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.
Batik Rifaiyah is a cultural heritage of the Rifaiyah Community located in Kalipucang Wetan Village, Batang Regency, Central Java. It serves as an expression of Islamic spirituality. This type of Batik represents artistic craftsmanship and is a medium for teaching Islamic values through symbols and motifs. However, understanding the philosophical values and foundational aspects of Rifaiyah Batik is currently limited to the oral traditions passed down by batik makers and the Rifaiyah Community, with insufficient written documentation. This study aims to explore and comprehend the fundamental principles of Rifaiyah Batik as a manifestation of Islamic spiritual art, focusing on the Pelo Ati and Materos Satrio motifs. The research employs a qualitative method utilizing a social phenomenology approach. It includes observations of the batik motifs, in-depth interviews with batik makers, and a review of relevant literature. The analysis is guided by the Pyramid Base Guiding Craft Innovation theory, which identifies four crucial dimensions in the batik tradition: Traditional Artifacts, Traditional Ecology, Teaching of Tradition, and Non-Material Culture. The findings reveal that Rifaiyah Batik reflects Islamic values through theological, aesthetic, and artistic ethics. Theologically, it avoids depicting living beings, using arabesque to express Tawheed. Aesthetically, its colors are inspired by the Qur'an. Artistically, its meticulous craftmanship is an act of worship.
The rapidly evolving business landscape, particularly in manufacturing and service sectors, demands effective marketing strategies to secure a competitive edge and sustain market presence. This paper focuses on the chemical industry, exemplified by CV Rizqy Anhu Kemala, which faces challenges such as product quality dissatisfaction and increased competition. The study explores the impact of the 4P marketing mix—Product, Price, Promotion, Place—on perceived consumer value, trust, and repurchasing decisions. By collecting questionnaire data and applying Structural Equation Modeling (SEM), the research aims to provide insights. The findings show that the relationship between the independent variable product (X1) has a positive effect on perceived value, price (X2) does not have a positive effect on perceived value, promotion (X3) and location (X4) cannot be proven because these variables are excluded. While the perceived value attachment variable (Y) has a positive and significant effect on trust.
Tasavvur-tasdik ayrımı, Fârâbî ile birlikte İslâm düşünce geleneğinde gerek mantık gerekse bilgi teorisi bağlamında önemli bir yer edinmiştir. Bu çerçevede, hem tasavvurların hem de tasdiklerin kazanımlı (mükteseb) ve kazanıma dayanmayan (gayr-i mükteseb) şeklinde tasnif edildiği görülmektedir. Ancak Fahreddin er-Râzî, başta el-Muḥaṣṣal olmak üzere el-Erbaʿîn, el-Âyâtü’l-beyyinât ve el-Meʿâlim gibi eserlerinde bu genel kabulden ayrılarak, tüm tasavvurların kazanıma dayanmaksızın, zorunlu biçimde meydana geldiğini savunmuştur. Râzî bu görüşünü iki temel argüman üzerinden temellendirmektedir: Bunlardan ilki, Menon paradoksunun yeniden formüle edilmesi; ikincisi ise tam tanımın gerek teorik gerekse pratik düzeyde imkânsızlığına dair ileri sürdüğü iddialardır. Menon paradoksu, bilgi edinme sürecinin imkânını sorgulayan bir epistemolojik açmaz ortaya koyar. Bu bağlamda, ya bilenin bilgiye hâlihazırda sahip olduğu ya da bilinmek istenen şeye dair hiçbir veriye sahip olmadığı varsayımı kabul edilmelidir. Her iki durumda da bilgi kazanma süreci anlamını yitirmektedir. Râzî’ye göre, elde edilmek istenen bir tasavvur ya önceden bilinmekte ya da hiçbir surette bilinmemektedir. İlk durumda, bilinen bir şeyin tekrar elde edilmesi anlamsız olduğundan; ikinci durumda ise, talep edilen şeye dair mutlak bir bilinemezlik söz konusu olduğundan tasavvurun kazanımı imkânsızdır. Diğer taraftan, tanımın imkânını sorgulayan ikinci delilde Râzî, tanımlanmak istenen şeyin mahiyetine ulaşmak için onun zâtî bileşenlerine dair bilgi sahibi olmanın çeşitli felsefi ve mantıksal sorunlara yol açtığını ileri sürer. Bu iki temel argüman üzerinden Râzî, bilgi sürecinin ilk aşamasını oluşturan tasavvurların kazanıma dayalı olmadığını ortaya koyar. Buna bağlı olarak, tasavvurlara dayanan tasdiklerin ve tasdiklere bağlı iradî fiillerin de özünde kazanımlı olamayacağı sonucuna ulaşır. Başka bir ifadeyle, et-Tefsîr’de dile getirdiği “İnsan, irade hürriyetine sahipmiş gibi görünse de aslında zorunludur” görüşünün arka planında, tasavvurların zorunlu meydana gelişi yönündeki bu temel varsayım yer alır. Bunu destekler nitelikte Râzî, argümanı el-Meṭâlib’in dokuzuncu bölümünde yer alan “İman ve küfrün kulların kalbinde meydana gelmesinin ancak Allah’ın yaratmasıyla mümkün olduğunu gösteren deliller” başlığı altında ve el-Erbaʿīn’de “Kulların fiillerinin yaratılmış olup olmadığı”na ilişkin başlık altında yeniden sunar. Ne var ki, özellikle irade hürriyetini epistemolojik zeminde reddetmesi nedeniyle, bu yaklaşım, Mu‘tezilî düşüncenin temel ilkeleriyle bağdaşmamaktadır. Bu yüzden, İbn Ebî’l-Hadîd, et-Taʿlîḳ ʿale’l-Muḥaṣṣal, Şerḥu’l-Âyâti’l-beyyinât ve Ziyâdâtü’n-Naḳżeyn gibi eserlerinde Râzî’nin bu görüşünü eleştirel bir bakışla ele almıştır. İbn Ebî’l-Hadîd’in eleştirileri, öncelikle tasavvurların kazanımlı olmadığının ne anlama geldiğini sorgulamakla başlar. Ona göre, genel olarak bilinen (eş-şuʿūr fî’l-cümle) şeylerin tanım aracılığıyla tam olarak bilinmesi (eş-şuʿūr et-tāmm) mümkündür. İkinci olarak, Râzî’nin gündeme taşıdığı Menon paradoksunu aşmak adına, bilen öznenin bilgiye ‘talep olmaksızın’ ulaşabilmesini mümkün kılan alternatif bir kazanım modeli önermektedir. Buna ek olarak, İbn Ebî’l-Hadîd, başta İbn Sînâ olmak üzere birçok İslâm düşünürünün benimsediği yönler (cihetler) anlayışını Râzî’nin görüşlerine karşı bir alternatif olarak sunar. Bu teoriye göre, özne, bilinen yönlerinden hareketle onun mahiyetine ulaşabilir. Râzî’nin ikinci delili olan tam tanımın imkânsızlığına yönelik eleştirisinde ise İbn Ebî’l-Hadîd, mahiyetin zâtî unsurları ile mahiyetin kendisi arasındaki farkı vurgular. Mahiyete dair bilginin ya faal aklın feyziyle ya da zâtî unsurlarından birinin bilinmesiyle mümkün olabileceğini savunur.
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.
Large Language Models (LLMs) have demonstrated impressive capabilities in complex reasoning tasks. However, they can be easily misled by unfaithful arguments during conversations, even when their original statements are correct. To this end, we investigate the problem of maintaining faithful integrity in LLMs. This involves ensuring that LLMs adhere to their faithful statements in the face of opposing arguments and are able to correct their incorrect statements when presented with faithful arguments. In this work, we propose a novel framework, named Alignment for Faithful Integrity with Confidence Estimation (AFICE), which aims to align the LLM responses with faithful integrity. Specifically, AFICE first designs a Bilateral Confidence Estimation (BCE) approach for estimating the uncertainty of each response generated by the LLM given a specific context, which simultaneously estimate the model's confidence to the question based on the internal states during decoding as well as to the answer based on cumulative probability ratios. With the BCE, we construct a conversational preference dataset composed of context, original statement, and argument, which is adopted for aligning the LLM for faithful integrity using Direct Preference Optimization (DPO). Extensive experimental results on a wide range of benchmarks demonstrate significant improvements in the LLM's ability to maintain faithful responses when encountering opposing arguments, ensuring both the practical utility and trustworthiness of LLMs in complex interactive settings. Code and data will be released via https://github.com/zhaoy777/AFICE.git
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.
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.
Mechanistic interpretability work attempts to reverse engineer the learned algorithms present inside neural networks. One focus of this work has been to discover 'circuits' -- subgraphs of the full model that explain behaviour on specific tasks. But how do we measure the performance of such circuits? Prior work has attempted to measure circuit 'faithfulness' -- the degree to which the circuit replicates the performance of the full model. In this work, we survey many considerations for designing experiments that measure circuit faithfulness by ablating portions of the model's computation. Concerningly, we find existing methods are highly sensitive to seemingly insignificant changes in the ablation methodology. We conclude that existing circuit faithfulness scores reflect both the methodological choices of researchers as well as the actual components of the circuit - the task a circuit is required to perform depends on the ablation used to test it. The ultimate goal of mechanistic interpretability work is to understand neural networks, so we emphasize the need for more clarity in the precise claims being made about circuits. We open source a library at https://github.com/UFO-101/auto-circuit that includes highly efficient implementations of a wide range of ablation methodologies and circuit discovery algorithms.
In many real natural language processing application scenarios, practitioners not only aim to maximize predictive performance but also seek faithful explanations for the model predictions. Rationales and importance distribution given by feature attribution methods (FAs) provide insights into how different parts of the input contribute to a prediction. Previous studies have explored how different factors affect faithfulness, mainly in the context of monolingual English models. On the other hand, the differences in FA faithfulness between multilingual and monolingual models have yet to be explored. Our extensive experiments, covering five languages and five popular FAs, show that FA faithfulness varies between multilingual and monolingual models. We find that the larger the multilingual model, the less faithful the FAs are compared to its counterpart monolingual models.Our further analysis shows that the faithfulness disparity is potentially driven by the differences between model tokenizers. Our code is available: https://github.com/casszhao/multilingual-faith.
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.
New studies are needed to illuminate the factors that affect customer satisfaction when using sharia banking services in Indonesia. This is especially significant given the existing study gap regarding Indonesia's relatively low number of sharia bank customers, despite the country's predominantly Muslim population. Instead, many of them prefer to use conventional banks, highlighting the need for a deeper understanding of the reasons behind this trend. This study examined the impact of customer perceived value and competitive advantage on customer loyalty mediated by trust. This investigation employed a quantitative methodology to examine the relationship between consumers' perceived value, competitive advantage, trust, and customer loyalty. The participants of this study were selected from the customer of Bank Syariah Indonesia (BSI), and data were collected through a questionnaire. The sample size was 210, and the researchers utilized random sampling to select participants. To analyze the collected data, the researchers employed structural equation modeling (SEM) analysis. The study results indicate that perceived value, competitive advantage, and trust significantly impact customer loyalty. This study also shows the mediating role of trust in the relationship between variables.
This current paper explores and analyzes the trend of Sufism discourses and practices among Indonesian Muslim modernist-reformer organizations with special reference to the Muhammadiyah movement. The public tends to categorize Muhammadiyah as an anti-Sufism movement. A close study, however, shows that the existence of Sufism discourse and practices is a clear religious phenomenon among its individual figures and members, although organizationally Muhammadiyah does not recognize the existence of Sufi orders (tarekat). Based on the data collected from subjective-ethnographic notes of the organization’s programs and activities as well as literature studies of the organization documents, this study implements Stephen Katz’s philosophical model. It shows that Muhammadiyah is searching for Islamic spirituality by promoting the authenticity of tasawuf aspects, namely tauh}id or monotheism and akhlaq al-karimah or noble characters as reflected in the concept of ih}san. In this regard, Sufism is defined as a system of values and spirituality, not involved in a particular Sufi order. Muhammadiyah interprets Sufism as ethical values and ethos to do virtuous actions in society.
Philosophy. Psychology. Religion, Islam. Bahai Faith. Theosophy, etc.
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.
Business and trade become an inseparable part of Islamic life. The rapidly growing market in cyberspace is a challenge for business players in the form of goods and services to expand and develop their business strategies, namely in terms of marketing. The purpose of the research in this article is to find out digital marketing from an Islamic perspective. The method used is a literature review, which is a literature search both internationally and nationally conducted using the Google Scholar database, ScienceDirect, ProQuest, JIM (Journal for Islamic Marketing), which is about 40 journal articles and 10 books of full quality and medium that is considered relevant is used in this study. The results show that sharia digital marketing activities must be based on Islamic principles. The implementation of digital marketing in sharia is almost the same as the digital marketing principle, yet the difference is the application of the character of business actors accompanied by sharia marketing ethics following the principles as described in the previous discussion. The steps of sharia digital marketing taken are as follows: Creating a Website and Social Media Account, Branding, Social Media Marketing, Content Marketing, Email Marketing, Video Production, and Segmentation.
Artikel ini akan mengulas tentang fenomena dakwah yang kini gencar dilakukan oleh berbagai lapisan masyarakat dalam berpartisipasi berdakwah melalui media sosial. Dakwah dengan narasi konservatif ini kian menjamur bahkan hingga saat ini, dimana menimbulkan kesalahpahaman materi dakwah hingga esensi dalam ajaran islam itu sendiri. Narasi dakwah yang cenderung konservatif ini kemudian juga melahirkan adanya penyempitan makna sehingga menimbulkan perselisihan paham hingga berakhir konflik. Media sosial yang memberikan akses penuh pada penggunanya, justru melahirkan sikap penyalahgunaan dalam menyampaikan opini maupun dalil-dalil tidak shahih yang kemudian turut berpartisipasi dalam riuh narasi dakwah konservatif. Penelitian ini berdiri pada rekam jejak media sosial maupun naskah dokumen yang menyoroti fenomena yang sama. Penelitian ini bertujuan untuk mengetahui serta menganalisis akar narasi dakwah konservatif muncul. Hasil penelitian telah diketahui bahwa: 1) Dakwah konservatif mengandung penyempitan makna yang dibawa, mengandalkan penafsiran yang keliru dan kaku, 2) Materi yang diangkat esensinya justru membahayakan kaum perempuan, sehingga agama Islam yang dikenal sebagai ajaran yang membawa solusi justru nihil dalam narasi konservatif, 3) Masyarakat yang memiliki pemikiran non ekstrem, moderat justru tidak mengambil sikap dan hanya diam sehingga narasi konservatif dalam dakwah hingga saat ini masih eksis bahkan tumbuh subur.
Crash events identification and prediction plays a vital role in understanding safety conditions for transportation systems. While existing systems use traffic parameters correlated with crash data to classify and train these models, we propose the use of a novel sensory unit that can also accurately identify crash events: microphone. Audio events can be collected and analyzed to classify events such as crash. In this paper, we have demonstrated the use of a deep Convolutional Neural Network (CNN) for road event classification. Important audio parameters such as Mel Frequency Cepstral Coefficients (MFCC), log Mel-filterbank energy spectrum and Fourier Spectrum were used as feature set. Additionally, the dataset was augmented with more sample data by the use of audio augmentation techniques such as time and pitch shifting. Together with the feature extraction this data augmentation can achieve reasonable accuracy. Four events such as crash, tire skid, horn and siren sounds can be accurately identified giving indication of a road hazard that can be useful for traffic operators or paramedics. The proposed methodology can reach accuracy up to 94%. Such audio systems can be implemented as a part of an Internet of Things (IoT) platform that can complement video-based sensors without complete coverage.
Md. Sajidul Islam, Razu Ahmed, Md. Mahamudujjaman
et al.
We present a detailed density functional theory based calculations of the structural, elastic, lattice dynamical, thermophysical, and optoelectronic properties of ternary semiconductors CaZn$_2$X$_2$ (X = N, P, As) in this paper. The obtained lattice parameters are in excellent agreement with the experimental values and other theoretical findings. These elastic constants satisfy the mechanical stability criteria. Moreover, many thermophysical parameters of these compounds are estimated, including the Debye temperature, average sound velocity, melting temperature, heat capacity, lattice thermal conductivity, etc. The comprehensive analysis of the elastic constants and moduli show that CaZn$_2$X$_2$ compounds possess reasonably good machinability, relatively high Vickers hardness and relatively low Debye temperature. The phonon dispersion curves and phonon density of states are investigated for the first time for the compounds CaZn$_2$P$_2$ and CaZn$_2$As$_2$. It is observed from the phonon dispersion curves that the bulk CaZn$_2$X$_2$ (X = N, P, As) compounds are dynamically stable. Electronic properties have been studied through the band structures and electronic energy density of states. The electronic band structures show that CaZn$_2$N$_2$ and CaZn$_2$As$_2$ possess direct band gaps while the compound CaZn$_2$P$_2$ show indirect band gap. The bonding characters of CaZn$_2$X$_2$ (X = N, P, As) compounds are investigated. Energy dependent optical parameters exhibit good correspondence with the electronic energy density of states features. We have thoroughly discussed the reflectivity, absorption coefficient, refractive index, dielectric function, optical conductivity and loss function of these semiconductors. The optical absorption, reflectivity spectra and the refractive index of CaZn$_2$X$_2$ (X = N, P, As) show that the compounds hold promise to be used in optoelectronic devices.