2.1 To gain access to the consultation rooms, users and visitors must show an official identification and fill out the Special Collection Visitor Registration Form provided by the Librarian. Access to the rooms and attention from staff are not guaranteed if an appointment has not been previously confirmed by the person in charge of the collection.

2.2 Users must not take food, drink, liquids and other materials that could damage the collections into the consultation rooms. When entering the facilities, users and visitors must leave any belongings outside that are not authorized by the Librarian or person responsible for the corresponding collection.

2.3 Visitors and users must respect the regulations on schedules and the code of conduct, and comply with all campus security measures and requirements.

3.1 Documentary collections may only be accessed in the consultation rooms; no materials may leave the facilities.

3.2 If the item to be consulted is already digitized, the user will be referred to the digital collection or to Tecnológico de Monterrey’s Institutional Repository. Researchers who need to physically consult such materials must justify this need on the original visitor registration form.

3.3 For any consultation of documentary collections, users and visitors must justify in writing the need for the material and schedule an appointment with the person in charge of the corresponding collection. In addition to having an appointment confirmed by the person in charge of the collection, to consult the documentary collections users must comply with the following requirements:

3.3.1 Students and active or retired employees of Tecnológico de Monterrey: must show their official credential and a written justification for requesting the material.

3.3.2 Professors and graduate students: only need to show their valid credential.

3.3.3 Researchers from other institutions: must show a personal identification and a letter establishing the nature and purpose of their research issued by the institution to which they are affiliated.

3.3.4 Independent researchers: must have a special authorization issued by the Documentary Heritage Administrative Office or the Administrative Office of the Library where the Special Collection is located.

3.4 The Librarian will be in charge of preparing the material and verifying that it is in appropriate condition to be consulted. Any material from the Documentary Heritage collections that, in the opinion of the Librarian or the Documentary Heritage Administrative Office, is not in adequate physical condition may not be consulted in its original format.

3.5 The Librarian will be solely responsible for delivering the requested item at the time and place previously confirmed to the user.

3.6 The user may enter the facilities to consult material with a pencil, a notebook and a laptop. Entering with any other article without the consent of the Librarian is prohibited.

3.7 Users may only take cell phones, tablets or cameras into the consultation room if the Librarian or person in charge of the collection has authorized it.

3.8 To access and use material from the special collections, users must comply with the Documentary Heritage Access and Consultation Manual.

3.9 This manual applies to all special documentary collections.

4.1 Have clean, unadorned hands (washed and dried, without cream or lotion, and without rings, bracelets or watches) and, if necessary, use the gloves and mask that the Librarian provides, or their own gloves and mask that have been previously checked and authorized.

4.2 Make notes with a pencil and paper or a laptop.

4.3 Avoid propping up reference materials or putting or leaving work tools or any other object on them.

4.4 Never scratch or mark any reference material, or erase the notes or marks it may contain.

4.5 Notify the Librarian if they find objects, utensils, manuscripts or anything other than the reference material.

4.6 Handle the consulted material with extreme care.

4.7 Ask the Librarian for support objects (weights, pillows or cushions, acid-free paper to turn pages) or assistance for handling the material.

4.8 Only take photographs of the material in accordance with the provisions of the “Material Reproduction” section of Tecnológico de Monterrey’s Cultural Heritage Guidelines. Any reproduction must be authorized and supervised by the Librarian or person in charge of the collection. The reproduced material may only be used for academic purposes and in accordance with Mexican Federal Copyright Law.

4.9 Return the requested material in the exact condition in which it was received from the Librarian.

4.10 Sanctions for non-compliance with any of these rules will be imposed at the discretion of the Documentary Heritage Office or the Office of the Library where the Special Collection is located.

2.1 Para el efecto de acceder a las instalaciones, el usuario o visitante, deberá mostrar una identificación oficial y llenar el formato de registro de visitas a Colecciones Especiales que le brinde el bibliotecario. No se garantiza el acceso a las salas ni la atención por parte del personal en los casos en los que no se cuente con una cita previa confirmada por el responsable de la colección.

2.2 Queda prohibido el acceso de comida, bebida, líquidos y otros materiales que puedan dañar las colecciones a las salas de consulta. Asimismo, al momento de ingresar a las instalaciones, los visitantes deberán dejar fuera cualquier pertenencia que no autorice el bibliotecario o responsable de las colecciones.

2.3 Los visitantes y usuarios deberán respetar los reglamentos correspondientes a horarios y código de comportamiento, además de cumplir con las medidas de seguridad y requisitos del campus.

3.1 El préstamo de los fondos documentales se efectuará únicamente en las salas de consulta y los materiales no podrán salir de las instalaciones.

3.2 En los casos en los cuales el material que se desea consultar ya se encuentre digitalizado, se referirá al usuario a la colección digital o al Repositorio Institucional del Tecnológico de Monterrey. Los investigadores que requieran consultar físicamente estos materiales, deberán justificar la necesidad de consultarlo en su formato original.

3.3 Para toda consulta de los fondos documentales será necesario justificar por escrito la necesidad del material y agendar una cita con el responsable de las colecciones. Para consultar los fondos documentales los usuarios deberán cumplir con los siguientes requisitos, además de la cita confirmada por el responsable de la colección:

3.3.1 Estudiantes, empleados activos o jubilados del Tecnológico de Monterrey: exhibir su credencial y una justificación por escrito de la solicitud del material.
3.3.2 Catedráticos y estudiantes de posgrado bastará con exhibir su credencial vigente.
3.3.3 Investigadores de otras instituciones: exhibir una identificación personal y una carta emitida por la institución a la que pertenezca, en la cual se establezca la naturaleza y propósito de su investigación.
3.3.4 Investigadores independientes: deberán contar con una autorización especial emitida por la Dirección de Patrimonio Documental o Dirección de la Biblioteca en donde se encuentre la Colección Especial.  

3.4 El Bibliotecario se encargará de preparar el material y verificar que se encuentre en condiciones adecuadas para ser consultado. Cualquier material de las colecciones de Patrimonio Documental que a juicio del bibliotecario o de la misma Dirección de Patrimonio Documental no se encuentre en condiciones físicas adecuadas, no podrá ser consultado en su formato original.

3.5 El Bibliotecario será el único responsable de entregar el material solicitado, la cual se efectuará en el horario y lugar confirmado previamente al usuario.

3.6 Al momento de consultar los materiales el usuario podrá ingresar con lápiz, libreta y una computadora portátil. Queda prohibida el ingreso de cualquier otro artículo sin el consentimiento del bibliotecario.

3.7 El ingreso de celulares, tabletas o cámaras fotográficas estará permitido solo en los casos en los que el bibliotecario o responsable de la colección lo autorice.

3.8 Para acceder y usar el material de colecciones especiales, el usuario deberá de cumplir con el Manual de Acceso y Consulta de Patrimonio Documental.

3.9 Este manual aplica para todas las colecciones especiales documentales.

4.1 Manos limpias (lavadas, secas, sin crema ni loción, sin anillos, pulseras ni relojes) y, en caso necesario, uso de guantes y cubreboca que le serán proporcionados por el bibliotecario, o bien que el mismo usuario lleve y que hayan sido previamente verificados y autorizados.

4.2 Usar lápiz y papel o bien computadora portátil para la toma de apuntes.

4.3 Evitar apoyar, poner o dejar útiles de trabajo o cualquier otro objeto sobre el material de consulta.

4.4 Queda prohibido rayar, marcar el material, así como también borrar los apuntes o marcas que contenga.

4.5 Notificar al bibliotecario en caso de encontrar objetos, utensilios, manuscritos o cualquier cosa ajena al material de consulta.

4.6 Manejar con extremo cuidado el material consultado.

4.7 Solicitar material de apoyo (pesos, almohadas o cojines, papel libre de ácido para pasar páginas) o ayuda para manipular el material al bibliotecario.

4.8 La toma de fotografías de los materiales deberá estar apegada a lo establecido en la sección de “Reproducción de material” de los Lineamientos de Patrimonio Cultural del Tecnológico de Monterrey. Toda reproducción deberá ser autorizada y supervisada por el bibliotecario o responsable de la colección. El material reproducido solamente podrá ser utilizado con fines académicos y con apego a la Ley Federal de Derechos de Autor.

4.9 Devolver el material solicitado en las condiciones exactas en las que fue recibido por el bibliotecario.

4.10 Las sanciones por el incumplimiento a cualquiera de las reglas, serán a discreción de la Dirección de Patrimonio Documental o Dirección de Biblioteca que custodie la colección.

• Cruz-Duarte, J. M., Amaya, I., Ortiz-Bayliss, J. C., Conant-Pablos, S. E., Terashima-Marín, H., & Shi, Y. Hyper-heuristics to customise metaheuristics for continuous optimisation. Swarm and Evolutionary Computation, 66. 2021.

• Diaz-Ramos, R.E.; Gomez-Cravioto, D.A.; Trejo, L.A.; Figueroa López, C.; Medina-Pérez, M.A. Towards a Resilience to Stress Index Based on Physiological Response: A Machine Learning Approach. Sensors, 21, 8293. Special Issue: Sensors and Digital Solutions for Human Health and Health Risk Monitoring. 2021.

• Hinojosa, S., Oliva, D., Cuevas, E. et al. Reducing overlapped pixels: a multi-objective color thresholding approach. Soft Comput 24, 6787–6807 (2020).

• Pérez-Torres, R., Torres-Huitzil, C. and Galeana-Zapién, H.: An On-Device Cognitive Dynamic Systems Inspired Sensing Framework for the IoT. IEEE Commun. Mag. 56(9): 154-161. 2018

• Ortiz-Bayliss, J.-C., Amaya, I. and Cruz-Duarte, J.-M., Gutierrez-Rodriguez, A.-E., Conant Pablos, S.-E. and Terashima-Marin, H. A General Framework based on Machine Learning for Algorithm Selection in Constraint Satisfaction Problems. Applied Sciences, 11(6), pp. 1-16. 2021.

• Martínez-Díaz, Y., Nicolás-Díaz, M., Méndez-Vázquez, H., Luevano, L. S., Chang, L., Gonzalez-Mendoza, M., Sucar, L. E. Benchmarking lightweight face architectures on specific face recognition scenarios. Artif Intell Rev 54, 6201–6244. 2021.

• Oliva, D., Hinojosa, S., Osuna-Enciso, V., Cuevas, E., Pérez-Cisneros, M., & Sanchez-Ante, G. Image segmentation by minimum cross entropy using evolutionary methods. Soft Computing, 23(2), 431-450. 2019.

• Falcón-Cardona, J.-G., Ishibuchi, H., Coello Coello, C.-A. and Michael Emmerich. Título del artículo: On the Effect of the Cooperation of Indicator-Based Multi-objective Evolutionary Algorithms. Revista: IEEE Transactions on Evolutionary Computation. 25(4): 681-695. 2021.

• Angeles-Ceron, J.-C., Ochoa-Ruiz, G., Chang, L. and Ali, S. Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusion, Under review at Medical Image Analysis, 2021.

• J. Rodríguez, J.-I. Mata-Sánchez, R. Monroy, O. Loyola-González, A. López-Cuevas. A one-class classification approach for bot detection on Twitter. Computers and Security 91, April 2020, article 101715 © Elsevier, 2020 ** 2021 Rómulo Garza's best paper award **

• Intelligence Artificielle pour la generation de microfictions littéraires (GenMicFic).
Raúl Monroy Borja (co-principal investigator). SEP - CONACYT - ANUIES - ECOS NORD Francia. Generación de microficciones, un género literario, mediante el uso de aprendizaje profundo, en particular transformadores (GPT-3) y SBERT. El mecanismo considera los elementos de un relato (inicio, desarrollo y conclusión), así como una variedad  de condiciones, tales como coherencia y narrativa en tercera persona.

• Proyecto Context-Aware Video Detection and Interpretation of Suspicious Behavior Using Distributed Robust Deep Learning
In collaboration with Dr. Paul Rad and Dr. David Han of the University of Texas at San Antonio.
Principal Investigator: Hugo Terashima Marín. Fondo Tec-UTSA $40000USD.
The proposed intelligent image/video analytics technology will allow users to: Search for images/videos by identifying not only objects but also structured relationships and attributes involving these objects, like: “find an image or video in which a man is carrying a bomb in the airport”; Understand the consistency between textual features and visual contents; Recognize all of the connections and effects between deep extracted features and retrieve images by considering all the dependencies of features; Identify and tag meaningful high-level situation descriptions presented in the retrieved video, like a person threatening another with a weapon, a person playing with a pet, etc.; and Based on results of previous objectives, work along a methodology for searching for meaningful features in order to detect suspicious behavior in video for a particular context.

• One Step closer to mental health: promptly detection of depression with wearables technology and voice analysis.
Principal Investigator: Luis Angel Trejo Rodríguez.
Symptoms of depression can be detected with machine learning algorithms techniques, using heart rate variability, sleeping patterns, type of personality and physical activity as attributes. The main objective is to develop an intelligent system to detect in time, through wearables, voice analysis, and machine learning, levels of depression of high risk in the final user.

• Space-time laminar computing: event-based spike neuromorphic processors for sensory computation.
Principal Investigator: César Torres Huitzil.
Computing in its diverse forms has become essential to most aspects of modern life, but it still fails in some of the basic tasks that biological systems (humans) perform easily and efficiently, such as perception, motor control, language processing, etc. Far beyond “intelligence”, computing based on current single-processor architectures and the associated semiconductor technology are facing fundamental physical limits (scalability, power consumption, process variations, noise margins, and fault tolerance, etc.) that prevent them achieving better performances only through improved processor technologies. The aim of this project is to provide the knowledge to develop a new class of event-driven spike neuroprocessors aimed at power efficient sensory computation under a neuro-inspired space-time laminar computational framework.

• Feature transformation for improving characterization of combinatorial optimization problems. Sectoral research fund for general education (CONACyT).
José Carlos Ortiz Bayliss, Research Associate.
The project seeks to improve the descriptive capabilities of hyper-heuristics by transforming the characterization of the problems they solve.

• Robust Surgical Tool Segmentation, Tracking and Depth Perception.
Gilberto Ochoa Ruiz in collaboration with Dr. Sharib Ali, from the University of Leeds (United Kingdom).
To develop new datasets, schemes and models for implement robust and real-time computer vision methods for Computer Integrated Surgery (CIS) applications and procedural quality assessment purposes. Students: Mansoor Ali Teevno.

• RECONDITE: Deep learning and image analysis methods for improving the endoscopic identification of kidney stones composition.
Gilberto Ochoa Ruiz in collaboration with Prof. Christian Daul the Centre de Recherche en Automatique de Nancy, CRAN (France) and the Institut National de la Santé et de la Recherche Medicale (INSERM).
To investigate deep learning algorithms for automatically classifying in vivo kidney stones from endoscopy images. Students: Francisco Lopez Tiro, Daniel Florez Araiza.

• ISOLATE: SegmentatIon and claSsification Of vascuLar pATtern symmEtries on cerebral vessels using DL.
Gilberto Ochoa Ruiz in collaboration with Dr. Christian Mata Miquel and Prof. Enrique Benitez from the Biomedical Engineering Research Center (CREB, Barcelona) of the Universitat Politecnica de Catalunya (Spain) and the Hospital Sant Joan de Deu (Barcelona).
To develop novel CADx tools for aiding physicians in the diagnosis of CP. Various algorithms for vessel segmentation and skeletonization have been explored and tested. The results of these preprocessing methods are to be used for classifying vascular pattern asymmetries.

• Arca Continental, Santiago E. Conant Pablos (investigador principal), IDEA: Innovación Mediante Ciencia de Datos e Inteligencia Artificial para Mejorar los Indicadores Clave de Negocio en Clientes del Canal Tradicional, 2019 - 2020.
• Google Inc. - APRU, Raúl Monroy Borja (experto) AI for all, 2017 - 2018.
• Google Inc., Raúl Monroy Borja (investigador principal) Formal Verification of Web Applications, 2015 - 2016.
• NIC - México, Raúl Monroy Borja (investigador principal) Dynamic Networks and Metrics for Ad Efficiency Ratings, 2017 - 2019.
• NIC - México, Raúl Monroy Borja (investigador principal) Countermeasures for DDoS Attacks Targeting the Domain Name System, 2017 - 2019.

• Cruz-Duarte, J. M., Amaya, I., Ortiz-Bayliss, J. C., Conant-Pablos, S. E., Terashima-Marín, H., & Shi, Y. Hyper-heuristics to customise metaheuristics for continuous optimisation. Swarm and Evolutionary Computation, 66. 2021.

• Diaz-Ramos, R.E.; Gomez-Cravioto, D.A.; Trejo, L.A.; Figueroa López, C.; Medina-Pérez, M.A. Towards a Resilience to Stress Index Based on Physiological Response: A Machine Learning Approach. Sensors, 21, 8293. Special Issue: Sensors and Digital Solutions for Human Health and Health Risk Monitoring. 2021.

• Hinojosa, S., Oliva, D., Cuevas, E. et al. Reducing overlapped pixels: a multi-objective color thresholding approach. Soft Comput 24, 6787–6807 (2020).

• Pérez-Torres, R., Torres-Huitzil, C. and Galeana-Zapién, H.: An On-Device Cognitive Dynamic Systems Inspired Sensing Framework for the IoT. IEEE Commun. Mag. 56(9): 154-161. 2018

• Ortiz-Bayliss, J.-C., Amaya, I. and Cruz-Duarte, J.-M., Gutierrez-Rodriguez, A.-E., Conant Pablos, S.-E. and Terashima-Marin, H. A General Framework based on Machine Learning for Algorithm Selection in Constraint Satisfaction Problems. Applied Sciences, 11(6), pp. 1-16. 2021.

• Martínez-Díaz, Y., Nicolás-Díaz, M., Méndez-Vázquez, H., Luevano, L. S., Chang, L., Gonzalez-Mendoza, M., Sucar, L. E. Benchmarking lightweight face architectures on specific face recognition scenarios. Artif Intell Rev 54, 6201–6244. 2021.

• Oliva, D., Hinojosa, S., Osuna-Enciso, V., Cuevas, E., Pérez-Cisneros, M., & Sanchez-Ante, G. Image segmentation by minimum cross entropy using evolutionary methods. Soft Computing, 23(2), 431-450. 2019.

• Falcón-Cardona, J.-G., Ishibuchi, H., Coello Coello, C.-A. and Michael Emmerich. Título del artículo: On the Effect of the Cooperation of Indicator-Based Multi-objective Evolutionary Algorithms. Revista: IEEE Transactions on Evolutionary Computation. 25(4): 681-695. 2021.

• Angeles-Ceron, J.-C., Ochoa-Ruiz, G., Chang, L. and Ali, S. Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusion, Under review at Medical Image Analysis, 2021.

• J. Rodríguez, J.-I. Mata-Sánchez, R. Monroy, O. Loyola-González, A. López-Cuevas. A one-class classification approach for bot detection on Twitter. Computers and Security 91, April 2020, article 101715 © Elsevier, 2020 ** 2021 Rómulo Garza's best paper award **

• Intelligence Artificielle pour la generation de microfictions littéraires (GenMicFic).
Raúl Monroy Borja (co-investigador principal). SEP - CONACYT - ANUIES - ECOS NORD Francia.
Generación de microficciones, un género literario, mediante el uso de aprendizaje profundo, en particular transformadores (GPT-3) y SBERT. El mecanismo considera los elementos de un relato (inicio, desarrollo y conclusión), así como una variedad  de condiciones, tales como coherencia y narrativa en tercera persona.

• Context-Aware Video Detection and Interpretation of Suspicious Behavior Using Distributed Robust Deep Learning
En colaboración con el Dr. Paul Rad y Dr. David Han de la Universidad de Texas en San Antonio. Investigador Principal: Hugo Terashima Marín. Fondo Tec-UTSA $40000USD.
The proposed intelligent image/video analytics technology will allow users to: Search for images/videos by identifying not only objects but also structured relationships and attributes involving these objects, like: “find an image or video in which a man is carrying a bomb in the airport”; Understand the consistency between textual features and visual contents; Recognize all of the connections and effects between deep extracted features and retrieve images by considering all the dependencies of features; Identify and tag meaningful high-level situation descriptions presented in the retrieved video, like a person threatening another with a weapon, a person playing with a pet, etc.; and Based on results of previous objectives, work along a methodology for searching for meaningful features in order to detect suspicious behavior in video for a particular context.

• One Step closer to mental health: promptly detection of depression with wearables technology and voice analysis.
Investigador principal: Luis Angel Trejo Rodríguez.
Symptoms of depression can be detected with machine learning algorithms techniques, using heart rate variability, sleeping patterns, type of personality and physical activity as attributes. The main objective is to develop an intelligent system to detect in time, through wearables, voice analysis, and machine learning, levels of depression of high risk in the final user.

• Space-time laminar computing: event-based spike neuromorphic processors for sensory computation.
Investigador principal: César Torres Huitzil.
Computing in its diverse forms has become essential to most aspects of modern life, but it still fails in some of the basic tasks that biological systems (humans) perform easily and efficiently, such as perception, motor control, language processing, etc. Far beyond “intelligence”, computing based on current single-processor architectures and the associated semiconductor technology are facing fundamental physical limits (scalability, power consumption, process variations, noise margins, and fault tolerance, etc.) that prevent them achieving better performances only through improved processor technologies. The aim of this project is to provide the knowledge to develop a new class of event-driven spike neuroprocessors aimed at power efficient sensory computation under a neuro-inspired space-time laminar computational framework.

• Feature transformation for improving characterization of combinatorial optimization problems. Fondo sectorial de investigación para la educación general (CONACyT).
José Carlos Ortiz Bayliss, investigador adjunto.
El proyecto busca mejorar las capacidades descriptivas de las hiper-heurísticas mediante la transformación de la caracterización de los problemas que resuelven.

• Robust Surgical Tool Segmentation, Tracking and Depth Perception. Gilberto Ochoa Ruiz in collaboration with Dr. Sharib Ali, from the University of Leeds (United Kingdom).
To develop new datasets, schemes and models for implement robust and real-time computer vision methods for Computer Integrated Surgery (CIS) applications and procedural quality assessment purposes. Students: Mansoor Ali Teevno.

• RECONDITE: Deep learning and image analysis methods for improving the endoscopic identification of kidney stones composition.
Gilberto Ochoa Ruiz in collaboration with Prof. Christian Daul the Centre de Recherche en Automatique de Nancy, CRAN (France) and the Institut National de la Santé et de la Recherche Medicale (INSERM).
To investigate deep learning algorithms for automatically classifying in vivo kidney stones from endoscopy images. Students: Francisco Lopez Tiro, Daniel Florez Araiza.

• ISOLATE: SegmentatIon and claSsification Of vascuLar pATtern symmEtries on cerebral vessels using DL.
Gilberto Ochoa Ruiz in collaboration with Dr. Christian Mata Miquel and Prof. Enrique Benitez from the Biomedical Engineering Research Center (CREB, Barcelona) of the Universitat Politecnica de Catalunya (Spain) and the Hospital Sant Joan de Deu (Barcelona).
To develop novel CADx tools for aiding physicians in the diagnosis of CP. Various algorithms for vessel segmentation and skeletonization have been explored and tested. The results of these preprocessing methods are to be used for classifying vascular pattern asymmetries.

• Arca Continental, Santiago E. Conant Pablos (investigador principal), IDEA: Innovación Mediante Ciencia de Datos e Inteligencia Artificial para Mejorar los Indicadores Clave de Negocio en Clientes del Canal Tradicional, 2019 - 2020.
• Google Inc. - APRU, Raúl Monroy Borja (experto) AI for all, 2017 - 2018.
• Google Inc., Raúl Monroy Borja (investigador principal) Formal Verification of Web Applications, 2015 - 2016.
• NIC - México, Raúl Monroy Borja (investigador principal) Dynamic Networks and Metrics for Ad Efficiency Ratings, 2017 - 2019.
• NIC - México, Raúl Monroy Borja (investigador principal) Countermeasures for DDoS Attacks Targeting the Domain Name System, 2017 - 2019.