Omar Hujran, Department of Statistics and Business Analytics, United Arab Emirates University, Al Ain, United Arab Emirates
This research report provides a comprehensive analysis of Compact Composite Descriptors (CCDs) as a highly ef icient alternative to deep learning embeddings for Content-Based Image Retrieval (CBIR) in resource-constrained environments. While Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) of er superior semantic performance, their computational overhead and storage requirements—often exceeding 8KB per image—limit their applicability in Edge AI and IoT scenarios. In contrast, engineered descriptors such as the Color and Edge Directivity Descriptor (CEDD), Fuzzy Color and Texture Histogram (FCTH), and Joint Composite Descriptor (JCD) utilize fuzzy inference systems to encode visual features into ultra-compact vectors ranging from 54 to 72 bytes. The study explores the algorithmic foundations of these descriptors, their implementation within the LIRE (Lucene Image Retrieval) framework, and benchmarks demonstrating their competitive retrieval accuracy against MPEG-7 standards. Finally, the report highlights the strategic utility of CCDs for privacy-preserving, low-bandwidth visual search on edge devices, proposing hybrid architectures that leverage the speed of fuzzy composites with the semantic power of neural re-ranking.
ChatGPT, Education, Conversational quality, Learning outcomes, Ethical concerns
Razwan Ahmed Tanvir and Greg Speegle, Department of Computer Science, Baylor University Waco, Texas, USA
Collaborative blockchain ecosystems allow diverse groups to cooperate on tasks while providing properties such as decentralization and transaction security. We provide a model that uses a repository blockchain to manage hard forks within a collaborative system such that a single process (assuming that it has knowledge of the requirements of each fork) can access all of the blocks within the system. The repository blockchain replaces the need for Inter Blockchain Communication (IBC) within the ecosystem by navigating the networks. The resulting construction resembles a tree instead of a chain. A proof-of-concept implementation performs a depth-first search on the new structure.
Hard Fork, Shared Governance, Inter Blockchain Communication (IBC), Blockchain Ecosystem
Jesus Antonio Motta1 and Pedro David Gomez 2, 1Laval University ,Quebec (Canada), 2 Foundation University of Health Sciences, Bogota (Colombia)
In this work, we present a highly efficient machine learning method for identifying DNA sequences that code for genes. The learning process is based on Human Genome Build 38 (GRCh38) sequences extracted from various specialized databases. The sequences were then translated into amino acid sequences and used to build matrices that facilitate the extraction of features with the TF*IDF metric for the creation of the training space. The prediction functions are learned using a convolutional neural network (CNN) deep learning model. The training spaces were created using the 24 chromosomes of the human genome and approximately 36,000 genes and pseudogenes whose names were fetched from the HUGO Gene Nomenclature Committee (HGNC). Performance analysis was performed on 24 genes associated with genetic disorders, as well as the surrounding DNA regions. The metrics used were precision, recall, F_score measure, accuracy and ROC curves for the genes of interest. The results achieved exceed all our expectations and place the work at the level of the state of the art for gene prediction.
DNA, Amino-Acids, TF×IDF, CNN, Genetic Disorder, Learning Model
Manuel Stoger, Mario Bernhart, and Thomas Grechenig, Research Group for Industrial Software (INSO), TU Wien, Vienna, Austria
Software repositories constitute rich, heterogeneous data sources whose effective exploitation is pivotalfor understanding software evolution and ensuring software quality. Knowledge graphs (KGs) and relatedgraph-based representations have emerged as a promising paradigm for structuring, querying, and rea-soning over repository data. This paper reviews 56 primary studies (2006–2025) to answer: “How haveknowledge graphs been applied to mining, analyzing, and visualizing software repositories?”. Follow-ing the Design Science Methodology by Wieringa, we classify proposed treatments, evaluate validationstrategies, and synthesize results into five application clusters: (1) ontology-based repository modeling,(2) code knowledge graph construction and querying, (3) developer and collaboration networks, (4) defect,maintenance, and traceability, and (5) software evolution and dependency analysis. The findings reveala clear evolution from early ontology-based approaches (2006–2012) through deep-learning-augmentedKGs (2017–2021) to LLM-integrated repository graphs (2023–2025). Open challenges include scalability,standardization, and the convergence of graph-based and neural approaches.
Knowledge Graph, Mining Software Repositories, Structured Literature Analysis, Ontology, Soft-ware Evolution
Chitti Srinivasa Phani1 Sedar Olmez 2 and Yokota Koichi 3 Antifragility Research Group, Fujitsu Research of Europe, Slough, United Kingdom
User-authored data-processing scripts are widely used for ETL workflows due to their flexibility and ease of development, but they frequently encode implicit assumptions about local file systems, execution order, and runtime context. These assumptions make such scripts fragile when migrated to cloud environments, where differences in storage semantics, resource constraints, and execution models can lead to silent failures or incorrect behaviour. Existing migration approaches typically rely on manual refactoring or narrowly scoped tooling, limiting scalability and reliability. This paper presents Platform Code Converter (PCC), a compiler-inspired middleware for transforming unstructured ETL scripts into portable, cloud-ready artefacts. PCC recovers operational semantics using a language-neutral Intent Grammar and a lightweight Intermediate Representation (IR), from which it derives backend-specific execution policies for chunking, batching, parallelism, and storage access. A tiered transformation engine applies correctnesspreserving rewrites, augmented by LLM-assisted, correctness-preserving refinement when deterministic rewriting is insufficient, while conservatively preserving code under semantic uncertainty. We evaluate PCC on forty heterogeneous ETL workloads, spanning synthetic patterns and real production scripts; across these, PCC applies 110 deterministic transformations, migrates over 300 file paths to cloud-safe representations, and validates all generated artefacts at execution time. These results demonstrate that intent-guided compilation provides a practical and reliable foundation for cloud migration of unstructured ETL code.
ETL migration, cloud computing, code transformation, intermediate representation, intent grammar
