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

Chenglong Song, Mazharul Islam, Lin Wang et al.

Conditional density estimation (CDE) is a fundamental task in machine learning that aims to model the full conditional law $\mathbb{P}(\mathbf{y} \mid \mathbf{x})$, beyond mere point prediction (e.g., mean, mode). A core challenge is free-form density estimation, capturing distributions that exhibit multimodality, asymmetry, or topological complexity without restrictive assumptions. However, prevailing methods typically estimate the probability density function (PDF) directly, which is mathematically ill-posed: differentiating the empirical distribution amplifies random fluctuations inherent in finite datasets, necessitating strong inductive biases that limit expressivity and fail when violated. We propose a CDF-first framework that circumvents this issue by estimating the cumulative distribution function (CDF), a stable and well-posed target, and then recovering the PDF via differentiation of the learned smooth CDF. Parameterizing the CDF with a Smooth Min-Max (SMM) network, our framework guarantees valid PDFs by construction, enables tractable approximate likelihood training, and preserves complex distributional shapes. For multivariate outputs, we use an autoregressive decomposition with SMM factors. Experiments demonstrate our approach outperforms state-of-the-art density estimators on a range of univariate and multivariate tasks.

Mohd Ramizu Abdullah, Uwais Aqili Mohd Zain

Multi-level marketing (MLM) businesses remain popular among today's society. This may be because this business is known to contribute significantly to increasing national income, in addition to the profits earned by companies and individuals involved in it. The involvement of various levels of society in this business has prompted the Malaysian Islamic Development Department (JAKIM) to provide comprehensive guidance in accordance with Islamic law. However, this study found that no previous academic studies have specifically described the MLM business from a maṣlaḥah perspective. Therefore, this highlight study aims to reveal the need for an assessment from a maṣlaḥah perspective of the MLM business, as it is a form of business that differs from conventional businesses and is said to provide numerous benefits while simultaneously posing disadvantages to society. This qualitative study employs thematic content analysis methods, utilising the library data collection method (library research) in its entirety. The findings of the study reveal that existing studies on MLM primarily discuss MLM businesses from the perspective of Islamic law without being linked to the perspectives of maṣlaḥah or mafsadah. Therefore, an assessment based on a maṣlaḥah perspective is necessary to further increase the public's understanding of the reality of the MLM business, while also preventing any manipulation of the maṣlaḥah element by any party.

Dian Puji Lestari, Jati Waskito

Talent management and career development are important factors in improving employee performance in various organizations, including government institutions. This study discusses the relationship between these two factors with employee job satisfaction and performance, as well as the role of job satisfaction as a mediator in the relationship. This study aims to explore the relationship between talent management, career development, and job satisfaction on employee performance at the National Police Institution in Central Java. Within this framework, job satisfaction is examined as a mediating variable. Using a quantitative approach by distributing questionnaires to 130 respondents, data analysis was conducted using the Partial Least Squares (PLS) method. The results of the study indicate that talent management and career development significantly affect employee job satisfaction and performance. In addition, job satisfaction is proven to mediate the relationship between talent management and employee performance, but not career development and employee performance. These findings highlight the importance of effective human resource management strategies to improve employee job satisfaction and performance. This study also provides practical implications, especially for the National Police institution, to optimize training programs, career development, and a supportive work environment.

Feronika Zendrato, Paidi Hidayat, Irsad Lubis

This study aims to examine the influence of health, per capita expenditure, population density, labor force, communication expenditure, and inflation on poverty across 34 provinces in Indonesia. The research employs a dynamic panel data method using the Generalized Method of Moments (GMM). The type of data used is secondary data obtained from the Central Bureau of Statistics (Badan Pusat Statistik/BPS) and Bank Indonesia (BI). The independent variables in this study include health, per capita expenditure, population density, labor force, communication expenditure, and inflation, while the dependent variable is poverty. The results indicate that health, per capita expenditure, labor force, and communication expenditure have a negative and significant effect on poverty. Conversely, population density, inflation, and the lag of poverty have a positive and significant effect on poverty in Indonesia.

Hanun Aufa Nur Khosyi, Sonja Andarini

This study aims to analyze the effect of work conflict, work stress, and work motivation on employee performance at Jiwa Muda Organizer. In the dynamic wedding organizer industry, work conflict and stress often arise due to high job demands and interactions with various stakeholders. Therefore, this study seeks to measure how these factors influence employee performance, both partially and simultaneously. The research method used is quantitative, with a multiple linear regression approach. Data were collected through questionnaires distributed to both permanent and freelance employees at Jiwa Muda Organizer. The results indicate that work conflict and work stress negatively affect employee performance, while work motivation has a positive effect. Simultaneously, these three variables significantly influence employee performance. These findings highlight that effective management of work conflict and stress, as well as increased motivation, can enhance productivity and work efficiency at Jiwa Muda Organizer.

Joey Mulé, Dhandeep Challagundla, Rachit Saini et al.

Event-based vision revolutionizes traditional image sensing by capturing asynchronous intensity variations rather than static frames, enabling ultrafast temporal resolution, sparse data encoding, and enhanced motion perception. While this paradigm offers significant advantages, conventional event-based datasets impose a fixed thresholding constraint to determine pixel activations, severely limiting adaptability to real-world environmental fluctuations. Lower thresholds retain finer details but introduce pervasive noise, whereas higher thresholds suppress extraneous activations at the expense of crucial object information. To mitigate these constraints, we introduce the Event-Based Crossing Dataset (EBCD), a comprehensive dataset tailored for pedestrian and vehicle detection in dynamic outdoor environments, incorporating a multi-thresholding framework to refine event representations. By capturing event-based images at ten distinct threshold levels (4, 8, 12, 16, 20, 30, 40, 50, 60, and 75), this dataset facilitates an extensive assessment of object detection performance under varying conditions of sparsity and noise suppression. We benchmark state-of-the-art detection architectures-including YOLOv4, YOLOv7, EfficientDet-b0, MobileNet-v1, and Histogram of Oriented Gradients (HOG)-to experiment upon the nuanced impact of threshold selection on detection performance. By offering a systematic approach to threshold variation, we foresee that EBCD fosters a more adaptive evaluation of event-based object detection, aligning diverse neuromorphic vision with real-world scene dynamics. We present the dataset as publicly available to propel further advancements in low-latency, high-fidelity neuromorphic imaging: https://ieee-dataport.org/documents/event-based-crossing-dataset-ebcd

Gemma Crowe, Islam Foniqi

In this paper, we construct an implementable algorithm which solves the conjugacy problem in twisted right-angled Artin groups (T-RAAGs). In certain cases, the complexity is known to be linear, by reducing the problem to the twisted conjugacy problem in right-angled Artin groups. We also show that T-RAAGs are biautomatic, providing an alternative solution to the conjugacy problem.

Dadi Adriana, Hartoyo Hartoyo, Rizal Syarief et al.

Based on the results of situational analysis, problematic situations, strategies, and the proposed model as explained above, several conclusions can be drawn. The high number of Muslim people in Indonesia encourages companies to innovate in creating new products that are easier for the public to understand, especially related to Sharia insurance. The existing condition of the insurance industry is driven by the real growth trend of Sharia insurance customers, which can be used as a measure of industry growth. When compared with other countries, there are similarities and differences between Sharia and conventional insurance governance. This can be used as a comparative reference to optimize the management of the Sharia insurance industry. Based on the results of the situation analysis, it is known that the current condition of the Sharia insurance industry is not optimal because there has been no serious effort from the regulator to integrate all potential from upstream to downstream in all elements of the Sharia insurance industry. From the results of the interview analysis, it was revealed that the strategy considered to be the most effective and the main priority in developing the Sharia insurance industry is increasing human resource competency through intensive and continuous training. This aims to overcome the shortage of professional human resources in the fields of insurance and sharia economics while ensuring quality improvement.

محمد حزام الخياطي

هذه الدراسة تهدف إلى بيان مظهر من مظاهر إعجاز القرآن، والمتمثل في الوحدة الموضوعية للسورة القرآنية، وكانت سورة فصلت هي المثال التطبيقي للوحدة الموضوعية في السورة القرآنية، وقد تناول البحث بيان الوحدة الموضوعية في سورة فصلت، وبيان مقاصدها وغرضها، ومحورها الرئيس، ومحاورها الفرعية. وقد تبين من خلال الدراسة أن السورة اشتملت على مجموعة من المعاني والمقاصد، مع وحدة موضوعها وترابط أجزائها، وأنها على غاية من الانسجام والاتساق في سياقها، وأن محاور السورة الفرعية مُفَصِّلة في أحداثها وسياقها لموضوع السورة العام.

Mahmudul Islam, Killian Sheriff, Yifan Cao et al.

Metallic alloys are routinely subjected to nonequilibrium processes during manufacturing, such as rapid solidification and thermomechanical processing. It has been suggested in the high-entropy alloy literature that chemical short-range order (SRO) could offer a new knob to tailor materials properties. While evidence of the effect of SRO on materials properties accumulates, the state of SRO evolution during alloy manufacturing remains obscure. Here, we employ high-fidelity atomistic simulations to track SRO evolution during the solidification and thermomechanical processing of alloys. Our investigation reveals that alloy processing can lead to nonequilibrium steady-states of SRO that are different from any equilibrium state. The mechanism behind nonequilibrium SRO formation is shown to be an inherent ordering bias present in nonequilibrium events. These results demonstrate that conventional manufacturing processes provide pathways for tuning SRO that lead to a broad nonequilibrium spectrum of SRO states beyond the equilibrium design space of alloys.

Rahul Islam, Tongze Zhang, Sang Won Bae

MoodCam introduces a novel method for assessing mood by utilizing facial affect analysis through the front-facing camera of smartphones during everyday activities. We collected facial behavior primitives during 15,995 real-world phone interactions involving 25 participants over four weeks. We developed three models for timely intervention: momentary, daily average, and next day average. Notably, our models exhibit AUC scores ranging from 0.58 to 0.64 for Valence and 0.60 to 0.63 for Arousal. These scores are comparable to or better than those from some previous studies. This predictive ability suggests that MoodCam can effectively forecast mood trends, providing valuable insights for timely interventions and resource planning in mental health management. The results are promising as they demonstrate the viability of using real-time and predictive mood analysis to aid in mental health interventions and potentially offer preemptive support during critical periods identified through mood trend shifts.

Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood et al.

Recently, a number of image-mixing-based augmentation techniques have been introduced to improve the generalization of deep neural networks. In these techniques, two or more randomly selected natural images are mixed together to generate an augmented image. Such methods may not only omit important portions of the input images but also introduce label ambiguities by mixing images across labels resulting in misleading supervisory signals. To address these limitations, we propose DiffuseMix, a novel data augmentation technique that leverages a diffusion model to reshape training images, supervised by our bespoke conditional prompts. First, concatenation of a partial natural image and its generated counterpart is obtained which helps in avoiding the generation of unrealistic images or label ambiguities. Then, to enhance resilience against adversarial attacks and improves safety measures, a randomly selected structural pattern from a set of fractal images is blended into the concatenated image to form the final augmented image for training. Our empirical results on seven different datasets reveal that DiffuseMix achieves superior performance compared to existing state-of the-art methods on tasks including general classification,fine-grained classification, fine-tuning, data scarcity, and adversarial robustness. Augmented datasets and codes are available here: https://diffusemix.github.io/

Anirudha Agrawal, Shaizeen Aga, Suchita Pati et al.

Concurrent computation and communication (C3) is a pervasive paradigm in ML and other domains, making its performance optimization crucial. In this paper, we carefully characterize C3 in ML on GPUs, which are most widely deployed for ML training and inference. We observe that while C3 leads to performance uplifts, the uplifts are far lower than ideal speedups (serial computation and communication versus maximum of computation or communication; all times from isolated executions). That is, C3 on average achieves only 21% of ideal speedup. This is so, due to known challenges of compute and memory interference between concurrent GPU kernels (that is, sharing of GPU's compute units, caches and HBM). To attain better performance for C3, first, we evaluate dual strategies of schedule prioritization and careful resource partitioning of compute units on GPUs to push performance attained with C3 (on average 42% of ideal speedup). We also provide heuristics that can guide a runtime while employing these strategies. To further enhance C3 performance, we propose to mitigate C3 interference by offloading communication tasks to the GPU's DMA engines. To this end, we build concurrent communication collectives (ConCCL) proof-of-concepts that harness DMA engines for communication. We show how ConCCL considerably closes the gap between realized and ideal speedup for C3 (on average 72% of ideal speedup is realized, up to 1.67x speedup). Overall, our work makes a strong case for GPU DMA engine advancements to better support C3 on GPUs.

Di Wu, Jia-Chen Gu, Fan Yin et al.

Retrieval-augmented language models (RALMs) have shown strong performance and wide applicability in knowledge-intensive tasks. However, there are significant trustworthiness concerns as RALMs are prone to generating unfaithful outputs, including baseless information or contradictions with the retrieved context. This paper proposes SynCheck, a lightweight monitor that leverages fine-grained decoding dynamics including sequence likelihood, uncertainty quantification, context influence, and semantic alignment to synchronously detect unfaithful sentences. By integrating efficiently measurable and complementary signals, SynCheck enables accurate and immediate feedback and intervention, achieving 0.85 AUROC in detecting faithfulness errors across six long-form retrieval-augmented generation tasks, improving prior best method by 4%. Leveraging SynCheck, we further introduce FOD, a faithfulness-oriented decoding algorithm guided by beam search for long-form retrieval-augmented generation. Empirical results demonstrate that FOD outperforms traditional strategies such as abstention, reranking, or contrastive decoding significantly in terms of faithfulness, achieving over 10% improvement across six datasets.

Baiq Nurlaela Rahmawati Rahman, Yudin Citriadin, Abdulloh Fuadi

There are problems in terms of teacher personality competencies that have not been maximized at MTs Nurussalam Reak, Pujut District, Central Lombok. Teachers do not come on time to enter their lessons, there are still teachers who are less sympathetic to students, there are still teachers who are not dressed neatly in class, and there are still teachers who cannot control their emotions when teaching. This causes a lack of response from students, it can be seen that students are depressed when the teacher delivers the subject matter. Based on the problems above, the teacher has not been able to set a good example in terms of discipline and responsibility as a teacher. This can be caused by the managerial head of the madrasa and the unfavorable climate of the madrasa. The type of research used is descriptive method with a quantitative approach. In this study the technique used in data collection was in the form of a closed questionnaire. The intensive research process lasted for almost three months involving 30 teachers at MTs Nurussalam Reak, Pujut District, Central Lombok. Data analysis in this study used multiple regression analysis. the results obtained are: (1) the managerial head of the madrasa has a positive and significant effect on the competence of the teacher's personality, (2) the climate of the madrasa has a positive and significant effect on the competence of the personality of the teacher, (3) the managerial head of the madrasa and the climate of the madrasa together have a significant influence significant to the teacher's personality competence. There is an influence between X1 on Y seen from the value of tcount > ttable, namely 2.397 >t table (2.0484), the managerial head of the madrasah has a positive and significant effect on teacher personality competence. There is an influence between X2 on Y seen from the value of tcount > ttable, namely 3.352 > t table (2.0484), madrasah climate has a positive and significant effect on teacher personality competence. There is an influence between X1 and X2 on Y seen as a significance value > 0.05 and madrasa head managerial and madrasah climate together have a significant influence on teacher personality competence.

Riska Amalia, Joko Setyono

This study uses religiosity as a moderating variable to analyze the influence of attitudes, subjective norms, perceptions of behavioral control, and financial literacy on the intention to save for the pilgrimage. The research population is the Muslim community on the island of Java. The distribution of questionnaires resulted in 173 respondents by convenience sampling and 157 respondents through purposive sampling. The data analysis technique uses the structural equation model partial least squares (SEM-PLS) with Smart PLS 3.0 software tools. The study results show that attitudes, subjective norms, and financial literacy significantly affect intention, while the perception of behavioral control and religiosity have no effect on intention. Indirectly religiosity cannot strengthen the relationship between the independent variables on the intention to save for the pilgrimage. This research can be used as input in making decisions for Bank Syariah Indonesia (BSI), where factors that have a significant influence can be used as a reference in deciding to increase the intention to save a prospective customer's pilgrimage.

Mingjie Li, Quanshi Zhang

In this technical note, we aim to explain a deep neural network (DNN) by quantifying the encoded interactions between input variables, which reflects the DNN's inference logic. Specifically, we first rethink the definition of interactions, and then formally define faithfulness and conciseness for interaction-based explanation. To this end, we propose two kinds of interactions, i.e., the AND interaction and the OR interaction. For faithfulness, we prove the uniqueness of the AND (OR) interaction in quantifying the effect of the AND (OR) relationship between input variables. Besides, based on AND-OR interactions, we design techniques to boost the conciseness of the explanation, while not hurting the faithfulness. In this way, the inference logic of a DNN can be faithfully and concisely explained by a set of symbolic concepts.

Syed Islam, Monika Filipovska

Traffic state forecasting is crucial for traffic management and control strategies, as well as user- and system-level decision making in the transportation network. While traffic forecasting has been approached with a variety of techniques over the last couple of decades, most approaches simply rely on endogenous traffic variables for state prediction, despite the evidence that exogenous factors can significantly impact traffic conditions. This paper proposes a multi-dimensional spatio-temporal graph attention-based traffic prediction approach (M-STGAT), which predicts traffic based on past observations of speed, along with lane closure events, temperature, and visibility across the transportation network. The approach is based on a graph attention network architecture, which also learns based on the structure of the transportation network on which these variables are observed. Numerical experiments are performed using traffic speed and lane closure data from the California Department of Transportation (Caltrans) Performance Measurement System (PeMS). The corresponding weather data were downloaded from the National Oceanic and Atmospheric Administration (NOOA) Automated Surface Observing Systems (ASOS). For comparison, the numerical experiments implement three alternative models which do not allow for the multi-dimensional input. The M-STGAT is shown to outperform the three alternative models, when performing tests using our primary data set for prediction with a 30-, 45-, and 60-minute prediction horizon, in terms of three error measures: Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). However, the model's transferability can vary for different transfer data sets and this aspect may require further investigation.

Yushi Hu, Benlin Liu, Jungo Kasai et al.

Despite thousands of researchers, engineers, and artists actively working on improving text-to-image generation models, systems often fail to produce images that accurately align with the text inputs. We introduce TIFA (Text-to-Image Faithfulness evaluation with question Answering), an automatic evaluation metric that measures the faithfulness of a generated image to its text input via visual question answering (VQA). Specifically, given a text input, we automatically generate several question-answer pairs using a language model. We calculate image faithfulness by checking whether existing VQA models can answer these questions using the generated image. TIFA is a reference-free metric that allows for fine-grained and interpretable evaluations of generated images. TIFA also has better correlations with human judgments than existing metrics. Based on this approach, we introduce TIFA v1.0, a benchmark consisting of 4K diverse text inputs and 25K questions across 12 categories (object, counting, etc.). We present a comprehensive evaluation of existing text-to-image models using TIFA v1.0 and highlight the limitations and challenges of current models. For instance, we find that current text-to-image models, despite doing well on color and material, still struggle in counting, spatial relations, and composing multiple objects. We hope our benchmark will help carefully measure the research progress in text-to-image synthesis and provide valuable insights for further research.

Khursid Alam, Tousif Islam

We explore the internal structures of the white dwarfs in two different modified theories of gravity: (i) scalar-tensor-vector gravity and (ii) beyond Horndeski theories of $G_3$ type. The modification of the gravitational force inside the white dwarf results in the modification of the mass and radius of the white dwarf. We use observational data from various astrophysical probes including $\textit{Gaia}$ to test the validity of these two classes of modified theories of gravity. We update the constraints on the parameters controlling the deviation from general relativity (and Newtonian gravity in the weak field limit) as : $0.007 \le α$ for the scalar-tensor-vector gravity and $-0.08 \le γ\le 0.007$ for the beyond Horndeski theories of $G_3$ type. Finally, we demonstrate the selection effect of the astrophysical data on the tests of the nature of gravity using white dwarf mass-radius relations specially in cases where the number of data-points are not many.