Daniela Godoy | Universidad del Centro de la Provincia de Buenos Aires (original) (raw)

Papers by Daniela Godoy

Knowledge and Information Systems, 2016

Matrix computations are both fundamental and ubiquitous in computational science, and as a result... more Matrix computations are both fundamental and ubiquitous in computational science, and as a result they are frequently used in numerous disciplines of scientific computing and engineering. Due to the highcomputational complexity of matrix operations, which makes them critical to the performance of a large number of applications, their efficient execution in distributed environments becomes a crucial issue. This work proposes a novel approach for distributing sparse-matrix arithmetic operations on computer clusters aiming at speeding-up the processing of high-dimensional matrices. The approach focuses on how to split such operations into independent parallel tasks by considering the intrinsic characteristics that distinguish each type of operation and the particular matrices involved. The approach was applied to the most commonly-used arithmetic operations between matrices. The performance of the presented approach was evaluated considering a high-dimensional text feature selection approach and two real-world datasets. Experimental evaluation showed that the proposed approach helped to significantly reduce the computing times of big-scale matrix operations, when compared to serial and multi-thread implementations as well as several linear-algebra software libraries.

Lucid dreams (LDs) and out-of-body experiences (OBEs) are phenomena characterized by the return o... more Lucid dreams (LDs) and out-of-body experiences (OBEs) are phenomena characterized by the return of higher cognitive abilities during sleep, including reflective self-awareness and abstract thought. Given the similarities in reflective self-awareness between LDs and OBEs, some authors consider them variations of the same phenomenon. This study aimed to compare the differences in content between non-LDs, LDs, and OBEs obtained from 60 participants over a two-month period, with 916 dream reports collected. The dream reports were analyzed using automatic methods based on Lexicons such as NRC Emotion Lexicon and Empath, and were scored based on Hall and Van de Castle's dream content scoring system with variations and additional measures. Results showed that OBE dreams were characterized by higher occurrences of negative emotions compared to both lucid and non-lucid dreams as measured by automatic and manual scoring systems. Also, more OBE dream reports contained words related to agen...

This dataset presents a large-scale collection of millions of Twitter posts related to the corona... more This dataset presents a large-scale collection of millions of Twitter posts related to the coronavirus pandemic in Spanish language. The collection was built by monitoring public posts written in Spanish containing a diverse set of hashtags related to the COVID-19, as well as tweets shared by the official Argentinian government offices, such as ministries and secretaries at different levels. Data was collected between March and June 2020 using the Twitter API, and will be periodically updated. In addition to tweets IDs, the dataset includes information about mentions, retweets, media, URLs, hashtags, replies, users and content-based user relations, allowing the observation of the dynamics of the shared information. Data is presented in different tables that can be analysed separately or combined. The dataset aims at serving as source for studying several coronavirus effects in people through social media, including the impact of public policies, the perception of risk and related disease consequences, the adoption of guidelines, the emergence, dynamics and propagation of disinformation and rumours, the formation of communities and other social phenomena, the evolution of health related indicators (such as fear, stress, sleep disorders, or children behaviour changes), among other possibilities. In this sense, the dataset can be useful for multi-disciplinary researchers related to the different fields of data science, social network analysis, social computing, medical informatics, social sciences, among others.

arXiv (Cornell University), Aug 20, 2018

Software systems naturally evolve, and this evolution often brings design problems that cause sys... more Software systems naturally evolve, and this evolution often brings design problems that cause system degradation. Architectural smells are typical symptoms of such problems, and several of these smells are related to undesired dependencies among modules. The early detection of these smells is important for developers, because they can plan ahead for maintenance or refactoring efforts, thus preventing system degradation. Existing tools for identifying architectural smells can detect the smells once they exist in the source code. This means that their undesired dependencies are already created. In this work, we explore a forward-looking approach that is able to infer groups of likely module dependencies that can anticipate architectural smells in a future system version. Our approach considers the current module structure as a network, along with information from previous versions, and applies link prediction techniques (from the field of social network analysis). In particular, we focus on dependency-related smells, such as Cyclic Dependency and Hub-like Dependency, which fit well with the link prediction model. An initial evaluation with two open-source projects shows that, under certain considerations, the predictions of our approach are satisfactory. Furthermore, the approach can be extended to other types of dependency-based smells or metrics.

Although hate speech detection has been extensively tackled in the literature as a classification... more Although hate speech detection has been extensively tackled in the literature as a classification task, recent works have raised concerns about the robustness of such systems. Understanding hate speech remains a significant challenge for creating reliable datasets and automatizing its detection. An essential goal for detection techniques is to ensure that they are not unduly biased towards or against particular norms of offense. For example, ensuring that models are not reproducing common biases in society associating certain terms with hateful content. This situation is known as unintended bias, in which models learn usual associations between words (commonly called identity terms) which causes them to classify content as hateful just because it contains one identity word. In this work, we tackle the issue of measuring and explaining the sensitivity of models to the presence of identity terms during model training. To this end, focusing on a misogyny detection task, we study how mo...

Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization

Recommender systems have recently been criticized for promoting bias and trapping users into filt... more Recommender systems have recently been criticized for promoting bias and trapping users into filter bubbles. This phenomenon not only limits potential user interactions but also threatens the broadness of content consumption. In a music recommender, for example, this situation can limit user perspective as music allows people to develop cultural knowledge and empathy. As a fundamental characteristic of users' content consumption is its diversity, it is necessary to break the bubbles and recommend potentially relevant and diverse songs from outside the influence of such bubbles. To address this problem, we present MRecuri (Music RECommender for filter bUbble diveRsIfication), a music recommendation technique to foster the diversity and novelty of recommendations. A preliminary evaluation over Last.fm listening data showed the potential of MRecuri to increase the diversity and novelty of recommendations compared with state-of-the-art techniques. CCS CONCEPTS • Information systems → Social recommendation; • Computing methodologies → Neural networks.

This volume contains the proceedings with the research contributions presented at the Second Work... more This volume contains the proceedings with the research contributions presented at the Second Workshop on Online Misinformation-and Harm-Aware Recommender Systems (OHARS’2021) co-located with the 15th ACM Recommender Systems Conference (RecSys’2021). These proceedings describe the specific workshop goals and format, and contain the papers presented during the online event held on October 2nd, 2021.

Personal and Ubiquitous Computing

Several intelligent agents have been developed in the last decade to help users with the vast amo... more Several intelligent agents have been developed in the last decade to help users with the vast amount of information available in the World Wide Web (WWW). Despite the efficiency of these agents depend on the knowledge they have about users, which is contained into user profiles, there are diverse considerations about what a profile should contain and how to construct it. Due to this fact, developers have to face the problem of, not only specifying the user profile content each time, but also its acquisition and adaptation to change in user interests. In this work we present an architecture which prescribes these aspects of the user profiling task. The goal of this architecture is to guide developers in the construction of agents involved with textual-based tasks. The results obtained from its application in a search agent, called P ersonalSearcher, are also reported in this work.

In order to be capable of exploiting context for pro-active information recommendation, agents ne... more In order to be capable of exploiting context for pro-active information recommendation, agents need to extract and understand user activities based on their knowledge of the user interests. In this paper, we propose a novel approach for context-aware recommendation in browsing assistants based on the integration of user profiles, navigational patterns and contextual elements. In this approach, user profiles built using an unsupervised Web page clustering algorithm are used to characterize user ongoing activities and behavior patterns. Experimental evidence show that using longer-term interests to explain active browsing goals user assistance is effectively enhanced. Keywords: User Profiling, Context-awareness, Browsing Assistants.

This work presents an approach in which user profiles, represented by ontologies that were learne... more This work presents an approach in which user profiles, represented by ontologies that were learned by an interface agent, are compared to foster collaboration for information retrieval from the web. It is shown how the interface agent represents the knowledge about the user along with the pro les that were empirically developped. Departing from a specific matching model, briey presented here, quantitative results were achieved by comparing such particular ontologies in a fully automatic way. The results are presented and their implications are discussed. We argue

Cyberbullying and cyberaggression are serious and widespread issues increasingly affecting Intern... more Cyberbullying and cyberaggression are serious and widespread issues increasingly affecting Internet users. With the widespread of social media networks, bullying, once limited to particular places, can now occur anytime and anywhere. Cyberaggression refers to aggressive online behaviour that aims at harming other individuals, and involves rude, insulting, offensive, teasing or demoralising comments through online social media. Considering the dangerous consequences that cyberaggression has on its victims and its rapid spread amongst internet users (specially kids and teens), it is crucial to understand how cyberbullying occurs to prevent it from escalating. Given the massive information overload on the Web, there is an imperious need to develop intelligent techniques to automatically detect harmful content, which would allow the large-scale social media monitoring and early detection of undesired situations. This paper presents the Isistanitos’s approach for detecting aggressive con...

ArXiv, 2021

Purpose. We present an approach for forecasting mental health conditions and emotions of a given ... more Purpose. We present an approach for forecasting mental health conditions and emotions of a given population during the COVID-19 pandemic based on language expressions used in social media. This approach permits anticipating high prevalence periods in shortto medium-term time horizons. Design. Mental health conditions and emotions are captured via markers, which link social media contents with lexicons. First, we build descriptive timelines for decision makers to monitor the evolution of markers, and their correlation with crisis events. Second, we model the timelines as time series, and support their forecasting, which in turn serve to identify high prevalence points for the estimated markers. Findings. Results showed that different time series forecasting strategies offer different capabilities. In the best scenario, the emergence of high prevalence periods of emotions and mental health disorders can be satisfactorily predicted with a neural network strategy, even when limited data...

Fourteenth ACM Conference on Recommender Systems

This volume contains the papers presented at the Workshop on Online Misinformation-and Harm-Aware... more This volume contains the papers presented at the Workshop on Online Misinformation-and Harm-Aware Recommender Systems (OHARS'2020) co-located with the 14th ACM Recommender Systems Conference (RecSys'2020). These proceedings describe the specific workshop goals, format and contain the papers that were presented during the online event held on September 25th, 2020.

Fifteenth ACM Conference on Recommender Systems

Recommender systems play a central role in online information consumption and user decision-makin... more Recommender systems play a central role in online information consumption and user decision-making by leveraging user-generated information at scale to assist users in finding relevant information and establishing new social relationships. Just as recommendation techniques have become powerful tools that are inserted in most social platforms, they could also involuntarily spread unwanted content and other types of online harms. The same fundamental concepts on which these techniques rely make them facilitators of such unwanted diffusion. To increase the user-perceived quality of recommender systems and mitigating the negative effects of the multiple forms of online harms, it is essential to provide recommender systems with harm-aware mechanisms. To further research in this direction, this Second edition of the Workshop on Online Misinformation-and Harm-Aware Recommender Systems (OHARS 2021) aimed at fostering research in recommender systems that can mitigate the negative effects of online harms by fostering the recommendation of safe content and trustworthy users, with a special interest in research tackling the negative effects of the propagation of harmful content referring to the COVID-19 crisis. CCS CONCEPTS • Information systems → Recommender systems.

Enfoque UTE

El etiquetado social consiste en clasificar recursos web, con el uso de palabras o etiquetas libr... more El etiquetado social consiste en clasificar recursos web, con el uso de palabras o etiquetas libremente elegidas por los usuarios. La simplicidad y apertura de los sistemas de etiquetado social para organizar recursos, es la clave de su éxito en Internet. Existen numerosos enfoques para facilitar al usuario el proceso de etiquetado, permitiéndole reutilizar etiquetas y optimizando así su limitado tiempo de lectura y escritura. Este documento propone un enfoque híbrido diferente, que resuelve de forma sencilla el problema de las recomendaciones basadas únicamente en el contenido del recurso, fusionando la lista de recomendaciones con las etiquetas más populares del historial de etiquetas del usuario, permitiéndole así reutilizar los términos asignados a otros recursos.

Enfoque UTE, Vol. 11 Nº 4 (2020), 2020

El etiquetado social consiste en clasificar recursos web, con el uso de palabras o etiquetas libr... more El etiquetado social consiste en clasificar recursos web, con el uso de palabras o etiquetas libremente elegidas por los usuarios. La simplicidad y apertura de los sistemas de etiquetado social para organizar recursos, es la clave de su éxito en Internet. Existen numerosos enfoques para facilitar al usuario el proceso de etiquetado, permitiéndole reutilizar etiquetas y optimizando así su limitado tiempo de lectura y escritura. Este documento propone un enfoque híbrido diferente, que resuelve de forma sencilla el problema de las recomendaciones basadas únicamente en el con-tenido del recurso, fusionando la lista de recomendaciones con las etiquetas más populares del historial de etiquetas del usuario, permitiéndole así reutilizar los términos asignados a otros recursos. Social tagging consists of classifying web resources using words or tags freely chosen by users. The simplicity and openness of social tagging systems to organize resources is the key to your success on the internet. ...

2018 IEEE International Conference on Software Architecture Companion (ICSA-C), Apr 1, 2018

The degree of dependencies among the modules of a software system is a key attribute to character... more The degree of dependencies among the modules of a software system is a key attribute to characterize its design structure and its ability to evolve over time. Several design problems are often correlated with undesired dependencies among modules. Being able to anticipate those problems is important for developers, so they can plan early for maintenance and refactoring efforts. However, existing tools are limited to detecting undesired dependencies once they appeared in the system. In this work, we investigate whether module dependencies can be predicted (before they actually appear). Since the module structure can be regarded as a network, i.e, a dependency graph, we leverage on network features to analyze the dynamics of such a structure. In particular, we apply link prediction techniques for this task. We conducted an evaluation on two Java projects across several versions, using link prediction and machine learning techniques, and assessed their performance for identifying new dependencies from a project version to the next one. The results, although preliminary, show that the link prediction approach is feasible for package dependencies. Also, this work opens opportunities for further development of software-specific strategies for dependency prediction.

IETE Journal of Research

In recent years, processing and analysing large graphs has become a major need in many research a... more In recent years, processing and analysing large graphs has become a major need in many research areas. Distributed graph processing programming models and frameworks arised as a natural solution to...

Knowledge and Information Systems, 2016

Matrix computations are both fundamental and ubiquitous in computational science, and as a result... more Matrix computations are both fundamental and ubiquitous in computational science, and as a result they are frequently used in numerous disciplines of scientific computing and engineering. Due to the highcomputational complexity of matrix operations, which makes them critical to the performance of a large number of applications, their efficient execution in distributed environments becomes a crucial issue. This work proposes a novel approach for distributing sparse-matrix arithmetic operations on computer clusters aiming at speeding-up the processing of high-dimensional matrices. The approach focuses on how to split such operations into independent parallel tasks by considering the intrinsic characteristics that distinguish each type of operation and the particular matrices involved. The approach was applied to the most commonly-used arithmetic operations between matrices. The performance of the presented approach was evaluated considering a high-dimensional text feature selection approach and two real-world datasets. Experimental evaluation showed that the proposed approach helped to significantly reduce the computing times of big-scale matrix operations, when compared to serial and multi-thread implementations as well as several linear-algebra software libraries.

Lucid dreams (LDs) and out-of-body experiences (OBEs) are phenomena characterized by the return o... more Lucid dreams (LDs) and out-of-body experiences (OBEs) are phenomena characterized by the return of higher cognitive abilities during sleep, including reflective self-awareness and abstract thought. Given the similarities in reflective self-awareness between LDs and OBEs, some authors consider them variations of the same phenomenon. This study aimed to compare the differences in content between non-LDs, LDs, and OBEs obtained from 60 participants over a two-month period, with 916 dream reports collected. The dream reports were analyzed using automatic methods based on Lexicons such as NRC Emotion Lexicon and Empath, and were scored based on Hall and Van de Castle's dream content scoring system with variations and additional measures. Results showed that OBE dreams were characterized by higher occurrences of negative emotions compared to both lucid and non-lucid dreams as measured by automatic and manual scoring systems. Also, more OBE dream reports contained words related to agen...

This dataset presents a large-scale collection of millions of Twitter posts related to the corona... more This dataset presents a large-scale collection of millions of Twitter posts related to the coronavirus pandemic in Spanish language. The collection was built by monitoring public posts written in Spanish containing a diverse set of hashtags related to the COVID-19, as well as tweets shared by the official Argentinian government offices, such as ministries and secretaries at different levels. Data was collected between March and June 2020 using the Twitter API, and will be periodically updated. In addition to tweets IDs, the dataset includes information about mentions, retweets, media, URLs, hashtags, replies, users and content-based user relations, allowing the observation of the dynamics of the shared information. Data is presented in different tables that can be analysed separately or combined. The dataset aims at serving as source for studying several coronavirus effects in people through social media, including the impact of public policies, the perception of risk and related disease consequences, the adoption of guidelines, the emergence, dynamics and propagation of disinformation and rumours, the formation of communities and other social phenomena, the evolution of health related indicators (such as fear, stress, sleep disorders, or children behaviour changes), among other possibilities. In this sense, the dataset can be useful for multi-disciplinary researchers related to the different fields of data science, social network analysis, social computing, medical informatics, social sciences, among others.

arXiv (Cornell University), Aug 20, 2018

Software systems naturally evolve, and this evolution often brings design problems that cause sys... more Software systems naturally evolve, and this evolution often brings design problems that cause system degradation. Architectural smells are typical symptoms of such problems, and several of these smells are related to undesired dependencies among modules. The early detection of these smells is important for developers, because they can plan ahead for maintenance or refactoring efforts, thus preventing system degradation. Existing tools for identifying architectural smells can detect the smells once they exist in the source code. This means that their undesired dependencies are already created. In this work, we explore a forward-looking approach that is able to infer groups of likely module dependencies that can anticipate architectural smells in a future system version. Our approach considers the current module structure as a network, along with information from previous versions, and applies link prediction techniques (from the field of social network analysis). In particular, we focus on dependency-related smells, such as Cyclic Dependency and Hub-like Dependency, which fit well with the link prediction model. An initial evaluation with two open-source projects shows that, under certain considerations, the predictions of our approach are satisfactory. Furthermore, the approach can be extended to other types of dependency-based smells or metrics.

Although hate speech detection has been extensively tackled in the literature as a classification... more Although hate speech detection has been extensively tackled in the literature as a classification task, recent works have raised concerns about the robustness of such systems. Understanding hate speech remains a significant challenge for creating reliable datasets and automatizing its detection. An essential goal for detection techniques is to ensure that they are not unduly biased towards or against particular norms of offense. For example, ensuring that models are not reproducing common biases in society associating certain terms with hateful content. This situation is known as unintended bias, in which models learn usual associations between words (commonly called identity terms) which causes them to classify content as hateful just because it contains one identity word. In this work, we tackle the issue of measuring and explaining the sensitivity of models to the presence of identity terms during model training. To this end, focusing on a misogyny detection task, we study how mo...

Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization

Recommender systems have recently been criticized for promoting bias and trapping users into filt... more Recommender systems have recently been criticized for promoting bias and trapping users into filter bubbles. This phenomenon not only limits potential user interactions but also threatens the broadness of content consumption. In a music recommender, for example, this situation can limit user perspective as music allows people to develop cultural knowledge and empathy. As a fundamental characteristic of users' content consumption is its diversity, it is necessary to break the bubbles and recommend potentially relevant and diverse songs from outside the influence of such bubbles. To address this problem, we present MRecuri (Music RECommender for filter bUbble diveRsIfication), a music recommendation technique to foster the diversity and novelty of recommendations. A preliminary evaluation over Last.fm listening data showed the potential of MRecuri to increase the diversity and novelty of recommendations compared with state-of-the-art techniques. CCS CONCEPTS • Information systems → Social recommendation; • Computing methodologies → Neural networks.

This volume contains the proceedings with the research contributions presented at the Second Work... more This volume contains the proceedings with the research contributions presented at the Second Workshop on Online Misinformation-and Harm-Aware Recommender Systems (OHARS’2021) co-located with the 15th ACM Recommender Systems Conference (RecSys’2021). These proceedings describe the specific workshop goals and format, and contain the papers presented during the online event held on October 2nd, 2021.

Personal and Ubiquitous Computing

Several intelligent agents have been developed in the last decade to help users with the vast amo... more Several intelligent agents have been developed in the last decade to help users with the vast amount of information available in the World Wide Web (WWW). Despite the efficiency of these agents depend on the knowledge they have about users, which is contained into user profiles, there are diverse considerations about what a profile should contain and how to construct it. Due to this fact, developers have to face the problem of, not only specifying the user profile content each time, but also its acquisition and adaptation to change in user interests. In this work we present an architecture which prescribes these aspects of the user profiling task. The goal of this architecture is to guide developers in the construction of agents involved with textual-based tasks. The results obtained from its application in a search agent, called P ersonalSearcher, are also reported in this work.

In order to be capable of exploiting context for pro-active information recommendation, agents ne... more In order to be capable of exploiting context for pro-active information recommendation, agents need to extract and understand user activities based on their knowledge of the user interests. In this paper, we propose a novel approach for context-aware recommendation in browsing assistants based on the integration of user profiles, navigational patterns and contextual elements. In this approach, user profiles built using an unsupervised Web page clustering algorithm are used to characterize user ongoing activities and behavior patterns. Experimental evidence show that using longer-term interests to explain active browsing goals user assistance is effectively enhanced. Keywords: User Profiling, Context-awareness, Browsing Assistants.

This work presents an approach in which user profiles, represented by ontologies that were learne... more This work presents an approach in which user profiles, represented by ontologies that were learned by an interface agent, are compared to foster collaboration for information retrieval from the web. It is shown how the interface agent represents the knowledge about the user along with the pro les that were empirically developped. Departing from a specific matching model, briey presented here, quantitative results were achieved by comparing such particular ontologies in a fully automatic way. The results are presented and their implications are discussed. We argue

Cyberbullying and cyberaggression are serious and widespread issues increasingly affecting Intern... more Cyberbullying and cyberaggression are serious and widespread issues increasingly affecting Internet users. With the widespread of social media networks, bullying, once limited to particular places, can now occur anytime and anywhere. Cyberaggression refers to aggressive online behaviour that aims at harming other individuals, and involves rude, insulting, offensive, teasing or demoralising comments through online social media. Considering the dangerous consequences that cyberaggression has on its victims and its rapid spread amongst internet users (specially kids and teens), it is crucial to understand how cyberbullying occurs to prevent it from escalating. Given the massive information overload on the Web, there is an imperious need to develop intelligent techniques to automatically detect harmful content, which would allow the large-scale social media monitoring and early detection of undesired situations. This paper presents the Isistanitos’s approach for detecting aggressive con...

ArXiv, 2021

Purpose. We present an approach for forecasting mental health conditions and emotions of a given ... more Purpose. We present an approach for forecasting mental health conditions and emotions of a given population during the COVID-19 pandemic based on language expressions used in social media. This approach permits anticipating high prevalence periods in shortto medium-term time horizons. Design. Mental health conditions and emotions are captured via markers, which link social media contents with lexicons. First, we build descriptive timelines for decision makers to monitor the evolution of markers, and their correlation with crisis events. Second, we model the timelines as time series, and support their forecasting, which in turn serve to identify high prevalence points for the estimated markers. Findings. Results showed that different time series forecasting strategies offer different capabilities. In the best scenario, the emergence of high prevalence periods of emotions and mental health disorders can be satisfactorily predicted with a neural network strategy, even when limited data...

Fourteenth ACM Conference on Recommender Systems

This volume contains the papers presented at the Workshop on Online Misinformation-and Harm-Aware... more This volume contains the papers presented at the Workshop on Online Misinformation-and Harm-Aware Recommender Systems (OHARS'2020) co-located with the 14th ACM Recommender Systems Conference (RecSys'2020). These proceedings describe the specific workshop goals, format and contain the papers that were presented during the online event held on September 25th, 2020.

Fifteenth ACM Conference on Recommender Systems

Recommender systems play a central role in online information consumption and user decision-makin... more Recommender systems play a central role in online information consumption and user decision-making by leveraging user-generated information at scale to assist users in finding relevant information and establishing new social relationships. Just as recommendation techniques have become powerful tools that are inserted in most social platforms, they could also involuntarily spread unwanted content and other types of online harms. The same fundamental concepts on which these techniques rely make them facilitators of such unwanted diffusion. To increase the user-perceived quality of recommender systems and mitigating the negative effects of the multiple forms of online harms, it is essential to provide recommender systems with harm-aware mechanisms. To further research in this direction, this Second edition of the Workshop on Online Misinformation-and Harm-Aware Recommender Systems (OHARS 2021) aimed at fostering research in recommender systems that can mitigate the negative effects of online harms by fostering the recommendation of safe content and trustworthy users, with a special interest in research tackling the negative effects of the propagation of harmful content referring to the COVID-19 crisis. CCS CONCEPTS • Information systems → Recommender systems.

Enfoque UTE

El etiquetado social consiste en clasificar recursos web, con el uso de palabras o etiquetas libr... more El etiquetado social consiste en clasificar recursos web, con el uso de palabras o etiquetas libremente elegidas por los usuarios. La simplicidad y apertura de los sistemas de etiquetado social para organizar recursos, es la clave de su éxito en Internet. Existen numerosos enfoques para facilitar al usuario el proceso de etiquetado, permitiéndole reutilizar etiquetas y optimizando así su limitado tiempo de lectura y escritura. Este documento propone un enfoque híbrido diferente, que resuelve de forma sencilla el problema de las recomendaciones basadas únicamente en el contenido del recurso, fusionando la lista de recomendaciones con las etiquetas más populares del historial de etiquetas del usuario, permitiéndole así reutilizar los términos asignados a otros recursos.

Enfoque UTE, Vol. 11 Nº 4 (2020), 2020

El etiquetado social consiste en clasificar recursos web, con el uso de palabras o etiquetas libr... more El etiquetado social consiste en clasificar recursos web, con el uso de palabras o etiquetas libremente elegidas por los usuarios. La simplicidad y apertura de los sistemas de etiquetado social para organizar recursos, es la clave de su éxito en Internet. Existen numerosos enfoques para facilitar al usuario el proceso de etiquetado, permitiéndole reutilizar etiquetas y optimizando así su limitado tiempo de lectura y escritura. Este documento propone un enfoque híbrido diferente, que resuelve de forma sencilla el problema de las recomendaciones basadas únicamente en el con-tenido del recurso, fusionando la lista de recomendaciones con las etiquetas más populares del historial de etiquetas del usuario, permitiéndole así reutilizar los términos asignados a otros recursos. Social tagging consists of classifying web resources using words or tags freely chosen by users. The simplicity and openness of social tagging systems to organize resources is the key to your success on the internet. ...

2018 IEEE International Conference on Software Architecture Companion (ICSA-C), Apr 1, 2018

The degree of dependencies among the modules of a software system is a key attribute to character... more The degree of dependencies among the modules of a software system is a key attribute to characterize its design structure and its ability to evolve over time. Several design problems are often correlated with undesired dependencies among modules. Being able to anticipate those problems is important for developers, so they can plan early for maintenance and refactoring efforts. However, existing tools are limited to detecting undesired dependencies once they appeared in the system. In this work, we investigate whether module dependencies can be predicted (before they actually appear). Since the module structure can be regarded as a network, i.e, a dependency graph, we leverage on network features to analyze the dynamics of such a structure. In particular, we apply link prediction techniques for this task. We conducted an evaluation on two Java projects across several versions, using link prediction and machine learning techniques, and assessed their performance for identifying new dependencies from a project version to the next one. The results, although preliminary, show that the link prediction approach is feasible for package dependencies. Also, this work opens opportunities for further development of software-specific strategies for dependency prediction.

IETE Journal of Research

In recent years, processing and analysing large graphs has become a major need in many research a... more In recent years, processing and analysing large graphs has become a major need in many research areas. Distributed graph processing programming models and frameworks arised as a natural solution to...