Reviewerly/exHarmony huggingface dataset:
Dolomitization of the Lower Part of the Sargelu Formation, Northeastern Iraq

The prominent characteristic of the lower part of the Sargelu Formation of the Middle Jurassic is the occurrence of destructive dolomitization. This dolomitized unit, cropped out in the high folded and Imbricated zones in Northern Iraq, is situated in the Gotnia Basin and has been distinctly identified and are documented in previous studies. Dolomite crystals are euhedral in shape; hence, they are classified as saccharoidal dolomite, resulting in the elimination of all biocontent and the destruction of the original rock fabrics. Despite the abundance of intercrystalline pores in this unit and their role in the quality of reservoirs in the oil fields, the origin or mechanism of dolomitization is not well understood yet. The aim of this investigation is to find out a suitable model for the dolomitization and to propose possible mechanisms that led to the dolomitization. At the top of the Lower Jurassic Sehkaniyan Formation, an extensive mass of destructive dolomitization has also occurred. The dolomitic unit of the Sargelu Formation, which extends for hundred kilometers constantly. Stratigraphic correlation reveals that the Sargelu Formation is juxtaposed with different successions, such as the Alan and Sehkaniyan formations, that show evidence about peritidal and sabkha environments, where the dolomitization units were associated with solution collapse breccia and microbial stromatolite. This study proposes that the reflux and burial dolomitization models for the lower part of the Sargelu Formation by comparing the paleoenvironmental conditions of the late Lower Jurassic and early Middle Jurassic in the Gotnia basin with a modern analogue model.
The Effectiveness of two Dimensional Haar Wavelet Image De-noising performance using Soft or Hard Thresholding Approach

Image de-noising and restoration represent basic problems in image processing with many different applications including engineering, reconstruction of missing data during their transmission and enhancement ..etc. this work is aimed at developing effective algorithm for denoising image using new strategy algorithm of wavelet techniques ,by applying two dimensions wavelet transform using Haar wavelet. Wavelets are hierarchically decomposing mathematical tools. A noisy picture is sent to the Haar wavelet transform to create four decomposed bands(for each level), and the noise is then removed by applying a threshold to the resultant image band. Then, an inverse transform is used to provide a noise-reduced picture. Performance characteristics are improved by the suggested design in contrast to those of the current approaches.
Research on low-light image enhancement algorithm based on filtering and data-driven composite

Low-light images have low visibility, poor contrast, and weakened details, which pose significant obstacles for subsequent computer vision tasks. When enhancing low-light images, multiple factors such as brightness, contrast, artifacts, and noise need to be considered, making this problem challenging. Low-light enhancement algorithms based on the Retinex theory are important methods in the field of image enhancement, with a wide range of applications and practicality. In this paper, starting from the low-light image imaging model, we address the issues of color distortion and limited brightness improvement exhibited by traditional Retinex-based algorithms and deep learning algorithms in actual low-light scenarios. We propose a filtering and data-driven composite enhancement algorithm (FADDC) that performs weighted fusion on output and intermediate quantities before outputting them. Experimental results combining SCI-Net with traditional algorithms SSR and MSR demonstrate that this fusion method can effectively enhance image brightness without color distortion, achieving better enhancement results.
<i>Is Gravity a Force or Not? A Tale of Scientific Models</i>

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Dan MacIsaac; Is Gravity a Force or Not? A Tale of Scientific Models. Phys. Teach. 1 February 2024; 62 (2): 158. https://doi.org/10.1119/10.0024604 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search
Digitalisation of Banking as a Catalyst for Inter-Country Money Transfers in Africa

The purpose of this research was to examine how banking digitalisation acts as a catalyst for money transfers and economic development between countries of origin and country of labour migration in Africa. This chapter used desktop research to understand how improvements in banking digitalisation using different platforms is transforming African movement of money and saving money senders a lot of money and time in inter account transfers. M-PESA; Mukuru, supermarket money market transfers, bank transfers have been dominant FinTech innovations transforming and improving quality of life and fostering financial inclusion within the African continent and diaspora. This chapter aids in understanding how digitalisation assists ordinary citizens, especially the poor who need to save every rand or dollar that they can save in order to make meaningful change in their lives and the lives of their loved ones anywhere in Africa and the world under these trying post-COVID 19 economic conditions.
To the 75th Anniversary of Academician of the Russian Academy of Sciences Valery Ivanovich Sergienko

January 5, 2024 marked the 75th anniversary of the birth of Doctor of Medical Sciences, Professor, Academician of the Russian Academy of Sciences Valery Ivanovich Sergienko, a leading Russian specialist in the field of physico-chemical medicine.
A study on the Regional regeneration and youth cultural activities in the island fishing village of Gukhwado

Korea is facing a declining population and an aging population, which is leading to a rise in sprawl. Various policies and measures are being explored at the national level to solve this problem, but they are not having much effect. The biggest problem with regional decline is the disappearance of young people and the aging population, which is caused by the influx of young people into large cities such as the Seoul metropolitan area and the natural marginalization of rural areas. This problem is more severe in island fishing villages than in general rural areas. The Gyeonggi-do Fishermen’s and Fisherwomen’s Support Center conducted capacity-building projects for fishermen and cultural activities for youth centered on Pungdo Island in 2020 and Gukhwado in 2021, and achieved significant results. In particular, the center conducted unconventional youth cultural activities by analyzing problems from the perspective of young people and exploring alternatives for promoting Gukhwado and revitalizing tourism. The youth, mainly university students, analyzed the strengths and weaknesses of Gukhwado and came up with revitalization plans based on them. For example, they pointed out problems in marginalized areas, such as signage problems, marine garbage, and problems receiving electronic boards. In order to resolve these inconveniences and promote the hardware-oriented development of the Fishing Village New Deal 300, the government proposed a plan to implement the ‘Friendly Chrysanthemum Seed’ program, install digital tourist information signs, campaigns and educational activities, Smart Island Ecotourism, establish smart city services, install public wireless internet (Wi-Fi), and provide convenience for tourists. It is of great significance that it solved the problem of Gukhwado through youth cultural activities and presented a new solution to solve the disappearance of island fishing villages. The government is carrying out the Fishing Village New Deal 300, and while such hardware-centered development is important, it is also necessary to transform the island fishing village into a livable island through cultural activities and establish it as a prosperous island. Gukhwado, which was presented as an example, is also a representative area where young people are disappearing, and by pointing out the problem and deriving solutions from the perspective of young people, we are diagnosing the fundamental problem of “why” young people are turning away from island fishing villages. In this respect, Gukhwado can be seen as a novel youth cultural activity success story.
COMPARISON OF MANUAL AND AUTOMATIC METHODS IN DETERMINATION OF HEMATOCRIT AND HEMOGLOBIN LEVELS IN GREEN SEA TURTLE

Sea turtles as protected animals in Indonesia need to recover and increase their population through comprehensive rehabilitation efforts. Examination of hematocrit and hemoglobin in green sea turtles (Chelonia mydas) at the rehabilitation center can function to determine the condition of green sea turtles because arriving turtles have different conditions. Dehydration and anemia in green sea turtles can occur due to transportation of turtles and unsuitable environmental conditions before the turtles were rescued. Examination of hematocrit and hemoglobin can be done manually or using a hematology analyzer. However, automated hematological analysis used for mammals generally not applicable for reptiles including green sea turtles due to the presence of nucleated erythrocytes. This study aims to compare the results of hematocrit and hemoglobin examination in green turtles using manual and automatic methods. This study used blood samples from 10 green turtles at the Turtle Conservation and Education Center (TCEC), Serangan Bali. Then a manual check was carried out at TCEC, Serangan, Bali and automatically using the Vet AutoRead IDEXX tool. Manual hematocrit examination uses the microhematocrit method and manual hemoglobin examination uses the Sahli method. The results were then analyzed using the Statistical Product and Service Solution (SPSS) application. Analysis of the significance of manual and automatic calculations of hematocrit and hemoglobin using paired sample t-test. The results of hematocrit (p=0.972) and hemoglobin (p=0.841) showed no significant difference (p&gt;0.05). It can be concluded that the examination of hematocrit and hemoglobin in green turtles is not different so that it can be done manually and automatically.
Super Neurons

Self-Organized Operational Neural Networks (Self-ONNs) have recently been proposed as new-generation neural network models with nonlinear learning units, i.e., the generative neurons that yield an elegant level of diversity; however, like its predecessor, conventional Convolutional Neural Networks (CNNs), they still have a common drawback: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">localized</i> (fixed) kernel operations. This severely limits the receptive field and information flow between layers and thus brings the necessity for deep and complex models. It is highly desired to improve the receptive field size without increasing the kernel dimensions. This requires a significant upgrade over the generative neurons to achieve the “non-localized kernel operations” for each connection between consecutive layers. In this article, we present superior (generative) neuron models (or super neurons in short) that allow random or learnable kernel shifts and thus can increase the receptive field size of each connection. The kernel localization process varies among the two super-neuron models. The first model assumes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">randomly localized</i> kernels within a range and the second one learns (optimizes) the kernel locations during training. An extensive set of comparative evaluations against conventional and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deformable</i> convolutional, along with the generative neurons demonstrates that super neurons can empower Self-ONNs to achieve a superior learning and generalization capability with a minimal computational complexity burden. PyTorch implementation of Self-ONNs with super-neurons is now publically shared.
Pancreatic cancer pathology image segmentation with channel and spatial long-range dependencies

Based on deep learning, pancreatic cancer pathology image segmentation technology effectively assists pathologists in achieving improved treatment outcomes. However, compared to traditional image segmentation tasks, the large size of tissues in pathology images requires a larger receptive field. While methods based on dilated convolutions or attention mechanisms can enhance the receptive field, they cannot capture long-range feature dependencies. Directly applying self-attention mechanisms to capture long-range dependencies results in intolerable computational complexity. To address these challenges, we introduce a channel and spatial self-attention (CS) Module designed for efficiently capturing both channel and spatial long-range feature dependencies in pancreatic cancer pathological images. Specifically, the channel and spatial self-attention module consists of an adaptive channel self-attention module and a window-shift spatial self-attention module. The adaptive channel self-attention module adaptively pools features to a fixed size to capture long-range feature dependencies. While the window-shift spatial self-attention module captures spatial long-range dependencies in a window-based manner. Additionally, we propose a re-weighted cross-entropy loss to mitigate the impact of long-tail distribution on performance. Our proposed method surpasses state-of-the-art on both our Pancreatic Cancer Pathology Image (PCPI) dataset and the GlaS challenge dataset. The mDice and mIoU have achieved 73.93% and 59.42% in our PCPI dataset.
Empirical Study of Agile Software Development Methodologies: A Comparative Analysis

The comparative analysis of software development models, also called the Software Development Life Cycle (SDLC), is an everyday discourse among software engineers, reflecting the dynamic nature of the field. Within this realm, various software development methodologies, such as prototyping, spiral development, and Rapid Action Development, have been established and recognised for their unique approaches to software creation. In recent years, Agile methodologies have emerged as prominent contenders in software development, offering flexibility, adaptability, and efficiency in delivering high-quality software within designated timeframes. Among the array of Agile methodologies, including Dynamic System Development Method (DSDM), Scrum, Feature-Driven Development (FDD), Extreme Programming (XP), Kanban, Adaptive Software Development (ASD), Mendix, Lean, and Crystal, several have garnered significant attention in the software development community. Specifically, ASD, DSDM, XP, FDD, Kanban, and Scrum have emerged as prominent choices among Agile methods utilised by software developers. This study conducts a comprehensive examination and comparison of these six Agile software models, aiming to elucidate their functionalities, strengths, and weaknesses. The findings of this comparative analysis seek to provide valuable insights for software industries, enabling informed decision-making when selecting software development models for upcoming projects. By understanding each Agile methodology's nuanced differences and capabilities, software developers and industry stakeholders can align their project requirements with the most suitable software development approach, ultimately optimising project outcomes and software quality.
From Clustering to Cluster Explanations via Neural Networks

A recent trend in machine learning has been to enrich learned models with the ability to explain their own predictions. The emerging field of explainable AI (XAI) has so far mainly focused on supervised learning, in particular, deep neural network classifiers. In many practical problems, however, the label information is not given and the goal is instead to discover the underlying structure of the data, for example, its clusters. While powerful methods exist for extracting the cluster structure in data, they typically do not answer the question why a certain data point has been assigned to a given cluster. We propose a new framework that can, for the first time, explain cluster assignments in terms of input features in an efficient and reliable manner. It is based on the novel insight that clustering models can be rewritten as neural networks&#x2014;or &#x201C;neuralized.&#x201D; Cluster predictions of the obtained networks can then be quickly and accurately attributed to the input features. Several showcases demonstrate the ability of our method to assess the quality of learned clusters and to extract novel insights from the analyzed data and representations.
Transformer-based visual object tracking via fine–coarse concatenated attention and cross concatenated MLP

Transformer-based trackers have demonstrated promising performance in visual object tracking tasks. Nevertheless, two drawbacks limited the potential performance improvement of transformer-based trackers. Firstly, the static receptive field of the tokens within one attention layer of the original self-attention learning neglects the multi-scale nature in the object tracking task. Secondly, the learning procedure of the multi-layer perception (MLP) in the feed forward network (FFN) is lack of local interaction information among samples. To address the above issues, a new self-attention learning method, fine–coarse concatenated attention (FCA), is proposed to learn self-attention with fine and coarse granularity information. Moreover, the cross-concatenation MLP (CC-MLP) is developed to capture local interaction information across samples. Based on the two proposed modules, a novel encoder and decoder are constructed, and augmented in an all-attention tracking algorithm, FCAT. Comprehensive experiments on popular tracking datasets, OTB2015, LaSOT, GOT-10K and TrackingNet, reveal the effectiveness of FCA and CC-MLP, and FCAT achieves the state-of-art on the datasets.
High-order relational generative adversarial network for video super-resolution

Video super-resolution can reconstruct a sequence of high-resolution frames with temporally consistent contents from their corresponding low-resolution sequences. The key challenge for this task is how to effectively utilize both inter-frame temporal relations and intra-frame spatial relations. The existing methods for super-resolving the videos commonly estimate optical flows to align the features of multiple frames based on temporal correlations. However, motion estimation is often error-prone and hence largely hinders the recovery of plausible details. Moreover, high-order contextual dependencies in the feature space are rarely exploited for further enhancing the spatio-temporal information fusion. To this end, we propose a novel generative adversarial network to super-resolve low-resolution videos, which makes full use of patch embeddings and is effective in exploring high-order spatio-temporal relations of the feature patches. Specifically, a motion-aware relation module is designed to handle the alignment between neighboring frames and reference ones. Depending on a patch-matching strategy for adaptive selection of multiple most similar patches, the cross-scale graph is constructed to reliably aggregate these patches using a feature pyramid. Based on the structure of multi-scale graph, a context-aware relation module is developed to capture high-order dependencies among resulting warped patches for better leveraging long-range complementary contexts. To further enhance reconstruction ability, the temporal position information of video sequences is also encoded into this module. Dual discriminators with cycle consistent constraints are adopted to provide more informative feedback to the generator while maintaining the global coherence. Extensive experiments have demonstrated the effectiveness of the proposed method in terms of quantitative and qualitative evaluation metrics.
Benchmarking Class Incremental Learning in Deep Learning Traffic Classification

Traffic Classification (TC) is experiencing a renewed interest, fostered by the growing popularity of Deep Learning (DL) approaches. In exchange for their proved effectiveness, DL models are characterized by a computationally-intensive training procedure that badly matches the fast-paced release of new (mobile) applications, resulting in significantly limited efficiency of model updates. To address this shortcoming, in this work we systematically explore Class Incremental Learning (CIL) techniques, aimed at adding new apps/services to pre-existing DL-based traffic classifiers without a full retraining, hence speeding up the model’s updates cycle. We investigate a large corpus of state-of-the-art CIL approaches for the DL-based TC task, and delve into their working principles to highlight relevant insight, aiming to understand if there is a case for CIL in TC. We evaluate and discuss their performance varying the number of incremental learning episodes, and the number of new apps added for each episode. Our evaluation is based on the publicly available MIRAGE19 dataset comprising traffic of 40 popular Android applications, fostering reproducibility. Despite our analysis reveals their infancy, CIL techniques are a promising research area on the roadmap towards automated DL-based traffic analysis systems.
Topic Particles, Agreement, and Movement in an Arabic Dialect

The dialect of North Hail, a variety of Najdi Arabic, has a set of sentence-initial particles marking topics of various kinds that correspond to those argued by Frascarelli and Hinterhölzl (2007) to be characteristic of Italian and German: Shift-Topic, Contrastive Topic, and Familiar Topic. In their work, as in much other work in the cartographic tradition, a hierarchy of abstract topic heads is postulated in the C-domain, which host the topical phrases as specifiers. In North Hail Arabic, the topic heads are overt, realized as particles. Some of the topic heads mark topics by attraction to the C-domain; other particles mark topics by ϕ-feature agreement. The particles in the C-domain agree in ϕ-features with a TP-internal DP, subject or object. Analyzed in terms of Agree (Chomsky 2001, 2008), arguments and adverbials are assigned particular topic values either by agreement or by movement. The particles thus provide evidence that topicality can be a syntactic feature, inherent in lexical items (the particles) and assigned to constituents by operations familiar from standard syntactic relations like subject agreement and case. The theory articulated observes the Inclusiveness Condition, known to be a problem for the cartographic theory of topic and focus.
Validating spatio-temporal environmental machine learning models: Simpson’s paradox and data splits

Abstract Machine learning has revolutionized environmental sciences by estimating scarce environmental data, such as air quality, land cover type, wildlife population counts, and disease risk. However, current methods for validating these models often ignore the spatial or temporal structure commonly found in environmental data, leading to inaccurate evaluations of model quality. This paper outlines the problems that can arise from such validation methods and describes how to avoid erroneous assumptions about training data structure. In an example on air quality estimation, we show that a poor model with an r2 of 0.09 can falsely appear to achieve an r2 value of 0.73 by failing to account for Simpson’s paradox. This same model’s r2 can further inflate to 0.82 when improperly splitting data. To ensure high-quality synthetic data for research in environmental science, justice, and health, researchers must use validation procedures that reflect the structure of their training data.