This paper aims to enhance the Graphical Set-based model (GSB) for ranking and classification tasks by incorporating node and word embeddings. The model integrates a textual graph representation with a set-based model for information retrieval. Initially, each document in a collection is transformed into a graph representation. The proposed enhancement involves augmenting the edges of these graphs with embeddings, which can be pretrained or generated using Word2Vec and GloVe models. Additionally, an alternative aspect of our proposed model consists of the Node2Vec embedding technique, which is applied to a graph created at the collection level through the extension of the set-based model, providing edges based on the graph’s structural information. Core decomposition is utilized as a method for pruning the graph. As a byproduct of our information retrieval model, we explore text classification techniques based on our approach. Node2Vec embeddings are generated by our graphs and are applied in order to represent the different documents in our collections that have undergone various preprocessing methods. We compare the graph-based embeddings with the Doc2Vec and Word2Vec representations to elaborate on whether our approach can be implemented on topic classification problems. For that reason, we then train popular classifiers on the document embeddings obtained from each model.

In document categorization, the escalating volume of digital data underscores the critical need for advanced techniques in generating semantically rich document embedding, particularly for topic modelling applications. This article presents a novel methodology that leverages the capabilities of SentenceBERT (SBERT) for sentence embedding, aiming to address the challenges associated with embedding longer texts. The process begins by decomposing documents into individual sentences that can be processed using the SBERT model which generates an embedding for each sentence. Subsequently, a clustering algorithm is employed to select representative sentence embeddings for each document. These embeddings serve as the foundation for constructing comprehensive document embeddings through an autoencoder. This research exclusively explores the autoencoder approach, discarding alternative methods for brevity and focus. The autoencoder strategy showed promising results, outperforming the base model, SBERT, in every scenario. This highlights the method's potential effectiveness in document embedding tasks where understanding long context is important. Moreover, when the Transformer Autoencoder was fine-tuned on test data, it achieved even grater performance, underscoring the viability of this approach. The findings suggest considerable room for improvement in various aspects of the methodology, including the clustering process and the encoder architecture. This article underscores the potential of autoencoders in advancing the state-of-the-art in document embedding technologies, setting the stage for further refinement and exploration.

The high costs of acquiring and maintaining high-performance computing (HPC) resources pose significant barriers for medium-sized enterprises and educational institutions, often forcing them to rely on expensive cloud-based solutions with recurring costs. This paper introduces CommC, a multi-purpose commodity hardware cluster designed to reduce operational expenses and extend hardware lifespan by repurposing underutilized computing resources. By integrating virtualization (KVM and Proxmox) and containerization (Kubernetes and Docker), CommC creates a scalable, secure, and cost-efficient computing environment. The proposed system enables seamless resource sharing, ensuring high availability and fault tolerance for both containerized and virtualized workloads. To demonstrate its versatility, we deploy big data engines like Apache Spark alongside traditional web services, showcasing CommC’s ability to support diverse workloads efficiently. Our cost analysis reveals that CommC reduces computing expenses by up to 77.93% compared to cloud-based alternatives while also mitigating e-waste accumulation by extending the lifespan of existing hardware. This significantly improves environmental sustainability compared to cloud providers, where frequent hardware turnover contributes to rising carbon emissions. This research contributes to the fields of cloud computing, resource management, and sustainable IT infrastructure by providing a replicable, adaptable, and financially viable alternative to traditional cloud-based solutions. Future work will focus on automating resource allocation, enhancing real-time monitoring, and integrating advanced security mechanisms to further optimize performance and usability.