This paper presents a longitudinal study on the transformation of programming practices among undergraduate students enrolled in the Computer Networks course at the Faculty of Computer Science, within “Alexandru Ioan Cuza” University of Iași. Spanning thirteen academic years (2012–2025) and analyzing over 4,300 individual project submissions, the study offers a rare, data-driven perspective on how software development behaviors evolve in response to technological shifts and the rise of AI-powered code generation tools. Through a hybrid methodology combining static code analysis, cyclomatic complexity evaluation, and LLM-based assessments of code quality, we uncover a profound inflection point following the public release of GPT-based tools. The findings reveal a sudden expansion in code volume accompanied by a counterintuitive simplification of structure, reflecting a transition from organically developed solutions to AI-accelerated, pattern-driven development. Skill and structure scores increased post-2022, but the stability of median values exposes a stratified adoption, raising questions about students’ conceptual mastery. These results mirror a wider global phenomenon represented by a cognitive realignment in how younger generations solve problems, reason, and create, shaped by ubiquitous access to intelligent systems. Rather than confirming a gradual shift, our data points to a disruptive pedagogical rupture, one that challenges foundational assumptions about learning, autonomy, and authorship in technical education. This study calls for a redefinition of learning outcomes in the era of generative AI, advocating for curricula that not only integrate such tools, but also cultivate reflective, critical engagement with them. The future of computing education depends not on resisting AI, but on teaching students to think beyond it.

In today’s data-driven environment, organizations increasingly depend on high-quality, interoperable data. This research presents a scalable, FAIR-compliant (Findable, Accessible, Interoperable, Reusable) data integration and governance framework for business-to-business (B2B) and business-to-government (B2G) contexts, addressing both metadata and data interoperability. The solution combines ETL pipelines, Semantic Web technologies, and Large Language Models (LLMs). Metadata interoperability is achieved via RDF schemas, SKOS vocabularies, and linked data principles; data-level integration is supported through schema mapping, format conversion, and LLM-assisted transformation. A dynamic ETL process uses semantic schema mapping and LLM-driven vocabulary suggestions to reduce manual alignment with domain ontologies. The framework is validated through a formal evaluation strategy combining real-world B2B and B2G case studies with comparative analysis against existing solutions. The system’s applicability spans across critical B2B and B2G scenarios in domains such as finance, healthcare, public administration and logistics, illustrating its capacity to enable trustworthy data sharing and governance across institutional and organizational boundaries.

This paper explores the sustainability implications of transitioning from on-premises infrastructure to cloud-based solutions. The research addresses the increasing importance of environmental sustainability in the rapidly growing cloud computing domain. Motivated by the desire to balance technological advancement with ecological responsibility, the study provides a comprehensive overview of cloud migration’s environmental impact. It assesses this transition through the lens of energy consumption, resource utilization, and carbon emissions, uncovering the "green footprint" accompanying such changes. Key findings reveal that cloud providers are making significant strides towards sustainability goals, including aspirations for 100% renewable energy and carbon neutrality. In addition to these provider-level efforts, user adoption of best practices such as right-sizing resources, limiting test environments, and adhering to optimized coding practices can further enhance the environmental benefits. Using the CodeCarbon tool, we focused on illustrating that design and architectural optimizations at the application level can lead to up to 99% emission reductions. Code-level improvements, including algorithmic efficiency and strategic use of programming languages and libraries, can yield further decreases between 88% and 99%. Moreover, database-focused strategies like indexing and the choice of database type (NoSQL vs. SQL) can lead to emission reductions of up to 92%. Additionally, the decision between monolithic and microservice architectures has significant ramifications for carbon output, depending on the context of their deployment. This study presents actionable recommendations for cloud users to maintain a minimal carbon footprint during migration activities. It underscores the importance of selecting appropriate cloud infrastructures and resources and aligning them with specific application requirements to maximize operational and environmental efficiency. Through meticulous research and extensive experimentation, the thesis contributes valuable insights toward achieving a more sustainable integration of cloud technologies.

The paper presents a comparative study of cloud migration for modular monolith Java Spring application, focusing on IaaS and PaaS deployment models across AWS and Azure. The study examines the technical and operational challenges of adopting each model, highlighting the trade-off between infrastructure control and management abstraction. Through a practical migration case study, we analyze the deployment process using AWS EC2 and Azure Virtual Machines for IaaS, as well as AWS Elastic Beanstalk and Azure App Services for PaaS. The findings illustrate that IaaS offers high configurability and allows for the precise replication of on-premise environments, but it demands significant technical expertise. In contrast, PaaS enables streamlined deployment and scalability but may limit customization and can introduce additional financial implications. The analysis further contrasts the two cloud platforms regarding ecosystem integration, deployment tools, hybrid cloud support, and service maturity. Key challenges encountered during migration, such as upgrading various frameworks, configuring secure connectivity, deploying workflows, and establishing CI/CD pipelines, are thoroughly documented. A comparative analysis provides insights into the financial impact of each model. This work serves as a practical guide for decision-makers involved in enhancing legacy applications. It offers criteria for selecting the appropriate cloud model and provider based on strategic, technical, and economic considerations.

Developing a passive monitoring prototype for elderly care is vital for ensuring their safety while respecting their autonomy. However, existing approaches often compromise confidentiality or necessitate the elderly to wear intrusive devices. To address this, we focus on recognizing behavioral patterns through Activities of Daily Living (ADLs). To detect and classify an ADL, data from various sensors is necessary, with motion sensors being the most prevalent. Consequently, our research delves deeper into understanding the functionality of these sensors and exploring the latest advancements in IoT communication protocols. Matter emerges as a standout choice, together with the OpenThread Border Router, which offer seamless interoperability, ensuring compatibility between devices and network infrastructure while prioritizing security and reliability. Leveraging the capabilities of these technologies, we developed a prototype for collecting data about an individual’s activity based on an occupancy sensor, found in a home setting. User-friendly management of the smart home is done through the Home Assistant platform, together with its HomeKit addon, enabling seamless interaction with commissioned devices directly from mobile phones. Our project advances through multiple stages, from laying the technical groundwork to defining data acquisition methods, successfully achieving these initial milestones. Our focus now shifts to data pre-processing, model development/selection for accurate sensor data classification within Activities of Daily Living (ADLs), and exploring system deployment options (cloud, edge, or on-device). Through our prototype, we showcase the feasibility of an elderly care monitoring system, emphasizing privacy and dignity with advanced IoT technologies.

Outdoor navigation often poses significant challenges for blind individuals due to the lack of accessibility features. Public accessibility systems often overlook the needs of the blind, leading to exclusionary experiences. Blind individuals frequently rely on alternative senses, such as creating mental maps, to navigate known spaces. Even commonplace activities like using transportation can be complex, requiring the blind to map known places with maneuvers made by the driver. Integrating a speech and gesture-based interface within accessibility frameworks is a crucial step towards inclusivity and improved user experiences. This approach offers alternative interaction methods beyond traditional graphical interfaces, empowering individuals with diverse abilities to navigate outdoor environments effortlessly. By implementing multi-modal feedback interfaces, our effort aims to create an intuitive interaction method that seamlessly provides navigation instructions, guiding users step-by-step along their routes. We prioritize minimizing cognitive load and architectural complexities by designing a notification system that complements the user’s actions without causing distraction.

This study primarily explores the feasibility of transferring a modular monolith from an on-premises to Google Cloud Platform (GCP) utilizing the Lift and Shift technique, with a focus on cost-effectiveness. Our objective is to identify cost-effective ways to host the application in the cloud. Our application, Hermit Portal, is built with Angular for the front end and .NET Core for the back end, with Clean Architecture serving as the primary design pattern. It examines the impact of architectural decisions on a modular monolith use case. In Section 2, we look at several cloud models, such as IaaS, PaaS, and Serverless. Our findings indicate that the IaaS model can be a very appealing option for GCP in terms of cost, with the potential to decrease costs by about 65%. We can conclude that, in terms of pricing, the GCP IaaS is the best choice based on our earlier research. We also discovered that the initial prices of PaaS can be higher due to the higher cost of SQL Server instances compared to on-premises deployments (this is also true for Amazon Web Services). To address this, we advise moving to a less expensive database (PostgreSQL or MySQL). This is made feasible by the flexibility of our architecture, which allows changes to other layers, such as databases, with relatively little influence on costs. Three cloud service providers (Amazon Web Services, Google Cloud Platform, and Microsoft Azure) will be compared in Section 3. Based on the insights gathered from this comparison, we will suggest enhancements to facilitate a seamless cloud migration for this category of applications. We will also include two essential components (Compute and Storage) that could provide useful data for a multi-cloud migration strategy or a hybrid-cloud migration strategy.

Public transportation is a critical part of modern society, allowing people to move efficiently and affordably between different locations. However, navigating public transportation stations can be challenging for visually impaired individuals, especially when multiple vehicles reach the station at the same time. This can lead to missed connections and delays, which can have a significant impact on their mobility and independence. In this paper, we propose an IoT solution that aims to help visually impaired individuals identify the vehicle they need to travel on. Our solution utilizes a device placed in every vehicle that communicates with cloud services to provide real-time location data. The mobile application used by the client then retrieves this information from the cloud services and provides audio or tactile feedback to the user, enabling them to identify the vehicle they need to board. We believe that our proposed solution can significantly enhance the mobility and independence of visually impaired individuals and contribute to a more inclusive society.

The current paper analyzes the feasibility of a modular web application's migration procedure from on-premises to the cloud. Our focus is on identifying cost savings and options for hosting the application in the cloud. The research specifically examines the impact of architectural decisions records (ADR) on a modular monolith use case, utilizing .NET Core for the backend and Angular for the front end, with Clean Architecture as the design pattern. We investigate different cloud models (IaaS, PaaS, Serverless) considering project management's triple constraints (time, cost, performance). The study demonstrates that a modular monolith can be migrated with varying effort and costs depending on the chosen cloud model and technology stack. We explore optimizations such as resource utilization, licensing fees, and cost reduction through infrastructure reservations. Our findings show that the migration cost can range from a 20% increase with IaaS to approximately 70% cost reduction with the Serverless strategy compared to the on-premises environment, using equivalent resources. We also explore methods to lower expenses for each model, including resource modifications, Linux operating systems, and longer resource reservations. Considering limitations, we propose a two-stage migration strategy: initially lifting and shifting the application to IaaS with cost optimization, and subsequently migrating to PaaS for scalability and simplified resource management in the long term.

Starting with everyday activities such as taking a walk at the outside environments, use of different public transportation methods or even buying different goods, visually impaired people need to put in a considerable effort to complete these so-called trivial tasks for sighted people. Given this, according to WHO (World Health Organization) it is estimated that there are at least 2.2 billion people who have a near or distance vision impairment. This paper presents an ongoing project that aims to address mainly two problems: the creation of a system designed to safely moving visually impaired people to points of interest using public transportation and designing of a communication mechanism that will balance between keeping the individual as safe as possible and also providing relevant indications. The current paper is divided in two main parts. In the first part, in addition to the study of similar systems, the factors underlying the architectural decisions on which the developed system was built, are debated and analysed. The second part presents the results obtained after a series of preliminary tests carried out with the help of visually impaired people. The application seems promising, being enthusiastically received by those who tested it, because it offers them safety and independence when travelling through the city.

This article presents new solutions for the difficult problem of data protection. The discussion in our approach will be based on the premise that data needs to be shared between the adequate number of entities under a set of strict rules. In this context, our proposal consists of a suite of privacy and data control focused approaches that become the building blocks for Self-Sovereign Applications (SSApps). SSApps are a new class of applications proposed for the first time in this paper and are aimed to ensure data ownership and proper data control for people and companies. The building blocks for SSApps consist of the insights that the role of blockchain should be reduced to ensure data anchoring of data shared between multiple participants. The blockchain ensures integrity and data provenance but should not be used for storing any private data. Therefore, the data is stored in off-chain storages that are structured in the form of a new approach called Data Sharing Units (DSU or Dossier). A method for performant implementation of SSApps, Dossiers and anchoring is proposed in this article.

InterPlanetary File System (IPFS) with its specific features is a novelty to the data distribution area. In this article we are analyzing this filesystem/protocol and we will conduct a parallel study based on the comparison to other distributed file systems (DFSs) and to HTTP (Hypertext Transfer Protocol). IPFS provides efficient data storage and distribution, data permanence, offline-oriented paradigms, and no-central-administration feature. Furthermore, we will employ an applied example in order to prove the strengths of IPFS, which is an Android based system that was implemented with the goal of helping people during emergency situations, in case when no access to the global Internet connectivity is available. The application has IPFS as the core system of distribution and messages propagation. Overall, we prove that the vision and the architecture of the Internet can be significantly improved in the (near) future if developers and engineers take into consideration and start to use the massive amount of modern, efficient existing technologies.

Visually challenged people may experience certain difficulties in their daily interaction with technology. That is essentially because the main way to exchange and process information is by written text, images or videos. Since the basic purpose of innovation is to improve people’s lifestyle, in this paper we propose a system that can make technology accessible to a broader group. Our prototype is presented as a mobile application based on vocal interaction, which can help people facing visual disorders consult their personal agenda, create an event, invite other friends to attend it, check the weather in certain areas and many other day-to-day tasks. Regarding the implementation, the project consists of a mobile application that interacts with a cloud based system, which makes it reliable and low in latency due to the resource availability in multiple global regions, provided by the newly emerging platform used in building the infrastructure. The novelty of the system lays in the highly flexible serverless architecture [1] that is open to extension and closed to modification through the set of autonomous cloud processing methods that sustain the base of the functionality. This distributed processing approach guarantees that the user always receives a response from his personal assistant, either by using artificial intelligence context generated phrases, by real-time cloud function processing or by fallback to the training answers.

The paper presents roGSEM (Romanian Governmental Semantic Portal), a platform able to provide access to legal open governmental data: normative documents published by the Romanian state. The proposed solution is based on Semantic Web technologies and uses the information according to the Linked Data initiative. Also, roGSEM semantic data repository can be used by independent plug-ins, encouraging developers to create and deploy innovative applications and mashups that give an improved access to existing legal knowledge.

The paper presents SIGMA (Semantic Government Mash-up Application), a platform able to create mash-ups by providing access to open governmental data. The proposed solution is based on the existing semantic Web technologies (especially RDF and SPARQL) and uses public distributed endpoints conforming to the Linked Data initiative. Also, SIGMA allows creating and deployment of independent plugins, encouraging the developers to build innovative applications, visualization solutions and mash-ups that give an improved access to the governmental data.

This paper is concerned with a problem in information organization and retrieval within Web communities. Most work in this domain is focused on reputation-based systems which exploit the experience gathered by previous users in order to evaluate resources at the community level. The current research focuses on a slightly different approach: a personalized evaluation system whose goal is to build a flexible and easy way to manage resources in a personalized manner. The functionality of such a model comes from local trust metrics which propagate the trust to a limited level into the system and, finally, lead to the appearance of minorities sharing some similar features/preferences. A modified PSO procedure is designed in order to analyze such a system and, in conjunction with a simple agglomerative clustering algorithm, identify homogenous groups of users.

The paper presents a Java-based object-oriented system that offers a layered infrastructure to create the adequate framework for complex interactions between Grid components (e.g., agents of Web services). Using a shared-memory modeled by sets of tuples, our proposal can be considered as an abstract communication architecture for building Grid services.