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

Halimat Afolabi, Zainab Afolabi, Elizabeth Friel et al.

Closed-source large language models (LLMs), such as ChatGPT and Gemini, are increasingly consulted for medical advice, yet their explanations may appear plausible while failing to reflect the model's underlying reasoning process. This gap poses serious risks as patients and clinicians may trust coherent but misleading explanations. We conduct a systematic black-box evaluation of faithfulness in medical reasoning among three widely used closed-source LLMs. Our study consists of three perturbation-based probes: (1) causal ablation, testing whether stated chain-of-thought (CoT) reasoning causally influences predictions; (2) positional bias, examining whether models create post-hoc justifications for answers driven by input positioning; and (3) hint injection, testing susceptibility to external suggestions. We complement these quantitative probes with a small-scale human evaluation of model responses to patient-style medical queries to examine concordance between physician assessments of explanation faithfulness and layperson perceptions of trustworthiness. We find that CoT reasoning steps often do not causally drive predictions, and models readily incorporate external hints without acknowledgment. In contrast, positional biases showed minimal impact in this setting. These results underscore that faithfulness, not just accuracy, must be central in evaluating LLMs for medicine, to ensure both public protection and safe clinical deployment.

Afreen Islam

In this work, Global Position System (GPS) data from a flock of homing pigeons are analysed. The flocking behaviour of the considered homing pigeons is formulated as a swarm optimal trajectory tracking control problem. The swarm problem in this work is modeled with the idea that one or two pigeons at the forefront lead the flock. Each follower pigeon is assumed to follow a leader pigeon immediately ahead of themselves, instead of directly following the leaders at the forefront of the flock. The trajectory of each follower pigeon is assumed to be a solution of an optimal trajectory tracking control problem. An optimal control problem framework is created for each follower pigeon. An important aspect of an optimal control problem is the cost function. A minimum principle based method for multiple flight data is proposed, which can help in learning the unknown weights of the cost function of the optimal trajectory tracking control problem for each follower pigeon, from flight trajectories' information obtained from GPS data.

Martin Tutek, Fateme Hashemi Chaleshtori, Ana Marasović et al.

When prompted to think step-by-step, language models (LMs) produce a chain of thought (CoT), a sequence of reasoning steps that the model supposedly used to produce its prediction. Despite much work on CoT prompting, it is unclear if reasoning verbalized in a CoT is faithful to the models' parametric beliefs. We introduce a framework for measuring parametric faithfulness of generated reasoning, and propose Faithfulness by Unlearning Reasoning steps (FUR), an instance of this framework. FUR erases information contained in reasoning steps from model parameters, and measures faithfulness as the resulting effect on the model's prediction. Our experiments with four LMs and five multi-hop multi-choice question answering (MCQA) datasets show that FUR is frequently able to precisely change the underlying models' prediction for a given instance by unlearning key steps, indicating when a CoT is parametrically faithful. Further analysis shows that CoTs generated by models post-unlearning support different answers, hinting at a deeper effect of unlearning.

Tanvir Islam

Histogram-Based Outlier Score (HBOS) is a widely used outlier or anomaly detection method known for its computational efficiency and simplicity. However, its assumption of feature independence limits its ability to detect anomalies in datasets where interactions between features are critical. In this paper, we propose the Extended Histogram-Based Outlier Score (EHBOS), which enhances HBOS by incorporating two-dimensional histograms to capture dependencies between feature pairs. This extension allows EHBOS to identify contextual and dependency-driven anomalies that HBOS fails to detect. We evaluate EHBOS on 17 benchmark datasets, demonstrating its effectiveness and robustness across diverse anomaly detection scenarios. EHBOS outperforms HBOS on several datasets, particularly those where feature interactions are critical in defining the anomaly structure, achieving notable improvements in ROC AUC. These results highlight that EHBOS can be a valuable extension to HBOS, with the ability to model complex feature dependencies. EHBOS offers a powerful new tool for anomaly detection, particularly in datasets where contextual or relational anomalies play a significant role.

Jiajian Xie, Hubery Yin, Chen Li et al.

Diffusion models have achieved remarkable generative quality but remain bottlenecked by costly iterative sampling. Recent training-free methods accelerate diffusion process by reusing model outputs. However, these methods ignore denoising trends and lack error control for model-specific tolerance, leading to trajectory deviations under multi-step reuse and exacerbating inconsistencies in the generated results. To address these issues, we introduce Error-aware Trend Consistency (ETC), a framework that (1) introduces a consistent trend predictor that leverages the smooth continuity of diffusion trajectories, projecting historical denoising patterns into stable future directions and progressively distributing them across multiple approximation steps to achieve acceleration without deviating; (2) proposes a model-specific error tolerance search mechanism that derives corrective thresholds by identifying transition points from volatile semantic planning to stable quality refinement. Experiments show that ETC achieves a 2.65x acceleration over FLUX with negligible (-0.074 SSIM score) degradation of consistency.

Nina Lutz, Benjamin Olsen, Weishung Liu et al.

Previous work has found a lack of research in HCI on religion, partly driven by misunderstandings of values and practices between religious and technical communities. To bridge this divide in an empirically rigorous way, we conducted an interview study with 48 religious people and/or experts from 11 faiths, and we document how religious people experience, understand, and imagine technologies. We show that religious stakeholders find non-neutral secular embeddings in technologies and the firms and people that design them, and how these manifest in unintended harms for religious and nonreligious users. Our findings reveal how users navigate technoreligious practices with religiously informed mental models and what they desire from technologies. Informed by this, we distill six design values -- wonder, humility, space, embodiedness, community, and eternity -- to guide technologists in considering and leveraging religion as an additional, valid sociocultural resource when designing for a holistic user. We further spell out directions for future research.

Shriyank Somvanshi, Md Monzurul Islam, Syed Aaqib Javed et al.

Bio-inspired algorithms utilize natural processes such as evolution, swarm behavior, foraging, and plant growth to solve complex, nonlinear, high-dimensional optimization problems. However, a plethora of these algorithms require a more rigorous review before making them applicable to the relevant fields. This survey categorizes these algorithms into eight groups: evolutionary, swarm intelligence, physics-inspired, ecosystem and plant-based, predator-prey, neural-inspired, human-inspired, and hybrid approaches, and reviews their principles, strengths, novelty, and critical limitations. We provide a critique on the novelty issues of many of these algorithms. We illustrate some of the suitable usage of the prominent algorithms in machine learning, engineering design, bioinformatics, and intelligent systems, and highlight recent advances in hybridization, parameter tuning, and adaptive strategies. Finally, we identify open challenges such as scalability, convergence, reliability, and interpretability to suggest directions for future research. This work aims to serve as a resource for both researchers and practitioners interested in understanding the current landscape and future directions of reliable and authentic advancement of bio-inspired algorithms.

Abyaan Basyaar Roofif, Ety Dwi Susanti

This study aims to determine the analysis of the effect of Return on Equity (ROE), Earning per Share (EPS), Inflation, and Interest Rates on the Share Price of food and beverage sector companies listed on the Indonesia Stock Exchange during the period 2018-2023. This research is quantitative research using secondary data taken from data published through the Indonesia Stock Exchange and Bank Indonesia websites. The population of all food and beverage companies listed on the Indonesia Stock Exchange for the period 2018-2023 with a total of 34 companies and sampled using the documentation method on an annual scale so that 78 samples were determined. The data analysis technique in this study used multiple linear regression analysis methods. The results found are Return on Equity (ROE) and Earning per Share (EPS) contribute to Stock Prices, while Inflation and Interest Rates do not contribute to Stock Prices.

Rr. Rachmalia Nadia Rizqina, Hery Pudjoprastyono, Nanik Hariyana

The Indonesian cosmetic industry has begun to develop from year to year. However, the development of the cosmetic industry in Indonesia is also followed by internal and external challenges. The existence of competition with foreign markets, especially China, has made the cosmetic industry in Indonesia begin to be affected. If it is not immediately addressed, the Indonesian cosmetic industry can be defeated by the foreign cosmetic industry. This study aims to determine and analyze the influence of social media marketing variables, price perception, and product quality on purchasing decisions for Citra body scrub products in the city of Surabaya. Samples were taken using purposive sampling technique with a total of 112 respondents. Data collection was carried out by distributing questionnaires. The scale used in the questionnaire is a Likert scale of 1-5. The results of the questionnaire answers were analyzed using SmartPLS software. This study tests the hypothesis using the Structural Equation Model (SEM) technique with the Partial Least Square (PLS) analysis method. The results of this study indicate that social media marketing has a positive and significant effect on purchasing decisions. Price perception has a positive and significant effect on purchasing decisions. Product quality has a positive and significant effect on purchasing decisions.

Satyadev Nandakumar, Subin Pulari, Akhil S

Hausdorff $Φ$-dimension is a notion of Hausdorff dimension developed using a restricted class of coverings of a set. We introduce a constructive analogue of $Φ$-dimension using the notion of constructive $Φ$-$s$-supergales. We prove a Point-to-Set Principle for $Φ$-dimension, through which we get Point-to-Set Principles for Hausdorff dimension, continued-fraction dimension and dimension of Cantor coverings as special cases. We also provide a Kolmogorov complexity characterization of constructive $Φ$-dimension. A class of covering sets $Φ$ is said to be "faithful" to Hausdorff dimension if the $Φ$-dimension and Hausdorff dimension coincide for every set. Similarly, $Φ$ is said to be "faithful" to constructive dimension if the constructive $Φ$-dimension and constructive dimension coincide for every set. Using the Point-to-Set Principle for Cantor coverings and a new technique for the construction of sequences satisfying a certain Kolmogorov complexity condition, we show that the notions of ``faithfulness'' of Cantor coverings at the Hausdorff and constructive levels are equivalent. We adapt the result by Albeverio, Ivanenko, Lebid, and Torbin to derive the necessary and sufficient conditions for the constructive dimension faithfulness of the coverings generated by the Cantor series expansion, based on the terms of the expansion.

Tousif Islam

We report an approximate, non-trivial mapping of angular (linear) momentum in gravitational waves obtained from numerical relativity (NR) and adiabatic point-particle black hole perturbation theory (BHPT) in the comparable mass regime for quasi-circular, non-spinning binary black holes. This mapping involves two time-independent scaling parameters, $α_{J}$ ($α_{P}$) and $β_{J}$ ($β_{P}$), that adjust the BHPT angular (linear) momentum and the BHPT time respectively such a way that it aligns with NR angular (linear) momentum. Our findings indicate that this scaling mechanism works really well until close to the merger. In addition to the comparison of $α_{J}$ ($α_{P}$) with relevant values obtained from the waveform and flux scalings, we explore the mass ratio dependence of the scaling parameter $α_{J}$ ($α_{P}$). Finally, we investigate their possible connection to the missing finite size correction for the secondary black hole within the BHPT framework and the implication of these scalings on the remnant properties of the binary.

Andreas Madsen, Sarath Chandar, Siva Reddy

Instruction-tuned Large Language Models (LLMs) excel at many tasks and will even explain their reasoning, so-called self-explanations. However, convincing and wrong self-explanations can lead to unsupported confidence in LLMs, thus increasing risk. Therefore, it's important to measure if self-explanations truly reflect the model's behavior. Such a measure is called interpretability-faithfulness and is challenging to perform since the ground truth is inaccessible, and many LLMs only have an inference API. To address this, we propose employing self-consistency checks to measure faithfulness. For example, if an LLM says a set of words is important for making a prediction, then it should not be able to make its prediction without these words. While self-consistency checks are a common approach to faithfulness, they have not previously been successfully applied to LLM self-explanations for counterfactual, feature attribution, and redaction explanations. Our results demonstrate that faithfulness is explanation, model, and task-dependent, showing self-explanations should not be trusted in general. For example, with sentiment classification, counterfactuals are more faithful for Llama2, feature attribution for Mistral, and redaction for Falcon 40B.

Rania Ben Belgacem, Anis Ben Amar , Valerio Brescia

This study conducts a comparative analysis of the relationship between Corporate Social Responsibility (CSR) and financial performance in Tunisian banks. The research focuses on an extensive sample of Tunisian banks operating between 2018 and 2022. Two models are employed: one based on Return on Equity (ROE) and the other on Return on Assets (ROA). The findings reveal that Islamic banks benefit from robust CSR practices, leading to enhanced ROA and aligning with ethical principles inherent in Islamic finance. In contrast, conventional banks demonstrate no significant correlation between CSR and ROE and exhibit a negative impact of CSR on ROA. These results underscore the sector-specific nuances of CSR and its influence on financial performance, highlighting the necessity for customized CSR strategies. The study offers valuable insights for banking professionals, policymakers, and stakeholders, aiding their comprehension of the role of CSR in shaping financial outcomes in distinct banking sectors.

Eva Naria, Piet Hizbullah Khaidir, M. Arromu Harmuzi

Abstract The pronunciation of dhikr in the Qur'an is mentioned 292 times, with various forms of the word derivation (sighat). One form of the word in the form of isim masdar is found in QS. Thaha [20]: 14. This research is a library research with an analysis approach of the Qur'an (interpretation) and philosophy. The method used in this study is the method of philosophical interpretation of Muhammad Fakhr al-Din al-Razi and Karl Buhler's Psycholinguistic Semiotics. The main objective of this study is to determine the relationship between dhikr and tauhid. The researcher describes data about the concept of dhikr in the Qur'an Surah Thaha [20]: 14 perspectives of al-Tafsir Mafatih al-Ghayb and Karl Buhler's Psycholinguistic Semiotics. The results obtained from this study are that dhikr as monotheistic awareness is not enough to be done only with theoretical knowledge. Monotheism that is carried out only with theoretical knowledge will only be informative. Therefore, it is necessary to have an awareness of knowledge and an actual awareness that is affirmative in nature, to achieve true monotheism.

Najirul Islam

In this article, we have realized the quantum Szilard engine (QZE) for non-interacting bosons. We have adopted the Bose-Einstein statistics for this purpose. We have considered fractional power law potential for this purpose and have used the artifact of the quantization of energy. We have calculated the work and the efficiency for non-interacting bosons in fractional power potential. We have shown the dependence of the number of particles for the work and the efficiency. We also have realized the QZE for a single-particle in a Morse potential revealing how the depth of the potential impacts both work and efficiency. Furthermore, we have examined the influence of temperature and the anharmonicity parameter on the work. Finally, we have conducted a comparative analysis, considering both non-interacting bosons in a fractional power law potential and a single-particle in a Morse potential under harmonic approximation conditions.

Tousif Islam

We investigate the interplay between numerical relativity (NR) and point-particle black hole perturbation theory (ppBHPT) for quasi-circular non-spinning binary black holes in the intermediate mass ratio regime: 7<=q<=128 (where $q:=m_1/m_2$ is the mass ratio of the binary with m_1 and m_2 being the mass of the primary and secondary black hole respectively). Initially, we conduct a comprehensive comparison between the dominant (l,m) = (2,2) mode of the gravitational radiation obtained from state-of-the-art NR simulations and ppBHPT waveforms along with waveforms generated from recently developed NR-informed ppBHPT surrogate model, BHPTNRSur1dq1e4. This surrogate model employs a simple but non-trivial rescaling technique known as the $α$-$β$ scaling to effectively match ppBHPT waveforms to NR in the comparable mass ratio regime. Subsequently, we analyze the amplitude and frequency differences between NR and ppBHPT waveforms to investigate the non-linearities, beyond adiabatic evolution, that are present during the merger stage of the binary evolution and propose fitting functions to describe these differences in terms of both the mass ratio and the symmetric mass ratio. Finally, we assess the performance of the $α$-$β$ scaling technique in the intermediate mass ratio regime.

Brian Barr, Noah Fatsi, Leif Hancox-Li et al.

The field of explainable artificial intelligence (XAI) aims to explain how black-box machine learning models work. Much of the work centers around the holy grail of providing post-hoc feature attributions to any model architecture. While the pace of innovation around novel methods has slowed down, the question remains of how to choose a method, and how to make it fit for purpose. Recently, efforts around benchmarking XAI methods have suggested metrics for that purpose -- but there are many choices. That bounty of choice still leaves an end user unclear on how to proceed. This paper focuses on comparing metrics with the aim of measuring faithfulness of local explanations on tabular classification problems -- and shows that the current metrics don't agree; leaving users unsure how to choose the most faithful explanations.

Joyanta Jyoti Mondal, Md. Farhadul Islam, Raima Islam et al.

The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our codes and dataset are available at https://github.com/lepotatoguy/aqi.

Wahyu Hidayat, Yefi Ardyanti, Elis Ratna Wulan

This study aims to analyze the effect of the internal quality assurance system on improving the accreditation quality of State Islamic Higher Education by using the Technology Acceptance Model (TAM). The research method used is quantitative. The research is an explanatory survey explaining the influence of the variables contained in the SPMI Information System in Higher Education. The research results show that; 1) There is a positive and significant effect of 62.4% both partially and simultaneously on all TAM variables on the use of the SPMI system; 2) There is a positive and significant effect of 53.5% both partially and simultaneously on all SPMI information system variables on improving the quality of accreditation. This research has implications for the importance of using other resources to improve the quality of education in tertiary institutions.

Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz

We study the faithfulness of an explanation system to the underlying prediction model. We show that this can be captured by two properties, consistency and sufficiency, and introduce quantitative measures of the extent to which these hold. Interestingly, these measures depend on the test-time data distribution. For a variety of existing explanation systems, such as anchors, we analytically study these quantities. We also provide estimators and sample complexity bounds for empirically determining the faithfulness of black-box explanation systems. Finally, we experimentally validate the new properties and estimators.