Authors - Bhavana Chalkalwar, Reena S. Satpute Abstract - In today’s world Artificial Intelligence is rapidly growing in modern tech industry which enhances the power of new IT services. Artificial Intelligence helps to Education, Government And Private Services for better future work. The new way of teaching and providing instruction requires an adaptive approach rather than the traditional way of teaching to meet the wide variety of student learners. This change in education is being led by technological advances in areas such as machine learning, natural language processing, and intelligent tutoring systems. This paper will examine Artificial Intelligence's role in education, including the various applications available and what potential benefits and challenges each might present, as well as how the use of Artificial Intelligence in education as a whole may affect education moving forward. Many of the current systems using AI today allow for customized arrangements through the use of automated assessment tools, intelligent tutoring, and adaptive algorithms. With the use of chatbots and virtual assistants, the amount of student interaction and the speed at which students can obtain information will increase as well as hold instructors accountable for identifying learning gaps. In addition, AI tools have an enormous impact on what happens inside the classroom and in institutional management. These tools will help streamline many of the non-teaching duties or administrative assignments assigned to educators, improve how resources are distributed, and better equip school administrators and policymakers to make decisions related to education. Additionally, AI tools can be used in research practices that are unique to institutions of higher education, such as automating literature reviews, analysing trend data within a compilation of data, and contributing to predictive modelling. However, the use of AI in education has created significant ethical, technical, and sociological concerns.
Authors - Aanchal Khandalkar, Reena S. Satpute Abstract - Mobile app developments exploded lately, and it’s not hard to see why. Things like 5G, AI, machine learning, and Mobile Edge Computing aren’t just making headlines they’re actually changing how apps work. Now, apps are smarter, more personalized, and packed with features that can show up overnight. Sounds amazing, but the flip side is tough: people expect everything instantly. They want quick responses, apps that basically read their minds, and zero downtime, no matter where they are. Honestly, building apps these days is anything but simple. Hardware is a real pain for developers. Phones just don’t have the power of regular computers. You get less memory, slower processors, and, of course, batteries that bail on you before you even realize. So, developers have to work magic keep the app running smoothly without draining the battery, or else people just uninstall. And with so many apps depending on outside libraries and analytics tools, there’s a whole new pile of problems. Sure, these tools help, but they open the door to security risks. Not every team has someone who lives and breathes security, so it’s easy for privacy issues or sketchy code to sneak in. All of that puts some cracks in the process and makes building solid apps a lot trickier. Such fragmentation exists on platform that developing AI/ML application for it, on the Android platform in particular, turns out to be extremely painful to integrate on. Then it becomes a question of tradeoffs from developers’ perspective-how the usage of cloud services versus local device processing fits, on each having its share of difficulties regarding scale, speed, power and security. In this paper, we go through the workflow and delve deeper into a comparative study on native application development.
Authors - Khushi Tijare, Reena S. Satpute Abstract - Bio-signals such as eye movements, electroencephalogram (EEG), functional magnetic resonance imaging (fMRI), and pupil dilation are real-time reactions to language cues that provide more data than static text representations used in classical NLP. The aim of this study is to examine how bio signals could be employed in deep natural language processing (NLP) to enhance task effectiveness and interpretability during reading comprehension, sentiment analysis, named entity recognition (NER), and syntax parsing. The following sections will describe the important aspects of design, including. Pre-processing steps for EEG and eye-tracking datasets. Model architecture types such as feature injection, multi-task learning based on auxiliary tasks, attention mechanisms, and multimodal transformers. Experimental design and metrics used for model evaluation. Ethical considerations regarding the use of cognitive signals. All design decisions made by the authors have been verified through experiments involving open access benchmark datasets like ZuCo, ZuCo 2.0, and recently developed EEG datasets.
Authors - Luong Tran Thi, Nguyen Van Long, Bac T. Nguyen, Hiep L Thi Abstract - Maximum Distance Separable (MDS) matrices play a crucial role in the design of diffusion layers in modern symmetric cryptographic primitives such as block ciphers, hash functions, and lightweight cryptographic schemes. Owing to their ability to achieve the optimal level of diffusion as measured by the branch number criterion, MDS matrices significantly enhance resistance against differential and linear cryptanalysis. However, the practical deployment of MDS matrices often faces challenges due to high computational cost, a large number of XOR operations, and substantial hardware resource requirements. Therefore, the construction of implementation-efficient MDS matrices has become an important research direction in modern cryptographic design. This paper presents a comprehensive survey of methods for constructing and optimizing MDS matrices with a focus on reducing implementation complexity in both software and hardware environments. Specifically, classical construction methods based on Cauchy, Vandermonde, and Reed–Solomon structures are reviewed, along with structured matrices such as circulant, recursive, and involutory forms. In addition, optimization techniques targeting XOR count, circuit depth, and memory usage are analyzed, and several open research directions in the design of efficient diffusion layers for modern cryptographic systems are discussed.
Authors - Hiep. L. Thi Abstract - Recent advances in quantum computing threaten the cryptographic foundations of blockchain systems. While existing surveys analyze quantum resistant blockchain architectures at the system level, wallet-layer migration and threshold post-quantum signing mechanisms remain underexplored. This paper proposes a post-quantum secure cryptocurrency wallet architecture based on code based threshold signatures. We introduce a formal quantum-adversarial wallet model, design a modular wallet framework, provide a security reduction argu ment under syndrome decoding hardness, and outline a migration-compatible de ployment strategy
Authors - Hiep. L. Thi Abstract - Secret-sharing is a fundamental cryptographic primitive that enables the secure distribution of sensitive information among multiple participants while guaranteeing correctness and privacy. In this paper, we present a comprehensive study of secret-sharing schemes from both information-theoretic and computational perspectives. We begin by reviewing classical threshold constructions, including Shamir’s polynomial-based scheme and its algebraic interpretation via linear codes. The notions of correctness and perfect secrecy are formalized using entropy, and the role of access structures in characterizing authorized subsets is emphasized. We then examine ideal secret-sharing schemes, where each share has the same size as the secret, and highlight their deep connection with representable matroids. In particular, we discuss how matroid representability over finite fields characterizes the existence of ideal linear secret-sharing schemes, thereby linking combinatorial independence with cryptographic access control. Additional structural results concerning field dependence, minimal non-ideal access structures, and connections to linear codes are also addressed. The paper further explores computational secret-sharing, which relaxes perfect privacy to computational indistinguishability under standard cryptographic assumptions. We describe constructions based on encryption and threshold key sharing, as well as realizations derived from monotone circuits. A central theme is the separation between information-theoretic and computational models: while certain access structures require exponential share size in the information-theoretic setting, they admit polynomial-size shares under computational assumptions. Finally, we discuss algebraic methods underlying secret-sharing, including polynomial interpolation and linear coding techniques, and illustrate how these tools support efficient distributed protocols such as secure multiparty computation and threshold cryptography. Overall, the paper provides a unified treatment of structural, algebraic, and computational aspects of secret-sharing, highlighting its foundational role in modern distributed cryptographic systems.
