Hasil untuk "Islam. Bahai Faith. Theosophy, etc."

Via Afrianti, Ma’mun Sarma

Global warming can be caused by various sectors, one of which is the transportation sector. The use of gasoline is one of the main contributors to energy consumption. Conventional cars typically use gasoline as fuel to support the mobility of their users. The use of this fuel can have negative impacts on the environment, such as greenhouse gas emissions and air pollution. Therefore, electric vehicles could be a potential solution to reduce these impacts. However, it cannot be denied that there are several barriers to the adoption of electric vehicles. Through the Theory of Planned Behavior (TPB) analysis, this study explores the influence of attitudes toward behavior, subjective norms, and perceived behavioral control on the intention to purchase electric vehicles among millennials. Additionally, it examines the motivations of millennials in their purchase intentions and the moderating role of gender in the intention to buy electric vehicles. The study sample consists of millennials living in the Jabodetabek area with an interest in electric vehicles. A quantitative method with a Structural Equation Modeling (SEM)-PLS approach is used to analyze the relationships among the variables studied. The results show that TPB and motivation influence the intention to purchase electric vehicles among millennials in Jabodetabek. Meanwhile, gender moderation only influences the relationship between perceived behavioral control and the intention to buy electric vehicles among millennials in Jabodetabek.

Fakhraddin Alwajih, Abdellah El Mekki, Hamdy Mubarak et al.

Large Language Models (LLMs) inherently reflect the vast data distributions they encounter during their pre-training phase. As this data is predominantly sourced from the web, there is a high chance it will be skewed towards high-resourced languages and cultures, such as those of the West. Consequently, LLMs often exhibit a diminished understanding of certain communities, a gap that is particularly evident in their knowledge of Arabic and Islamic cultures. This issue becomes even more pronounced with increasingly under-represented topics. To address this critical challenge, we introduce PalmX 2025, the first shared task designed to benchmark the cultural competence of LLMs in these specific domains. The task is composed of two subtasks featuring multiple-choice questions (MCQs) in Modern Standard Arabic (MSA): General Arabic Culture and General Islamic Culture. These subtasks cover a wide range of topics, including traditions, food, history, religious practices, and language expressions from across 22 Arab countries. The initiative drew considerable interest, with 26 teams registering for Subtask 1 and 19 for Subtask 2, culminating in nine and six valid submissions, respectively. Our findings reveal that task-specific fine-tuning substantially boosts performance over baseline models. The top-performing systems achieved an accuracy of 72.15% on cultural questions and 84.22% on Islamic knowledge. Parameter-efficient fine-tuning emerged as the predominant and most effective approach among participants, while the utility of data augmentation was found to be domain-dependent.

Momen Khandoker Ope, Akif Islam, Mohd Ruhul Ameen et al.

Cultural heritage restoration in Bangladesh faces a dual challenge of limited resources and scarce technical expertise. Traditional 3D digitization methods, such as photogrammetry or LiDAR scanning, require expensive hardware, expert operators, and extensive on-site access, which are often infeasible in developing contexts. As a result, many of Bangladesh's architectural treasures, from the Paharpur Buddhist Monastery to Ahsan Manzil, remain vulnerable to decay and inaccessible in digital form. This paper introduces Oitijjo-3D, a cost-free generative AI framework that democratizes 3D cultural preservation. By using publicly available Google Street View imagery, Oitijjo-3D reconstructs faithful 3D models of heritage structures through a two-stage pipeline - multimodal visual reasoning with Gemini 2.5 Flash Image for structure-texture synthesis, and neural image-to-3D generation through Hexagen for geometry recovery. The system produces photorealistic, metrically coherent reconstructions in seconds, achieving significant speedups compared to conventional Structure-from-Motion pipelines, without requiring any specialized hardware or expert supervision. Experiments on landmarks such as Ahsan Manzil, Choto Sona Mosque, and Paharpur demonstrate that Oitijjo-3D preserves both visual and structural fidelity while drastically lowering economic and technical barriers. By turning open imagery into digital heritage, this work reframes preservation as a community-driven, AI-assisted act of cultural continuity for resource-limited nations.

Mohammad AL-Smadi

This paper presents our approach and results for SubTask 1: Islamic Inheritance Reasoning at QIAS 2025, a shared task focused on evaluating Large Language Models (LLMs) in understanding and reasoning within Islamic inheritance knowledge. We fine-tuned the Fanar-1-9B causal language model using Low-Rank Adaptation (LoRA) and integrated it into a Retrieval-Augmented Generation (RAG) pipeline. Our system addresses the complexities of Islamic inheritance law, including comprehending inheritance scenarios, identifying eligible heirs, applying fixed-share rules, and performing precise calculations. Our system achieved an accuracy of 0.858 in the final test, outperforming other competitive models such as, GPT 4.5, LLaMA, Fanar, Mistral and ALLaM evaluated with zero-shot prompting. Our results demonstrate that QU-NLP achieves near state-of-the-art accuracy (85.8%), excelling especially on advanced reasoning (97.6%) where it outperforms Gemini 2.5 and OpenAI's o3. This highlights that domain-specific fine-tuning combined with retrieval grounding enables mid-scale Arabic LLMs to surpass frontier models in Islamic inheritance reasoning.

Mohammad Amaan Sayeed, Mohammed Talha Alam, Raza Imam et al.

Centuries-old Islamic medical texts like Avicenna's Canon of Medicine and the Prophetic Tibb-e-Nabawi encode a wealth of preventive care, nutrition, and holistic therapies, yet remain inaccessible to many and underutilized in modern AI systems. Existing language-model benchmarks focus narrowly on factual recall or user preference, leaving a gap in validating culturally grounded medical guidance at scale. We propose a unified evaluation pipeline, Tibbe-AG, that aligns 30 carefully curated Prophetic-medicine questions with human-verified remedies and compares three LLMs (LLaMA-3, Mistral-7B, Qwen2-7B) under three configurations: direct generation, retrieval-augmented generation, and a scientific self-critique filter. Each answer is then assessed by a secondary LLM serving as an agentic judge, yielding a single 3C3H quality score. Retrieval improves factual accuracy by 13%, while the agentic prompt adds another 10% improvement through deeper mechanistic insight and safety considerations. Our results demonstrate that blending classical Islamic texts with retrieval and self-evaluation enables reliable, culturally sensitive medical question-answering.

Wisnu Uriawan, Imany Fauzy Rahman, Muhamad Zidan et al.

The rapid development of deepfake technology powered by AI has raised global concerns regarding the manipulation of information, the usurpation of digital identities, and the erosion of public trust in the authenticity of online content. These challenges extend beyond technical issues and involve complex moral dimensions, rendering conventional, technologically driven, and reactive management approaches insufficient to address underlying causes such as intent, ethical responsibility, and intangible social harm. In response to these challenges, this study aims to formulate a comprehensive Islamic ethical framework as a preventive approach to mitigate the misuse of deepfake technology. This study employed a Systematic Literature Review (SLR) guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), selecting ten primary sources published between 2018 and 2025 to identify ethical gaps, regulatory needs, and appropriate normative solutions. The analysis demonstrates that integrating the principles of Maqasid al-Shariah, particularly hifz al-ird and hifz al-nafs, provides a strong normative foundation for governing the responsible use of digital technology. Based on the findings, this study proposes three strategic recommendations: regulatory reforms that recognize the intangible and psychological harms resulting from reputational damage; strengthened technology governance grounded in moral accountability and the values of adl, amanah, and transparency; and enhanced public digital literacy based on the principle of tabayyun. Overall, the findings suggest that the application of Islamic ethical principles shifts governance paradigms from punitive mechanisms toward preventive approaches that emphasize the protection of human dignity, the prevention of harm, and the promotion of the common good in the digital age.

Ronna Sari Daulay, Meldyana Priadina Siregar, Hadi Saputra Panggabean

This research aims to analyze the forms of innovation in the teaching of Kitab Kuning in Islamic boarding schools (Pesantren) to strengthen religious literacy. The study employs a library research approach, utilizing literature sources such as journals, books, dictionaries, documents, magazines, and other references. The research method involves a literature review. The results indicate that Pesantren is capable of producing students proficient in Arabic language and fundamental knowledge of religious studies. Various innovations can be implemented to enhance religious literacy, such as the use of technology through e-learning and online forums. Additionally, innovations in teaching models or methods, such as the ArRumuz method, which facilitates quick reading of religious texts, and problem-based learning methods, involving the study of issues in society, can contribute to strengthening religious literacy. Moreover, innovations in extracurricular activities, including programs like Qira’atul Kutub tutoring, Qira’atul Kutub study sessions, and achievement award and art performance programs, are also noteworthy.

Faith Johnson, Bryan Bo Cao, Ashwin Ashok et al.

Visual navigation takes inspiration from humans, who navigate in previously unseen environments using vision without detailed environment maps. Inspired by this, we introduce a novel no-RL, no-graph, no-odometry approach to visual navigation using feudal learning to build a three tiered agent. Key to our approach is a memory proxy map (MPM), an intermediate representation of the environment learned in a self-supervised manner by the high-level manager agent that serves as a simplified memory, approximating what the agent has seen. We demonstrate that recording observations in this learned latent space is an effective and efficient memory proxy that can remove the need for graphs and odometry in visual navigation tasks. For the mid-level manager agent, we develop a waypoint network (WayNet) that outputs intermediate subgoals, or waypoints, imitating human waypoint selection during local navigation. For the low-level worker agent, we learn a classifier over a discrete action space that avoids local obstacles and moves the agent towards the WayNet waypoint. The resulting feudal navigation network offers a novel approach with no RL, no graph, no odometry, and no metric map; all while achieving SOTA results on the image goal navigation task.

Ruiqi Li, Maowei Jiang, Kai Wang et al.

Time Series Forecasting plays a crucial role in various fields such as industrial equipment maintenance, meteorology, energy consumption, traffic flow and financial investment. However, despite their considerable advantages over traditional statistical approaches, current deep learning-based predictive models often exhibit a significant deviation between their forecasting outcomes and the ground truth. This discrepancy is largely due to an insufficient emphasis on extracting the sequence's latent information, particularly its global information within the frequency domain and the relationship between different variables. To address this issue, we propose a novel model Frequency-domain Attention In Two Horizons, which decomposes time series into trend and seasonal components using a multi-scale sequence adaptive decomposition and fusion architecture, and processes them separately. FAITH utilizes Frequency Channel feature Extraction Module and Frequency Temporal feature Extraction Module to capture inter-channel relationships and temporal global information in the sequence, significantly improving its ability to handle long-term dependencies and complex patterns. Furthermore, FAITH achieves theoretically linear complexity by modifying the time-frequency domain transformation method, effectively reducing computational costs. Extensive experiments on 6 benchmarks for long-term forecasting and 3 benchmarks for short-term forecasting demonstrate that FAITH outperforms existing models in many fields, such as electricity, weather and traffic, proving its effectiveness and superiority both in long-term and short-term time series forecasting tasks. Our codes and data are available at https://github.com/LRQ577/FAITH.

Faith Johnson, Ryan Meegan, Jack Lowry et al.

Agricultural domains are being transformed by recent advances in AI and computer vision that support quantitative visual evaluation. Using aerial and ground imaging over a time series, we develop a framework for characterizing the ripening process of cranberry crops, a crucial component for precision agriculture tasks such as comparing crop breeds (high-throughput phenotyping) and detecting disease. Using drone imaging, we capture images from 20 waypoints across multiple bogs, and using ground-based imaging (hand-held camera), we image same bog patch using fixed fiducial markers. Both imaging methods are repeated to gather a multi-week time series spanning the entire growing season. Aerial imaging provides multiple samples to compute a distribution of albedo values. Ground imaging enables tracking of individual berries for a detailed view of berry appearance changes. Using vision transformers (ViT) for feature detection after segmentation, we extract a high dimensional feature descriptor of berry appearance. Interpretability of appearance is critical for plant biologists and cranberry growers to support crop breeding decisions (e.g.\ comparison of berry varieties from breeding programs). For interpretability, we create a 2D manifold of cranberry appearance by using a UMAP dimensionality reduction on ViT features. This projection enables quantification of ripening paths and a useful metric of ripening rate. We demonstrate the comparison of four cranberry varieties based on our ripening assessments. This work is the first of its kind and has future impact for cranberries and for other crops including wine grapes, olives, blueberries, and maize. Aerial and ground datasets are made publicly available.

Faith Ellen, Gal Sela

Because strongly-linearizable objects provide stronger guarantees than linearizability, they serve as valuable building blocks for the design of concurrent data structures. Yet, many objects that have linearizable implementations from base objects weaker than compare&swap objects do not have strongly-linearizable implementations from the same base objects. We focus on one such object: the bag, a multiset from which processes can take unspecified elements. We present the first lock-free, strongly-linearizable implementation of a bag from interfering objects (specifically, registers, and test&set objects). This may be surprising, since there are provably no such implementations of stacks or queues. Since a bag can contain arbitrarily many elements, an unbounded amount of space must be used to implement it. Hence, it makes sense to also consider a bag with a bound on its capacity. However, like stacks and queues, a bag with capacity $b$ shared by more than $2b$ processes has no lock-free, strongly-linearizable implementation from interfering objects. If we further restrict a bounded bag so that only one process can insert into it, we are able to obtain a lock-free, strongly-linearizable implementation from $O(b + n)$ interfering objects, where $n$ is the number of processes. Our goal is to understand the circumstances under which strongly-linearizable implementations of bags exist and, more generally, to understand the power of interfering objects.

Faith Johnson, Bryan Bo Cao, Ashwin Ashok et al.

Visual navigation follows the intuition that humans can navigate without detailed maps. A common approach is interactive exploration while building a topological graph with images at nodes that can be used for planning. Recent variations learn from passive videos and can navigate using complex social and semantic cues. However, a significant number of training videos are needed, large graphs are utilized, and scenes are not unseen since odometry is utilized. We introduce a new approach to visual navigation using feudal learning, which employs a hierarchical structure consisting of a worker agent, a mid-level manager, and a high-level manager. Key to the feudal learning paradigm, agents at each level see a different aspect of the task and operate at different spatial and temporal scales. Two unique modules are developed in this framework. For the high-level manager, we learn a memory proxy map in a self supervised manner to record prior observations in a learned latent space and avoid the use of graphs and odometry. For the mid-level manager, we develop a waypoint network that outputs intermediate subgoals imitating human waypoint selection during local navigation. This waypoint network is pre-trained using a new, small set of teleoperation videos that we make publicly available, with training environments different from testing environments. The resulting feudal navigation network achieves near SOTA performance, while providing a novel no-RL, no-graph, no-odometry, no-metric map approach to the image goal navigation task.

Hans-Bernd Schaefer, Rok Spruk

We examine the contribution of Islamic legal institutions to the comparative economic decline of the Middle East behind Latin Europe, which can be observed since the late Middle Ages. To this end, we explore whether the sacralization of Islamic law and its focus on the Sharia as supreme, sacred and unchangeable legal text, which reached its culmination in the 13th century had an impact on economic development. We use the population size of 145 cities in Islamic countries and 648 European cities for the period 800-1800 as proxies for the level of economic development, and construct novel estimates of the number of law schools (i.e. madaris) and estimate their contribution to the pre-industrial economic development. Our triple-differences estimates show that a higher density of madrasas before the sacralization of Islamic law predicts a more vibrant urban economy characterized by higher urban growth. After the consolidation of the sharia sacralization of law in the 13th century, greater density of law schools is associated with stagnating population size. We show that the economic decline of the Middle East can be partly explained by the absence of legal innovations or substitutes of them, which paved the way for the economic rise of Latin Europe, where ground-breaking legal reforms introduced a series of legal innovations conducive for economic growth. We find that the number of learned lawyers trained in universities with law schools is highly and positively correlated with the western European city population. Our counterfactual estimates show that almost all Islamic cities under consideration would have had much larger size by the year 1700 if legal innovations comparable to those in Western Europe were introduced. By making use of a series of synthetic control and difference-in-differences estimators our findings are robust against a large number of model specification checks.

Youcef L Soufi

This article provides a sketch of the historical antecedent to the 11th century theory of maqāṣid al-sharī‘a (the purposes of the law). I examine the role of human benefit (maṣlaḥa) within the classical Shafi‘i school, focusing on the 10th and 11th centuries. I show that Shafi‘i law gave consideration of benefit a central role in the interpretation of scripture. This is attested to in both texts of legal theory (uṣūl al-fiqh) and substantive law (furū‘ al-fiqh). Importantly, I also explain how Shafi‘is subjected their claims about benefit to contestation and debate, acknowledging the limits to humans’ ability to apprehend God’s law. In presenting this classical model of benefit in the Shafi‘i school, the essay offers an alternative for reformists who invoke the maqāṣid al-sharī‘a today—an alternative that has a deep pedigree within the Islamic tradition and promotes the democratization of debate over the benefits of the law.

E. Burak Şaman

The relation of priority and posteriority between the affirmation and negation, which Aristotle put forth in Peri Hermêneias, has had some important consequences in terms of logical attribution and judgement. The problem encountered here is the question of whether affirmation (i.e., affirming something of something) and negation (i.e., denying something of something) share the same status as a statement (qawl). In the fifth chapter of the first article (I.5) of al-‘Ibāra, the volume from al-Shifā corpus that corresponds to Peri Hermêneias, Avicenna deals with affirmation and negation in terms of these logical consequences and reveals his own position on the subject by way of distinguishing between attribution and judgement. However, the text of al-Shaikh al-Raīs presents some obscurities for the reader. The reason behind this obscurity is that a debate taking place among Hellenistic commentators lies in the background of Avicenna’s text. This article proposes to study this text alongside the Hellenistic commentators in order to better understand the logical problem in the relevant passages from al-‘Ibāra. Our guide in this reading will be Boethius, who wrote a Latin commentary on Peri Hermêneias. In the present study, I will try to reveal how the positions of the Hellenistic commentators (i.e., Alexander of Aphrodisias, Porphyry, and Syrianus), whose views Boethius conveyed, coincide with the views Avicenna defended and criticized regarding affirmation and negation. In this respect, my reading in this article aims to better understand Avicenna’s relevant text and its logical extensions as well as the dimensions of his relationship with Hellenistic commentators.

Md. Alamin Talukder, Md. Manowarul Islam, Md Ashraf Uddin et al.

Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disability in humans. The histopathological detection of such malignancies is usually the most important component in determining the best course of action. Early detection of the ailment on either front considerably decreases the likelihood of mortality. Machine learning and deep learning techniques can be utilized to speed up such cancer detection, allowing researchers to study a large number of patients in a much shorter amount of time and at a lower cost. In this research work, we introduced a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. The model is evaluated on histopathological (LC25000) lung and colon datasets. According to the study findings, our hybrid model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively. The study's findings show that our proposed strategy outperforms existing models significantly. Thus, these models could be applicable in clinics to support the doctor in the diagnosis of cancers.

Boyuan Feng, Tianqi Tang, Yuke Wang et al.

Transformer verification draws increasing attention in machine learning research and industry. It formally verifies the robustness of transformers against adversarial attacks such as exchanging words in a sentence with synonyms. However, the performance of transformer verification is still not satisfactory due to bound-centric computation which is significantly different from standard neural networks. In this paper, we propose Faith, an efficient framework for transformer verification on GPUs. We first propose a semantic-aware computation graph transformation to identify semantic information such as bound computation in transformer verification. We exploit such semantic information to enable efficient kernel fusion at the computation graph level. Second, we propose a verification-specialized kernel crafter to efficiently map transformer verification to modern GPUs. This crafter exploits a set of GPU hardware supports to accelerate verification specialized operations which are usually memory-intensive. Third, we propose an expert-guided autotuning to incorporate expert knowledge on GPU backends to facilitate large search space exploration. Extensive evaluations show that Faith achieves $2.1\times$ to $3.4\times$ ($2.6\times$ on average) speedup over state-of-the-art frameworks.

Hasanul Banna, Md Rabiul Alam

This paper examines the nexus between digital financial inclusion (DFI) and levels of bank risk-taking, using a sample of 283 commercial banks (Islamic and conventional) from six countries over the period 2011 to 2019 and deploying panel-corrected standard errors, two-stage least squares-instrumental variables and dynamic panel two-step generalized method of moments estimators. The findings suggest that Islamic banks take more risks than their counterpart conventional banks. The empirical evidence also indicates that an increase in the DFI index score reduces the overall level of bank risktaking and increases that of banking stability for commercial and conventional banks compared to Islamic ones. A strong association between DFI and bank risk-taking suggests that DFI not only reduces the default risk, leverage risk and portfolio risk of banks, but also increases financial mobility in the sample countries. Consequently, an inclusive digitalised banking industry ensures sustainable economic growth, which is likely to help maintain financial sustainability in times of crisis such as the Covid-19 pandemic. Our results are shown to be robust by various robustness checks. The study contributes to both the Islamic and conventional banking, as well as the digital financial inclusion, literature. The findings of the study provide various policy implications for policymakers and standard-setters in the countries examined.

Yoachim Agus Tridiatno

The paper explores forgiveness in Islam starting from the Islamic bases, forgiveness as moral response, forgiveness needs repentance, and implementing forgiveness for promoting a peaceful world. It is library research based on articles and books which discussed the topics, and the secondary data about forgiveness in Islam. It aims to expose the soft side of Islam especially its struggle for peace and harmonious life.

Tanveer Khan, Alexandros Bakas, Antonis Michalas

Over the past few years, a tremendous growth of machine learning was brought about by a significant increase in adoption of cloud-based services. As a result, various solutions have been proposed in which the machine learning models run on a remote cloud provider. However, when such a model is deployed on an untrusted cloud, it is of vital importance that the users' privacy is preserved. To this end, we propose Blind Faith -- a machine learning model in which the training phase occurs in plaintext data, but the classification of the users' inputs is performed on homomorphically encrypted ciphertexts. To make our construction compatible with homomorphic encryption, we approximate the activation functions using Chebyshev polynomials. This allowed us to build a privacy-preserving machine learning model that can classify encrypted images. Blind Faith preserves users' privacy since it can perform high accuracy predictions by performing computations directly on encrypted data.