arXiv subjects

Ievgeniia Kuzminykh

Publications and source records attributed to Ievgeniia Kuzminykh.

9 recordsLinked to original sources

arXiv2026

Understanding the Skills Gap between Higher Education Institutions and the Software Engineering Industry

In the rapidly evolving field of software engineering, the skills required of graduates entering the job market are constantly changing. Several studies have identified a gap between the skills taught in university curricula and those demanded by the software engineering industry. This chapter investigates the technical skill and expertise gap between higher education institutions (HEIs) and the UK software engineering industry by mapping job descriptions to the skills included in computer science degree programmes. A custom web scraping and text analysis tool, utilising fuzzy matching, was developed to extract and categorise skills from 300 job postings and undergraduate curricula from 30 UK universities. The analysis showed that the curricula place a strong emphasis on Programming Languages (18%) and Database Management (12.83%). In contrast, the industry s most frequently requested skill category is Software Design and Planning, which appears in approximately 88.68% of job descriptions, highlighting its critical importance. General Programming Language and System Structures also show strong demand, present in over 78.30% and 66.04% of postings, respectively. The mapping indicates that areas such as System Structures and Software Domains are significantly underrepresented in curricula, while Database Management and Compiler Design may be overemphasised. These insights can support HEIs in aligning their programmes with industry needs, supporting the preparation of graduates for dynamic careers in software engineering.

cs.SE↗

arXiv2025

Generative AI-Enabled Adaptive Learning Platform: How I Can Help You Pass Your Driving Test?

This study aims to develop an adaptive learning platform that leverages generative AI to automate assessment creation and feedback delivery. The platform provides self-correcting tests and personalised feedback that adapts to each learners progress and history, ensuring a tailored learning experience. The study involves the development and evaluation of a web-based application for revision for the UK Driving Theory Test. The platform generates dynamic, non-repetitive question sets and offers adaptive feedback based on user performance over time. The effectiveness of AI-generated assessments and feedback is evaluated through expert review and model analysis. The results show the successful generation of relevant and accurate questions, alongside positive and helpful feedback. The personalised test generation closely aligns with expert-created assessments, demonstrating the reliability of the system. These findings suggest that generative AI can enhance learning outcomes by adapting to individual student needs and offering tailored support. This research introduces an AI-powered assessment and feedback system that goes beyond traditional solutions by incorporating automation and adaptive learning. The non-memoryless feedback mechanism ensures that student history and performance inform future assessments, making the learning process more effective and individualised. This contrasts with conventional systems that provide static, one-time feedback without considering past progress.

cs.HC↗

arXiv2024

Personalised Feedback Framework for Online Education Programmes Using Generative AI

AI tools, particularly large language modules, have recently proven their effectiveness within learning management systems and online education programmes. As feedback continues to play a crucial role in learning and assessment in schools, educators must carefully customise the use of AI tools in order to optimally support students in their learning journey. Efforts to improve educational feedback systems have seen numerous attempts reflected in the research studies but mostly have been focusing on qualitatively benchmarking AI feedback against human-generated feedback. This paper presents an exploration of an alternative feedback framework which extends the capabilities of ChatGPT by integrating embeddings, enabling a more nuanced understanding of educational materials and facilitating topic-targeted feedback for quiz-based assessments. As part of the study, we proposed and developed a proof of concept solution, achieving an efficacy rate of 90% and 100% for open-ended and multiple-choice questions, respectively. The results showed that our framework not only surpasses expectations but also rivals human narratives, highlighting the potential of AI in revolutionising educational feedback mechanisms.

cs.HC↗

arXiv2024

Industry Perception of Security Challenges with Identity Access Management Solutions

Identity Access Management (IAM) is an area posing significant challenges, particularly in the context of remote connectivity and distributed or cloud-based systems. A wide range of technical solutions have been proposed by prior research, but the integration of these solutions in the commercial sector represent steps that significantly hamper their acceptance. The study aims to outline the current perception and security issues associated with IAMs solutions from the perspective of the beneficiaries. The analysis relies on a series of interviews with 45 cyber security professionals from different organisations all over the world. As results showed, cloud IAM solutions and on premises IAM solutions are affected by different issues. The main challenges for cloud based IAM solutions were Default configurations, Poor management of Non-Human Identities such as Service accounts, Poor certificate management, Poor API configuration and limited Log analysis. In contrast, the challenges for on premise solutions were Multi Factor Authentication, insecure Default configurations, Lack of skillsets required to manage IAM solution securely, Poor password policies, Unpatched vulnerabilities, and compromise of Single-Sign on leading to compromise of multiple entities. The study also determined that, regardless the evolving functionality of cloud based IAM solutions, 41% of respondents believe that the on premise solutions more secure than the cloud-based ones. As pointed out by the respondents, cloud IAM may potentially expose organisations to a wider range of vulnerabilities due to the complexity of the underlying solutions, challenges with managing permissions, and compliance to dynamic IAM policies.

cs.CR↗

arXiv2024

Understanding the Skills Gap between Higher Education and Industry in the UK in Artificial Intelligence Sector

As Artificial Intelligence (AI) changes how businesses work, there is a growing need for people who can work in this sector. This paper investigates how well universities in United Kingdom offering courses in AI, prepare students for jobs in the real world. To gain insight into the differences between university curricula and industry demands we review the contents of taught courses and job advertisement portals. By using custom data scraping tools to gather information from job advertisements and university curricula, and frequency and Naive Bayes classifier analysis, this study will show exactly what skills industry is looking for. In this study we identified 12 skill categories that were used for mapping. The study showed that the university curriculum in the AI domain is well balanced in most technical skills, including Programming and Machine learning subjects, but have a gap in Data Science and Maths and Statistics skill categories.

cs.AI↗

arXiv2023

Data Driven Approaches to Cybersecurity Governance for Board Decision-Making -- A Systematic Review

Cybersecurity governance influences the quality of strategic decision-making to ensure cyber risks are managed effectively. Board of Directors are the decisions-makers held accountable for managing this risk; however, they lack adequate and efficient information necessary for making such decisions. In addition to the myriad of challenges they face, they are often insufficiently versed in the technology or cybersecurity terminology or not provided with the correct tools to support them to make sound decisions to govern cybersecurity effectively. A different approach is needed to ensure BoDs are clear on the approach the business is taking to build a cyber resilient organization. This systematic literature review investigates the existing risk measurement instruments, cybersecurity metrics, and associated models for supporting BoDs. We identified seven conceptual themes through literature analysis that form the basis of this study's main contribution. The findings showed that, although sophisticated cybersecurity tools exist and are developing, there is limited information for Board of Directors to support them in terms of metrics and models to govern cybersecurity in a language they understand. The review also provides some recommendations on theories and models that can be further investigated to provide support to Board of Directors.

cs.CR↗

arXiv2021

Audio Interval Retrieval using Convolutional Neural Networks

Modern streaming services are increasingly labeling videos based on their visual or audio content. This typically augments the use of technologies such as AI and ML by allowing to use natural speech for searching by keywords and video descriptions. Prior research has successfully provided a number of solutions for speech to text, in the case of a human speech, but this article aims to investigate possible solutions to retrieve sound events based on a natural language query, and estimate how effective and accurate they are. In this study, we specifically focus on the YamNet, AlexNet, and ResNet-50 pre-trained models to automatically classify audio samples using their respective melspectrograms into a number of predefined classes. The predefined classes can represent sounds associated with actions within a video fragment. Two tests are conducted to evaluate the performance of the models on two separate problems: audio classification and intervals retrieval based on a natural language query. Results show that the benchmarked models are comparable in terms of performance, with YamNet slightly outperforming the other two models. YamNet was able to classify single fixed-size audio samples with 92.7% accuracy and 68.75% precision while its average accuracy on intervals retrieval was 71.62% and precision was 41.95%. The investigated method may be embedded into an automated event marking architecture for streaming services.

cs.SD↗

arXiv2020

Impact of Network and Host Characteristics on the Keystroke Pattern in Remote Desktop Sessions

Authentication based on keystroke dynamics is a convenient biometric approach, easy in use, transparent, and cheap as it does not require a dedicated sensor. Keystroke authentication, as part of multi factor authentication, can be used in remote display access to guarantee the security of use of remote connectivity systems during the access control phase or throughout the session. This paper investigates how network conditions and additional host interaction may impact the behavioural pattern of keystrokes when used in a remote desktop application scenario. We focus on the timing of adjacent keys and investigate this impact by calculating the variations of the Euclidean distance between a reference profile and resulting profiles following such impairments. The experimental results indicate that variations of congestion latency, whether produced by adjacent traffic sources or by additional remote desktop interactions, have a substantive impact on the Euclidian distance, which in turn may affect the effectiveness of the biometric authentication algorithm. Results also indicate that data flows within remote desktop protocol are not prioritized and therefore additional traffic will have a significant impact on the keystroke timings, which renders continuous authentication less effective for remote access and more appropriate for one-time login.

cs.CR↗

arXiv2020

The Challenges with Internet of Things for Business

Many companies consider IoT as a central element for increasing competitiveness. Despite the growing number of cyberattacks on IoT devices and the importance of IoT security, no study has yet primarily focused on the impact of IoT security measures on the security challenges. This paper presents a review of the current state of security of IoT in companies that produce IoT products and have begun a transformation towards the digitalization of their products and the associated production processes. The analysis of challenges in IoT security was conducted based on the review of resources and reports on IoT security, while mapping the relevant solutions/measures for strengthening security to the existing challenges. This mapping assists stakeholders in understanding the IoT security initiatives regarding their business needs and issues. Based on the analysis, we conclude that almost all companies have an understanding of basic security measures as encryption, but do not understand threat surface and not aware of advanced methods of protecting data and devices. The analysis shows that most companies do not have internal experts in IoT security and prefer to outsource security operations to security providers.

cs.CR↗