In recent years, blockchain technology has garnered significant interest from both academic and professional researchers, primarily due to its innovative approach to distributed record-keeping. Originally conceived as a value transfer mechanism, blockchain now finds applications across various fields including healthcare, banking, and the Internet of Things (IoT). Despite the considerable attention given to this field, the potential applications of blockchain in education remain largely unexplored. In light of the COVID-19 pandemic and the growing need for online and automated solutions in academia, this paper proposes an implementation of a blockchain solution that leverages its unique features such as decentralization, data integrity, and security. The presented solution aims to reduce paperwork, secure stored data, increase transparency, and offer new recruitment possibilities and statistical capabilities to the current educational system.Keywords: Blockchain, Education, Transparency, Reliability, Security, Academic Career

As energy consumption grows and green energy technologies are implemented, effective energy management schemes are crucial for improving energy use in households and industrial compounds. These schemes aim to economically reduce users' electric bills and energy generation costs, while environmentally contributing to climate change mitigation by reducing CO2 levels. Smart grid structures have gained prominence in recent years, with one of their main contributions being enhanced energy usage. Additionally, they leverage Internet of Things (IoT) monitoring, sensing, and communication capabilities to collect data and react accordingly. This combination allows for intelligent real-time interventions on appliance usage that surpass human accuracy and reaction time. This paper presents current trends in energy management techniques from academic literature and proposes a context-aware approach utilizing the flexibility and extensiveness of software policies. Moreover, the proposed procedure demonstrates great scalability, as its implementation is not restricted to households but extends to managing energy in residential neighborhoods and industrial applications.Keywords: Policies, Context-Awareness, Energy Management Systems

Software design and architecture involve transforming system specifications into executable programs. This process begins with designing a software structure that realizes and achieves the software specifications, followed by translating the generated structure into automated software. This paper presents a comprehensive overview of the field of software design and architecture by compiling information from various peer-reviewed sources. It describes the main domains of software design and architecture practices and highlights key open research issues in the field.Keywords: Software Engineering, Software Design, Software Architecture, Quality Attributes, Design Patterns

Software testing is an essential means of ensuring software quality, with test case generation being a crucial component of program evaluation. However, test case generation is often one of the most tedious and time-consuming tasks in software testing. Various approaches have been developed to significantly reduce its complexity and increase throughput by carefully selecting test cases based on defined criteria. This research paper addresses the complexity issue by applying robust algorithms to identify relevant edges in Control Flow Graphs (CFGs) and assign them additional weight during test case generation. The novelty of this approach lies in the application of bridge-finding algorithms and min-cut algorithms to extract critical edges in CFGs.Keywords: Control Flow Graph, Test Case Generation, Graph Algorithms, Bridges, Min-Cut, Max-Flow

Efficient energy management has become crucial in smart grid environments to meet increasing energy demands. A key feature of smart grids is their ability to empower users to make more informed decisions. Through real-time information networks, users can access dynamic energy prices, allowing them to respond accordingly. This paper proposes an optimal appliance scheduling algorithm that establishes a well-balanced trade-off between minimizing user discomfort and energy costs. Simulated results, generated from real-life data environments, demonstrate that our proposed algorithm leads to significant reductions in energy bills across a wide range of scenarios. Additionally, this technique employs machine learning to effectively reduce energy loads during peak hours, making it advantageous for both users and energy providers.Keywords: Smart Grid, Scheduling, Energy Cost Reduction, Machine Learning, Dynamic Environment, Time Series Forecasting

Driven by significant advancements in deep learning, this paper explores the application of Generative Adversarial Networks (GANs) in producing human-like faces. GANs, an innovative branch of machine learning, encompass two intertwined neural networks: a generator and a discriminator. The generator creates new samples based on the distribution of input data, while the discriminator differentiates between these samples and the original training set. By doing so, GANs aim to generate samples highly reminiscent of authentic input data. While these networks yield photorealistic outcomes, training them poses notable challenges, including non-convergence and generator collapse, resulting in a restricted variety of outputs. This research also discusses the Boltzmann machine, a unique deep learning model characterized by undirected links and the ability to generate data, setting a contrast to conventional fully connected networks. By learning potential connections and their mutual influences, the Boltzmann machine can distinguish normal from abnormal within a system, a feat impossible for humans to accomplish when dealing with a large number of parameters.Keywords: Machine Learning, Human Face Generation, Deep Learning, Convolutional Neural Networks, Discriminator, Generator