The digitalization of organizational processes has accelerated the adoption of efficient attendance management systems. This study systematically reviews AppSheet-based digital attendance implementations, focusing on outcomes, features, and sectoral adoption. Following a PRISMA-guided systematic literature review, 23 studies published between 2020 and 2025 were analyzed. Findings indicate that AppSheet enhances efficiency, accuracy, transparency, and user satisfaction, primarily through mobile access, GPS-based geolocation, QR code verification, and cloud-based reporting. Selfie photo with GPS emerged as the most common feature, followed by QR code–based attendance, while additional functionalities such as digital signatures and form-based attendance provide complementary solutions. Adoption is concentrated in the education sector, particularly primary and secondary schools, with limited uptake in government and corporate contexts. Challenges include scalability limits in free versions, proxy attendance risks, internet dependency, and storage constraints. The study underscores AppSheet’s flexibility as a no-code platform, offering practical, cost-effective, and adaptive solutions for digital attendance management across diverse organizational settings.

Variational Quantum Circuits (VQCs) have emerged as a cornerstone of hybrid quantum–classical algorithms designed to harness the computational potential of near-term quantum devices. By combining parameterized quantum gates with classical optimization, VQCs provide a flexible framework for tackling machine learning, chemistry, and optimization problems intractable for classical methods. This review comprehensively overviews VQC design principles, ansatz structures, optimization strategies, and real-world applications. Furthermore, we discuss fundamental challenges such as barren plateaus, the expressibility–trainability trade-off, and current noisy intermediate-scale quantum (NISQ) hardware limitations. Finally, we highlight emerging directions that could enable scalable, noise-resilient, and physically interpretable variational quantum models for future quantum computing applications

The use of Search-Based Software Engineering (SBSE) for optimizing software architecture has evolved from fully automated to interactive approaches, integrating human expertise. However, current interactive tools face limitations: they typically support only single decision-makers, confine architects to passive roles, and induce significant cognitive fatigue from repetitive evaluations. These issues disconnect them from modern, team-based software development, where collaboration and consensus are crucial. To address these shortcomings, we propose "ArchEvolve," a novel framework designed to facilitate collaborative, multi-architect decision-making. ArchEvolve employs a cooperative coevolutionary model that concurrently evolves a population of candidate architectures and distinct populations representing each architect's unique preferences. This structure guides the search towards high-quality consensus solutions that accommodate diverse, often conflicting, stakeholder viewpoints. An integrated Artificial Neural Network (ANN) serves as a preference learning module, trained on explicit team feedback to act as a surrogate evaluator. This active learning cycle substantially reduces the number of required human interactions and alleviates user fatigue. Empirical evaluation on two industrial case studies (E-Commerce System and Healthcare Management System) compared ArchEvolve to a state-of-the-art interactive baseline. Results indicate that ArchEvolve achieves statistically significant improvements in both solution quality and consensus-building. The preference learning module demonstrated over 90% accuracy in predicting team ratings and reduced human evaluations by up to 46% without compromising final solution quality. ArchEvolve provides a practical, scalable framework supporting collaborative, consensus-driven architectural design, making interactive optimization a more viable and efficient tool for real-world software engineering teams by intelligently integrating cooperative coevolutionary search with a preference learning surrogate.

The rapid growth of open-source software (OSS) in machine learning (ML) has intensified the need for reliable, automated methods to assess project quality, particularly as OSS increasingly underpins critical applications in science, industry, and public infrastructure. This study evaluates the effectiveness of a diverse set of machine learning and deep learning (ML/DL) algorithms for classifying GitHub OSS ML projects as engineered or non-engineered using a SMOTE-enhanced and explainable modeling pipeline. The dataset used in this research includes both numerical and categorical attributes representing documentation, testing, architecture, community engagement, popularity, and repository activity. After handling missing values, standardizing numerical features, encoding categorical variables, and addressing the inherent class imbalance using the Synthetic Minority Oversampling Technique (SMOTE), seven different classifiers—K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), XGBoost (XGB), Logistic Regression (LR), Support Vector Machine (SVM), and a Deep Neural Network (DNN)—were trained and evaluated. Results show that LR (84%) and DNN (85%) outperform all other models, indicating that both linear and moderately deep non-linear architectures can effectively capture key quality indicators in OSS ML projects. Additional explainability analysis using SHAP reveals consistent feature importance across models, with documentation quality, unit testing practices, architectural clarity, and repository dynamics emerging as the strongest predictors. These findings demonstrate that automated, explainable ML/DL-based quality assessment is both feasible and effective, offering a practical pathway for improving OSS sustainability, guiding contributor decisions, and enhancing trust in ML-based systems that depend on open-source components.

We introduce DiFACE2 causal DAG, a framework for generating Diverse, Feasible, and Actionable Counterfactual Explanations under causal Directed Acyclic Graph (DAG) constraints. Leveraging background knowledge from an educational intervention program in post-conflict Nigeria, we construct an application-specific causal DAG to identify causal relationships and immutable features during counterfactual generation for black-box ML predictions. Using this DAG, we develop a causally constrained sparse ML model that integrates prior frameworks to generate diverse, feasible, and actionable counterfactuals for the Strengthening Education in Northeast – Early Grade Reading Assessment (SENSE-EGRA) dataset. DiFACE2 produces up to four counterfactuals per prediction, demonstrating high feasibility and actionability for categorical variables. While numeric features occasionally yield infeasible recommendations, this is mitigated by additional non-causal constraints. Although the sparse ML model yields lower accuracy and F1 scores than benchmark frameworks, this stems from the higher complexity and noisiness of the real-world dataset rather than methodological weakness. This trade-off underscores that meaningful, interpretable counterfactuals are preferable to near-perfect accuracy on simplified benchmarks. Therefore, when fairly evaluated on the same complex dataset, DiFACE2 outperforms prior methods across all counterfactual quality metrics.

This study investigates the influence of overprotective parenting on independent character development among out-of-town university students enrolled in a Home Economics Education Program. Independence in emerging adulthood encompasses emotional, behavioral, and value-based autonomy, essential for functioning as responsible adults. Using a quantitative approach with simple linear regression analysis, data were collected from 103 students selected through purposive sampling. The results show a negative relationship between overprotective parenting and student independence; however, the effect was not statistically significant (p = 0.744). These findings suggest that while overprotective parenting may inhibit autonomy to some extent, student independence is more significantly shaped by broader contextual factors such as peer interaction, campus engagement, and experiential learning. Peer interaction can provide opportunities for students to practice independence in a supportive environment, while campus engagement and experiential learning can foster autonomy by encouraging students to take initiative and make decisions. The study highlights the need for holistic strategies within higher education to foster independence beyond family influences alone. Implications of this study indicate that universities should create structured programs that support student autonomy and self-regulation. At the same time, future research is encouraged to explore mediating variables that may better explain the development of independence during emerging adulthood.

Physics learning in senior high school faces challenges in developing students' scientific reasoning ability. This study aims to examine students' scientific reasoning ability through project-based learning of briquette making and its effect on improving students' critical thinking ability, especially in SMA Negeri 1 Jatiroto, to improve a more effective understanding of physics. The research was conducted at SMA Negeri 1 Jatiroto, Lumajang Regency, in semester one of the 2024/2025 scholastic year. The population of the research sample was class X students, which was determined using homogeneity test and random sampling technique or purposive sampling. The research design used Quasi Experimental with pre-test and post-test. Data collection was done through tests, observations, interviews, and documentation. The pre-test and post-test results showed a significant increase in the experimental class, with a higher mean post-test result (84) than the control class (50). Data analysis displayed a significant difference between the two groups, indicating that the project-based learning model had a beneficial effect on enhancing performance students' scientific reasoning skills. Thus, project-based learning in briquette making is proven to be able to encourage increased understanding of physics concepts while developing students' critical thinking skills more effectively.

The existence of organizations is essential because human beings, as social creatures, cannot fulfill all of their needs individually. This study examines the fundamental concepts underlying organizations. Its primary objective is to identify and understand the key elements encompassed within the basic framework of organizational theory. Employing a qualitative methodology with a literature review approach, the research draws upon digital literacy sources and other relevant data pertinent to the topic. Through analyzing various scholarly references, particularly conceptual frameworks and the Qur'anic perspective on organizational principles, this study explores definitions, essential components, organizational management principles, structural elements, and the broader social role of organizations. The findings indicate that robust theoretical foundations support organizational theory and harmonize the spiritual values embedded in Islamic teachings. Findings from the literature also emphasize that an ideal organization is not solely built upon structural systems, procedures, and work efficiency, but also supported by rooted values of ethics, morality, and spirituality deeply. 

This study aims to evaluate the assessment standards of Rare Muchtary Kindergarten Denpasar through an evaluation of the suitability of the assessment standards with early childhood development. Using an evaluation research approach, this study seeks to deeply understand the ongoing assessment practices and their relevance to the stages of child development. Data were collected through direct observation in the classroom, in-depth interviews with teachers and principals, and analysis of assessment standard documents and child development records. The results of this study indicate that Rare Muchtary Kindergarten Denpasar is very administratively compliant with the National Assessment Standards, with high scores on objectives, principles, and various assessment techniques (observations, anecdotal notes, conversations, parent reports). However, there is still an opportunity to improve the accuracy and suitability of the assessment implementation with actual child development. 

The pursuit of Artificial General Intelligence (AGI) requires learning frameworks that not only optimize task performance but also align with complex human values. Reinforcement Learning with Human Feedback (RLHF) has emerged as a promising approach to address this challenge; however, conventional RLHF pipelines face scalability issues, reward-model brittleness, and safety concerns. In this study, we propose a PPO-based RLHF framework enhanced with hybrid human–AI oversight and predictive reward evaluation metrics. The framework integrates human annotations with AI-generated critiques, improving data efficiency and robustness against reward hacking. Experimental evaluations on language alignment and control benchmarks demonstrate that the proposed approach achieves a preference win-rate of 78% (vs. 65% in standard RLHF and 54% in supervised fine-tuning), improves task accuracy to 83% (a 12% increase over Sparrow), and reduces safety violations by 31% compared to baseline RLHF. Furthermore, the hybrid oversight strategy enhanced sample efficiency by 1.5×, reducing overall training episodes and annotation costs. These results confirm that the proposed method significantly improves alignment, efficiency, and safety, positioning RLHF with hybrid oversight and predictive evaluation as a practical substrate for advancing safe and scalable AGI systems.