DOI: 10.3897/jucs.161865

Authors: Vinicius Eduardo Ferreira, João Pedro Vidotti Cesaro, Gabriela Rosa, Edson OliveiraJr, Gislaine Camila Lapasini Leal, Renato Balancieri

Abstract: In July 2024, a failed update in the CrowdStrike Falcon security software disrupted critical services across the United States and several other countries, affecting sectors such as aviation, healthcare, and finance. Despite advancements in automated testing, an error introduced during development was rapidly propagated, exposing vulnerabilities in digital infrastructure and underscoring the risks of technological interdependence. This study examines the incident through an integrative, multivocal literature review that combines academic and gray literature to address three core research questions: the impacts of the outage (RQ1), the technical causes of its severity (RQ2), and the lessons learned for software engineering and cybersecurity (RQ3). The analysis reveals critical failures in validation processes, integration mechanisms, and organizational prac-tices, highlighting the need for robust quality assurance and software governance. The findings contribute to both research and practice by informing more effective strategies for preventing and responding to large-scale cybersecurity incidents.

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DOI: 10.3897/jucs.160046

Authors: Sang Suh, Numery Zaber

Abstract: Traditional resume screening is manual, slow, and susceptible to bias, and it struggles to keep pace with today’s application volumes. This paper presents a dual-engine, AI-powered resume screening system designed for transparency and reproducibility. The primary (classical) pipeline encodes resumes and job descriptions using Sentence-BERT (SBERT), computes a resume–job match score via cosine similarity, classifies candidates into 25 job categories using XGBoost, and provides model interpretability through SHAP. In parallel, a prompted large language model (LLM) baseline (GPT-4o/4o-mini) outputs a match score and predicted category for comparative analysis. A Streamlit-based interface integrates both engines to support recruiter workflows and human-in-the-loop oversight. Using a dataset of 962 resumes across 25 categories with an 80/20 stratified split, the system reports standard multi-class performance metrics (classification report and normalized confusion matrix) for the classical pipeline. At the same time, an evaluation harness enables the same artifacts for the LLM baseline on sample subsets. The interface further exposes dataset distribution, top predicted categories, and explanation widgets (SHAP for the classical pipeline; TF-IDF key-term highlighting for the LLM), while applying basic PII filtering. Considerations related to fairness, privacy, and deployment drift are discussed. Comparison with prior SBERT-only systems (e.g., Enhanced Resume Screening using S-BERT) highlights key contributions: (1) a reproducible SBERT→XGBoost→SHAP classification pipeline, (2) an LLM comparator with a standardized evaluation template, and (3) a practitioner-oriented user interface that operationalizes explainability and auditability.

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DOI: 10.3897/jucs.128779

Authors: Carlos Goetz, Rodrigo Simon Bavaresco, Wesllei Felipe Heckler, Gustavo Lazarotto Schroeder, Rafael Kunst, Jorge Luis Victória Barbosa

Abstract: Stress is a mental disorder responsible for impacting the industry through psychosomatic illnesses, loss of productivity, and accidents caused by stressful workplaces. Conversely, the literature indicates that fostering mental well-being among workers can boost motivation and performance while alleviating symptoms of stress. The fourth industrial revolution incorporated technologies into work that allowed the automation of processes and control of environments. The fifth revolution introduced the application of research and innovation aimed at a human-centered consciousness, enabling the advancement of mental health through sensors and wearables. Despite advancements in stress classification technologies, there remain opportunities for further research into identifying stress motivators within industrial work environments. In this sense, this paper proposes an ontology to identify stressors considering personal and environmental data, allowing knowledge generation related to work stressors for mitigating the problem. The methodology utilized in this ontology development consisted of seven stages and two evaluation phases. The findings addressed four key questions related to competency as outlined in the model. The results revealed potential stressful scenarios, including the timing of occurrence, shared locations, environmental factors, and identifying groups experiencing moments of stress. This study presents as a scientific contribution the first ontology to address the identification of work-related stressors in the industrial environment.

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DOI: 10.3897/jucs.121185

Authors: Emre Önal, Abdullah Bülbül

Abstract: Optimizing game elements through iterative human playtests can be time-consuming and insufficient for games with complex intransitive mechanics. Imbalances in games often require the release of numerous balance patches. We present a computational method for making each game unit equally preferable against a uniform play strategy. We leverage concepts from game theory to model intricate relationships among intransitive entities. Matching units against each other is modeled as a symmetric zero-sum game, where unit selection represents a strategy and the error is quantified using payoff values derived from unit parameters. The algorithm takes the initial unit parameters provided by the game designer and optimizes them with minimal changes using gradient descent. Consequently, the payoff matrix converges to a state where a uniform strategy is a near Nash equilibrium, ensuring that each unit is equally preferable under the optimized condition. We implemented a testing environment based on fictitious play and verified our results on different scenarios. While the majority of game theory research focuses on finding optimal strategies given specific environmental conditions, this paper takes a different perspective within the context of game design. We explore game theoretic concepts to address the goal of designing environments that lead to desired strategy choices.

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DOI: 10.3897/jucs.115016

Authors: Aluizio Haendchen Filho, Adson Marques da Silva Esteves, Hércules Antonio do Prado, Edilson Ferneda, André Luis Alice Raabe

Abstract: The high dropout rate in Information Technologies courses is a relevant problem in many countries, mainly because of the increasing demand for professionals in this sector. Usually, high dropout rates in these courses are related to difficulties in algorithms and programming subjects. Content recommendation systems are proposed to mitigate this problem, employing adaptive learning environments that facilitate the learning process. This study presents a content recommendation system that uses learning paths to group students and provide personalized recommendations based on peers' progress. The work follows the many efforts of group-based recommendation systems reported in the literature. The system uses intelligent agents and clustering algorithms to implement the recommendation system and was evaluated by submitting the simulation results to the judgment of human experts who significantly agreed with them. This initiative could make programming teaching more adaptive, using the groups' knowledge. Facilitating learning is one of the key issues to reduce dropout rates and resolve the shortage of labor in the technological area in Portuguese-speaking countries.

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DOI: 10.3897/jucs.67593

Authors: Florencia Vega, Guillermo Rodríguez, Fabio Rocha, Rodrigo Pereira dos Santos

Abstract: Agile Methods propose an approach for developing software based on an iterative and incremental life cycle model, in which needs and solutions evolve through collaboration between multi-functional and self-organized teams. As such, agile practices in work teams are gaining much momentum. To meet the demanding level of projects, agile software development also has to keep up with several challenges. In this context, software industry has chosen to use several tools to ease development and communication between different teams’ members. However, these tools generate overwhelming volumes of data that hamper decision-making by project managers. To address this issue, we present Scrum Watch, a tool-based approach that focuses on generating, through cloud-based technologies, graphic elements and reports that assist project managers with information to support decision making. Results obtained from an undergraduate Systems Engineering course through a capstone project confirm the feasibility of the proposed approach, which exploits the benefits of the availability and visualization of process and product metrics.

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DOI: 10.3897/jucs.68852

Authors: Agnes Kukulska-Hulme, Jorge Arus-Hota, Jesus Garcia Laborda

Abstract: The emerging paradigm of mobile open social learning for languages (MOSL4L) integrates the three elements of mobile, open and social, and in so doing it creates the idea of a conceptually different language learning space. It is a space full of opportunity and challenge, relevant to a post-pandemic world in which we are looking for ways to build back better. The paper discusses tensions between formal and informal language learning and the nature of learning outcomes in MOSL4L. It focuses on the needs of individuals while also considering the characteristics of the virtual spaces in which they participate. It highlights the potential of micro experiences and learning moments as structures that are well aligned with MOSL4L. It suggests developments in language curricula to take account of communication challenges being identified in the contemporary world. Many more new learning designs and software architectures will have to be developed to match the possibilities generated by the MOSL4L space.

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DOI: 10.3217/jucs-025-09-1199

Authors: François Bouchaud, Gilles Grimaud, Thomas Vantroys, Pierrick Buret

Abstract: The Internet of Things (IoT) is everywhere around us. Smart communicating objects are offering the digitalization of lives. They create new opportunities within criminal investigations. In recent years, the scientific community sought to develop a common digital framework and methodology adapted to IoT-based infrastructure. However, the difficulty in exploiting the IoT lies in the heterogeneous nature of the devices, the lack of standards and the complex architecture. Although digital forensics are considered and adopted in IoT investigations, this work only focuses on the collection. The identification phase is quite unexplored. It addresses the challenges of locating hidden devices and finding the best evidence to be collected. The matter of facts is the traditional method of digital forensics does not fully fit the IoT environment. Furthermore, the investigator can no longer consider a connected object as a single device, but as an interconnected whole one, anchored in a cross-disciplinary environment. This paper presents the methodology for identifying and classifying connected objects in search of the best evidence to be collected. It offers techniques for detecting and locating the appropriate equipment. Based on frequency mapping and interactions, it transfers the concept of "fingerprinting" into the field of crime scene. It focuses on the technical and data criteria to successfully select the relevant IoT devices. It gives a general classiffication as well as the limits of such an approach. It shows the collection of digital evidence by focusing on pertinent information from the Internet of Things.

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DOI: 10.3217/jucs-024-12-1731

Authors: Amit Rathee, Jitender Chhabra

Abstract: During various phases of software development lifecycle, the internal structure of the software degrades which finally results in increased maintenance efforts and hence cost. One quick solution to this problem is software remodularization in which restructuring of different software elements such as classes/ packages/ methods is done (without changing their original meaning and functionality). Several researchers have proposed different techniques for software remodularization. Each technique considers two points of view: dependency measurement among software elements (based on structural, conceptual and/or change history based relations) and performing clustering using different algorithms. So, in this paper, first of all, an empirical evaluation is carried out in order to test the role of different dependency relations in modeling dependency among different software elements. From an empirical evaluation, it is observed that the change history of a software system plays a major role in modeling dependency relations and hence must be used along with other relations for more accurate measures. Then, a new weighted dependency measurement scheme is proposed by combining structural, conceptual and change history based relations among software elements together, with more importance to evolutionary dependency relations. Finally, different dependency schemes are evaluated with six clustering algorithms by applying them to four standard open source Java software of variable sizes and belonging to different domains. The obtained results show that our proposed approach is capable of accurately determining dependence relations among various software elements as compared to other similar approaches present in literature and thus increases restructuring accuracy.

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DOI: 10.3217/jucs-024-09-1217

Authors: Difeng Yu, Hai-Ning Liang, Feiyu Lu, Vijayakumar Nanjappan, Konstantinos Papangelis, Wei Wang

Abstract: Target selection is one of the most common and important tasks in interactive systems. Within virtual reality environments, target selection can pose extra challenges to users because targets can be located far away, clustered together, and occluded from view. Although selection techniques have been explored, it is often unclear which techniques perform better across different environmental target density levels and which have higher levels of usability especially for recently released commercial head-mounted display (HMD) virtual reality systems and input devices. In this paper, we first review previous studies on target selection in HMD VR environments. We then compare the performances of three main techniques or metaphors (RayCasting, Virtual Hand, and Hand-Extension) using recently marketed VR headsets and input devices under different density conditions and selection areas. After, we select the best two techniques (RayCasting and Virtual Hand) for the second experiment to explore their relative performance and usability by adding different feedback to these two techniques. In the third experiment, we implemented three techniques with pointing facilitators and compared them against the best techniques from the second experiment, RayCasting with visual feedback, to assess their performance, error rates, learning effects, and usability. The three studies altogether suggest the best target selection features, based on techniques, feedback, and pointing facilitators for target density conditions in HMD VR environments.

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DOI: 10.3217/jucs-024-03-0302

Authors: Bruno Fernandes, Fábio Silva, Cesar Analide, José Neves

Abstract: Upcoming cities must undoubtedly reason upon the knowledge they have acquired through data gathered by sensorization. Those who do that will be at the forefront, closing to become Smart Cities. To achieve this goal, we must evolve from an Internet of Things to an Internet of People, defined as an ecosystem where everyone and everything can sense the other and the world, and act upon such data and knowledge, aiming to enhance people's quality of life. Considering the Vulnerable Road Users' (VRUs) problem, this work provides a proof of concept on crowd sensing for urban security in Smart Cities, confirming that our concept is viable and has practical potential. The goal is to sense the density of people at certain points of interest for VRUs, such as pedestrian crossings or busier roads, by detecting Wi-Fi probe requests with a Smart Scanner. Such information can be relevant to many applications, allowing, for example, the promotion of better safety measures on crowed spots, enhance crowd control and assemble interesting insights on traffic characteristics. To complement this work, and considering that smart clothing will certainly play an important role in promoting the citizen sensor and the smart cities' approach to VRUs' safety, a case study will be presented and discussed in which their clothing is equipped with a Bluetooth Low Energy transmitter. This allows such users to be recognized on the road, which may help avoid dangerous situations. The proof of concept was a success, with the developed software showing promising results at an extremely low price.

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DOI: 10.3217/jucs-018-11-1483

Authors: Marc Forment, María Casany, Enric Mayol, Jordi Piguillem, Nikolas Galanis, Francisco García-Peñalvo, Miguel Conde

Abstract: Google Docs is a well-known suite of online collaborative tools for document processing, spreadsheets, online presentations, drawing and even forms. The last versions of the major open source LMS Moodle, offers weak integrations with Google Docs treating it as a content repository. But these integrations are neglecting the collaborative qualities of the Google Docs suite and its potential as a learning activity within the LMS course. This paper presents an integration proposal that using the IMS Basic Learning Tools Interoperability (IMS BLTI) standard turns Google Docs into an engine that powers collaborative learning activities within the LMS Moodle.

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DOI: 10.3217/jucs-018-01-0106

Authors: Marc Alier, Enric Mayol, Maria Casañ, Jordi Piguillem, Jeffrey Merriman, Miguel Conde, Francisco García-Peñalvo, Wouter Tebben, Charles Severance

Abstract: Since the beginning of the discipline, eLearning has been about innovation. New software, systems, contents and tools are being created and experimented with and in constant evolution. But when systems, contents and tools become successful and part of the regular infrastructure of educational institutions, interoperability becomes an issue. Systems that are consolidated and regularly used need to be able to interoperate with new ones. And the new tendencies need to fit within the current infrastructure. This paper states how several research and development projects with heterogeneous funding sources and locations worldwide, gathered together to find a solution to this common problem, providing open specifications and standards, plus Free/Libre, Open Source reference implementations.

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DOI: 10.3217/jucs-015-13-2608

Authors: Vitoantonio Bevilacqua, Lucia Cariello, Marco Giannini, Giuseppe Mastronardi, Vito Santarcangelo, Rocco Scaramuzzi, Antonella Troccoli

Abstract: This paper describes a comparative study between an Artificial Neural Network (ANN) and a geometric technique to detect for biometric applications,the bifurcation points of blood vessels in the retinal fundus. The first step is an image preprocessing phase to extract retina blood vessels. The contrast of the blood vessels from the retinal image background is enhanced in order to extract the blood vessels skeleton. Successively, candidate points of bifurcation are individualized by approximating the skeleton lines in segments. The distinction between bifurcations and vessel bends is carried out through the employment of two methods: geometric (through the study of intersections within the region obtained thresholding the image portion inside a circle centered around the junctions point and the circumference of the same circle) and an ANN. The results obtained are compared and discussed.

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DOI: 10.3217/jucs-015-04-0826

Authors: Vitoantonio Bevilacqua, Giuseppe Mastronardi, Filippo Menolascina, Paolo Pannarale, Giuseppe Romanazzi

Abstract: Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When time-course data is available, gene interactions may be modeled by a Bayesian Network (BN). Given a structure, that models the conditional independence between genes, we can tune the parameters in a way that maximize the likelihood of the observed data. The structure that best fit the observed data reflects the real gene network's connections. Well known learning algorithms (greedy search and simulated annealing) devoted to BN structure learning have been used in literature. We enhanced the fundamental step of structure learning by means of a classical evolutionary algorithm, named GA (Genetic algorithm), to evolve a set of candidate BN structures and found the model that best fits data, without prior knowledge of such structure. In the context of genetic algorithms, we proposed various initialization and evolutionary strategies suitable for the task. We tested our choices using simulated data drawn from a gene simulator, which has been used in the literature for benchmarking [Yu et al. (2002)]. We assessed the inferred models against this reference, calculating the performance indicators used for network reconstruction. The performances of the different evolutionary algorithms have been compared against the traditional search algorithms used so far (greedy search and simulated annealing). Finally we individuated as best candidate an evolutionary approach enhanced by Crossover-Two Point and Selection Roulette Wheel for the learning of gene regulatory networks with BN. We show that this approach outperforms classical structure learning methods in elucidating the original model of the simulated dataset. Finally we tested the GA approach on a real dataset where it reach 62% of recovered connections (sensitivity) and 64% of direct connections (precision), outperforming the other algorithms.

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DOI: 10.3217/jucs-014-14-2343

Authors: Botond Virginas, Marian Ursu, Edward Tsang, Gilbert Owusu, Chris Voudouris

Abstract: This paper considers the problem of resource allocation in the service industries approached from an agent-based perspective. Agent technologies seem to be well suited to this domain by providing a distributed environment, are network centric, semi-autonomous and collaborative and can communicate with each other to achieve better optimisation with little human intervention. The paper describes the context of this solution, a general power model and several pathways with corresponding example implementations with results and discussion The novelty of the solution resides in the fact that it is a natural and versatile formulation that combines an agent-based model with various artificial intelligence and operations research techniques such as rule-based expressions of allocation strategies and multi-criteria optimisation expressions of allocation objectives.

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DOI: 10.3217/jucs-012-02-0214

Authors: Belhassen Zouari

Abstract: Characterising liveness using a structure based approach is a key issue in theory of Petri nets. In this paper, we introduce a structure causality relation from which a topological characterisation of liveness in Petri nets is defined. This characterisation relies on a controllability property of siphons and allows to determine the borders of the largest abstract class of Petri nets for which equivalence between liveness and deadlock-freeness holds. Hence, interesting subclasses of P/T systems, for which membership can be easily determined, are presented. Moreover, this paper resumes, from a new point of view, similar results related to this issue and, provides a unified interpretation of the causes of the non-equivalence between liveness and deadlock-freeness.

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DOI: 10.3217/jucs-010-05-0620

Authors: Isabel Nepomuceno-Chamorro

Abstract: Membrane Computing is a recent area of Natural Computing, a topic where much work has been done but still much remains to be done. There are some applications which have been developed in imperative languages, like C++, or in declaratives languages, as Prolog, working in the framework of P systems. In this paper, a software tool (called SimCM, from Spanish Simulador de Computacion con Membranas) for handling P systems is presented. The program can simulate basic transition P Systems where dissolution of membranes and priority rules are allowed. The software application is carried out in an imperative and object-oriented language - Java. We choose Java because it is a scalable and distributed language. Working with Java is the first step to cross the border between simulations and a distributed implementation able to capture the parallelism existing in the membrane computing area. This tool is a friendly application which allows us to follow the evolution of a P system easily and in a visual way. The program can be used to move the P system theory closer to the biologist and all the people who wants to learn and understand how this model works.

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DOI: 10.3217/jucs-004-03-0308

Authors: Christine Thackaberry, Roy Rada

Abstract: Software engineering methods and metrics to estimate development time for the development and maintenance of computer-based training (CBT) differ from methods and metrics used to develop large information systems. The estimation techniques for large information systems employ Lines-Of-Code and Feature/Function points to calculate project effort in staff-months [Boehm 1981], techniques that are difficult to apply to CBT effort estimation. For the development of computer-based training systems, Development to Delivery Time Ratios have been the usual estimation metric, but these also have limitations [Marshall et al. 1995]. Metrics to accurately measure the development effort of Multimedia Information Systems (MIS) are currently being developed and investigated [Fletcher et al. 1997], but still differ from computer-based training systems development. This paper presents an estimation model for effort development of small courseware projects (less than 2 staff-months). By identifying the sub-tasks of the development phase, an individual estimation technique is suggested for each sub-task. The sum of all sub-tasks estimations determines the total effort estimation for the development phase of a particular lesson. Incorporating historical data as a baseline and identifying risk cost factors, this method is accurate for estimating effort of some sub-tasks and for the lesson unit as a whole. This method is not meant to be a "silver bullet" [Brooks 1995] but a start toward building an accurate estimation tool and a refinement of the development process.

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DOI: 10.3217/jucs-003-12-1337

Authors: Giampaolo Bella, Elvinia Riccobene

Abstract: The Gurevich's Abstract State Machine formalism is used to specify the well known Kerberos Authentication System based on the Needham-Schroeder authentication protocol. A complete model of the system is reached through stepwise refinements of ASMs, and is used as a basis both to discover the minimum assumptions to guarantee the correctness of the system and to analyse its security weaknesses. Each refined model comes together with a correctness refinement theorem.

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