Search Results (532)

Image semantics, by revealing rich structural information, provides crucial guidance for image inpainting. However, current semantic-guided inpainting frameworks generally operate unidirectionally, relying on pre-trained segmentation networks without a feedback mechanism to adapt segmentation dynamically during inpainting. To address this limitation, we propose an innovative inpainting methodology that incorporates semantic segmentation feedback recorrection via semi-supervised learning. Specifically, the fundamental concept involves enabling the initial inpainting network to deliver feedback to the semantic segmentation model, which subsequently refines its predictions by leveraging cross-image semantic consistency. The iteratively corrected semantic segmentation maps serve to direct the inpainting neural network toward improved reconstruction quality, fostering a synergistic interaction that enhances both segmentation accuracy and inpainting performance. Furthermore, a semi-supervised learning strategy is implemented to reduce reliance on ground truth labels and improves generalization by utilizing both labeled and unlabeled datasets. We conduct our methodology on the CelebA-HQnd Cityscapes datasets, employing multiple quantitative metrics including LPIPS, PSNR, and SSIM. Results demonstrate that the proposed algorithm surpasses current methodologies: on CelebA-HQ dataset, it achieves a 5.89% reduction in LPIPS and a 0.52% increase in PSNR, with notable improvements in SSIM; on the Cityscapes dataset, LPIPS decreases by 6.15% and SSIM increases by 1.58%. Ablation studies confirm the effectiveness of the feedback recorrection mechanism. This research provides novel insights into synergistic interactions between segmentation and inpainting, demonstrating that fostering such interactions can substantially improve image processing performance. Full article

This paper explores the optimal control issue for a linear–quadratic–Gaussian (LQG) system under the conditions of imperfect feedback and constraints related to energy harvesting. The system is equipped with various energy options, which allow it to gather energy for information transmission while also receiving imperfect feedback from an auxiliary filter that estimates packet loss. The primary goal of this study is to jointly design the energy selector and the controller to achieve an optimal balance between transmission costs and control performance. Initially, we separate the controller’s synthesis task from the energy selection task. The subproblem of optimal controller synthesis is characterized by a Riccati equation that takes continuous packet loss into account. Simultaneously, the energy selection task, influenced by imperfect feedback and constraints on energy costs, is reformulated as a Markov decision process (MDP) that operates with perfect acknowledgments through iterative updates of state information. Ultimately, the optimal energy selection policy that guarantees filtering performance is derived by solving a Bellman equation. The effectiveness of the proposed approach is confirmed through simulation results. Full article

Formative assessment is an integral component of higher education, fostering student learning through feedback, reflection, and iterative improvement. However, despite its pedagogical importance, widespread adoption of formative assessment is often hindered by time constraints, resource limitations, and scalability challenges. The objective of this study is to examine how large language models (LLMs) offer a potential solution to support and enhance formative assessment in higher education across diverse educational contexts by enabling automated, personalized, and scalable feedback that is sustainable and accessible. In this review, we comprehensively examine cutting-edge research and applications of LLMs in various components of formative assessment, including feedback generation, student self-assessment, peer review, and instructor support within the context of higher education. We explore the opportunities LLMs present in enhancing learning outcomes associated with formative assessments and current research gaps while critically discussing the challenges in practical implementations of integrating LLM-driven formative assessments in real-world classrooms. By synthesizing current advancements, this review provides educators and researchers with insights into the transformative potential and responsible implementation of LLM-driven formative assessments in higher education. Full article

Service Function Chain (SFC) is a framework that dynamically orchestrates Virtual Network Functions (VNFs) and is essential to enhancing resource scheduling efficiency. However, traditional scheduling methods face several limitations, such as low matching efficiency, suboptimal resource utilization, and limited global coordination capabilities. To this end, we propose a multi-objective scheduling algorithm for SFCs based on matching games (SFC-GS). First, a multi-objective cooperative optimization model is established that aims to reduce scheduling time, increase request acceptance rate, lower latency, and minimize resource consumption. Second, a matching model is developed through the construction of preference lists for service nodes and VNFs, followed by multi-round iterative matching. In each round, only the resource status of the current and neighboring nodes is evaluated, thereby reducing computational complexity and improving response speed. Finally, a hierarchical batch processing strategy is introduced, in which service requests are scheduled in priority-based batches, and subsequent allocations are dynamically adjusted based on feedback from previous batches. This establishes a low-overhead iterative optimization mechanism to achieve global resource optimization. Experimental results demonstrate that, compared to baseline methods, SFC-GS improves request acceptance rate and resource utilization by approximately 8%, reduces latency and resource consumption by around 10%, and offers clear advantages in scheduling time. Full article

Voice assistants have become increasingly common in everyday devices such as smartphones and smart speakers. Improving their user experience (UX) is crucial to ensuring usability, acceptance, and long-term effectiveness. Heuristic evaluation is a widely used method for UX evaluation due to its efficiency in detecting problems quickly and at low cost. Nonetheless, existing usability/UX heuristics were not designed to address the specific challenges of voice-based interaction, which relies on spoken dialog and auditory feedback. To overcome this limitation, we developed HEUXIVA, a set of 13 heuristics specifically developed for evaluating UX with voice assistants. The proposal was created through a structured methodology and refined in two iterations. We validated HEUXIVA through heuristic evaluations, expert judgment, and user testing. The results offer preliminary but consistent evidence supporting the effectiveness of HEUXIVA in identifying UX issues specific to the voice assistant “Google Nest Mini”. Experts described the heuristics as clear, practical, and easy to use. They also highlighted their usefulness in evaluating interaction features and supporting the overall UX evaluation process. HEUXIVA therefore provides designers, researchers, and practitioners with a specialized tool to improve the quality of voice assistant interfaces and improve user satisfaction. Full article

This study examines the potential of the AI chatbot ChatGPT-4o to support human-centered tasks such as qualitative research analysis. It focuses on a case study involving an initial university teaching training program at the Universidad Politécnica de Madrid (UPM), evaluated through student feedback. The findings indicate that ChatGPT can assist in the qualitative analysis of student assessments by identifying specific issues and suggesting possible solutions. However, expert oversight remains necessary as the tool lacks a full contextual understanding of the actions evaluated. The study concludes that AI systems like ChatGPT offer powerful means to complement complex human-centered tasks and anticipates their growing role in the evaluation of formative programs. By examining ChatGPT’s performance in this context, the study lays the groundwork for prototyping a customized automated system built on the insights gained here, capable of assessing program outcomes and supporting iterative improvements throughout each module, with the ultimate goal of enhancing the quality of the training program Full article

As an important supplement to ground computing, edge computing can effectively alleviate the computational burden on ground systems. In the context of integrating edge computing with low-Earth-orbit satellite networks, this paper proposes an edge computing task offloading algorithm based on distributed multi-agent deep reinforcement learning (DMADRL) to address the challenges of task offloading, including low transmission rates, low task completion rates, and high latency. Firstly, a Ground–UAV–LEO (GUL) three-layer architecture is constructed to improve offloading transmission rate. Secondly, the task offloading problem is decomposed into two sub-problems: offloading decisions and resource allocation. The former is addressed using a distributed multi-agent deep Q-network, where the problem is formulated as a Markov decision process. The Q-value estimation is iteratively optimized through the online and target networks, enabling the agent to make autonomous decisions based on ground and satellite load conditions, utilize the experience replay buffer to store samples, and achieve global optimization via global reward feedback. The latter employs the gradient descent method to dynamically update the allocation strategy based on the accumulated task data volume and the remaining resources, while adjusting the allocation through iterative convergence error feedback. Simulation results demonstrate that the proposed algorithm increases the average transmission rate by 21.7%, enhances the average task completion rate by at least 22.63% compared with benchmark algorithms, and reduces the average task processing latency by at least 11.32%, thereby significantly improving overall system performance. Full article

This paper presents the development and evaluation of a method for rendering realistic haptic textures in virtual environments, with the goal of enhancing immersion and surface recognizability. By using Blender for the creation of geometric models, Unity for real-time interaction, and integration with the Touch haptic device from 3D Systems, virtual surfaces were developed with parameterizable characteristics of friction, stiffness, and relief, simulating different physical textures. The methodology was assessed through two experimental phases involving a total of 47 participants, examining both tactile recognition accuracy and the perceived realism of the textures. Results demonstrated improved overall performance and reduced variability between textures, suggesting that the approach can provide convincing haptic experiences. The proposed method has potential applications across a wide range of domains, including education, medical simulation, cartography, e-commerce, entertainment, and artistic creation. The main contribution of this research lies in the introduction of a simple yet effective methodology for haptic texture rendering, which is based on the flexible adjustment of key parameters and iterative optimization through human feedback. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a major global health threat, with children at higher risk due to developmental differences in drug metabolism, limited treatment options and inappropriate antibiotic use. Pediatric antimicrobial stewardship programs (ASPs) face implementation challenges, often relying on adult-based guidelines and limited pediatric-specific evidence. This study aimed to identify and prioritize the most critical areas for pediatric ASP intervention development through a structured, multi-round Delphi consensus process with experts in antimicrobial stewardship and infectious diseases. Method: A four-round modified Delphi process was conducted to identify and prioritize key pediatric ASP interventions. Experts in antimicrobial stewardship and infectious diseases were recruited through an existing clinical trial. Using an iterative survey and in-person discussions, experts provided input on priority areas, which were thematically grouped and refined across rounds. Structured feedback supported real-time refinement and consensus-building. Results: Twenty experts participated in the process, generating 25 priority items in Round 1 through open-ended responses. These were narrowed to seven key priorities through structured voting and discussion. The final items were clustered into three intersecting themes: Care Settings, Prescriptions, and Strategies. Care Settings focused on high-impact areas such as outpatient clinics and intensive care units, where misuse is common and/or care is complex. The prescriptions theme prioritized shorter durations and narrow-spectrum agents. The strategy theme highlighted the need for outcome-based metrics, improved diagnostic stewardship, and routine tracking of patient outcomes to guide and assess stewardship efforts. Conclusions: This expert consensus identified key priorities for pediatric ASPs, providing a foundation for future interventions. Findings can be used to inform policy and practice, improving the appropriate use of antimicrobials in pediatrics and combating AMR. Full article

Object detection is a critical technology for smart city development. As request volumes surge, inference is increasingly offloaded from centralized clouds to user-proximal edge sites to reduce latency and backhaul traffic. However, heterogeneous workloads, fluctuating bandwidth, and dynamic device capabilities make offloading and scheduling difficult to optimize in edge environments. Deep reinforcement learning (DRL) has proved effective for this problem, but in practice, it relies on manually engineered reward functions that must be redesigned whenever service objectives change. To address this limitation, we introduce an LLM-driven framework that retargets DRL policies for edge object detection directly through natural language instructions. By leveraging understanding of the text and encoding capabilities of large language models (LLMs), our system (i) interprets the current optimization objective; (ii) generates an executable, environment-compatible reward function code; and (iii) iteratively refines the reward via closed-loop simulation feedback. In simulations for a real-world dataset, policies trained with LLM-generated rewards adapt from prompts alone and outperform counterparts trained with expert-designed rewards, while eliminating manual reward engineering. Full article

This study reports on a Design-Based Research (DBR) project that implemented Project-Based Learning (PjBL) in an undergraduate psychology course on Human Resource Development (HRD). The purpose was to move beyond lecture-based instruction and explore how open pedagogy can create authentic, student-centered learning experiences that bridge theory and practice. Over two course iterations (n = 31), students co-designed, implemented, and evaluated HRD interventions for their peers, supported by peer and instructor feedback and complemented by a co-created open-book exam. Quantitative pre- and post-tests revealed significant improvements in students’ knowledge of HRD methods, learning theories, and application competencies, as well as enhanced confidence in their professional qualifications. Students valued the openness of the design, its practical orientation, and the error-friendly learning environment, though challenges emerged regarding workload, communication, and intrinsic motivation. Educators reported a transformation of their role from knowledge transmitter to facilitator and co-learner, while also identifying opportunities to use AI for generating authentic case tasks. The findings suggest that PjBL, combined with open pedagogy, fosters self-directed learning, transparency, and collaboration, thereby contributing to cultural change in higher education toward openness, participation, and innovation. Full article

The growing penetration of distributed photovoltaic (PV) systems presents significant operational challenges for power grids, driven by the scarcity of historical data and the high spatiotemporal variability of PV generation. To address these challenges, we propose Generative Reconstruction and Adaptive Identification via Latents (GRAIL), a unified, end-to-end framework that integrates generative modeling with adaptive clustering to discover latent structures and representative scenarios in PV datasets. GRAIL operates through a closed-loop mechanism where clustering feedback guides a cluster-aware data generation process, and the resulting generative augmentation strengthens partitioning in the latent space. Evaluated on a real-world, multi-site PV dataset with a high missing data rate of $45.4\%$, GRAIL consistently outperforms both classical clustering algorithms and deep embedding-based methods. Specifically, GRAIL achieves a Silhouette Score of 0.969, a Calinski–Harabasz index exceeding $4.132\times {10}^6$, and a Davies–Bouldin index of $0.042$, demonstrating superior intra-cluster compactness and inter-cluster separation. The framework also yields a normalized entropy of $0.994$, which indicates highly balanced partitioning. These results underscore that coupling data generation with clustering is a powerful strategy for expressive and robust structure learning in data-sparse environments. Notably, GRAIL achieves significant performance gains over the strongest deep learning baseline that lacks a generative component, securing the highest composite score among all evaluated methods. The framework is also computationally efficient. Its alternating optimization converges rapidly, and clustering and reconstruction metrics stabilize within approximately six iterations. Beyond quantitative performance, GRAIL produces physically interpretable clusters that correspond to distinct weather-driven regimes and capture cross-site dependencies. These clusters serve as compact and robust state descriptors, valuable for downstream applications such as PV forecasting, dispatch optimization, and intelligent energy management in modern power systems. Full article

This research investigates the optimization of operational efficiency and cost reduction through the enhancement of material flow management within production line stations. Departing from conventional static analyses, the study employs advanced simulation tools to pinpoint performance bottlenecks and inefficiencies via dynamic modelling techniques. The Ishikawa diagram serves as the primary tool for conducting root-cause analysis. Simultaneously, the 5S methodology is implemented to foster workplace organization, standardization, and hygiene practices. In contrast to traditional optimization frameworks, the proposed strategy integrates real-time performance tracking systems, complemented by adaptive feedback mechanisms. This integration permits ongoing assessment of the production process, facilitating iterative improvement cycles. Empirical data gathered from monitored cycle times, equipment utilization rates, and defect frequencies substantiate the validation of implemented changes. The resulting optimized system significantly minimizes downtime and waste, thereby advancing sustainable and scalable operations. Ultimately, this research demonstrates that the fusion of simulation-based insights with lean management principles leads to considerable improvements in manufacturing productivity and overall product quality. Full article

Background/Objectives: Psychosocial care for siblings and caregivers of youth with cancer (SCYC) is a critical yet under-implemented component of comprehensive pediatric oncology care, as outlined by the Standards for Psychosocial Care for Children with Cancer and Their Families. Despite evidence supporting psychosocial interventions, such as integrative care interventions, as effective for stress mitigation and coping, barriers to implementation include revenue-generating funding models and siloed psychosocial disciplines, which hinder accessibility for adult caregivers within pediatric institutions and geographically dispersed families. This manuscript describes the relevant extant literature as well as a model for leveraging short-term funding opportunities and interdisciplinary collaboration to develop integrative care programs for these underserved groups. Methods: Philanthropic funding supported part-time child life specialist and creative arts therapist deployment to develop and implement integrative virtual group programs, as well as interdisciplinary integrative programs, to serve SCYC. Attendance, engagement, and qualitative feedback were used for program iteration and supported the transition to institutional funding. Results: Integrative programs provided 331 caregiver and sibling encounters during the two-year pilot. Qualitative feedback from caregivers highlighted the value of virtual services in reaching geographically dispersed families and addressing feelings of isolation among SCYC at the universal and targeted levels of care. Communication about these key outcomes led to operational funding and sustained integrated care programs. Conclusions: This manuscript illustrates a successful model of leveraging philanthropic funding to support the development of integrative care programs to serve SCYC. Future research should focus on refining the clinical and financial feasibility of such models and assessing their impact on family well-being. Full article

Background: Inappropriate antibiotic prescribing by dental practitioners is a significant problem in low- and middle-income settings, such as India, where there are no guidelines for dental prescribing. This study aims to report, in a step-by-step process, the co-development of a computer-based stewardship educational intervention with Indian stakeholders to reduce inappropriate antibiotic prescribing by primary care dental practitioners in India. Methods: The development process of our intervention was guided by the Medical Research Council framework for developing and evaluating complex interventions. In alignment with the framework’s core elements, a co-production research approach was employed. Engagement with local stakeholders, including primary care dental practitioners, academic dentists, and those from the Indian Dental Association, facilitated the development of a contextually appropriate intervention that was informed by a prior needs assessment (a systematic review and a policy document analysis conducted in India) and evidence from global literature. The intervention was refined through iterative feedback from stakeholders and pre-testing. Results: An educational antibiotic stewardship intervention was co-developed in collaboration with stakeholders from Chennai, a major city in southern India. The final intervention comprised three components: 1. A one-page chairside guide summarising common areas of dental antibiotic use for easy reference in clinical settings; 2. A training module based on the chairside guide; and 3. A patient information sheet to facilitate dentists’ communication with patients. The intervention components were designed to be clear, practical, and contextually relevant, with the potential to enhance clinical decision-making and promote evidence-based antibiotic prescribing practices. Conclusions: This research paper describes, in a structured manner, how an educational antibiotic stewardship intervention for dental practitioners in India was co-developed by researchers and local stakeholders. Further feasibility testing is required to address uncertainties identified at the conclusion of the development process, including those related to dentists’ perceptions of the intervention, the utility of the intervention tools, and prescription recording. Full article