In this work, we consider combinatorial games in which two players alternately choose vertices from a finite graph until a winning condition is achieved. Specifically, we focus our investigation on the well-known game Kayles, in which the selected vertices must form an independent set, and the player who makes the last valid move wins (i.e., the player who chooses a vertex that completes a maximal independent set). Zermelo's Theorem guarantees that, in this scenario, one of the players has a winning strategy---that is, a sequence of moves that ensures a win regardless of the opponent's choices. Given a graph, the typical decision problem associated with this type of game consists of determining which player has a winning strategy. Answering this question means solving the game. We first consider Kayles played on caterpillars. Since caterpillars are interval graphs, an O(n3)-time algorithm for solving Kayles on this graph class is already known [Bodlaender and Kratsch, 2002]. However, we investigate scenarios in which this time complexity can be reduced to O(1) by extending the periodicity property presented in [Guignard and Sopena, 2009] to caterpillars. We prove that the nimber of any caterpillar is equal to the nimber of an equivalent reduced caterpillar, obtained by appropriately removing certain leaves from the original graph. By partitioning these reduced caterpillars into classes, we show that a period of 34 emerges in each investigated class, allowing the computation of nimbers to scale to graphs with a large number of vertices. We present a sufficient condition for a class of caterpillars to exhibit periodicity 34, using it to identify many periodic classes and to calculate the nimber of their caterpillars in O(1) time. Furthermore, we present an O(n2)-time dynamic programming algorithm for solving Kayles on powers of paths, improving upon the O(n4)-time complexity given in [Bodlaender and Kratsch, 2002]for graphs with an asteroidal number of at most 2. Finally, we show how this same O(n2)-time algorithm can be adapted to solve Kayles on powers of cycles, thereby reducing the O(n3)-time complexity previously established in [Bodlaender and Kratsch, 2002] for circular-arc graphs.
Real-virtual object pairing is a central strategy for enabling convincing visuo-haptic experiences in Extended Reality (XR), allowing users to touch and manipulate physical proxies while perceiving aligned virtual counterparts. This paper presents a systematic review of XR systems that pair real and virtual objects to support tangible interaction, passive and encountered haptics, and illusion-based remapping. We organize the literature around three guiding questions: which pairing solutions and archetypes are used to achieve real-virtual alignment, how technological advances in head-mounted displays, sensing, and software pipelines shape these solutions over time, and what open challenges and future directions remain for scalable, robust deployments. Across the included studies, we synthesize recurring solution patterns, such as one-to-one tracked proxies, proxy reuse via retargeting and reset strategies, dynamic redirection for safe contact, and approaches that reduce or exploit mismatch through perceptual manipulations. We further consolidate reported challenges into cross-cutting categories (e.g., misalignment, tracking and latency issues, hardware and safety limitations, ergonomics, cybersickness, and experimental setup constraints), highlighting the persistent engineering and methodological barriers that limit generalization beyond controlled settings. Finally, we summarize the experimental landscape using a participant-tier matrix, covering 74 experiments and emphasizing the dominance of small-to-medium participant evaluations. Based on these findings, we outline actionable research directions toward perception-aware co-registration, standardized reporting and benchmarking, safer dynamic/robot-mediated pairing, and authoring tools that scale pairing to complex, multi-object XR scenarios.
While higher education is the backbone for human capital development and economic growth, its high dropout rates remain a global concern that leads to wasted resources and unfulfilled student potential. Understanding dropout requires integrating social, economic, academic, and technical factors across students’ trajectories, often interrelated in intricate, non-obvious ways. In this context, Process Mining (PM) offers a promising approach by uncovering patterns in students’ interactions with academic programs and courses. However, traditional PM methods are typically established over mono perspectives of processes, which limits their ability to capture the multi-factor and correlated nature of educational trajectories. To address this gap, this paper proposes an extended PM-based approach that incorporates enriched labeling strategies that allow the simultaneous analysis of multiple dimensions of students' academic trajectories. Furthermore, the article presents a detailed application of the labeled method over real data of a Brazilian public university with 437,690 events from eight different programs, including students from the Unified Selection System (SISU). By comparing students' outcomes and paths, while considering their enrollment method, course option, and demographic information, we discovered that admission score, program, high school type, gender, and place of origin are the variables with a higher correlation to successful and less successful students. A deeper analysis of a specific program is also outlined to show how the approach can be customized for particular cases, under minor effort, while keeping standard input data.
A multilayer architecture was developed for real-time health data collection and processing, designed for outdoor environments with high population density and significant network interference. By integrating fog and cloud computing, the system addresses the growing demand for continuous health monitoring driven by the proliferation of Internet of Things (IoT) devices and Wireless Body Area Networks (WBANs) using smartbands. Traditional cloud-centric solutions often face challenges such as high latency and data integrity issues in unstable network conditions. The proposed architecture overcomes these limitations by employing fog computing for edge data preprocessing, reducing reliance on cloud connectivity and enhancing system responsiveness. The architecture was originally evaluated under diverse network conditions (3G, 4G, 5G) and in real-world scenarios such as football stadiums, metro systems, and urban beaches, demonstrating over 96% packet delivery success and significant latency reductions compared to cloud-only approaches. In this extended version, additional real-world scenarios are analyzed, including domestic flights, large-scale events in stadiums with over 60,000 attendees, and new evaluations along urban beachfronts. Furthermore, this version provides a more detailed explanation of key mechanisms, such as the use of the Transactional Outbox pattern to ensure data consistency in unstable networks and the integration of distributed processing techniques for real-time alert generation. These contributions offer deeper insights into the architecture’s scalability and reliability, confirming its effectiveness in maintaining data integrity and achieving low latency in connectivity-challenged environments, providing a solution for health monitoring.
Modern vehicles increasingly rely on interconnected systems, combining internal networks (e.g., CAN) with external interfaces (e.g., embedded Wi-Fi). While this connectivity enables advanced functionalities, it also expands the potential attack surface for cyber threats. Existing intrusion detection solutions often address these layers in isolation, but we identify a need for integrated, explainable, and user-centered approaches. In this work, we propose a conceptual architecture for explainable intrusion detection in connected vehicles. Our solution simultaneously analyzes CAN and Wi-Fi traffic, using supervised learning models (XGBoost) for anomaly detection. To ensure interpretability, we apply SHAP to quantify feature importance and leverage Large Language Models (LLMs) to generate clear, textual explanations from the results. For validation, we conduct experiments using the X-CANIDS and AWID2 datasets, simulating common attacks such as fuzzing, fabrication, Evil Twin, and Hirte. Our results demonstrate that combining XAI and LLMs produces accurate, auditable narratives about attacker behavior, improving transparency in automotive security systems.
Embedded systems are present in a wide range of products, and advances in the Internet of Things, wireless sensor networks, and cyber-physical systems have increased their use. This growth has made embedded software more complex, requiring new real-time operating systems, development kits, libraries, and platforms. Despite these tools, the need for better software engineering practices remains a significant issue in embedded software, to prevent lower software quality and increased maintenance costs. To make matters worse, the knowledge transfer of research advances from academia to industry is still challenging. This paper presents ZBus, an advanced software bus that enables many-to-many communication between threads, enhancing modularity and decoupling in embedded software development. ZBus is a result of a collaborative effort between industry and academia. We also detail the development process, the challenges encountered, the lessons learned, and the collaborative efforts that culminated in integrating ZBus into the widely adopted Zephyr real-time operating system. This integration serves as an experience report on technological knowledge transfer from academia to industry through open-source contributions, as well as a practical guide for researchers and practitioners seeking to engage with industrial open-source initiatives in the embedded software domain.
Transformers have achieved state-of-the-art results, with Large Language Models (LLMs) leading many NLP tasks. However, it remains unclear whether LLMs always outperform first-generation Transformers (aka Small Language Models, SLMs) across different text classification tasks and scenarios (e.g., movie reviews, topic classification). This study compares four SLMs (BERT, RoBERTa, Qwen, BART) with four open LLMs (LLaMA 3.1, Mistral, Falcon, DeepSeek) across nine sentiment and four topic classification datasets, totaling over 1000 results. Results show that open LLMs only moderately outperform or tie with SLMs when fine-tuned, and at a very high computational cost. To address this trade-off, we propose “Call My Big Sibling” (CMBS), a novel confidence-based framework that integrates calibrated SLMs and open LLMs using advanced instance selection techniques. CMBS assigns high-confidence instances to the cheaper SLM, while low-confidence instances are routed to an LLM in zero-shot, in-context, or partially tuned modes, optimizing cost-effectiveness. Experiments show CMBS significantly outperforms SLMs and delivers LLM-level performance at a fraction of the cost, offering a cost-sensitive solution for NLP applications.
The stochastic nature of Large Language Models (LLMs) challenges traditional evaluation paradigms, which rely on single-response metrics and often mask complex behavioral patterns. This paper introduces Trait and Consistency Evaluation for LLMs (TraCE-LLM), an evaluation protocol that quantifies latent behavioral traits and model consistency within a black-box paradigm. Through a factorial design combining five LLMs, three benchmarks and a systematic stratification by prompt style (Naive, Chain-of-Thought and Adversarial), the framework employs a multidimensional rubric to measure Depth of Reasoning (DoR) and Originality (ORI) of model responses. The primary empirical contribution of this study is the identification and formalization of the Adversarial Compensation Effect (ACE), a phenomenon wherein smaller-capacity models under adversarial stress exhibit a paradoxical gain in accuracy metrics while suffering a severe degradation in behavioral stability. Our results also demonstrate an asymmetric stability with DoR being a significantly more stable trait than ORI and the prevalence of compressed reasoning, where 17.8% of correct answers lack adequate justification. By decoupling response correctness from process quality, TraCE-LLM provides a blueprint for more granular and reliable evaluation, arguing that LLM auditing must be multidimensional, context-sensitive and psychometrically informed to ensure the development of safer and more interpretable AI.
Depression is a serious global mental health illness that causes significant suffering to the individual and social impairment in their lives. Compared to the general population, depression shows a higher prevalence among college students. With recent advancements in digital phenotyping data analysis to infer depressive symptoms, machine learning (ML) techniques have been increasingly employed to indicate behaviors related to potential depressive profiles (PDP). However, despite the growing body of work on ML usage to detect depression, few studies have focused on data preprocessing approaches to handle missing values in datasets that go beyond common data imputation. In this study, we conducted a series of experiments to evaluate the combination of data preprocessing methods and ML algorithms for effectively classifying PDP and non-PDP students using data from the Amive project. The primary challenges were implementing a data processing workflow to address missing values and class imbalance, common issues in digital phenotyping datasets, and selecting algorithms capable of handling such data. The experimental results showed promising outcomes, with individual classification models, including Random Forest, XGBoost, and SVM(rbf), achieving accuracies of 77%, 75%, and 76%, respectively. The best performance was obtained by training on datasets that went through outlier filtering, specifically removing rows with four or more missing values. This combination of data preprocessing approaches and ML algorithms resulted in a Random Forest classification model with the best performance ranging between 77% of accuracy and with mean errors metrics of AUC and MCC above 0.5.
The widespread adoption of digital technologies and internet connectivity, particularly in developing regions, has drawn attention to the growing diversity of software users, who come from different cultural and linguistic backgrounds. Traditionally, human-computer interaction (HCI) research has treated language communities as static and isolated units, often equating them with national boundaries. However, linguistic research demonstrates that cultures are dynamic and interconnected, languages transcend borders, and people frequently speak two or more languages. To optimize HCI design, developers must account for these complex sociolinguistic realities, incorporating both social and individual dimensions of multilingualism. In this work, our aim is to contrast the demands of multilingual users with current design solutions. First, the behavior and difficulties of technology users in multilingual contexts were investigated through an online survey conducted with the academic communities of two universities: the Università della Svizzera italiana (USI), in Switzerland, and the Pontifical Catholic University of Rio Grande do Sul (PUCRS), in Brazil. Next, we present a pattern language that describes solutions aimed at multilingualism issues found in current websites and applications. In a final discussion, we compare the findings of the survey with the patterns, summarizing the challenges and opportunities for future research that aim to propose HCI design approaches with a focus on multilingualism.
Different strategies in the literature seek to help support awareness mechanisms in collaborative applications. However, the most common approaches are often applied in a specific context and do not focus on evaluating these mechanisms or the support provided from the user's perspective. Few studies present methods or processes that provide awareness aspects in collaborative systems; thus, finding a good starting point in the literature can be challenging for beginners in awareness design. We build upon the findings presented by Mantau and Benitti [2023a], providing contributions towards developing an awareness assessment process that enables accessing awareness and, consequently, collaboration support by measuring awareness mechanisms from the participant's viewpoint. Then, we expose the model’s artifacts to HCI and collaborative system examiners to gauge their appreciation and verify the suitability of the process based on reliability and usefulness criteria. The case study scenario demonstrated suitable indicators from the perspective of demographic data and IRT parameterization. As a result, the applicability of the awareness scale from the participant's perspective was achieved.
Computing is everywhere, and children observe, use, and are affected by the technology around them. They must understand the ”digital world” as they understand the ”real world”. Involving them in collaborative design processes can increase their engagement and understanding of technology and allow children to learn. However, there is a notable gap in understanding how preschool-aged children (4–6 years old) can be effectively involved, and existing studies often focus on the products children create during these processes. We conducted a Systematic Mapping (SM) study of research published between 2015 and 2025, reviewing 34 empirical studies involving children aged 4–6 using Participatory Design or Co-design methodologies. This SM synthesizes the following information: year of publication; retrieval sources and databases; publication outlets and locations; time frame of the research; research approach; and, finally, participants and those responsible for the study. Our analysis reveals a growing interest in involving preschool-aged children in design, with various techniques employed to engage them. However, there is a lack of standardized methodologies adapted to this age group, and few studies examine learning outcomes for the children involved.
Camera trap is an important non-invasive technique for wildlife monitoring. A typical camera-trap workflow involves various relevant tasks, such as filtering empty images, classifying animal species and identifying animal behavior. In this study, we explore the application of large-scale multimodal language models (MLLMs) for processing camera trap images to perform these three tasks. We evaluate the performance of four state-of-the-art models across these tasks, precisely BLIP, CLIP, Gemini, and GPT with zero-shot and few-shot learning methodologies. Our experiments showed several interesting results. First, few-shot learning significantly enhanced model performance in filtering empty images, with BLIP achieving a much higher accuracy (91.0%) compared to only 7.61% of its zero-shot counterpart. In the task of animal species classification, Gemini showed strong baseline performance, reaching 75.89 % of accuracy with zero-shot. In terms of identifying animal behavior, two scenarios were investigated: using single image or sequences of images. The results indicate that sequence-based processing improves behavioral analysis, with BLIP attaining the highest accuracy (75.57 %) in this task. In general, our study emphasizes the limitations of the zero-shot approach in complex tasks while highlights the effective potential of few-shot and sequence-based learning to address challenging problems such as empty images, and species misclassifications. These findings demonstrate the efficacy of advanced MLLMs in automating biodiversity monitoring, offering a scalable and accurate solution for processing large-scale datasets, and advancing conservation science.
To address sample scarcity, a common challenge in medical imaging, we investigate the Reptile meta-learning algorithm for few-shot disease classification in fundus images. In a setup with eight training classes and four testing classes, we investigate different architectures, training strategies, varying N-way K-shot configurations, and different data augmentation techniques. Our results show that Reptile outperforms standard transfer learning approaches in several settings and that, when combined with data augmentation, especially during evaluation, correct predictions tend to receive higher confidence scores. The quantitative results are further supported by an analysis of prediction confidence levels and activation-map visualizations.
Research on Human-Computer Interaction (HCI) interfaces has gained increasing relevance in both corporate and academic environments, particularly in adaptive systems that offer personalized interventions. Adaptive systems are crucial for enhancing user experience and promoting well-being by dynamically adjusting to individual needs and contexts. Well-being, which encompasses physical, mental, and social dimensions, can significantly influence user behavior and task performance. However, measuring well-being remains a complex challenge due to its subjective and multidimensional nature. This study aims to map and analyze the state of the art in computational interfaces that adapt to the user’s context to promote well-being. Specifically, the study addresses the gap in adaptive systems, which are still underdeveloped in the field. Despite significant progress in measuring well-being, most systems focus on monitoring well-being states or training predictive models, rather than offering fully adaptive interventions. To explore this, a systematic mapping study was conducted, investigating three key questions: What is the purpose of the study regarding the well-being dimension explored, as well as the approaches and techniques used to promote it? What methods were employed to measure users’ well-being? What interventions were implemented to promote well-being? The analysis of 36 selected studies reveals that research primarily concentrates on the mental and physical dimensions of well-being, with artificial intelligence techniques and physiological sensors, particularly electrocardiograms (ECG), being the most frequently used. However, there is a notable lack of adaptive systems in the literature. These findings underscore the need for further development of adaptive interventions that actively improve well-being, providing valuable insights to guide the design of adaptive interfaces. By leveraging these insights, future systems can be developed to enhance user experience and promote well-being across diverse domains.
NTRU is one of the most important post-quantum cryptosystems nowadays and since its introduction several variants have been proposed in the literature. In particular, the Matrix NTRU is a variant which replaces the NTRU polynomials by integer matrices. In this work, we develop a lattice-based reduction attack on the Matrix NTRU cryptosystem that allows us to recover the plaintext. We also show that this system is completely vulnerable to the proposed attack for parameters that could be used in practice. We show that this practical attack can also be extended by reducing the lattice dimension. In addition, we give sufficient conditions to avoid decryption failure for the Matrix NTRU.
Accessibility is a key requirement in any software system that requires user interaction since it is concerned with the development of products and services that can be used by a wide range of users, regardless of their physical or cognitive abilities. Many studies were conducted to understand the lack of accessibility observed in many digital products and concluded that the lack of knowledge and awareness, and the lack of management commitment and executive support were the main factors that hinder accessibility adoption in the software development process, suggesting that many organizations do not recognize the business importance of accessibility in digital markets. On the other hand, some organizations consistently invest in accessibility. In this manuscript, we present a study conducted with professionals involved in the decision-making processes of some of those organizations to uncover their strategic views on the development of accessible products, mainly regarding their motivations, policies, barriers, and benefits obtained from investing in accessibility. To accomplish our goals, we adopted an exploratory sequential mixed methods, starting with a qualitative study to explore the views of participants, followed by a quantitative study designed based on factors identified in the first study. In total 15 professionals participated in our qualitative study and 31 professionals participated in our quantitative study. Our findings suggest that in these organizations: i) accessibility demands are mostly generated by internal policies and cultures in tactical and strategic layers; ii) organizations are mostly driven by a mix of ethical, business, and regulatory factors, such as to promote digital inclusion, increase brand reputation and yet comply with current legislation; iii) policies to ensure accessibility adoption mostly include hiring people with disabilities, making accessibility validation mandatory with well-defined processes, and including accessibility in the definition of done (DoD) for all features; iv) perceived benefits are primarily associated with stronger brand reputation, better software quality and increased digital inclusion; v) the more critical barriers to accessibility adoption are linked to the lack of awareness and knowledge among stakeholders and the technical team, in addition to the lack of commitment across management levels and struggles with legacy systems.
Empathic games simulate real-life challenges to foster players’ understanding of complex situations, such as daily medical practice, financial stress, or coping with grief. While these games have been used to support physical and cognitive health across age groups, little is known about older adults’ engagement with them. To understand the breath and lenght of this knowledge gap, a systematic literature review (SLR) following a structured PRISMA protocol was performed. The papers inside the Scopus, Web of Science, PubMed, IEEE, DBLP and ACM databases were examined, without consideration for a set time period, using keywords related to “empathic games” and “older adults.” Studies were included if they investigated digital games with empathetic elements targeting adults aged 60+. Two HCI specialists independently screened titles, abstracts, and full texts in a two-round process, applying predefined inclusion/exclusion criteria. Of 205 identified records, 15 met the final criteria. Findings suggest empathic games can positively influence issues such as loneliness, depression, and family dynamics among the senior citizens. However, significant gaps remain regarding usability needs, player preferences, and profiles. These results highlight the need for further research to guide the effective design of empathetic games for older adults and to explore sensitive emotional topics.
In recent years, a wide range of new Machine Learning (ML) techniques with high accuracy have been developed for Android malware detection. Despite their high accuracy, these techniques are seldom implemented in production environments due to their limited generalization capabilities, leading to reduced performance when applied to real-world scenarios. In light of this, this paper introduces a novel multi-view Android malware detection model implemented in two stages. The first stage involves extracting multiple feature sets from the analyzed Android application package, offering complementary behavioral representations that improve the system's generalization in the classification process. In the second stage, a multi-objective optimization is conducted to identify the optimal feature subset from each view and fine-tune the hyperparameters of individual classifiers, enabling an ensemble-based classification approach. The core innovation of our approach lies in the proactive selection of feature subsets and the optimization of hyperparameters that together enhance classification accuracy while minimizing processing overhead within a multi-view framework. Experiments conducted on a newly developed dataset, consisting of over 40 thousand Android application samples, validate the effectiveness of our proposal. The results indicate that our model can increase true-positive rates by up to 18% while reducing inference processing costs by as much as 72%.
The Information Gathering stage in web Pentests is crucial as it lays the foundation for all subsequent activities. However, comprehensive information gathering requires the manual use of various tools that demand advanced technical knowledge. In this context, we propose Web xKaliBurr, an open-source web tool that automates the information gathering stage of web Pentest. With a simple and user-friendly interface, the proposed tool performs extensive scans from the site's URL, providing a wide range of information and recommendations, allowing users without advanced knowledge to assess their site's security and detect potential flaws or vulnerabilities. To evaluate Web xKaliBurr, we applied the System Usability Scale (SUS) questionnaire to measure aspects of usability in accordance with the user's subjective assessment and the Net Promoter Score (NPS) method to measure user satisfaction and willingness to recommend it to others. This study involved 10 respondents. The SUS method had a score of 80, which indicates a good to excellent product, and the results of using NPS reached a value of 70%, reflecting a very good level of user satisfaction. Besides, we performed an evaluation with 3 experts in web Pentests.
