10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
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Authors - Md Asif Ahmed, Md Sadatuzzaman Saagoto, Farhan Mahbub, Protik Barua Abstract - Graphene is emerging as a strong candidate for qubit applications in quantum computing due to its unique properties and recent technological advancements. Graphene, as a two-dimensional material with high carrier mobility and distinct electron behavior, presents potential advantages for qubit applications. However, its zero-band-gap nature poses challenges for stable quantum states, requiring innovative solutions to realize its full potential in quantum computing. This review explores graphene's unique properties and their impact on qubit design, analyzing recent breakthroughs aimed at overcoming its inherent limitations, such as techniques for band-gap modulation and substrate engineering. We delve into various methodologies, including the integration of hexagonal boron nitride (hBN) and electrostatic gating, to enhance graphene's performance for quantum applications. Additionally, we examine the integration of graphene with other 2D materials and hybrid structures to achieve tunable quantum properties, essential for advancing scalable quantum architectures. This comprehensive analysis aims to bridge the material science challenges with the practical demands of qubit technology, providing a roadmap for leveraging graphene in future quantum systems.
Authors - Kobus Kemp, Lynette Drevin, Magda Huisman Abstract - This paper reports on a study that explores and addresses security challenges in the development of enterprise mobile applications (EMAs). Despite the growing prevalence of mobile applications, security considerations are often overlooked or insufficiently addressed in mobile application software development methodologies. This gap highlights the need to incorporate security training into software developer education. The study used a literature review of software development methodologies (SDMs) and security practices, complemented by case studies involving interviews with industry experts on EMA development processes. Using thematic and cross-case analyses, the study produced a framework designed to guide the integration of security measures into EMA development. Findings revealed a limited emphasis on security aspects in current mobile application development practices. Consequently, a partial framework is presented in this paper, detailing key security considerations and countermeasures specific to EMA development. This research contributes to the discipline by offering developers guidelines to enhance security in EMAs, emphasizing the importance of integrating these practices into developer training programs.
Authors - Marisol Roldan-Palacios, Aurelio Lopez-Lopez Abstract - Limited available data becomes a critical problem in specific machine learning tasks, where approaches, such as large language models, turn impractical. Reaching solutions in such situations requires alternative methods, especially whether the object of study contributes to data scarcity while preventing using techniques such as data augmentation. This scenario led us to formulate the research question on how to squeeze hidden information from small data. In this work, we propose a data processing and evaluation technique to increase information extraction from scarce data. Attributes expressed as trajectories are further pair-related by proximity and assessed by customary learning algorithms. The efficacy of the proposed approach is tested and validated in language samples from individuals affected by a brain injury. Direct classification on raw and normalized data from three sets of lexical attributes works as a baseline. Here, we report two learning algorithms out of five explored, showing consistent behavior and demonstrating satisfactory discriminatory capabilities of the approach in most cases, with encouraging percentages of improvement in terms of f1-measure. We are in the process of testing the approach in language data sets of syntactic and fluency features, but other fields can take advantage of the technique.
Authors - Khalaf Elwadya, Khosro Salmani Abstract - The evolution of social media platforms has led to the creation of a dynamic ecosystem, abundant in user-generated content. This, however, has also resulted in raising concerns about data privacy. Beyond potential threats like scammers exploiting freely shared information on social media for spying, financial scams, social media companies can leverage user data to sell targeted advertising. Addressing these issues necessitates heightened user awareness. Hence, this paper first examines the privacy policies of major social media platforms including TikTok, Twitter, Facebook, Instagram, and LinkedIn, providing a comparative analysis of their data storage practices, utilization of user information, account verification requirements, and default privacy settings. Next, we undertake an extensive survey utilizing the data gathered in the initial phase to evaluate user awareness regarding the utilization of their data, highlighting a notable gap between policy stipulations and user expectations. We conclude with four recommendations based on our findings to help social media companies refine their privacy policies, promoting more comprehensible guidelines.
