Hasil untuk "Computer software"

M. Grave, Y. LeLous, D. Duce et al.

Sun Xinguo, Peng Anbang, Ma Shuaifei et al.

Urban areas are increasingly affected by intense rainstorm-induced flooding, posing serious risks to human life and property. To reduce the impact of such disasters and promote urban safety and sustainable development, a systematic assessment of Urban Flood Disaster Risk (UFDR), along with appropriate management strategies, is essential. Due to the high cost and complexity of acquiring and processing numerous potential indicators, identifying the most influential predictive variables is critical. This study integrates adaptive fuzzy logic with machine learning techniques to predict flood probabilities and develop evidence-based mitigation protocols. The proposed framework incorporates 13 carefully selected evaluation indicators, categorized into three dimensions: hazard triggers, environmental susceptibility, and community vulnerability. Indicator weights are determined through a combination of subjective (Analytic Hierarchy Process) and objective (CRITIC) weighting methods. Dynamic risk assessment is conducted using the Variable Fuzzy Pattern Evaluation (VFPE) model, while temporal features are automatically extracted using one-dimensional convolutional neural networks (1D-CNN). Flood probability is predicted using several machine learning algorithms, including Random Forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). The contribution of each input variable is assessed using feature importance scores derived from the RF model, averaged over a leave-one-out cross-validation (LOOCV) process. Results indicate that the SVM model achieves the highest accuracy and reliability for the multi-class classification task, particularly in identifying high-risk events. RF and XGBoost also demonstrate strong performance, offering a balance between predictive accuracy and model interpretability. Overall, the proposed methodology provides an effective and data-driven approach to support urban flood risk assessment and disaster mitigation planning.

Rickman Roedavan, Dimas Ramdhan, Bambang Pudjoatmodjo et al.

Designing serious games often faces the challenge of over-scoping, where a single game attempts to address multiple learning objectives simultaneously. This results in overly complex experiences that are difficult to develop, navigate, and sustain, especially within the limited structure of traditional Learning Management Systems (LMS). These platforms are typically linear, making it difficult to integrate interactive learning experiences. To address this, we introduce GATE (Game Architecture in Transmedia Education), a framework designed to support the development of focused and lightweight serious games through microlearning strategies. GATE leverages a transmedia-based open learning space that spans multiple platforms beyond institutional LMS boundaries. The framework was tested through a pilot implementation involving 10 student teams, each developing a 15-level gampelay that focused on a single topic. All games were evaluated using the Game Experience Questionnaire (GEQ), with the following average scores: Competence (3.003), Sensory and Imaginative Immersion (3.080), Flow (2.65), Challenge (2.288), Tension/Annoyance (1.787), Positive Affect (3.399), and Negative Affect (1.807).  The results indicate that microlearning-based game development through the GATE framework can offer a more focused, engaging, and accessible learning experience.       

Sathishkumar Moorthy, Sachin Sakthi Kuppusami Sakthivel, Young Hoon Joo et al.

Nonholonomoic mobile robots (NMRs) are widely used in logistics transportation and industrial production, with motion control remaining a key focus in current WMR research. However, most previously developed controllers assume ideal conditions without considering motion slippage. Neglecting slippage factors often leads to reduced control performance, causing instability and deviation from the robot’s path. To address such a challenge, this paper proposes an intelligent method for estimating the longitudinal wheel slip, enabling effective compensation for the adverse effects of slippage. The proposed algorithm relies on the development of an adaptive trajectory tracking controller for the leader robot. This controller enables the leader robot to accurately follow a virtual reference trajectory while estimating the actual slipping ratio with precision. By employing this approach, the mobile robot can effectively address the challenge of wheel slipping and enhance its overall performance. Next, a distributed observer is developed for each NMR that uses both its own and adjacent robot’s information to determine the leader’s state. To solve this difficulty for the follower robot to receive the states of the leader in a large group of robots, distributed formation controllers are designed. Further, Lyapunov stability theory is utilized to analyze the convergence of tracking errors that guarantees multi-robot formation. At last, numerical simulations on a group of NMR are provided to illustrate the performance of the designed controller. The leader robot achieved a low RMSE of 1.7571, indicating accurate trajectory tracking. Follower robots showed RMSEs of 2.7405 (Robot 2), 3.0789 (Robot 4), and 4.3065 (Robot 3), reflecting minor variations due to the distributed control strategy and local disturbances.

Gustavo Domingues, Leticia de Oliveira, Leina Yoshida et al.

Background: Head-mounted displays (HMDs) offer compelling virtual and augmented experiences, yet their influence on everyday accuracy and efficiency is not fully understood. In particular, video see-through (VST) and optical see-through (OST) devices may introduce perceptual distortions that degrade performance. Methods: We compared a VST HMD (Meta Quest 3) and an OST HMD (Microsoft HoloLens) in two representative motor tasks: dart throwing (far-field interaction) and bottle filling (near-field interaction). Eighty volunteers were split into two experiments, each using one HMD type. Every participant performed both tasks twice—once with the assigned HMD and once with normal vision. Completion time, dart-board error, water-level deviation, and selfreported visual-discomfort symptoms (eyestrain, blurred vision, nausea) were recorded. Results: Wearing either HMD lengthened task completion and reduced precision relative to the naked-eye baseline. Dart throws landed farther from the bullseye and showed greater score variability under HMD conditions. In the bottle-filling task, participants overfilled more frequently and deviated further from the target water level when using an HMD. Mild visual discomfort was reported by some users, whereas severe symptoms were rare. Conclusions: Both VST and OST HMDs can impose perceptual and cognitive demands that impair speed and accuracy in common near- and farfield activities. Refining calibration procedures and real-time visual feedback may mitigate these effects; broader studies across diverse user groups and task domains are warranted.

Jinjing Ma, Yongcheng Lin, Lanmei Qian et al.

Abstract Facial expression recognition (FER), as a critical task in computer vision and affective computing, has gained considerable attention in recent years. However, current methods often suffer from high computational costs and limited capability in extracting key discriminative features. To address these issues, this paper proposes SPAYOLO (Spectral-aware Perception and Aggregation YOLOv8), a novel FER network based on the YOLOv8 architecture. We introduce a new Spectral-aware Perception and Aggregation Module (SPAM), designed to enhance expression recognition performance by systematically modeling spatial and frequency features. SPAM comprises three components: a Hierarchical Receptive Modeling (HRM) path that uses multi-scale convolutional branches to capture fine-grained and mid-level spatial variations; a Frequency Enhancement Path (FEP) that leverages Fast Fourier Transform (FFT) to extract high-frequency texture and micro-expression features; and a Gated Attention Mechanism (GAM) that adaptively fuses spatial and frequency features to mitigate feature distribution inconsistency and improve discriminative stability. Experimental results show that the proposed model achieves an accuracy of 70.74% on the FER2013 dataset and 67.88% on the AffectNet dataset, while maintaining high computational efficiency. These results highlight its suitability for real-time facial expression recognition tasks.Our findings validate the effectiveness of hierarchical feature fusion and frequency-domain enhancement in FER tasks, offering valuable insights for future research in computer vision.The custom code for this study is available at GitHub repository: https://github.com/YociLam/Spectral-Spatial-Feature-Fusion-for-Real-Time-Facial-Expression-Recognition .

Shuangjun Li, Zhixin Huang, Yuanming Li et al.

Traditional materials informatics leverages big data and machine learning (ML) to forecast material performance based on structural features but often overlooks valuable textual information. In this work, we proposed a novel methodology for predicting material performance through context-based modeling using large language models (LLMs). This method integrates both numerical and textual information, enhancing predictive accuracy and scalability. In the case study, the approach is applied to predict the performance of solid amine CO2 adsorbents under direct air capture (DAC) conditions. ChatGPT 4o model was used to employ in-context learning to predict CO2 adsorption uptake based on input features, including material properties and experimental conditions. The results show that context-based modeling can reduce prediction error in comparison to traditional ML models in the prediction task. We adopted Sapley Additive exPlanations (SHAP) to further elucidate the importance of various input features. This work highlights the potential of LLMs in materials science, offering a cost-effective, efficient solution for complex predictive tasks.

Connor T. Hann, Kyungjoo Noh, Harald Putterman et al.

Dissipative cat qubits are a promising physical platform for quantum computing, since their large noise bias can enable more hardware-efficient quantum error correction. In this work we theoretically study the long-term prospects of a hybrid cat-transmon quantum computing architecture where dissipative cat qubits play the role of data qubits, and error syndromes are measured using ancillary transmon qubits. The cat qubits’ noise bias enables more hardware-efficient quantum error correction, and the use of transmons allows for practical, high-fidelity syndrome measurement. While correction of the dominant cat Z errors with a repetition code has recently been demonstrated in experiment, here we show how the architecture can be scaled beyond a repetition code. In particular, we propose a cat-transmon entangling gate that enables the correction of residual cat X errors in a thin rectangular surface code, so that logical error can be arbitrarily suppressed by increasing code distance. We numerically estimate logical memory performance, finding significant overhead reductions in comparison to architectures without biased noise. For example, with current state-of-the-art coherence, physical error rates of 10^{−3} and noise biases in the range 10^{3}–10^{4} are achievable. With this level of performance, the qubit overhead required to reach algorithmically relevant logical error rates with the cat-transmon architecture matches that of an unbiased-noise architecture with physical error rates in the range 10^{−5}–10^{−4}.

Janaka Godevithana, Champa Jayalakshmie Wijesinghe, Millawage Supun Dilara Wijesinghe

BackgroundAn unhealthy diet is a well-established risk factor for the development of noncommunicable diseases, and office workers are at a higher risk of noncommunicable diseases due to their sedentary work style. Stage of change (SOC) theory–based and stage-matched interventions effectively influence dietary and behavior changes. The effectiveness of such interventions in the context of low- and middle-income countries is yet to be assessed. ObjectiveThis protocol describes a cluster randomized trial planned to evaluate the effectiveness of an intervention for changing dietary behavior among government office workers in the Galle district in Sri Lanka. MethodsA cluster randomized trial was conducted in 20 clusters divided into intervention and control arms. A cluster was an office with 30 clerical-type workers who were sedentary at work. A stage-matched intervention based on behavior change processes was implemented in the intervention clusters for 3 months. Participants were provided with an intervention matched to their SOC at baseline. Precontemplators and contemplators received awareness-raising and emotional arousal interventions. Others received goal setting and self-monitoring interventions. The SOC and dietary intake were assessed at baseline and the postintervention stage through a staging algorithm, and 24-hour dietary recall was supplemented with a picture guide and computer software. Adherence to the intervention was assessed monthly. We hypothesized that participants would achieve a progressive change in the SOC and healthy dietary intake in the intervention clusters compared to the control clusters. ResultsBy December 2024, the planned intervention was completed. Data analysis on the effectiveness of the intervention is to be completed and published in 2025. ConclusionsThis protocol reports a stage-matched intervention based on SOC theory, enriching the current knowledge base with new evidence from office workers in a low- to middle-income country. Trial RegistrationSri Lanka Clinical Trials Registry SLCTR/2020/025; https://slctr.lk/trials/slctr-2020-025 International Registered Report Identifier (IRRID)DERR1-10.2196/70293

Priyavanshi Pathania, Rohit Mehra, Vibhu Saujanya Sharma et al.

With the climate crisis looming, engineering sustainable software systems become crucial to optimize resource utilization, minimize environmental impact, and foster a greener, more resilient digital ecosystem. For developers, getting access to automated tools that analyze code and suggest sustainabilityrelated optimizations becomes extremely important from a learning and implementation perspective. However, there is currently a dearth of such tools due to the lack of standardized knowledge, which serves as the foundation of these tools. In this paper, we motivate the need for the development of a standard knowledge base of commonly occurring sustainability weaknesses in code, and propose an initial way of doing that. Furthermore, through preliminary experiments, we demonstrate why existing knowledge regarding software weaknesses cannot be re-tagged "as is" to sustainability without significant due diligence, thereby urging further explorations in this ecologically significant domain.

Ying Xiao, Shangwen Wang, Sicen Liu et al.

Traditional software fairness research typically emphasizes ethical and social imperatives, neglecting that fairness fundamentally represents a core software quality issue arising directly from performance disparities across sensitive user groups. Recognizing fairness explicitly as a software quality dimension yields practical benefits beyond ethical considerations, notably improved predictive performance for unprivileged groups, enhanced out-of-distribution generalization, and increased geographic transferability in real-world deployments. Nevertheless, existing bias mitigation methods face a critical dilemma: while pre-processing methods offer broad applicability across model types, they generally fall short in effectiveness compared to post-processing techniques. To overcome this challenge, we propose Correlation Tuning (CoT), a novel pre-processing approach designed to mitigate bias by adjusting data correlations. Specifically, CoT introduces the Phi-coefficient, an intuitive correlation measure, to systematically quantify correlation between sensitive attributes and labels, and employs multi-objective optimization to address the proxy biases. Extensive evaluations demonstrate that CoT increases the true positive rate of unprivileged groups by an average of 17.5% and reduces three key bias metrics, including statistical parity difference (SPD), average odds difference (AOD), and equal opportunity difference (EOD), by more than 50% on average. CoT outperforms state-of-the-art methods by three and ten percentage points in single attribute and multiple attributes scenarios, respectively. We will publicly release our experimental results and source code to facilitate future research.

Nina Andrejevic, Tao Zhou, Qingteng Zhang et al.

Abstract Coherent X-ray scattering (CXS) techniques are capable of interrogating dynamics of nano- to mesoscale materials systems at time scales spanning several orders of magnitude. However, obtaining accurate theoretical descriptions of complex dynamics is often limited by one or more factors—the ability to visualize dynamics in real space, computational cost of high-fidelity simulations, and effectiveness of approximate or phenomenological models. In this work, we develop a data-driven framework to uncover mechanistic models of dynamics directly from time-resolved CXS measurements without solving the phase reconstruction problem for the entire time series of diffraction patterns. Our approach uses neural differential equations to parameterize unknown real-space dynamics and implements a computational scattering forward model to relate real-space predictions to reciprocal-space observations. This method is shown to recover the dynamics of several computational model systems under various simulated conditions of measurement resolution and noise. Moreover, the trained model enables estimation of long-term dynamics well beyond the maximum observation time, which can be used to inform and refine experimental parameters in practice. Finally, we demonstrate an experimental proof-of-concept by applying our framework to recover the probe trajectory from a ptychographic scan. Our proposed framework bridges the wide existing gap between approximate models and complex data.

Ebtesam Al Haque, Chris Brown, Thomas D. LaToza et al.

About 32% of a software practitioners' day involves seeking and using information to support task completion. Although the information needs of software practitioners have been studied extensively, the impact of AI-assisted tools on their needs and information-seeking behaviors remains largely unexplored. To addresses this gap, we conducted a mixed-method study to understand AI-assisted information seeking behavior of practitioners and its impact on their perceived productivity and skill development. We found that developers are increasingly using AI tools to support their information seeking, citing increased efficiency as a key benefit. Our findings also amplify caveats that come with effectively using AI tools for information seeking, especially for learning and skill development, such as the importance of foundational developer knowledge that can guide and inform the information provided by AI tools. Our efforts have implications for the effective integration of AI tools into developer workflows as information retrieval systems and learning aids.

Vittorio Cortellessa, J. Andres Diaz-Pace, Daniele Di Pompeo et al.

Several approaches have recently used automated techniques to generate architecture design alternatives by means of optimization techniques. These approaches aim at improving an initial architecture with respect to quality aspects, such as performance, reliability, or maintainability. In this context, each optimization experiment usually produces a different set of architecture alternatives that is characterized by specific settings. As a consequence, the designer is left with the task of comparing such sets to identify the settings that lead to better solution sets for the problem. To assess the quality of solution sets, multi-objective optimization commonly relies on quality indicators. Among these, the quality indicator for the maximum spread estimates the diversity of the generated alternatives, providing a measure of how much of the solution space has been explored. However, the maximum spread indicator is computed only on the objective space and does not consider architectural information (e.g., components structure, design decisions) from the architectural space. In this paper, we propose a quality indicator for the spread that assesses the diversity of alternatives by taking into account architectural features. To compute the spread, we rely on a notion of distance between alternatives according to the way they were generated during the optimization. We demonstrate how our architectural quality indicator can be applied to a dataset from the literature.

Jie Zhu, Leye Wang, Xiao Han et al.

The size of deep learning models in artificial intelligence (AI) software is increasing rapidly, hindering the large-scale deployment on resource-restricted devices (e.g., smartphones). To mitigate this issue, AI software compression plays a crucial role, which aims to compress model size while keeping high performance. However, the intrinsic defects in a big model may be inherited by the compressed one. Such defects may be easily leveraged by adversaries, since a compressed model is usually deployed in a large number of devices without adequate protection. In this article, we aim to address the safe model compression problem from the perspective of safety-performance co-optimization. Specifically, inspired by the test-driven development (TDD) paradigm in software engineering, we propose a test-driven sparse training framework called SafeCompress. By simulating the attack mechanism as safety testing, SafeCompress can automatically compress a big model to a small one following the dynamic sparse training paradigm. Then, considering two kinds of representative and heterogeneous attack mechanisms, i.e., black-box membership inference attack and white-box membership inference attack, we develop two concrete instances called BMIA-SafeCompress and WMIA-SafeCompress. Further, we implement another instance called MMIA-SafeCompress by extending SafeCompress to defend against the occasion when adversaries conduct black-box and white-box membership inference attacks simultaneously. We conduct extensive experiments on five datasets for both computer vision and natural language processing tasks. The results show the effectiveness and generalizability of our framework. We also discuss how to adapt SafeCompress to other attacks besides membership inference attack, demonstrating the flexibility of SafeCompress.

John H. Maloney, K. Peppler, Y. Kafai et al.

Gene H. Kim, E. Spafford

Diego André Cerqueira, Rafael Maiani de Mello, Guilherme Horta Travassos

Recent laws to ensure the security and protection of personal data establish new software requirements. Consequently, new technologies are needed to guarantee software quality under the perception of privacy and protection of personal data. Therefore, we created a checklist-based inspection technique (LGPDCheck) to support the identification of defects in software artifacts based on the principles established by the Brazilian General Data Protection Law (LGPD). Objective/Aim: To evaluate the effectiveness and efficiency of LGPDCheck for verifying privacy and data protection (PDP) in software artifacts compared to ad-hoc techniques. Method: To assess LGPDCheck and ad-hoc techniques experimentally through a quasi-experiment (two factors, five treatments). The data will be collected from IoT-based health software systems built by software engineering students from the Federal University of Rio de Janeiro. The data analyses will compare results from ad-hoc and LGPDCheck inspections, the participant's effectiveness and efficiency in each trial, defects' variance and standard deviation, and time spent with the reviews. The data will be screened for outliers, and normality and homoscedasticity will be verified using the Shapiro-Wilk and Levene tests. Nonparametric or parametric tests, such as the Wilcoxon or Student's t-tests, will be applied as appropriate.

Raquel Blanco, Manuel Trinidad, Maria Jose Suarez-Cabal et al.

Software testing is an essential knowledge area required by industry for software engineers. However, software engineering students often consider testing less appealing than designing or coding. Consequently, it is difficult to engage students to create effective tests. To encourage students, we explored the use of gamification and investigated whether this technique can help to improve the engagement and performance of software testing students. We conducted a controlled experiment to compare the engagement and performance of two groups of students that took an undergraduate software testing course in different academic years. The experimental group is formed by 135 students from the gamified course whereas the control group is formed by 100 students from the non-gamified course. The data collected were statistically analyzed to answer the research questions of this study. The results show that the students that participated in the gamification experience were more engaged and achieved a better performance. As an additional finding, the analysis of the results reveals that a key aspect to succeed is the gamification experience design. It is important to distribute the motivating stimulus provided by the gamification throughout the whole experience to engage students until the end. Given these results, we plan to readjust the gamification experience design to increase student engagement in the last stage of the experience, as well as to conduct a longitudinal study to evaluate the effects of gamification.

Raghava Gowda