Publications

  • B. He, X. Wei, M. Wei, Y. Shen, M Alvarez, and S. Schwartz. A shallow slow slip event in 2018 in the semidi segment of the alaska subduction zone detected by machine learning. Earth and Planetary Science Letters, 2023.
  • K. DeMedeiros, A. Hendawi, and M. Alvarez. A survey of AI-based anomaly detection in IoT and sensor networks. Sensors, 2023.
  • J. Rondeau, D. Deslauriers, T. Howard III, and M. Alvarez. A deep learning framework for finding illicit images/videos of children. Machine Vision and Applications, 33(5):66, 2022.
  • F. Borges, J. Balta, M. Roghanian, A. Gonçalves, M. Alvarez, and H. Pistori. The interference of optical zoom in human and machine classification of pollen grain images. In Workshop de Visão Computacional (WVC), 100–106. 2021.
  • D. Moreira, E. Pereira, and M. Alvarez. Improving real age estimation from apparent age data. In International Joint Conference on Neural Networks (IJCNN), 1–7. 2021.
  • J. Hope, M. Gjergji, J. Di Girolamo, M. Alvarez, and A. Qasem. Characterizing input-sensitivity in tightly-coupled collaborative graph algorithms. In IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), 287–296. 2021. Best Paper Award.
  • J. Couret, D. Moreira, D. Bernier, A. Loberti, E. Dotson, and M. Alvarez. Delimiting cryptic morphological variation among human malaria vector species using convolutional neural networks. PLOS NTDs, 14(12):1–21, 2020.
  • J. Souza, V. Weber, A. Gonçalves, M. Alvarez, M. Cereda, W. Gonçalves, V. Odakura, and H. Pistori. Viable yeast identification using bag of visual words in colored images. In Workshop de Visão Computacional (WVC). Uberlandia, Brasil, 2020.
  • G. Astolfi, A. Gonçalves, G. Menezes, F. Borges, A. Astolfi, E. Matsubara, M. Alvarez, and H. Pistori. Pollen73s: an image dataset for pollen grains classification. Ecological Informatics, 60:101165, 2020.
  • A. Jilling and M. Alvarez. Optimizing recommendations for clustering algorithms using meta-learning. In International Joint Conference on Neural Networks (IJCNN), 1–10. 2020.
  • M. Gjergji, V. Weber, L. Silva, R. Gomes, T. De Araujo, H. Pistori, and M. Alvarez. Deep learning techniques for beef cattle body weight prediction. In International Joint Conference on Neural Networks (IJCNN), 1–8. 2020.
  • D. Moreira, E. Pereira, and M. Alvarez. Peda 376k: a novel dataset for deep-learning based porn-detectors. In International Joint Conference on Neural Networks (IJCNN), 1–8. 2020.
  • P. Asadi, M. Gindy, M. Alvarez, and A. Asadi. A computer vision based rebar detection chain for automatic processing of concrete bridge deck gpr data. Automation in Construction, 112:103106, 2020.
  • E. Tetila, B. Machado, G. Menezes, A. Oliveira Jr, M. Alvarez, W. Amorim, N. Belete, G. da Silva, and H. Pistori. Automatic recognition of soybean leaf diseases using uav images and deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 17(5):903–907, 2020.
  • P. Asadi, M. Gindy, and M. Alvarez. A machine learning based approach for automatic rebar detection and quantification of deterioration in concrete bridge deck ground penetrating radar b-scan images. KSCE Journal of Civil Engineering, 23:2618–2627, 2019.
  • J. Rondeau and M. Alvarez. Deep modeling of human age guesses for apparent age estimation. In International Joint Conference on Neural Networks (IJCNN), 01–08. 2018.
  • L. Xu, D. Zhang, M. Alvarez, J. Morales, X. Ma, and J. Cavazos. Dynamic android malware classification using graph-based representations. In International Conference on Cyber Security and Cloud Computing (CSCloud), 220–231. Beijing, China, 2016.
  • W. Killian, R. Miceli, E. Park, M. Alvarez, and J. Cavazos. Performance improvement in kernels by guiding compiler auto-vectorization heuristics. White Paper, Performance Prediction, Partnership for Advanced Computing in Europe (PRACE), 2014.
  • L. Xu, W. Wang, M. Alvarez, J. Cavazos, and D. Zhang. Parallelization of shortest path graph kernels on multi-core cpus and gpus. In International Workshop on Programmability Issues for Heterogeneous Multicores (MULTIPROG). Vienna, Austria, 2014. Best Paper Award.
  • L. Xu, W. Wang, M. Alvarez, and J. Cavazos. Parallelization of the shortest path graph kernel on the gpu. In International Workshop on OpenCL (IWOCL). Atlanta, GA, USA, 2013.
  • H. Pistori, M. Pereira, M. Alvarez, and X. Qi. Open source tools and project-based teaching as enablers of research experience in computer vision students. In Congresso Brasileiro de Educação em Engenharia (COBENGE). Gramado, RS, Brasil, 2013.
  • M. Alvarez and C. Yan. A new protein graph model for function prediction. Computational Biology and Chemistry, 37:6–10, 2012.
  • E. Park, J. Cavazos, and M. Alvarez. Using graph-based program characterization for predictive modeling. In International Symposium on Code Generation and Optimization (CGO), 196–206. San Jose, CA, USA, 2012.
  • M. Alvarez. Graph Kernels and Applications in Bioinformatics. Ph. D. Dissertation, Department of Computer Science, Utah State University, 2011. Committee: Adele Cutler, Changhui Yan, Minghui Jiang, Vicki Allan, and Xiaojun Qi.
  • M. Alvarez, X. Qi, and C. Yan. A shortest-path graph kernel for estimating gene product semantic similarity. Journal of Biomedical Semantics, 2(1):3, 2011.
  • M. Alvarez and C. Yan. A graph-based semantic similarity measure for the gene ontology. Journal of Bioinformatics and Computational Biology, 2011.
  • M. Alvarez, X. Qi, and C. Yan. Go-based term semantic similarity. In Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances, chapter IX. IGI Publishing, 2011.
  • M. Alvarez and C. Yan. Exploring structural modeling of proteins for kernel-based enzyme discrimination. In Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 1–5. Montreal, Canada, 2010.
  • B. Shelton, J. Scoresby, T. Stowell, M. Capell, M. Alvarez, and C. Coats. A frankenstein approach to open source: the construction of a 3d game engine as meaningful educational process. IEEE Transactions on Learning Technologies, 3:85–90, 2010.
  • B. Shelton, M. Alvarez, M. Capell, C. Coats, J. Scoresby, and T. Stowell. The heat engine: a demonstration of sustainable design from an open-source 3d game engine. In Open Education Conference 2008: Celebrating Ten Years of Open Content. Logan, UT, USA, 2008.
  • B. Shelton, M. Alvarez, M. Capell, C. Coats, J. Scoresby, and T. Stowell. Iterations of an open-source 3d game engine: multiplayer environments for learners. In Meaningful Play. East Lansing, MI, USA, 2008.
  • M. Alvarez and S. Lim. A machine learning approach for one-stop learning. In Data Mining and Knowledge Discovery Technologies, chapter XIV. IGI Publishing, 2008.
  • M. Alvarez, J. Baiocchi, and J. Pow-Sang. Computing and higher education in peru. Inroads, 40:35–39, 2008.
  • R. Viana, R. Rodrigues, M. Alvarez, and H. Pistori. Svm with stochastic parameter selection for bovine leather defect classification. In Pacific Rim Conference on Advances in Image and Video Technology (PSIVT), 600–612. Santiago, Chile, 2007.
  • R. Rodrigues, R. Viana, A. Pasquali, H. Pistori, and M. Alvarez. Máquinas de vetores de suporte aplicadas à classificação de defeitos em couro bovino. In Workshop de Visão Computacional (WVC). São José do Rio Preto, SP, Brasil, 2007.
  • M. Alvarez and S. Lim. Discovering interchangeable words from string databases. In International Conference on Digital Information Management (ICDIM), 25–30. Lyon, France, 2007.
  • M. Alvarez and S. Lim. A graph modeling of semantic similarity between words. In International Conference on Semantic Computing (ICSC), 355–362. Irvine, CA, USA, 2007.
  • M. Alvarez. Modernización de la infraestructura de software, hardware y comunicaciones en los sistemas de información de la municipalidad provincial de tacna. 2005. Proyecto SNIP 20494 (US\$ 579. 852), Ministerio de Economía y Finanzas, Lima, Perú.
  • M. Alvarez. Um estudo comparativo de técnicas de pruning para redes neurais artificiais. Master's thesis, Instituto de Ciencias Matemáticas e de Computação, Universidade de São Paulo, 1999. Thesis Committee: André de Carvalho, Heloisa Camargo, and Maria Carolina Monard.
  • E. Cuadros, M. Alvarez, and A. de Carvalho. A multi-threaded object oriented simulator for ontogenic neural networks. In International Conference on Computational Intelligence and Multimedia Applications (ICCIMA), 276–281. Gippsland, Australia, 1998.
  • E. Cuadros, M. Alvarez, and A. de Carvalho. Kipu: um simulador de redes neurais ontogênicas orientado a objetos. In Simpósio Brasileiro de Redes Neurais (SBRN). Goiânia, GO, Brasil, 1997.
  • E. Cuadros, M. Alvarez, and A. de Carvalho. Simulador multi-threads de redes neurais ontogênicas orientado a objetos. Technical Report 59, ICMC-USP, 1997.