Our Work

Deep convolutional neural networks deliver high predictive performance in image classification. However, they often require substantial computation and memory. This limits their use in resource-constr…

Deep convolutional neural networks deliver high predictive performance in image classification. However, they often require substantial computation and memory. This limits their use in resource-constrained environments. Many existing compression methods apply pruning and quantization separately. They also focus mainly on supervised learning with fixed label spaces. As a result, they may cause accuracy loss, high optimization cost, and limited adaptability to unseen classes. To address these limitations, this study proposes a hybrid Network Pruning and Quantization framework integrated with zero-shot learning for compressed deep convolutional neural networks. The proposed NPQ framework combines magnitude-based pruning, penalty-based sparsity regularization, and quantization-aware optimization within a unified design. It also preserves semantic embedding consistency to support recognition of unseen classes. The framework is evaluated on CIFAR 10, AWA2, and CUB benchmark datasets. Its performance is compared with recent pruning and quantization methods including LQ NET, RESREP, AutoPruner, and SIGMA. The experimental results show that NPQ achieves competitive accuracy while reducing model complexity, memory usage, and inference latency. Ablation studies further confirm that each component contributes to compression efficiency and zero shot generalization performance. Overall, the proposed NPQ framework offers an effective approach for building compact and efficient deep learning models for real time and resource-constrained deployment.

This study addresses the persistent challenges in implementing e-governance in developing countries, with a focus on Nepal where infrastructural limitations, institutional fragmentation, and low digit…

This study addresses the persistent challenges in implementing e-governance in developing countries, with a focus on Nepal where infrastructural limitations, institutional fragmentation, and low digital literacy continue to hinder effective adoption. The research aims to identify key determinants influencing e-governance implementation and to propose a computational framework that integrates technological, institutional, and human dimensions. Using a quantitative survey of 250 respondents across Nepal’s seven provinces, the study applies multiple regression analysis to examine relationships among critical factors. The findings reveal that institutional support [β = .35, p < .001] and digital literacy [β = .29, p < .001] are the most significant predictors, explaining 63% of the variance in adoption [R² = .63]. Based on these insights, a four-layer computational framework is proposed. The study concludes that effective e-governance implementation requires coordinated institutional reforms, enhanced digital capacity, and inclusive infrastructure development. The findings contribute both theoretically and practically by offering a scalable model for developing nations.

The zip file contains dataset related to the publication and minimum code to reproduce graphs

La formación docente contemporánea exige metodologías activas que promuevan competencias pedagógicas, críticas y digitales. Este estudio analiza el impacto de dichas…

La formación docente contemporánea exige metodologías activas que promuevan competencias pedagógicas, críticas y digitales. Este estudio analiza el impacto de dichas estrategias en estudiantes de la carrera de Educación en la Universidad Politécnica Salesiana, sede Guayaquil, Ecuador, mediante un enfoque mixto que combinó encuestas, observación y análisis documental. Los resultados revelan un uso frecuente del Aprendizaje Basado en Proyectos, el trabajo colaborativo y los debates, y una relación significativa entre su aplicación y el desarrollo de competencias cognitivas, sociales y reflexivas. La discusión se fundamenta en enfoques como la pedagogía crítica y el aprendizaje experiencial, subrayando la importancia de una integración pedagógica intencionada de las tecnologías. Se concluye que estas metodologías fortalecen la formación docente integral, aunque persisten desafíos en el uso autónomo de las Tecnologías de la Información y Comunicación, así como en su consolidación institucional.

A pesquisa analisou a evolução e a natureza da dívida com empreiteiros e fornecedores nas 11 Universidades Federais do Estado de Minas Gerais entre os anos de 2010 e 2018, conside…

A pesquisa analisou a evolução e a natureza da dívida com empreiteiros e fornecedores nas 11 Universidades Federais do Estado de Minas Gerais entre os anos de 2010 e 2018, considerando os impactos da crise fiscal brasileira intensificada a partir de 2014. O estudo concluiu que a dívida com fornecedores nessas instituições representa um montante financeiro expressivo. Em 2015 (pico do endividamento), a dívida total acumulada superou o orçamento discricionário de 7 das 11 universidades mineiras.

The administrative and legal regulation of personal data processing in state information systems (SIS) is becoming increasingly important because of the development of e-government and digital identif…

The administrative and legal regulation of personal data processing in state information systems (SIS) is becoming increasingly important because of the development of e-government and digital identification systems. The aim of the study is to establish how legal instruments ensure lawful, transparent, and secure data processing. The methodology includes comparative legal analysis, documentary review, and case studies, with a focus on jurisdictions with developed e-government.Significant discrepancies between norms and practice were identified, especially in the areas of accountability, data minimization, and cross-border exchange. In Germany, the BundID system provides legal certainty thanks to clear obligations enshrined in federal law. Legal transformation is ongoing in Ukraine (the Diia platform). Most systems lack effective oversight and are not adapted to technological changes. The user access to the X-Road-based platform complies with Articles 5 and 6 of the General Data Protection Regulation (GDPR) regarding the lawfulness of processing and identification. The Delphi procedure revealed only 12% agreement on the criteria of minimization and user autonomy.It is necessary to update the legal framework, implement risk-based control, unify standards with international norms. Research into automated tools and adaptive management models using artificial intelligence (AI) and biometrics is promising.

In very large-scale integration (VLSI) design, power consumption estimation has become something very crucial, having a direct effect on reliability, energy efficiency, management of thermal propertie…

In very large-scale integration (VLSI) design, power consumption estimation has become something very crucial, having a direct effect on reliability, energy efficiency, management of thermal properties, and other performances at a higher level. With such complementary metal-oxide-semiconductor (CMOS) technology scaling and the complex nature of the integrated circuit, power prediction at an early stage is now of prime importance for design optimization of circuits. Everything with simulation was considered better in terms of power estimation, but it has always been too slow for design iterations. In view of resolving issues in current power estimation systems, this study introduces a novel power estimation scheme using Graph Autoencoder (GAE) combined with Graph Convolutional Network (GCN). Such a model exploits the graph nature of CMOS VLSI circuits, where logic gates and their interconnections are treated as nodes and edges, respectively. The GAE encodes the circuit graphs into low-dimensional latent structural features with preservation of both local and global dependency relationships, while the GCN learns these features to predict power consumption at the circuit level with high accuracy. Initially, gate-level attributes like gate types, flip-flops, inputs, and outputs were considered from the ISCAS’89 benchmark dataset for training and evaluating the model. Experimental results signify the excellent performance of the proposed GAE-GCN model as it has yielded a prediction accuracy with a regression coefficient of 0.9999, RMSE of 0.00010, and a correlation coefficient of 0.999. The results are compared with existing models outlined in the survey for validation purposes. The results comparison indicates that the developed GAE-GCN model outperformed all other models.

Las universidades públicas enfrentan desafíos permanentes en gobernanza, eficiencia y transparencia. El objetivo de este estudio es analizar de manera sistemática el impacto de la…

Las universidades públicas enfrentan desafíos permanentes en gobernanza, eficiencia y transparencia. El objetivo de este estudio es analizar de manera sistemática el impacto de las herramientas de gestión en la gobernanza de las universidades públicas, considerando su efecto en la eficiencia administrativa, la toma de decisiones estratégicas y el desempeño institucional. Este estudio presenta una revisión sistemática de investigaciones publicadas entre 2020 y 2024 sobre el uso de herramientas de gestión en la administración universitaria. La búsqueda en Scopus, ProQuest, Springer, Taylor & Francis y SciELO, identificó 102 registros, de los cuales 10 cumplieron con los criterios de inclusión según PRISMA 2020. Los hallazgos muestran que los instrumentos estratégicos y orientados a procesos se asocian con mayor frecuencia a eficiencia, alineación institucional y rendición de cuentas; mientras que las herramientas basadas en conocimiento fortalecen el aprendizaje organizacional. Se identificaron barreras como restricciones financieras, estructuras rígidas y resistencia al cambio. Se concluye que la integración de competencias directivas, transformación digital y participación de actores académicos, es esencial para consolidar una gobernanza universitaria sostenible.

A pesquisa investigou os fatores determinantes do desempenho no Exame Nacional do Ensino Médio (ENEM) para os estudantes concluintes do ensino médio regular na cidade de Viçosa-MG…

A pesquisa investigou os fatores determinantes do desempenho no Exame Nacional do Ensino Médio (ENEM) para os estudantes concluintes do ensino médio regular na cidade de Viçosa-MG no período de 2015 a 2017. O estudo concluiu que o desempenho dos alunos é fortemente influenciado por uma combinação de fatores individuais, familiares (family background) e, de forma preponderante, pela dependência administrativa da escola frequentada

El Diseñador de Cursos con IA es una aplicación web gratuita en Google Apps Script que permite a docentes crear cursos académicos completos de forma rápida y profesional. G…

El Diseñador de Cursos con IA es una aplicación web gratuita en Google Apps Script que permite a docentes crear cursos académicos completos de forma rápida y profesional. Genera contenido profundo con citas APA 7ª edición (priorizando fuentes recientes) y cuestionarios interactivos con retroalimentación inmediata y opciones aleatorizadas. Su interfaz tipo LMS facilita la navegación paso a paso y permite exportar todo el material directamente a Google Docs, funcionando 100% en la nube sin necesidad de instalaciones.