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Item type:Publication, Fast High Resolution Blood Flow Estimation and Clutter Rejection via an Alternating Optimization Problem(Cornell University, 2020-11-03)This paper introduces a computationally efficient technique for estimating high-resolution Doppler blood flow from an ultrafast ultrasound image sequence. More precisely, it consists in a new fast alternating minimization algorithm that implements a blind deconvolution method based on robust principal component analysis. Numerical investigation carried out on \textit{in vivo} data shows the efficiency of the proposed approach in comparison with state-of-the-art methods. - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Interpreter and Applied Development Environment for Learning Concepts of Object Oriented Programming(European Organization for Nuclear Research, 2020-10-26)The programing languages are classified by its paradigms, some paradigms are easier to understand than others, or at least we have that impression. But when we learn our first language, what paradigm is the best? Many people could say that structured is better, because is a recipe to follow, or maybe an object oriented, because it-s a natural definition, others could say that functional is better. Actually the debate of which should be the first language to learn is growing, and it will continue growing. Nowadays we decide which language to learn as a result of a necessity, more than analyse which one is easier to understand or which should be the correct learning process. This paper will present a tool in Spanish that could be used as an instrument to understand these concepts of object oriented and its management before starting the programming. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Efficient strategies for hierarchical text classification: External knowledge and auxiliary tasks(Cornell University, 2020-05-05)In hierarchical text classification, we perform a sequence of inference steps to predict the category of a document from top to bottom of a given class taxonomy. Most of the studies have focused on developing novels neural network architectures to deal with the hierarchical structure, but we prefer to look for efficient ways to strengthen a baseline model. We first define the task as a sequence-to-sequence problem. Afterwards, we propose an auxiliary synthetic task of bottom-up-classification. Then, from external dictionaries, we retrieve textual definitions for the classes of all the hierarchy's layers, and map them into the word vector space. We use the class-definition embeddings as an additional input to condition the prediction of the next layer and in an adapted beam search. Whereas the modified search did not provide large gains, the combination of the auxiliary task and the additional input of class-definitions significantly enhance the classification accuracy. With our efficient approaches, we outperform previous studies, using a drastically reduced number of parameters, in two well-known English datasets. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 3D Shape Matching for Retrieval and Recognition(Springer, 2020-01-01)Nowadays, multimedia information such as images and videos are present in many aspects of our lives. Three-dimensional information is also becoming important in different applications, for instance, entertainment, medicine, security, art, just to name a few. It is therefore necessary to study how to properly process 3D information taking advantage of the properties that it provides. This chapter gives an overview of 3D shape matching and its applications in shape retrieval and recognition. In order to present the subject, we opted for describing in detail four approaches with good balance among maturity and novelty, namely, the PANORAMA descriptor, spin images, functional maps, and Heat Kernel Signatures for retrieval. We also aim at stressing the importance of this field in areas such as computer vision and computer graphics, as well as the importance of addressing the main challenges on this research field.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Designing valid humanitarian logistics scenario sets: Application to recurrent Peruvian floods and earthquakes(IGI Global, 2020-09-18)Literature about humanitarian logistics (HL) has developed a lot of innovative decision support systems during the last decades to support decisions such as location, routing, supply, or inventory management. Most of those contributions are based on quantitative models but, generally, are not used by practitioners who are not confident with. This can be explained by the fact that scenarios and datasets used to design and validate those HL models are often too simple compared to the real situations. In this chapter, a scenario-based approach based on a five-step methodology has been developed to bridge this gap by designing a set of valid scenarios able to assess disaster needs in regions subject to recurrent disasters. The contribution, usable by both scholars and practitioners, demonstrates that defining such valid scenario sets is possible for recurrent disasters. Finally, the proposal is validated on a concrete application case based on Peruvian recurrent flood and earthquake disasters. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Soft Sensor Design for Restricted Variable Sampling Time(Elsevier, 2020-01-01)Difficult-to-obtain variables in industrial applications have led to the rise of soft sensors, which use prior system information and measurements to estimate these difficult-to-obtain variables. In real systems, the measurements that need to be estimated by a soft sensor are often infrequently measured or delayed. Sometimes, these delays and sampling time are variable in time. Though there are papers considering soft sensors in the presence of time delays and different sampling times, the variation of those parameters has not been considered when evaluating the adequacy of the soft sensors. Therefore, this paper will evaluate the impact of such variations for a data-driven soft sensor and propose modifications of the soft sensor that increase its robustness. The reliability of its estimate will be shown using the Bauer-Premaratne-Durán Theorem. Furthermore, the soft sensor will be simulated applying it to a continuous stirred tank reactor. Simulation showed that the modified soft sensor gives good estimates, whereas the traditional soft sensor gives an unstable estimate. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design of a 3D control system using PTV-VISSIM to manage Vehicle traffic(The World Academy of Research in Science and Engineering, 2020-05-25)Vehicle congestion has become a conflict that affects an increasing number of countries. To avoid this, it is necessary to design a control system that can benefit those affected by traffic congestion. In this work, PTV-VISSIM will be applied to obtain real-time data when simulating a traffic control system in Lima, Peru. This case study explains the architecture of the prototype which has 2 stages: PTV-VISSIM and the development of the prototype that includes 4 materials (controller, wireless network connection, motion detector and LED traffic lights), components that are critical for the correct functioning of the simulator. Three trials were carried out on different days for the study, based on the traffic flow of each day, which shows that there was a reduction in vehicle traffic. The results could help the authorities to obtain a measure that reduces the time of vehicular traffic congestion at an accessible cost, improving the quality of life of citizens. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Assessment of supervised classifiers for the task of detecting messages with suicidal ideation(Elsevier, 2020-08-01)According to the World Health Organization (WHO) close to 800,000 people worldwide die by suicide each year, and many more attempts to do it. In consequence, the WHO recognizes suicide as a global public health priority, which affects not only rich countries but poor and middle-income countries as well. This study makes a systematic analysis of 28 supervised classifiers using different features of the corpus Life to detect messages with suicidal ideation and depression to know if these can be used in an automatic prevention online system. The Life Corpus, used in this research, is a bilingual text corpus (English and Spanish) oriented to the detection of suicide ideation. This corpus was constructed retrieving texts from several social networks and its quality was measured using mutual annotation agreement. The different experiments determined that the classifier with the best performance was KStar, with the corpus features POS-SYNSETS-NUM, achieving the best results with the ROC Area metrics of 0,81036 and F-measure of 0,7148. The present research fulfilled the objective of discovering which supervised classifiers and which features are the most suitable for the automatic classification of messages with suicidal ideation using the Life Corpus. Also, given the imbalance of the results, a new precision measure was developed called the Two-dimensional Accuracy and Recovery Index (GDP), which can provide better results, in unbalanced systems, than the usual measures to assess the quality of the results (measure F, Area ROC), and thus increase the number of messages at risk of suicidal ideation, detected at the cost of receiving more messages that are not related to suicide or vice versa. Computer Science; Suicidal ideation; supervised classifiers; Machine Learning; Social networks; automatic classification; suicid10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design of a Web Application for the Detection of Diabetes using Machine Learning(The World Academy of Research in Science and Engineering, 2020-07-25)Diabetes is a disease that has always had an impact on humanity because of its terrible effects.Machine learning is the method used in this work, which allows us, through its classifiers, to predict upcoming events, in this case, probability of disease.The case study is Lima, Peru, where most cases of diabetes are seen, also using a chat Bot, to represent the doctor in the consultation.The result of this research was the design of this online doctor, which is used for the best care of patients and predicts diabetes, thus helping people who need a consultation.This research was designed for its next development, in order to serve as a starting point for other research with different diseases.