de-sign recommendations for visual analytics to help analysts with progressive levelsof situation awareness

Intelligent agents solving problems in the real world require domain models containing widespread knowledge of the world.