Ciencias Exactas y Ciencias de la Salud

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Feature transformations for improving the performance of selection hyper-heuristics on job shop scheduling problems
(Instituto Tecnológico y de Estudios Superiores de Monterrey) Garza Santisteban, Fernando; Terashima Marín, Hugo; Özcan, Ender; School of Engineering and Sciences; School of Engineering and Sciences; Campus Monterrey; Amaya Contreras, Ivan
Solving Job Shop (JS) scheduling problems is a hard combinatorial optimization problem. Nevertheless, it is one of the most present problems in real-world scheduling environments. Throughout the recent computer science history, a plethora of methods to solve this problem have been proposed. Despite this fact, the JS problem remains a challenge. The domain it- self is of interest for the industry and also many operations research problems are based on this problem. The solution to JS problems is overall beneficial to the industry by generating more efficient processes. Authors have proposed solutions to this problem using dispatch- ing rules, direct mathematical methods, meta-heuristics, among others. In this research, the application of feature transformations for the generation of improved selection constructive hyper-heuristics (HHs) is shown. There is evidence that applying feature transformations on other domains has produced promising results; Also, no previous work was found where this approach has been used for the JS domain. This thesis is presented to earn the Master’s degree in Computer Science of Tecnolo ́gico de Monterrey. The research’s main goals are: (1) the assessment of the extent to which HHs can perform better on JS problems than single heuristics, and that they are not specific to the instances used to train them; and (2), the degree to which HHs generated with feature transformations are revamped. Experiments were carried out using instances of various sizes published in the literature. The research involved profiling the set of heuristics chosen, ana- lyzing the interactions between the heuristics and feature values throughout the construction of a solution, and studying the performance of HHs without transformations and by using two transformations found in the literature. Results indicate that for the instances used, HHs were able to outperform the results achieved by single heuristics. Regarding feature transforma- tions, it was found that they induce a scaling effect to feature values throughout the solution process, which produces more stable HHs, with a median performance comparable to HHs without feature transformations, but not necessarily better. Results are conclusive in terms of the objectives of this research. Nevertheless, there are several ideas that could be explored to improve the HHs, which are outlined and discussed in the final Chapter of the thesis. The following major contributions are derived from this research: (1) applying a se- lection constructive HH approach, with feature transformations, to the JS domain; (2) the rationale behind the JS subproblem dependance in terms of the solution paths followed by the heuristics, which has a great impact in the training process of the HHs; (3) a method to deter- mine the most suitable parameters to apply feature transformations, which could be extended for other domains of combinatorial optimization problems; and (4) a framework for studying HHs in the Job Shop domain.
Detection of Violent Behavior in Open Environments Using Pose Estimation and Neural Networks
(Instituto Tecnológico y de Estudios Superiores de Monterrey) Chong Loo, Kevin Brian Kwan; TERASHIMA MARIN, HUGO; 65879; Terashima Marín, Hugo; tolmquevedo, emipsanchez; Conant Pablos, Santiago Enrique; Escuela de Ingeniería y Ciencia; Campus Monterrey
People’s safety and security have always been an issue to attend. With the coming of techno- logical advances, part of it has been used to improve safeguards, though other aspects, without precautions, have made people even more vulnerable. People can get their sensitive data stolen or become victims of transaction fraud. These may be crimes done without physical interac- tion, but felonies with physical violence still exist. Some solutions for pedestrian safety are guards, police cars patrolling, sensors and security cameras. Nonetheless, these methods only react when the crime is happening or, even more critical, when it has already occurred, and the damage has been done. Therefore, numerous methods have been implemented using Arti- ficial Intelligence in order to solve this problem. Many approaches to detect violent behavior and action recognition rely on 3D convolutional neural networks (3D CNNs), spatial tempo- ral models, long short term memory networks, pose estimation among other implementations. However, in the current state of the art, how these approaches are used do not work perfectly and are not adapted to an uncontrolled environment. Therefore, a significant contribution from this work was the development of a new solu- tion model that is able to detect violent behavior. This approach focuses on using pedestrian detection, tracking, pose estimation and neural networks to predict pedestrian behavior in video frames. This method uses a time window frame to extract joint angles, given by the pose estimation algorithm, as features for classifying behavior. At the moment of developing this thesis project, there were not many databases with violent behavior videos. The ones that existed were low quality; cluttered were pedestrians cannot be seen clearly, and with unfixed camera angles. Consequently, another important contribution of this work was creating a new database, Kranok-NV, with a total of 3,683 normal and violent videos. This database was used to train and test the solution model. For the evaluation, a protocol was designed using 10-fold cross- validation. With the implemented solution model, accuracy of more than 98% was achieved on the Kranok-NV database. This approach surpassed the performance of state of the art methods for violence detection and action recognition in the developed database. Though this new solution model is able to detect violent and normal behavior, it can be easily extended to classify more types of behaviors. Further work requires to test this approach in emerging databases of videos and optimize specific areas of the solution model. Additionally, the contributions of this work can aid in the development of new approaches.

Ciencias Exactas y Ciencias de la Salud

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