Alberto G. Salguero

Training to capture software requirements by role playing

Capture client requirements is considered one of the most important steps in the field of information technology projects. In University courses related to Computer Sciences, this subject is sometimes trained through interviews with real companies. However, voluntaries of companies participating in the interviews do not act like real interlocutors, as their interest is not the project itself, but just the interview. In this regard, we miss custom dynamics such as conflicts or demanding requests, which are inherent parts of interviews. To include these conditions for a more realistic experience, we propose the students themselves to also take the role of clients, randomly playing different characters that are based on a set of features that define their personalities and technical skills. In this way, teams of analysts interview teams of customers, generating scenarios not only derived from the project requirements, but also the personal and strategic interests of each part. Results show that the main problems of analysts to handle meetings are precisely related to the emotional behaviors, which influenced quality, fluency, empathy and appropriateness in the analysts conduct. Moreover, results show that after this experience the students achieved a strong improvement of abilities to dynamically manage an interview process, self-control skills, adequately express their ideas and anticipate client needs, compared to those who performed classical pre-designed interviews with real costumers. Students reported a gain of auto-assessment and a better empathy with clients, which increased the chances to successfully capture and prioritize requirements.

A flexible text analyzer based on ontologies: an application for detecting discriminatory language

AbstractLanguage can be a tool to marginalize certain groups due to the fact that it may reflect a negative mentality caused by mental barriers or historical delays. In order to prevent misuse of language, several agents have carried out campaigns against discriminatory language, criticizing the use of some terms and phrases. However, there is an important gap in detecting discriminatory text in documents because language is very flexible and, usually, contains hidden features or relations. Furthermore, the adaptation of approaches and methodologies proposed in the literature for text analysis is complex due to the fact that these proposals are too rigid to be adapted to different purposes for which they were intended. The main novelty of the methodology is the use of ontologies to implement the rules that are used by the developed text analyzer, providing a great flexibility for the development of text analyzers and exploiting the ability to infer knowledge of the ontologies. A set of rules for detecting discriminatory language relevant to gender and people with disabilities is also presented in order to show how to extend the functionality of the text analyzer to different discriminatory text areas.

Description Logic Class Expression Learning Applied to Sentiment Analysis

Description Logic (DL) Class Expression Learning (CEL) is a recent research topic of interest in the field of machine learning. Given a set of positive and negative examples of individuals in an ontology, the learning problem consists of finding a new class expression or concept such that most of the positive examples are instances of that concept, whereas the negatives examples are not. Therefore, the class expression learning can be seen as a search process in the space of concepts. In this chapter, the use of CEL algorithms is proposed as a tool to find the class expression that describes as much of the instances of positive documents as possible, being the main novelty of the proposal that the ontology is focused on inferring knowledge at syntactic level to determine the orientation of opinion. Furthermore, the use of CEL algorithms can be an alternative to complement other types of classifiers for sentiment analysis, incorporating such description classes as relevant new features into the knowledge base. To do so, an ontology-based text model for the representation of text documents is presented. The process for the ontology population and the use of the class expression learning of sentiment concepts are also described. To show the usefulness and effectiveness of our proposal, we use a set of documents about positive feedback focused on films to learn the positive sentiment concept and to classify the documents, comparing the results obtained against the result obtained by a C4.5 decision tree classifier, using the standard bag of words structure. Finally, we describe the problems that have arisen and solutions that have been adopted in our proposal.

Information management in interactive and non-interactive suspenseful storytelling

Suspense is one key feature in modern storytelling. One of the mechanisms to deliver a suspenseful experience to an audience is by means of controlling the information provided. The media, however, has a very strong impact on what kind of information can be delivered and how. Moreover, modern storytelling is usually conveyed interactively, in such a way that the audience is also part of the story. In this paper, we experiment and analyse the different impact of information management in interactive and non-interactive storytelling. We report on an experiment measuring the reported perceived amusement in interactive and non-interactive versions of a potentially suspenseful story, and we provide evidence that a passive, non-interactive audience usually prefers less information than an active interactive audience. The study provides informed insight on how these results could be used in real scenarios to deliver appropriate levels of information to enhance the perception of suspense.

Teaching concurrent and parallel programming by patterns

The use of programming patterns is considered to be a conceptual aid for programmers for developing understandable and testable concurrent and parallel code which is not only well built but also safe. By using programming patterns and their implementations as computer programs, difficult new concepts can be smoothly taught in lectures to students who before trying this teaching approach would have been reluctant to enroll on Parallel and Concurrent Programming courses. The approach presented in this paper consists in changing the traditional programming teaching and learning model to one where students are first introduced to syntactical constructs through selected introductory program code-patterns. In the theory lessons that follow, through the use of laptops with multi-core processors and access to the Virtual Campus services of our university, the students are easily able to implement and master the new concepts as they are taught. This teaching experiment was implemented to teach a concurrent and real-time programming course which is part of the computer engineering (CE) degree and taught during the third semester of the CE curriculum. Evaluation of the students academic performance when they had been taught with this approach revealed a 20.6% improvement in the students end-of-course grades. A new teaching model for undergraduate Parallel and Distributed Programming (PDC) courses which uses code-patterns.Assessment of students academic results compared with other courses on the same subject.More didactic PDC teaching based on shared resources from the outset.Petersons mutual exclusion and tumor growth simulation to introduce our approach.Set the fundamentals for developing our own self-study cloud courses and tools.

Using Ontologies for the Online Recognition of Activities of Daily Living

The recognition of activities of daily living is an important research area of interest in recent years. The process of activity recognition aims to recognize the actions of one or more people in a smart environment, in which a set of sensors has been deployed. Usually, all the events produced during each activity are taken into account to develop the classification models. However, the instant in which an activity started is unknown in a real environment. Therefore, only the most recent events are usually used. In this paper, we use statistics to determine the most appropriate length of that interval for each type of activity. In addition, we use ontologies to automatically generate features that serve as the input for the supervised learning algorithms that produce the classification model. The features are formed by combining the entities in the ontology, such as concepts and properties. The results obtained show a significant increase in the accuracy of the classification models generated with respect to the classical approach, in which only the state of the sensors is taken into account. Moreover, the results obtained in a simulation of a real environment under an event-based segmentation also show an improvement in most activities.

Parallel Cellular Automaton Tumor Growth Model

“In silico” experimentation allows us to simulate the effect of different therapies by handling model parameters. Although the computational simulation of tumors is currently a well-known technique, it is however possible to contribute to its improvement by parallelizing simulations on computer systems of many and multi-cores. This work presents a proposal to parallelize a tumor growth simulation that is based on cellular automata by partitioning of the data domain and by dynamic load balancing. The initial results of this new approach show that it is possible to successfully accelerate the calculations of a known algorithm for tumor-growth.

Ontology-based feature generation to improve accuracy of activity recognition in smart environments

Abstract In recent years, many techniques have been proposed for automatic recognition of Activities of Daily Living from smart home sensor data. However, classifiers usually use features created ad hoc. In this work, the use of ontologies is proposed for the fully automatic generation of these features. The process consists of converting the original dataset into an ontology and then combine all the concepts and relations in that ontology to obtain relevant class expressions. The high formalization of ontologies allows us to reduce the search space by discarding many meaningless expressions, such as contradictory or unsatisfiable expressions. The relevant class expressions are then used as features by the classifiers to build the classification model. To validate our proposal, we have used as reference the results obtained by four different classification algorithms that use the most commonly used features.

Improving Activity Classification Using Ontologies to Expand Features in Smart Environments

Activity recognition is a promising field of research aiming to develop solutions within smart environments to provide relevant solutions on ambient assisted living, among others. The process of activity recognition aims to recognize the actions and goals of one or more person in a environment with a set of sensors are deployed, basing on the sensor data stream that capture a series of observations of actions and environmental conditions. This contributions presents the initial results from a new methodology that considers the use of ontologies to expand the set of feature vector, which is computed by using the sensor data stream, that is used in the process of activity recognition by data-driven approaches. The obtained results indicates that the use of extended feature vectors provided by the use of ontology offers a better accuracy regarding the original feature vectors used in the process of activity recognition with different data-driven approaches.

The Long Path of Frustration : A Case Study with Dead by Daylight

Playability is a key factor in video-games. From a narrative standpoint, the play process is usually designed as sequences of episodes triggered by the player’s motivations, which unfold along a sense of suspense-relief. Suspense, as a factor on engagement, has a strong impact on the narrative of video-games: when it decreases, so does the engagement. This is a common pattern when players are aware that losing is unavoidable. As we point out, many players disconnect from the game in this situation. In this paper we evaluate how suspense affects playability, to analyse how the lack of uncertainty due to the knowledge of the rules may degrade Dead by Daylight game players experience when they are bound to fail. We have observed that players acknowledging that there are no chances to win tend to leave the game. Results also reveal that suspense is modulated by the player’s knowledge of the game.