Reviewerly/exHarmony huggingface dataset:
Calculation of nth Derivative with Minimum Error Based on Function’s Measurement

A solution of the problem that arises in all cases where it is required to approximately calculate the derivatives of an a priori smooth function by its experimental discrete values is proposed. The problem is reduced to finding an “optimal” step of difference approximation. This problem has been studied by many mathematicians. It turned out that to find an optimal approximation step for the kth-order derivative, it is required to know a highly accurate estimate of the modulus of the derivative of order k+1. The proposed algorithm, which gives such an estimate, is applied to the problem of thrombin concentration, which determines the dynamics of blood coagulation. This dynamics is represented by plots and provides a solution of the thrombin concentration problem, which is of interest to biophysicists.
THE FIRST INFORMATION ABOUT THE GOLD-COPPER MINERALIZATION OF THE KONTORSKOYE ORE OPENING (ARIADNE INTRUSION OF ULTRABASITES, PRIMORYE)

Within the Sikhote-Alin orogenic belt, a new promising type of mineral resources has been identified – complex ores and placers spatially and genetically related to Late Mesozoic ultramafic intrusions. An example of this is the Ariadne massif of ultramafic rocks, in the center of which there is a manifestation of gold-titanium mineralization, and in the exocontact – gold-antimony. The main features of the mineralogy and geochemistry of gold-copper formations in its near-intrusive zone are determined. To reconstruct the conditions for the formation of these ores, the isotopic composition of sulfur in sulfide minerals was studied. The obtained materials testify to the significant metal-bearing potential of the ultramafic rocks of the Sikhote-Alin.
PUSAT PEMBINAAN DAN PENGEMBANGAN E-SPORT DI KOTA BANJARBARU

Banjarbaru has become a thriving city with various existing communities, from art, food, clothing, conventional sports, and electronic sports, which can invite people from outside the city to come. The electronic sports in Banjarbaru has the potential to be developed as an opportunity to show the local children's potential and have quality players who are no less competitive. Along with the rapid development of E-Sports, and the increasing number of enthusiasts who are highly expected in Banjarbaru, a Guidance and Development are needed to accommodate E-Sport enthusiasts themselves to practice and obtain adequate facilities. The Eco-Futuristic Architecture theme is applied to the design because E-Sport is closely related to technology that has developed until today.
PERANCANGAN KOMPLEK MASJID AL FURQAN DI BANJARMASIN

The mosque serves as the hub for all activities, particularly those that are religious and linked to prayer. When a mosque's current structure is unable to handle the activities of its congregation as a result of societal change and population growth, problems arise. Comfort in serving the congregation and the community supports the construction of ideal circumstances in a mosque. Planning functions and forms that make the most of current circumstances can facilitate easy accessibility and the requirements of the congregation's activities. Lastly, a mosque is a container or location that accommodates all activities linked to prayer and other religious activities. It is not only an identity or a symbol.
PERANCANGAN MADRASAH ALIYAH RAUDHATUL AMININ DI KECAMATAN SAMPANAHAN KABUPATEN KOTABARU

Education is one of the basic needs for humans. As the population increases, the need for education will also continue to increase. Madrasah Aliyah is one of the secondary education levels in formal education in Indonesia which is equivalent to Senior High School managed by the Ministry of Religious Affairs. The equalization and quality of Islamic education services is very important to do, especially in remote areas that may not have it at all, one of which is the Sampanahan District area of Kotabaru Regency. The problem raised from the design of this Madrasah Aliyah is how the concept of designing Madrasah Aliyah can optimize its function as an Islamic Education building. Architectural Programming method is used to optimize the function of Islamic education. The design concept applied to support the Islamic Education area is the concept of Islamic Architecture. The application on the design is with the division and arrangement of building masses in the area which is sorted according to the function and the culprit.
The Required Value of Open Digital Platforms: an Example of Connection to Third Party Applications.

Summary New digital journeys should easily integrate with already well-proven technology adding value to open digital platforms that account for some Application Programming Interfaces (APIs) that allow users to connect to third party applications. The digital footprint of all this data becomes essential, with new ways of analysing the results and new workflows that can utilise other cloud and non-cloud based existing solutions to create new insights and value to decision makers. Interconnecting applications by exporting results, exchanging tokens and validating users as well as ingesting results from other applications seamlessly becomes key to maximise technology investments. In that way we can expand the capabilities of new workflows, without having to focus on developing technology that is already existing, nor having to duplicate data. The results of the running external engines, once dynamic simulation has been completed, get stored in the original contextualised cloud service for further analysis and results evaluation. Extracting value from digital data should therefore not be about a scattered search for some relationships in data, but having a deliberate approach to query the data for the information the energy industry could utilise for specific decision making.
Data Foundations: Unlocking the Potential of Subsurface Machine Learning Workflows

Summary There is increased utilization of machine learning in subsurface workflows, with the objective of enabling interpreters to produce more accurate results in less time. Many authors have published results of machine learning workflows, and frequently they conclude that high quality input data is required to deliver reliable results. This paper reviews various machine learning workflows that have been utilized by the subsurface community and analyses the data quality requirements to support those workflows. Based on multiple data foundations projects the key components of a solution to these data quality challenges are presented. The outcome is an aggregated conditioned dataset which allows both humans and machines to rapidly find relevant and quality-controlled data for the workflows they are looking to perform. The aggregated data along with the data standards and business rules developed for these varied data types can be utilised in future digital initiatives including the OSDU where data standards for many data types are still being developed.
Training Experimental Language Models with Low Resources, for the Hungarian Language

In recent years, natural language processing tasks, like sentiment analysis, can be solved with high performance techniques, if a pre-trained language model is fine-tuned.However, in most cases, the pre-training of language models require huge computational resources and training corpora.Our paper addresses the issue of developing deep neural network language models for low resourced languages, such as Hungarian.Pre-training language models like BERT, requires a prohibitive amount of computational power and huge amount of training data.Unfortunately, neither of these prerequisites are commonly available for low resource languages.The question is how well the system can perform with limited resources (both in data and hardware).We focus our research on five transformer models: ELECTRA, ELECTRIC, RoBERTa, BART and GPT-2.To evaluate our models, we fine-tuned the models in six different natural language processing tasks: sentence-level sentiment analysis, named entity recognition, noun phrase chunking, extractive summarization and abstractive summarization.Our results suggest that while our experimental models obviously cannot surpass the performance of the state-of-the-art Hungarian BERT model, they require a smaller carbon footprint, may bring neural network technology to mobile applications and, finally, they may lower the threshold to engaging with neural network technology in low resourced languages, which has been an obstacle so far, in the synergistic co-development of cognitive info-communication systems and its related disciplines.
The components of the pedagogical model for the formation of metacognitive competence in primary school students through didactic communication

The pedagogical model represents a "didactic-methodological conception", "a schematic pattern" that unifies the theories and paradigms of the education system with practice, offering the educational practitioner a concrete example of a theoretical-methodological approach, in the direction of the formation of a behavior or a competence. The assimilation of behavior oriented towards deep information processing requires the design of an instructional model that capitalizes on the "self-reflexive awareness" of the young schoolchild. The principles that are the basis of the pedagogical model we propose are formulated like instructive axes, approaches that broadly outline the vision of the authors regarding the compatibility of theory with school practice so that the subject of learning can take note of internal-cognitive actions, of the executive functions at his disposal, and at the same time, to call on them whenever the school or curricular context requires it. Didactic communication has an essential role in the formation and development of the metacognitive factor. That is why a series of specific principles are developed to make the informational flow more flexible, to facilitate the access of the young schoolboy to the set of metacognitive skills and attitudes that are intended to be formed through the didactic action of the teacher.
The Problems, Reasons, and Countermeasures of Cultivating Scientific Spiritual Literacy in Universities

This article combines the problems of insufficient attention to the cultivation of students' scientific spiritual literacy, lack of a sound evaluation mechanism for scientific spiritual literacy, emphasis on traditional knowledge teaching, and neglect of scientific spiritual literacy cultivation, some college students are eager for quick success and do not pay attention to their own innovation ability cultivation.From the perspectives of universities, students and teachers, in-depth analysis was conducted on the reasons for the existing problems.And proposed relevant countermeasures for cultivating scientific spiritual literacy in universities, including creating a good atmosphere for cultivating scientific spiritual literacy, encouraging students to participate in various subject competitions, establishing a multi-dimensional student performance evaluation system, and leveraging the guidance role of teachers.
QR video steganography using Python

Video steganography is a way to hide secret messages in videos. It is becoming more and more important because we need to keep our communications secure and because video is a popular way to share information. Video steganography may be divided into three basic categories: intra-embedding, pre- embedding, and post-embedding. Intra-embedding conceals the covert message within the actual video frames. Pre-embedding conceals the covert message in the uncompressed raw video data. The hidden message is concealed by post- embedding in the compressed video bitstream. The paper also discusses how to measure how well video steganography methods work and what the future of video steganography research looks like. Finally, the paper identifies some of the challenges that video steganography researchers face.
Data poisoning: issues, challenges, and needs

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.
REVITALIZATION OF THE BANUA HALAT SACRED MOSQUE

The area Banua Halat Sacred Mosque in Tapin Regency, which has degraded its environment due to inadequate facilities and infrastructure services, and does not have a forum for development and introduction to religious and cultural history, it is feared that cultural heritage will be damaged, couple with the case of the COVID-19 pandemic which hampers activities. Baayun Maulid Nabi Muhammad, which is held every 12 rabiul awal every year, reduces the existence of areas. With the development of cultural heritage that elevates historical and cultural values, it will have an impact on the environment and the surrounding community. The revitalization of the mosque area is carried out with the aim of rediscovering the potential that has or was owned or should have been owned by the sacred Banua Halat mosque. Trough the method of forming the area through the concept of God to express directly the relationship between religion and culture produces clarity of form and space, shape, and order by increasing the vitality of the area through service network facilities and infrastructure, as well as developing the value of the location of the cultural heritage area. &#x0D; Keywords: Banua Halat Sacred Mosque, Cultural Conservation, Devine Rantau, Revitalization.
Advanced Geothermal prospectivity mapping with Machine Learning using Multi-model Regression and Geology-dependent Multicriteria-Decision Analysis

Summary Geothermal prospectivity mapping using multiple-criteria decision analysis is well-documented in the scientific literature. However, most previous works suffer from two drawbacks: no consideration of the geology for defining the weights of the decision criteria, and no metrics of reliability for the prospectivity prediction. In this work, we introduce a method that overcomes these drawbacks and extends on a large scale the mapping provided manually by the experts. The method is based on (i) automatic regressions of both heat flow and thermal gradient maps using a multi-model approach, (ii) introduction of geology-dependent criteria weights for the decision analysis, and (iii) combination of multiple decision maps to obtain a more robust prospectivity mapping and a quantification of the reliability of the prediction. Applied to geothermal data from British Columbia, the method appears to produce prospectivity maps with higher spatial resolution than conventional ones. Besides, it substantially attenuates the footprint of data acquisition and facilitates the identification of geographical zones where the prediction is the most uncertain, thus where a human subjective interpretation would have the most added value. The proposed workflow is easily transposable to any context of subsurface exploration or exploitation, for instance in the petroleum, mining, or hydrogeological industries.
Unveiling sentiment analysis: A comparative study of LSTM and Logistic regression models with XAI insights

In this study, we delve into sentiment analysis and the role of Explainable Artificial Intelligence (XAI), with a focus on techniques such as Lime that bring transparency to machine learning (Logistic Regression) and deep learning (LSTM) models. We explore how ML predictions can be biased using XAI and how XAI helps us understand DL models used in sentiment analysis through research that has been made. Examining various research, we notice a gap – the lack of training and interpretation for both ML and DL models on the same dataset using XAI. Our research fills this gap, shedding light on ML and DL model predictions through XAI's lens. By completing our research work, we come to know that even with an accuracy level of 83% for the DL model, they outperform the ML model with an accuracy level of 92% in some cases. This distinction is only identified with XAI techniques, particularly Lime.
Recyclable garbage detection algorithm under occlusion conditions

In the recyclable garbage detection task, an improved SSD garbage occlusion detection method combining data enhancement methods with fused features is proposed to address the problem of accuracy degradation caused by garbage targets occluding each other. First, the recyclable garbage occlusion dataset is self-made using the improved occlusion data enhancement algorithm. Secondly, the VGG network in the SSD model is replaced with the improved MobileNet V3 network. Finally, the improved Feature Pyramid Network (FPN) is used to fuse SSD from deep to shallow layers of the network step by step to detect the mutually obscured spam targets more accurately. The experimental results show that the average mean accuracy of the proposed method in this paper is improved by 13.34% compared with the traditional SSD model.
Neural Machine Translation Research Progress and Its Implications for Education Technology: A Bibliometric Analysis

Neural machine translation (NMT) stands as one of the most prominent domains in contemporary natural language processing research. In this study, employing VOSviewer software, we conducted a bibliometric analysis based on 1386 NMT-related publications retrieved from the Web of Science database as of September 2023. Co-citation analysis revealed that influential sources in the NMT field were primarily concentrated in top-tier conferences and journals in areas such as artificial intelligence, neural computing, and computer vision. Notably influential authors focused on machine translation techniques, deep learning, and natural language processing technologies. Co-occurrence analysis delineated two prominent research directions within the NMT domain: machine translation and deep learning. Based on the outcomes of bibliometric analysis, we provide insights into the current state, challenges, and prospects of NMT research and offer an understanding of NMT applications in the realm of educational technology.
MDCYOLO: Improved YOLOv5 Algorithm with Modified Deformable Convolution

Convolution is a fundamental operation for feature extraction in deep neural networks. However, traditional convolutional operations use fixed-shaped kernels, resulting in a stable receptive field for each point on the output feature map. This stability limits their adaptability to irregularly shaped objects. The mainstream deep learning network model uses convolution as the basic method of feature extraction, which also results in the model having poor recognition effect on data sets with large differences in shape and size. Deformable Convolutional Networks (DCN) series of work calculate corresponding offsets from the perspective of shape for the points on the feature map participating in the convolution operation, thereby changing the shape of the receptive field. However, DCN does not consider adjusting the feature map weights. Therefore, we proposed a modified deformable convolution (MDC), adding a mask to adjust the weight of the feature map based on Deformable ConvNets v2 (DCNv2) to simultaneously adjust the shape and weight of the feature map participating in the convolution operation. Furthermore, we used MDC in YOLOv5 and named the improved YOLOv5 MDCYOLO. Experimental results show that the detection accuracy of the MDC is significantly higher than DCNv2, and ultimately increased by 1.9% on the Pascal VOC data set and 3.1% on the COCO data set.
Enterprise Talent Demand Prediction Model Based on Optimized Generalized Regression Neural Network

This paper establishes a comprehensive framework for the Generalized Regression Neural Network (GRNN) and focuses on optimizing its functionality. GRNN, recognized for its robustness in handling limited or noisy data, plays a significant role in addressing real-world problems characterized by unstable and uncertain data. The study emphasizes the refinement of GRNN's underlying theory, highlighting the integration of the Sparrow Search Algorithm (SSA) to optimize the network's smoothing parameter. The paper delves into the construction and optimization of the GRNN model, emphasizing the development of its essential architectural layers. Finally, it showcases the practical implications of the optimized GRNN model, demonstrating its application in predicting enterprise talent demand within the context of this study.
Effective and Efficient Training for Sequential Recommendation Using Cumulative Cross-Entropy Loss

An Increasing research interests focus on sequential recommender systems, aiming to model dynamic sequence representation precisely. However, the most commonly used loss function in state-of-the-art sequential recommendation models has essential limitations. To name a few, Bayesian Personalized Ranking (BPR) loss suffers the vanishing gradient problem from numerous negative sampling and prediction biases; Binary Cross-Entropy (BCE) loss subjects to negative sampling numbers, thereby it is likely to ignore valuable negative examples and reduce the training efficiency; Cross-Entropy (CE) loss only focuses on the last timestamp of the training sequence, which causes low utilization of sequence information and results in inferior user sequence representation. To avoid these limitations, we propose calculating Cumulative Cross-Entropy (CCE) loss over the sequence in this paper. CCE is simple and direct, enjoying painless deployment, no negative sampling, and effective and efficient training. We conducted extensive experiments on five benchmark datasets to demonstrate the effectiveness and efficiency of CCE. The results show that employing CCE loss on three state-of-the-art models GRU4Rec, SASRec, and S3-Rec can reach 83.92%, 53.86%, and 45.86% average improvement of full ranking NDCG@5, respectively. Using CCE, the performance curve of the models on the test data increases rapidly with the wall clock time and is superior to that of other loss functions in almost the whole process of model training.