DOI: 10.3897/jucs.119828

Authors: Seyedeh Aridis Ahadi, Kian Jazayeri, Sahand Tebyani

Abstract: The identification of individuals who indicate suicidal behaviors on social media platforms has become more significant in recent years. The utilization of textual data may help in the development of systems aimed at predicting individuals' mental health. This article proposes an integrated framework for the identification of suicidal thoughts in social media through the implementation of a layered classifier model consisting of a convolutional neural network (CNN) and a long short-term memory (LSTM) model. Various combinations of embedding techniques, activation functions, and solver algorithms are applied to the network. The mixture of these techniques forms 82 distinct methodologies employed, followed by comparing the results obtained. A collection of approximately 60,0000 user posts from 2018 to 2020 was compiled from Reddit for the study. It has resulted in the combination of TF-IDF (word embedding), RReLU (activation function), and Adam (solver algorithm) reaching the highest overall performance. The model achieved impressive accuracy, F1 Score, and AUC of 86%, with precision and recall score of 91% and 82% respectively. It was fitted in just 8.69 seconds, demonstrating its time efficiency as well. This approach has great potential for creating a platform in real life to not only reduce the social impacts of suicidality and mental illness, but also increase social access to mental health resources for all individuals.

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DOI: 10.3897/jucs.109080

Authors: F. Didem Alay, Nagehan İlhan, M. Tahir Güllüoğlu

Abstract: Photovoltaic (PV) energy systems are a leading type of renewable energy systems globally. Predicting PV energy production accurately is crucial for maintaining efficient energy grids, making informed decisions in the energy market, and reducing maintenance costs. To ensure high accuracy and optimal production, it is essential to monitor and analyze these variables regularly. Solar radiation and temperature are two meteorological variables that directly affect the quantity of PV energy generated in PV facilities. The Performance Ratio (PR) is a critical parameter for assessing PV plant performance. A comprehensive model was constructed in this study to forecast solar radiation and temperature using multiple machine learning methods, including Instance-Based K-Nearest Neighbor Algorithm (IBK), Linear Regression, Random Forests, Random Tree, Multilayer Perceptron (MLP), and MLP Regression. Moreover, we used time series approaches, such as Simple Exponential Smoothing (SES), Error-Trend-Seasonality (ETS), Autoregressive Integrated Moving Average (ARIMA) and Holt Winter's Seasonal Method (HWES) models for PV systems prediction. Initially, we conducted daily forecasts as well as 1-step ahead forecasts at 5-minute intervals for both solar radiation and temperature. It is crucial to subject both variables to the same methodology in order to construct precise models for forecasting PV. Secondly, we compared the predicted values of solar radiation and temperature with the actual energy yield of the power plant to calculate energy production. Subsequently, a relative analysis of data mining models and time series models have been performed depending on the statistical error criteria like RMSE, MAPE, MABE, MAE, MSE, and direction accuracy (DAC).

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DOI: 10.3897/jucs.69711

Authors: Nahia Ugarte, Mikel Larrañaga, Ana Arruarte

Abstract: Recommender Systems provide users with content or products they are interested in. The main purpose of Recommender Systems is to find, among the vast amount of information that is available or advertised on the Internet, content that meets the user’s needs i.e., a product or content that satisfies his or her wishes. These systems are being used more and more in many of the services of our daily lives. In this paper, a systematic mapping review that explores the use of Rec- ommender Systems in formal learning stages is presented. The paper analyzes what kinds of items the Recommender Systems suggest, who the users that receive the recommendations are, what kinds of information the Recommender Systems use to carry out the recommendation process, the algorithms and techniques the Recommender Systems employ and, finally, how the Recommender Systems have been evaluated. The results obtained in the review will make it possible to iden- tify not only the current situation in this field but also some of the challenges that are still to be faced.

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DOI: 10.3217/jucs-025-02-0122

Authors: Koldo Zabaleta, Unai Lopez-Novoa, Ivan Pretel, Diego López-De-Ipiña, Vincenzo Cartelli, Giuseppe Modica, Orazio Tomarchio

Abstract: Human computation or Human-based computation (HBC) is a paradigm that considers the design and analysis of information processing systems in which humans participate as computational agents performing small tasks and being orchestrated by a computer system. In particular, humans perform small pieces of work and a computer system is in charge of orchestrating their results. In this work, we want to exploit this potential to improve the take-up of e-service United States of Americage by citizens interacting with governments. To that end, we propose Citizenpedia, a human computation framework aimed at fostering citizen's involvement in the public administration. Citizenpedia is presented as a web application with two main components: the Question Answering Engine, where citizens and civil servants can post and solve doubts about e-services and public administration, and the Collaborative Procedure Designer, where citizens can collaborate with civil servants in the definition and improvement of new administrative procedures and e-services. In this work, we present the design and prototype of Citizenpedia and two evaluation studies conducted: the first one, a set of on-line surveys about the component's design, and the second one, a face-to-face user evaluation of the prototype. These evaluations showed us that the participants of the tests found the platform attractive, and pointed out several improvement suggestions regarding user experience of e-services.

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DOI: 10.3217/jucs-022-03-0375

Authors: Georgios Lappas, Amalia Triantafillidou, Prodromos Yannas, Anastasia Kavada, Alexandros Kleftodimos, Olga Vasileiadou

Abstract: The purpose of this study is to examine the use of social media such as Facebook, Twitter, and YouTube by candidates running for the 2014 Greek Municipal Elections by addressing the following questions: (1) which factors affect social media adoption by municipal candidates?, and (2) whether social media usage along with the popularity of candidates' social media pages influence candidates' vote share. Results indicate that social media are not very popular campaigning tools among municipal candidates in Greece. This implies that Greek candidates still rely on traditional ways to lure their voters. Furthermore, findings reveal that candidates running in large municipalities are more likely to utilize social media (i.e., Facebook, Twitter, and YouTube) as means of political marketing. In addition, challengers seem to prefer Facebook and Twitter as campaign tools while males tend to focus on YouTube to attract voters. Despite the low adoption rate, results suggest that candidates who made use of social media won more votes compared to candidates who were not social media users. Moreover, it was found that a candidate's Facebook page and YouTube channel popularity are good indicators of the candidate's vote share.

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DOI: 10.3217/jucs-022-03-0302

Authors: Masoud Sagharichian, Morteza Langouri, Hassan Naderi

Abstract: The variety of applications requiring graph analysis is growing rapidly. Diameter is one of the most important metrics of a graph. The diameter is important in both designing algorithms for graphs and understanding the nature and evolution of graphs. So, detecting diameter of large graphs is very important. We propose an algorithm to calculate the diameter of such graphs. The main goal of this algorithm is to reduce the number of breadth-first searches required to determine the diameter of the graph by finding a better upper bound for the eccentricity of vertices. Based on experimental results, our proposed algorithm can quickly detect the exact diameter of the large-scale real world graphs with a few number of breadth-first searches.

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DOI: 10.3217/jucs-021-11-1425

Authors: Hsing-Wen Wang, Yen-Chun Wu, Tse-Ping Dong

Abstract: Social networking websites have become increasingly popular, and have also become the main media not only to connect lives socially, but also to affect brand image and consumers' purchase intention. The purpose of this paper is to incorporate the Facebook fan page and e-journal provide over the Internet (cloud e-journal) with the uses and gratification theory to test the impact on brand image and purchase intention through the use of cloud learning. We used cloud learning material from the Ivy League in Taiwan in our case study. This paper also applied structural equation modeling to analyze the data collected from members of the Ivy League Facebook fan page and the Cloud users’ e-journal. The results of this study demonstrated that for the members of the Ivy League Facebook fan page, purchase intention was positively and significantly influenced, regardless of any use intention factors, based on the uses and gratification theory. In addition, using the Facebook fan page and Cloud e-journal would also positively and significantly affect the brand image for Internet users. Moreover, with the Ivy League fan page and the Cloud e-journal's improved brand image, there is an increase in intention to buy the journals and relevant services. This paper also demonstrated that six features of the Cloud e-journal did have a moderating effect on the purchase intention. Our results provide suggestions to those who attempt to build cloud learning solutions for customers, and are also helpful to those who wish to apply the Facebook fan page to customer relationship marketing platforms.

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DOI: 10.3217/jucs-020-11-1583

Authors: Gia Vo, Richard Lai

Abstract: In recent years, there has been a tremendous growth in e-Government services due to advances in Information Communication Technology and the number of citizens engaging in e-Government transactions. In government administration, it is very time consuming to process different types of documents and there are many data input problems. There is also a need to satisfy citizens’ requests to retrieve government information and to link these requests to build an online document without asking the citizen to input the data more than once. To provide an e-Government service which is easy to access, fast and secure, the e-Document plays an important role in the management and interoperability of e-Government Systems. To meet these challenges, this paper presents a Secure Multilayer e-Application (SMeA) method for improving e-Government processes. This method involves five steps: namely (i) identifying an e-Template; (ii) building a SMeA; (iii) mapping the data; (iv) processing the e-Application; and (v) approving the e-Application. The first step involves requirements analysis and the last four involve data analysis for building a SMeA. To demonstrate its usefulness, we applied SMeA to a case study of an application for a licence to set up a new business in Vietnam.

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DOI: 10.3217/jucs-019-12-1761

Authors: Alois Paulin

Abstract: In this paper we present a novel model for governing societies based on modern information technology, which neither relies on manual bureaucratic labour, nor depends on process-based e-government services. We analyse the flaws of the latter and argue that e-government is not feasible for sustainable governance due to permanently changing regulation; instead we propose a model in which people can govern themselves in a self-service manner by relying on constellations of data stored in a network of governmental databases to which citizens and government agents have read- and write access under conditions defined by then-valid regulation.

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DOI: 10.3217/jucs-019-09-1257

Authors: Pilar Garrido, Irene Ruiz, Miguel Gómez-Nieto

Abstract: This paper describes an alert system for people monitoring based on multi-agent using maps. This system monitors the users' physical context using their mobile phone. The data acquisition is made using the available sensors on mobile phone. A set of agents on mobile phones are responsible for collecting, processing and sending data to the server. Another set of agents on server stores the data and checks the preconditions of the restrictions associated with the user, in order to trigger the appropriate alarms. These alarms are sent not only to the user that violates a restriction, but also to the one responsible for supervising the person monitored. The supervisor can control all the supervised people through a map interface with functionality such as sending a SMS or making a call directly from the map. The applicability of the system will be illustrated with an example for Alzheimer patient monitoring. These patients will carry on normal activity in the home environment or home for the elderly, monitored by their family or by nurses.

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DOI: 10.3217/jucs-018-08-1048

Authors: Victor Ströele, Geraldo Zimbrão, Jano Souza

Abstract: Social networks are dynamic social structures consisting of individuals or organizations, usually represented by nodes tied by one or more relationship type. Analyzing these structures enables us to detect several inter and intra connections between people in and outside their organizations. In this context, we construct a multi-relational scientific social network where researchers may have four different types of relationships with each other. We adopt some criteria such as relationship age in order to assign a weight to relationships and to enable the modeling of a scientific social network as close as possible to reality. Using clustering techniques with maximum flow measure, we identify the social structure and research communities in a way that allows us to evaluate the knowledge flow in the Brazilian scientific community.

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DOI: 10.3217/jucs-018-05-0623

Authors: Ismael Hasan, Javier Parapar, Álvaro Barreiro

Abstract: Text preprocessing and segmentation are critical tasks in search and text mining applications. Due to the huge amount of documents that are exclusively presented in PDF format, most of the Data Mining (DM) and Information Retrieval (IR) systems must extract content from the PDF files. In some occasions this is a difficult task: the result of the extraction process from a PDF file is plain text, and it should be returned in the same order as a human would read the original PDF file. However, current tools for PDF text extraction fail in this objective when working with complex documents with multiple columns. For instance, this is the case of official government bulletins with legal information. In this task, it is mandatory to get correct and ordered text as a result of the application of the PDF extractor. It is very usual that a legal article in a document refers to a previous article and they should be offered in the right sequential order. To overcome these difficulties we have designed a new method for extraction of text in PDFs that simulates the human reading order. We evaluated our method and compared it against other PDF extraction tools and algorithms. Evaluation of our approach shows that it significantly outperforms the results of the existing tools and algorithms.

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DOI: 10.3217/jucs-018-04-0487

Authors: John Lee, Gerard Cabunducan, Francis George C. Cabarle, Raphael Castillo, Jasmine Malinao

Abstract: Past work analysing elections in online domains has largely ignored the underlying social networks present in such environments. Here, the Wikipedia Request for Adminship (RfA) process is studied within the context of a social network and several factors influencing different stages of the voting process are pinpointed. Machine-learning problems were formulated to test the identified factors. The different facets explored are: election participation, decision making in elections, and election outcome. Our results show that voters tend to participate in elections that their contacts have participated in. Furthermore, there is evidence showing that an individual's decision-making is influenced by his contacts' actions. The properties of voters within the social graph were also studied; results reveal that candidates who gain the support of an influential coalition tend to succeed in elections. Additionally, detailed analyses on different classes of voters and candidates were made. Finally, the structural properties corresponding to networks of election participants were analysed and these networks were found to exhibit higher degrees of community structure versus graphs of participants selected at random.

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DOI: 10.3217/jucs-017-14-1951

Authors: Victor Ströele, Ricardo Silva, Moises De Souza, Carlos Eduardo R. De Mello, Jano Souza, Geraldo Zimbrão, Jonice Oliveira

Abstract: Social networks are social structures consisting of individuals or organizations, usually represented by nodes tied by one or more types of relationships. Although these structures are often complex, analyzing them enables us to detect several inter and intra connections amongst people in and outside their organizations. In this context, we present an approach using data mining techniques in order to identify intra and inter organizational linkages amongst groups of people with similar profiles. Using clustering techniques, we identify groups of people in a way that allows us to evaluate how researchers collaborate in the Brazilian scientific scenario of Computing Science. Besides this, we are able to understand how research flows amongst the best universities and research centres in Brazil. Understanding the Scientific Brazilian scenario can help the development of research in other scenario or even in other Social Network Types.

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DOI: 10.3217/jucs-017-02-0311

Authors: Andreas Kuswara, Debbie Richards

Abstract: With the emergence of the Web 2.0 phenomena, technology-assisted social networking has become the norm. The potential of social software for collaborative learning purposes is clear, but as yet there is little evidence of realisation of the benefits. In this paper we consider Information and Communication Technology student attitudes to collaboration and via two case studies the extent to which they exploit the use of wikis for group collaboration. Even when directed to use a particular wiki designed for the type of project they are involved with, we found that groups utilized the wiki in different ways according to the affordances ascribed to the wiki. We propose that the integration of activity theory with an affordances perspective may lead to improved technology, specifically Web 2.0, assisted collaboration.

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DOI: 10.3217/jucs-016-08-1056

Authors: Manuel J. Fernández Iglesias, Luis M. Álvarez Sabucedo

Abstract: Europe is currently transitioning to digital terrestrial television and isaimed to replace all analog infrastructures by 2012. Besides replacing all broadcasting networks in Europe, the transition requires updating household televisions andantennas. As with any major change, public administrations must keep citizens informed and provide support, especially when dealing with a communication mediumexpected to support a new portfolio of services and information. State-of-the-art technologies enable universal coverage in locations where television services are currentlyunavailable. This paper evaluates the transition to digital television in several European regions and analyzes novel approaches and solutions to achieving universal accessto digital television and citizen awarenes.

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DOI: 10.3217/jucs-013-07-0970

Authors: Erla Morales, Francisco García-Peñalvo, Ángela Barrón

Abstract: The activities developed in this paper were aimed at providing an awareness of the elements that should be considered in quality learning objects instructional design for e-learning systems. We thus propose our own LO definition taking into account aggregation level number 2. On this basis, we analyze cognitive theories for promoting learning and we explain issues relating to the LO characteristics that help to improve their quality for suitable management. To achieve this we propose an instructional design based on an ontological model which explains the relationship between the instructional design elements and a specific classification to improve their management.

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DOI: 10.3217/jucs-011-04-0605

Authors: Karsten Böhm, Wolf Engelbach, Joerg Härtwig, Martin Wilcken, Martin Delp

Abstract: Process-oriented Knowledge Management aims to provide adequate information for employees, especially in weakly structured and information-intensive business processes. Beside a technical software solution, which uses a pre-structured, context-aware and collaborative information space that combines processes, domain specific semantic structures and document parts, this requires a methodology to model the process and other context-dimensions, such as roles. Moreover, a guideline and clear service modules are necessary to introduce process-oriented Knowledge Management in companies, especially in small and medium-sized enterprises (SME). Such solutions were developed in the cooperative research project PreBIS (Pre-Build Information Space).

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DOI: 10.3217/jucs-009-12-1398

Authors: Wolfgang Hiermann, Max Höfferer

Abstract: BEKO-SMS is a knowledge-based skill management system that combines project planning and human resource management. Application and system functions model specific skills and relationships used in a particular project. The definition of skills, skill trees, skill updating and other processes form the basis for the success factors of the SMS. We conclude that efficient project resource planning would not be possible without SMS and the skill manager.

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