Machine Learning Models for Educational Platforms

Scaling up education online and onlife is presenting numerous key challenges, such as hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely. However, thanks to the wider availability of learning-related data and increasingly higher performance computing, Artificial Intelligence has the potential to turn such challenges into an unparalleled opportunity. One of its sub-fields, namely Machine Learning, is enabling machines to receive data and learn for themselves, without being programmed with rules. Bringing this intelligent support to education at large scale has a number of advantages, such as avoiding manual error-prone tasks and reducing the chance that learners do any misconduct. Planning, collecting, developing, and predicting become essential steps to make it concrete into real-world education. This thesis deals with the design, implementation, and evaluation of Machine Learning models in the context of online educational platforms deployed at large scale. Constructing and assessing the performance of intelligent models is a crucial step towards increasing reliability and convenience of such an educational medium. The contributions result in large data sets and high-performing models that capitalize on Natural Language Processing, Human Behavior Mining, and Machine Perception. The model decisions aim to support stakeholders over the instructional pipeline, specifically on content categorization, content recommendation, learners’ identity verification, and learners’ sentiment analysis. Past research in this field often relied on statistical processes hardly applicable at large scale. Through our studies, we explore opportunities and challenges introduced by Machine Learning for the above goals, a relevant and timely topic in literature. Supported by extensive experiments, our work reveals a clear opportunity in combining human and machine sensing for researchers interested in online education. Our findings illustrate the feasibility of designing and assessing Machine Learning models for categorization, recommendation, authentication, and sentiment prediction in this research area. Our results provide guidelines on model motivation, data collection, model design, and analysis techniques concerning the above applicative scenarios. Researchers can use our findings to improve data collection on educational platforms, to reduce bias in data and models, to increase model effectiveness, and to increase the reliability of their models, among others. We expect that this thesis can support the adoption of Machine Learning models in educational platforms even more, strengthening the role of data as a precious asset. The thesis outputs are publicly available at https://www.mirkomarras.com

The Threat of Offensive AI to Organizations

AI has provided us with the ability to automate tasks, extract information from vast amounts of data, and synthesize media that is nearly indistinguishable from the real thing. However, positive tools can also be used for negative purposes. In particular, cyber adversaries can use AI to enhance their attacks and expand their campaigns. Although offensive AI has been discussed in the past, there is a need to analyze and understand the threat in the context of organizations. For example, how does an AI-capable adversary impact the cyber kill chain? Does AI benefit the attacker more than the defender? What are the most significant AI threats facing organizations today and what will be their impact on the future? In this study, we explore the threat of offensive AI on organizations. First, we present the background and discuss how AI changes the adversary’s methods, strategies, goals, and overall attack model. Then, through a literature review, we identify 32 offensive AI capabilities which adversaries can use to enhance their attacks. Finally, through a panel survey spanning industry, government and academia, we rank the AI threats and provide insights on the adversaries

Transdisciplinary AI Observatory -- Retrospective Analyses and Future-Oriented Contradistinctions

In the last years, AI safety gained international recognition in the light of heterogeneous safety-critical and ethical issues that risk overshadowing the broad beneficial impacts of AI. In this context, the implementation of AI observatory endeavors represents one key research direction. This paper motivates the need for an inherently transdisciplinary AI observatory approach integrating diverse retrospective and counterfactual views. We delineate aims and limitations while providing hands-on-advice utilizing concrete practical examples. Distinguishing between unintentionally and intentionally triggered AI risks with diverse socio-psycho-technological impacts, we exemplify a retrospective descriptive analysis followed by a retrospective counterfactual risk analysis. Building on these AI observatory tools, we present near-term transdisciplinary guidelines for AI safety. As further contribution, we discuss differentiated and tailored long-term directions through the lens of two disparate modern AI safety paradigms. For simplicity, we refer to these two different paradigms with the terms artificial stupidity (AS) and eternal creativity (EC) respectively. While both AS and EC acknowledge the need for a hybrid cognitive-affective approach to AI safety and overlap with regard to many short-term considerations, they differ fundamentally in the nature of multiple envisaged long-term solution patterns. By compiling relevant underlying contradistinctions, we aim to provide future-oriented incentives for constructive dialectics in practical and theoretical AI safety research

Critically Envisioning Biometric Artificial Intelligence in Law Enforcement

This report presents an overview of the Critically Exploring Biometric AI Futures project led by the University of Edinburgh in partnership with the University of Stirling. This short 3-month project explored the use of new Biometric Artificial Intelligence (AI) in law enforcement, the challenges of fostering trust around deployment and debates surrounding social, ethical and legal concerns

Telecommunication Network Security

YesOur global age is practically defined by the ubiquity of the Internet; the worldwide interconnection of cyber networks that facilitates accessibility to virtually all ICT and other elements of critical infrastructural facilities, with a click of a button. This is regardless of the user’s location and state of equilibrium; whether static or mobile. However, such interconnectivity is not without security consequences. A telecommunication system is indeed a communication system with the distinguishing key word, the Greek tele-, which means "at a distance," to imply that the source and sink of the system are at some distance apart. Its purpose is to transfer information from some source to a distant user; the key concepts being information, transmission and distance. These would require a means, each, to send, convey and receive the information with safety and some degree of fidelity that is acceptable to both the source and the sink. Chapter K begins with an effort to conceptualise the telecommunication network security environment, using relevant ITU-T2* recommendations and terminologies for secure telecommunications. The chapter is primarily concerned with the security aspect of computer-mediated telecommunications. Telecommunications should not be seen as an isolated phenomenon; it is a critical resource for the functioning of cross-industrial businesses in connection with IT. Hence, just as information, data or a computer/local computer-based network must have appropriate level of security, so also a telecommunication network must have equivalent security measures; these may often be the same as or similar to those for other ICT resources, e.g., password management. In view of the forgoing, the chapter provides a brief coverage of the subject matter by first assessing the context of security and the threat-scape. This is followed by an assessment of telecommunication network security requirements; identification of threats to the systems, the conceivable counter or mitigating measures and their implementation techniques. These bring into focus various cryptographic/crypt analytical concepts, vis a vis social engineering/socio-crypt analytical techniques and password management. The chapter noted that the human factor is the most critical factor in the security system for at least three possible reasons; it is the weakest link, the only factor that exercises initiatives, as well as the factor that transcends all the other elements of the entire system. This underscores the significance of social 2*International Telecommunications Union - Telecommunication Standardisation Sector 12 engineering in every facet of security arrangement. It is also noted that password security could be enhanced, if a balance is struck between having enough rules to maintain good security and not having too many rules that would compel users to take evasive actions which would, in turn, compromise security. The chapter is of the view that network security is inversely proportional to its complexity. In addition to the traditional authentication techniques, the chapter gives a reasonable attention to locationbased authentication. The chapter concludes that security solutions have a technological component, but security is fundamentally a people problem. This is because a security system is only as strong as its weakest link, while the weakest link of any security system is the human infrastructure. A projection for the future of telecommunication network security postulates that, network security would continue to get worse unless there is a change in the prevailing practice of externality or vicarious liability in the computer/security industry; where consumers of security products, as opposed to producers, bear the cost of security ineffectiveness. It is suggested that all transmission devices be made GPS-compliant, with inherent capabilities for location-based mutual authentication. This could enhance the future of telecommunication security.Petroleum Technology Development Fun