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Your search keyword '"Luis Rivero"' showing total 198 results
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198 results on '"Luis Rivero"'

1. Proposing Usability-UX technologies for the design and evaluation of text-based chatbots

Author

Malu Gabriele Silva Mafra, Kennedy Nunes, Simara Rocha, Geraldo Braz Junior, Aristofanes Silva, Davi Viana, Williamson Silva, and Luis Rivero

Subjects
Inspection Checklist, Chatbots Evaluation, Usability, User Experience, Design Patterns, Computer software, QA76.75-76.765, Computer engineering. Computer hardware, TK7885-7895
Abstract

Chatbots are interactive systems that communicate using natural language with human users, via a textual interface or voice activation. These tools are useful for many spheres of business such as Customer Service, Sales, Education and Learning, Health and Entertainment. Recently, chatbots have become popular, with significant growth in the software industry, especially text-based chatbots. This is encouraging developers to create their own tools, as well as attracting efforts from researchers into this area. Despite this highlight, technologies to guarantee the quality of chatbots and user satisfaction are not keeping up with the growing demand for these tools. Considering this, there is a need to propose technologies capable of supporting developers and development teams in the process of building and evaluating chatbots. Therefore, this research proposes to develop artifacts applicable to the design and evaluation process of chatbots, based on quality attributes identified in systematic literature reviews related to Usability and User Experience (UX), due to the importance and impact that these aspects have on user satisfaction and the perceived quality of the system. The first artifact is the U2Chatbot inspection checklist, developed to assist development teams in the process of identifying defects in text-based chatbots. The second artifact is a set of interface design patterns, DP-U2Chatbot, containing useful examples to support developers in the process of building chatbots. The technologies were subjected to the necessary evaluations. The results of the empirical study regarding the U2Chatbot inspection checklist indicated that participants considered the technology useful for discovering defects in chatbots, however, ease of use could be improved. The participants' experience discreetly influenced the effectiveness and efficiency of the technique, leading us to believe that professionals with a certain level of inspection experience can benefit more from the checklist. Regarding the evaluation of DP-U2Chatbot design patterns, the results generally indicated that the technology is easy to understand and useful in supporting the design of chatbots, helping to build better tools.

Published
2024
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3. Characterization of a Shallow Coastal Aquifer in the Framework of a Subsurface Storage and Soil Aquifer Treatment Project Using Electrical Resistivity Tomography (Port de la Selva, Spain)

Author

Alex Sendrós, Aritz Urruela, Mahjoub Himi, Carlos Alonso, Raúl Lovera, Josefina C. Tapias, Luis Rivero, Ruben Garcia-Artigas, and Albert Casas

Subjects
aquifer geometry, electrical resistivity tomography, managed aquifer recharge, saltwater intrusion, soil aquifer treatment, sustainable development goal 6, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

Water percolation through infiltration ponds is creating significant synergies for the broad adoption of water reuse as an additional non-conventional water supply. Despite the apparent simplicity of the soil aquifer treatment (SAT) approaches, the complexity of site-specific hydrogeological conditions and the processes occurring at various scales require an exhaustive understanding of the system’s response. The non-saturated zone and underlying aquifers cannot be considered as a black box, nor accept its characterization from few boreholes not well distributed over the area to be investigated. Electrical resistivity tomography (ERT) is a non-invasive technology, highly responsive to geological heterogeneities that has demonstrated useful to provide the detailed subsurface information required for groundwater modeling. The relationships between the electrical resistivity of the alluvial sediments and the bedrock and the difference in salinity of groundwater highlight the potential of geophysical methods over other more costly subsurface exploration techniques. The results of our research show that ERT coupled with implicit modeling tools provides information that can significantly help to identify aquifer geometry and characterize the saltwater intrusion of shallow alluvial aquifers. The proposed approaches could improve the reliability of groundwater models and the commitment of stakeholders to the benefits of SAT procedures.

Published
2021
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4. Reseñas de libros

Author

Emilio Suárez de la Torre, Marisa Alía, Luis Alberto de Cuenca, Ángel López Chala, Pedro Redondo Reyes, Sara Macías Otero, José Antonio Fernández Delgado, Luis Rivero García, Ana Rodríguez Mayorgas, Luis Calero Rodríguez, and Natalio Fernández Marcos

Subjects
Philology. Linguistics, P1-1091
Published
2015

20. A Comparison of Algorithms for Intrusion Detection on Batch and Data Stream Environments

Author

Pérez, Jorge Luis Rivero, Ribeiro, Bernardete, and Ortiz, Kadir Hector

Subjects
Computer Science - Data Structures and Algorithms
Abstract

Intruders detection in computer networks has some deficiencies from machine learning approach, given by the nature of the application. The principal problem is the modest display of detection systems based on learning algorithms under the constraints imposed by real environments. This article focuses on the machine learning approach for network intrusion detection in batch and data stream environments. First, we propose and describe three variants of KDD99 dataset preprocessing including attribute selection. Secondly, a thoroughly experimentation is performed from evaluating and comparing representative batch learning algorithms on the variants obtained from KDD99 pre processing. Finally, since network traffic is a constant data stream, which can present concept drifting with high rate of false positive, along with the fact that there are not many researches addressing intrusion detection on streaming environments, lead us to make a comparison of various representative data stream classification algorithms. This research allows determining the algorithms that better perform on the proposed variants of KDD99 for both batch and data stream environments., Comment: in Spanish

Published
2017

33. Proposal of Data Processing Platform for Direct Marketing Data

Author

Pérez, Jorge Luis Rivero, Santana, Yaimara Peñate, and López, Pedro Harenton Martínez

Subjects
Computer Science - Computers and Society
Abstract

Data mining has been widely used to identify potential customers for a new product or service. In this article is done a study of previous work relating to the application of data mining methodologies for software projects, specifically for direct marketing projects. Several data sets of demographic and historical customer purchases data available for evaluation of algorithms in this area, some of them very new and current are described. The main contribution of this paper is the proposal of a platform for distributed data stream processing for the processes of targeting customers and building predictive models required response; thus facilitating several of the functional requirements for development environments., Comment: in Spanish

Published
2016

34. Attribute Learning for Network Intrusion Detection

Author

Pérez, Jorge Luis Rivero and Ribeiro, Bernardete

Subjects
Computer Science - Cryptography and Security, Computer Science - Learning
Abstract

Network intrusion detection is one of the most visible uses for Big Data analytics. One of the main problems in this application is the constant rise of new attacks. This scenario, characterized by the fact that not enough labeled examples are available for the new classes of attacks is hardly addressed by traditional machine learning approaches. New findings on the capabilities of Zero-Shot learning (ZSL) approach makes it an interesting solution for this problem because it has the ability to classify instances of unseen classes. ZSL has inherently two stages: the attribute learning and the inference stage. In this paper we propose a new algorithm for the attribute learning stage of ZSL. The idea is to learn new values for the attributes based on decision trees (DT). Our results show that based on the rules extracted from the DT a better distribution for the attribute values can be found. We also propose an experimental setup for the evaluation of ZSL on network intrusion detection (NID).

Published
2016

35. Mahalanobis Distance Metric Learning Algorithm for Instance-based Data Stream Classification

Author

Perez, Jorge Luis Rivero, Ribeiro, Bernardete, and Perez, Carlos Morell

Subjects
Computer Science - Learning
Abstract

With the massive data challenges nowadays and the rapid growing of technology, stream mining has recently received considerable attention. To address the large number of scenarios in which this phenomenon manifests itself suitable tools are required in various research fields. Instance-based data stream algorithms generally employ the Euclidean distance for the classification task underlying this problem. A novel way to look into this issue is to take advantage of a more flexible metric due to the increased requirements imposed by the data stream scenario. In this paper we present a new algorithm that learns a Mahalanobis metric using similarity and dissimilarity constraints in an online manner. This approach hybridizes a Mahalanobis distance metric learning algorithm and a k-NN data stream classification algorithm with concept drift detection. First, some basic aspects of Mahalanobis distance metric learning are described taking into account key properties as well as online distance metric learning algorithms. Second, we implement specific evaluation methodologies and comparative metrics such as Q statistic for data stream classification algorithms. Finally, our algorithm is evaluated on different datasets by comparing its results with one of the best instance-based data stream classification algorithm of the state of the art. The results demonstrate that our proposal is better

Published
2016

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