Unsupervised methods are widely used to induce latent semantic structure from large text collections, yet their outputs often contain incoherent, redundant, or poorly grounded clusters that are difficult to validate without labeled data. We propose a reasoning-based refinement framework that leverages large language models (LLMs) not as embedding generators, but as semantic judges that validate and restructure the outputs of arbitrary unsupervised clustering algorithms.Our framework introduces three reasoning stages: (i) coherence verification, where LLMs assess whether cluster summaries are supported by their member texts; (ii) redundancy adjudication, where candidate clusters are merged or rejected based on semantic overlap; and (iii) label grounding, where clusters are assigned interpretable labels in a fully unsupervised manner. This design decouples representation learning from structural validation and mitigates common failure modes of embedding-only approaches. We evaluate the framework on real-world social media corpora from two platforms with distinct interaction models, demonstrating consistent improvements in cluster coherence and human-aligned labeling quality over classical topic models and recent representation-based baselines. Human evaluation shows strong agreement with LLM-generated labels, despite the absence of gold-standard annotations. We further conduct robustness analyses under matched temporal and volume conditions to assess cross-platform stability. Beyond empirical gains, our results suggest that LLM-based reasoning can serve as a general mechanism for validating and refining unsupervised semantic structure, enabling more reliable and interpretable analyses of large text collections without supervision.
Nowadays, social media is pivotal in shaping public discourse, especially on polarizing issues like vaccination, where diverse moral perspectives influence individual opinions. In NLP, data scarcity and complexity of psycholinguistic tasks, such as identifying morality frames, make relying solely on human annotators costly, time-consuming, and prone to inconsistency due to cognitive load. To address these issues, we leverage large language models (LLMs), which are adept at adapting new tasks through few-shot learning, utilizing a handful of in-context examples coupled with explanations that connect examples to task principles. Our research explores LLMs' potential to assist human annotators in identifying morality frames within vaccination debates on social media. We employ a two-step process: generating concepts and explanations with LLMs, followed by human evaluation using a "think-aloud" tool. Our study shows that integrating LLMs into the annotation process enhances accuracy, reduces task difficulty, lowers cognitive load, suggesting a promising avenue for human-AI collaboration in complex psycholinguistic tasks.
In this research a real time power hardware in loop configuration has been implemented for an microgrid with the combination of distribution energy resources such as photovoltaic, grid tied inverter, battery, utility grid, and a diesel generator. This paper introduces an unique adaptive multi-objective optimization approach that employs weighted optimization techniques for real-time microgrid systems. The aim is to effectively balance various factors including fuel consumption, load mismatch, power quality, battery degradation, and the utilization of renewable energy sources. A real time experimental data from power hardware in loop system has been used for dynamically updating system states. The adaptive preference-based selection method are adjusted based on state of battery charging thresholds. The technique has been integrated with six technical objectives and complex constraints. This approach helps to practical microgrid decision making and optimization of dynamic energy systems. The energy management process were also able to maximize photovoltaic production where minimizing power mismatch, stabilizing battery state of charge under different condition. The research results were also compared with the baseline system without optimization techniques, and a reliable outcome was found.
We propose VISP: Volatility Informed Stochastic Projection, an adaptive regularization method that leverages gradient volatility to guide stochastic noise injection in deep neural networks. Unlike conventional techniques that apply uniform noise or fixed dropout rates, VISP dynamically computes volatility from gradient statistics and uses it to scale a stochastic projection matrix. This mechanism selectively regularizes inputs and hidden nodes that exhibit higher gradient volatility while preserving stable representations, thereby mitigating overfitting. Extensive experiments on MNIST, CIFAR-10, and SVHN demonstrate that VISP consistently improves generalization performance over baseline models and fixed-noise alternatives. In addition, detailed analyses of the evolution of volatility, the spectral properties of the projection matrix, and activation distributions reveal that VISP not only stabilizes the internal dynamics of the network but also fosters a more robust feature representation.
Salah Eddine Bekhouche, Abdellah Zakaria Sellam, Hichem Telli
et al.
Islamic inheritance law (Ilm al-Mawarith) requires precise identification of heirs and calculation of shares, which poses a challenge for AI. In this paper, we present a lightweight framework for solving multiple-choice inheritance questions using a specialised Arabic text encoder and Attentive Relevance Scoring (ARS). The system ranks answer options according to semantic relevance, and enables fast, on-device inference without generative reasoning. We evaluate Arabic encoders (MARBERT, ArabicBERT, AraBERT) and compare them with API-based LLMs (Gemini, DeepSeek) on the QIAS 2025 dataset. While large models achieve an accuracy of up to 87.6%, they require more resources and are context-dependent. Our MARBERT-based approach achieves 69.87% accuracy, presenting a compelling case for efficiency, on-device deployability, and privacy. While this is lower than the 87.6% achieved by the best-performing LLM, our work quantifies a critical trade-off between the peak performance of large models and the practical advantages of smaller, specialized systems in high-stakes domains.
Evariste Sindani, Simon Ntumba Badibanga, Pierre Kafunda Katalay
et al.
This study proposes an integrated approach to digitalizing human resources (HR) in African public institutions by developing a performance optimization model. Based on five key variables—processing time, operational cost, service quality, degree of automation, and employee satisfaction—this model aims to enhance the overall efficiency of HR processes. The study is applied to the case of the National Office for Population Identification (ONIP) in the Democratic Republic of Congo and highlights substantial improvements in human resource management. Theoretically, the approach contributes to the digital transformation field through modeling, and practically, by offering a reproducible and adaptable framework for other public organizations with limited resources.
Keywords: Digitalization, HR process optimization, ONIP, HR performance, HRIS.
Rifa'iyah women in Pekalongan managed to break free from constraints of patriarchal culture, enabling them to actively participate in the public sphere. Over time, they have initiated changes in their understanding and interpretation of religion, adapting these to contemporary developments. Their courage and consistency in these efforts have facilitated evolutionary adaptability within the social and cultural systems of the Rifa'iyah community, ultimately realizing complete freedom for women in public activities. This study employs the historical research method, encompassing heuristics, criticism, interpretation, and historiography. The findings reveal, first, that Rifa'iyah women have driven societal change by challenging cultural norms through innovative interpretations of the Qur'an and Hadith. They also hold that their actions align with the teachings of K.H. Ahmad Rifa'i. Second, the social roles of Rifa'iyah women in Pekalongan include participation in the Wagean Islamic Study, establishing the UMRI organization, engaging in professional work, participating in activities outside the home, and being politically active. Moreover, Rifa'iyah women successfully balance dual roles by fulfilling their responsibilities as mothers and wives while collaborating with their husbands.
Hasrat Efendi Samosir, Md Noor Bin Hussin, Sudianto
et al.
This study explores the Constitution of Medina as an early model of prophetic political communication grounded in consensus, offering a relevant framework for managing diversity and fostering social cohesion in pluralistic societies. Utilizing a qualitative library research method, the study draws upon the primary source—the text of the Constitution of Medina—and integrates secondary literature from the field of political communication. The data were analyzed through content analysis to identify underlying communicative principles and political strategies within the Charter. The findings reveal that the Constitution operationalizes participatory dialogue and peaceful conflict resolution mechanisms, aligning closely with contemporary consensus-based political communication theories. These principles served not only to manage inter-group tensions but also to build a cooperative and just social order. The study concludes that the Constitution of Medina is not merely a historical document but a normative model that offers practical insights into inclusive governance, interfaith cooperation, and the ethical foundations of political discourse. Its relevance is especially significant today, where polarized societies seek coexistence and constructive engagement frameworks. Thus, the Medina Charter is relevant as a prophetic guide for inclusive political communication in multicultural and multireligious contexts. This study contributes to Islamic political thought and communication by providing a normative and historically grounded model for inclusive governance. It bridges classical Islamic sources with contemporary political communication theory, offering a framework applicable to modern pluralistic societies seeking ethical and participatory governance models.
This paper explores the mechanisms behind extreme financial events, specifically market crashes, by employing the theoretical framework of phase transitions. We focus on endogenous crashes, driven by internal market dynamics, and model these events as first-order phase transitions critical, stochastic, and dynamic. Through a comparative analysis of early warning signals associated with each type of transition, we demonstrate that dynamic phase transitions (DPT) offer a more accurate representation of market crashes than critical (CPT) or stochastic phase transitions (SPT). Unlike existing models, such as the Log-Periodic Power Law (LPPL) model, which often suffers from overfitting and false positives, our approach grounded in DPT provides a more robust prediction framework. Empirical findings, based on an analysis of S&P 500 stocks from 2019 to 2024, reveal significant trends in volatility and anomalous dimensions before crashes, supporting the superiority of the DPT model. This work contributes to a deeper understanding of the predictive signals preceding market crashes and offers a novel perspective on their underlying dynamics.
Grasping the themes of social media content is key to understanding the narratives that influence public opinion and behavior. The thematic analysis goes beyond traditional topic-level analysis, which often captures only the broadest patterns, providing deeper insights into specific and actionable themes such as "public sentiment towards vaccination", "political discourse surrounding climate policies," etc. In this paper, we introduce a novel approach to uncovering latent themes in social media messaging. Recognizing the limitations of the traditional topic-level analysis, which tends to capture only overarching patterns, this study emphasizes the need for a finer-grained, theme-focused exploration. Traditional theme discovery methods typically involve manual processes and a human-in-the-loop approach. While valuable, these methods face challenges in scalability, consistency, and resource intensity in terms of time and cost. To address these challenges, we propose a machine-in-the-loop approach that leverages the advanced capabilities of Large Language Models (LLMs). To demonstrate our approach, we apply our framework to contentious topics, such as climate debate and vaccine debate. We use two publicly available datasets: (1) the climate campaigns dataset of 21k Facebook ads and (2) the COVID-19 vaccine campaigns dataset of 9k Facebook ads. Our quantitative and qualitative analysis shows that our methodology yields more accurate and interpretable results compared to the baselines. Our results not only demonstrate the effectiveness of our approach in uncovering latent themes but also illuminate how these themes are tailored for demographic targeting in social media contexts. Additionally, our work sheds light on the dynamic nature of social media, revealing the shifts in the thematic focus of messaging in response to real-world events.
We present a complete rewriting system for twisted right-angled Artin groups. Utilizing the normal form coming from the rewriting system, we provide applications that illustrate differences and similarities with right-angled Artin groups, both at the geometric and algebraic levels.
The purpose of this research is to explore the values of local wisdom reflected in the stories, which include aspects of Pacitan community life such as religious system or religious ceremonies, community system, knowledge system, language, art, livelihood system, and tool system. The research method used is qualitative with a literary anthropology approach. The data were obtained through the data collection technique of listening and recording of quotations or sentences contained in the book “Kumpulan Cerita dari Kota 1001 Goa”. The analysis was carried out by dividing the data based on the identified local wisdom and then analyzing through description and interpretation of the research data. The results showed the existence of seven elements of local wisdom in the collection of stories, namely 9 data on religious systems and religious ceremonies, 3 data on community systems, 5 data on knowledge systems, 3 data on language, 4 data on art, 4 data on livelihood systems, and 8 data on tool systems. The existence of these seven elements is reflected in “Kumpulan Cerita dari Kota 1001 Goa” and can be revealed through an anthropology of literature approach.
The Islamic view of change is that change is dynamic, change is a necessity that humans themselves can try their best to make changes. The concept of change in Islam is commonly referred to as hijrah, namely the direction of human change to move from an environment that is not good to be good, from disgraceful behavior to commendable, from immoral humans to human beings with good morals. The purpose of this research is to bring change in the form of a positive impact in every line of life and of course, the world of education is also constantly changing. The method used in this study is the method of observation and in-depth reference excavation from various sources or media. The results of research on changes in Islamic education include curriculum changes, changes in teaching methods, changes in teaching materials, learning strategies and media. What is clear is that this change will bring good and benefit us all.
We present a technique to represent anisotropic heavy-quark potentials as effective potentials. This involves employing an effective screening mass linked to the quantum numbers $l$ and $m$ of a specific state. Our approach utilizes the resulting 1D effective potential model, enabling the solution of a 1D Schrödinger equation. Remarkably, this model accurately reproduces the energies and binding energies of low-lying heavy-quarkonium bound states in 3D, including the differentiation of various p-wave polarizations. The derived 1D effective model offers a means to incorporate momentum anisotropy effects into simulations of heavy-quarkonium dynamics in the quark-gluon plasma within open quantum systems.
Crafting effective deep learning models for medical image analysis is a complex task, particularly in cases where the medical image dataset lacks significant inter-class variation. This challenge is further aggravated when employing such datasets to generate synthetic images using generative adversarial networks (GANs), as the output of GANs heavily relies on the input data. In this research, we propose a novel filtering algorithm called Cosine Similarity-based Image Filtering (CosSIF). We leverage CosSIF to develop two distinct filtering methods: Filtering Before GAN Training (FBGT) and Filtering After GAN Training (FAGT). FBGT involves the removal of real images that exhibit similarities to images of other classes before utilizing them as the training dataset for a GAN. On the other hand, FAGT focuses on eliminating synthetic images with less discriminative features compared to real images used for training the GAN. Experimental results reveal that employing either the FAGT or FBGT method with modern transformer and convolutional-based networks leads to substantial performance gains in various evaluation metrics. FAGT implementation on the ISIC-2016 dataset surpasses the baseline method in terms of sensitivity by 1.59% and AUC by 1.88%. Furthermore, for the HAM10000 dataset, applying FABT outperforms the baseline approach in terms of recall by 13.75%, and with the sole implementation of FAGT, achieves a maximum accuracy of 94.44%.
This study aims to find the meaning and describe the management of organizational culture change in the educational environment at the Sumatra Thawalib Islamic Boarding School, Parabek. The research method used in this research is qualitative with the type of phenomenological study. The instrument in this research is the researcher himself. Data collection techniques are used in the form of in-depth interviews, participant observation, and documentation. The guarantee of the validity of the data is carried out by triangulation of sources and increasing the persistence of researchers in understanding each data obtained. The results showed that the management of organizational culture change in the educational environment at the Sumatra Thawalib Islamic Boarding School, Parabek Evidencettinggi is contained in the acronym "THAWALIB" which consists of (1) Taqwa; (2) himmah; (3) Ahl al-'ilmi wa al-'worship; (4) Wara'; (5) Trust; (6) Layin; (7) Istiqomah; (8) Birrul. The core values of the management of organizational culture change as stated in the eight core values are reflected in various aspects of life that exist in the Educational Institutions at the Sumatra Thawalib Islamic Boarding School, Parabek, Bukittinggi. The management of organizational culture change which is reflected in the acronym for the name of the Islamic boarding school is also the core values of how the educational institution is run to achieve the vision, mission, and goals that have been set.
This research aims to explore and examine the civil law implications for individuals who are Khuntsa and have changed their identities, with a focus on the perspectives of Islamic Law and Civil Law in Indonesia. The research adopts a normative legal approach with a prescriptive nature, emphasizing the analysis of legislation and case studies. If this process is carried out and followed by a legal application to change their gender identity through the court system, and the judge approves it, there will be legal implications. Additionally, it is essential to consider religious perspectives, especially Islam. To address the issues arising from the civil law implications for Khuntsa individuals who have changed their identities based on court rulings, specific regulations governing their status and position are necessary. This is aimed at ensuring legal certainty in such cases.
Yani Zimatullah, Rozaanah Rozaanah, Ilham Tumanggor
The purpose of this study was to determine the correlation between ability to pronounce Arabic letters and Imla' ablity in first grade students of SMP Ma'had Tahfizul Qur'an Fajar Islam Tanjung Morawa. The method used is a quantitative approach with a correlation method and the data analysis method uses statistical data with the Product Moment formula to find the results of the study. The result is that the alternative hypothesis (Ha) is accepted and the null hypothesis (H0) is rejected. That is, there is a correlation between ability to pronounce Arabic letters and Imla’ ability in first grade students of SMP Ma'had Tahfizul Qur'an Fajar Islam Tanjung Morawa with a correlation value of 0.765 which shows a strong and unidirectional correlation.