Reviewerly/exHarmony huggingface dataset:
Feature Extraction and Classification of Tissue Mammograms Based on Grayscale and Gray Level Co-occurrence Matrix

Breast cancer is a major cause of death for women in the world. Breast cancer can be diagnosed by various means of examination, including mammography examination, which indicates abnormalities in the breast. Doctors need other information, such as a biopsy to detect breast cancer further. However removal of some tissue can cause bleeding, hematoma formation, and infection. A pattern recognition system is needed using mammogram images for breast cancer detection to avoid unnecessary biopsies. According to research conducted by experts from Kaiser Permanente in Oakland, California, breast tissue density can be one of the factors that determine whether a woman is at risk for breast cancer or not. Breast tissue density is always associated with cancer risk. The denser the breast, the more vulnerable it is to be attacked by cancer. This paper proposes a technique classification of breast tissue density into Glandular, Dense Glandular, or Fatty Glandular groups. The features used are mean, kurtosis, skewness, contrast, correlation, energy, and homogeneity. The proposed system consists of two main stages, namely (a) Performing feature extraction using Grayscale and Gray Level Co-occurrence Matrix (GLCM); (b) Compile transaction data and build a classification model. The evaluation results using the Tree and Random Forest algorithms are the accuracy rate is 92% (Tree), 95% (Random Forest).
Stochastic Gradient

We describe the stochastic gradient method, the fundamental algorithm for several important problems in data science, including deep learning. We give several example problems for which this method is suitable, then described its operation for the simple problem of computing a mean of a collection of values. We related it to a classical method, the Kaczmarz method for solving a system of linear equalities and inequalities. Next, we describe the key assumptions to be used in convergence analysis, then describe the convergence rates attainable by several variants of stochastic gradient under several scenarios. Finally, we discuss several aspects of practical implementation of stochastic gradient, including minibatching and acceleration.
Iron speciation throughout a karst pedosedimentary record in South of Italy

&amp;lt;p&amp;gt;A 3-m thick sediment was found in a limestone mine located in the Apulia region (south of Italy), at a depth of 25-30 m from the current ground level. Samples from 5 layers were investigated by X-ray Absorption Near Edge Spectroscopy (XANES) and Extended X-ray Absorption Fine Structure (EXAFS) at the iron (Fe) K-edge, paired with Raman spectroscopy (RS) and thermal analysis.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The pedosediment record under investigation represents a peculiar stratigraphic series showing complex features related to the Fe dynamic.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;From a technical point of view, the challenges of phase identification in Fe oxides are many, but the application of multiple techniques provides sufficient evidence for the identification and discrimination of Fe phases. The combination of Fe XANES and EXAFS allowed to get information on the Fe speciation and its local structure. In detail, ferrihydrite is the most abundant Fe species, followed by goethite and minor amounts of hematite. Ferrihydrite content decreases with depth, where goethite and hematite occur. The presence of ferrihydrite, only detected by Linear Combination Fitting (LCF) on the first-derivative XANES spectra and on the EXAFS spectra, suggests that redox cycling of Fe occurred in these sediments. In addition, higher contents in Fe(III) complexed by organic matter is found in the top and deepest layer.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Generally speaking, RS and thermal analysis might present a promising tool to unravel some mineralogical components such as calcite, goethite and some Mn-oxides. Unlike for the identification of some Fe-bearing minerals like ferrihydrite, RS seems to be not well suited and this aspect require further investigations.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Definitely, this study confirms that each layer has preserved the distinct features that relate to its time of deposition. We underline how a multidisciplinary approach is strongly required to obtain reliable records when peculiar environments like karst pedosequences are investigated.&amp;lt;/p&amp;gt;
Erosion and chemical weathering of siliceous rocks at the supply and kinetic limits

&amp;lt;p&amp;gt;The uplift and erosion of active mountain ranges and the consequent weathering of minerals modulates the global carbon cycle and impacts Earth&amp;amp;#8217;s climate on geologic timescales. However, the link between erosion and weathering is complex because weathering rates can be limited by the supply of minerals to the weathering zone, by the supply of acidic fluids, or by the kinetics of mineral weathering. Existing approaches that model the carbon cycle over geologic timescales assume that with increasing erosion rates, weathering transitions from a supply limit where weathering rates scale linearly with erosion to an &amp;amp;#8216;acid&amp;amp;#8217; or a kinetic limit where weathering is insensitive to erosion. Alternative models fit a single power-law to the relationship between erosion and weathering across multiple orders of magnitude. The validity of these two approaches remains difficult to assess at the landscape scale because existing data do not cover all limits or because co-variation between runoff and erosion obscures the driver of changes in weathering rates. Here, we compile five datasets of solute concentrations in streams that span well-defined erosion rate gradients in relatively uniform lithologies and with limited or well-constrained variations in runoff. Across 2-3 orders of magnitude of erosion rates, and for both metasedimentary and granitic lithologies, we find that silicate weathering rates are insensitive to erosion rates. In turn, weathering of sulfide and carbonate minerals increase with erosion rates, consistent with a limitation by mineral supply. However, contrary to existing models of supply-limited weathering, we observe a non-linear increase of sulfide and carbonate weathering rates with erosion. These new findings suggests that supply-limited and kinetically limited zones of weathering co-exist within a single landscape across multiple orders of magnitude of erosion rate. The distribution of these zones is most likely controlled by erosion processes. As a consequence, existing weathering models that assume a linear relationship between erosion and weathering at the supply limit may overestimate the sensitivity of weathering rates to erosion and underestimate the impact of climate on these reactions, with implications for the effect of mountain building on the carbon cycle and on Earth&amp;amp;#8217;s climate.&amp;lt;/p&amp;gt;
Towards a comprehensive High Resolution 3D P- and S-Wave Velocity Model for the Alpine Mountain Chain using Local Earthquake Data

&amp;lt;p&amp;gt;Seismic data availability and automated picking algorithms drastically improved in the European Alps since the last orogen wide crustal P-wave velocity model was compiled by Diehl et al. (2009). Especially, the abundant seismic data recorded by the AlpArray Seimic Network (AASN) which was in operation from 2015-2021 provides a unique high resolution seismic data set. The aim of our project therefore is to create a comprehensive 3D P- and S-wave crustal velocity model for the European Alpine region using Local Earthquake Tomography (LET). Such a model is not only needed to sharpen high resolution teleseismic tomography studies imaging subducted slabs but also to relate surface structures to mountain building processes in the mantle.&amp;lt;br&amp;gt;To achieve this aim precise onset times of seismic crustal phases are needed. Here we show our first results of automatic onset time determination obtained through the deep-neural-network PhaseNet. When compared to catalogues of manual travel time picks, we find its performance as accurate as a human analyst's. This confirms the transferability of machine learning approaches to our area and data set.&amp;lt;br&amp;gt;The large amount of evenly distributed seismic stations yields up to a total of 720 P and S arrival picks with epicentral distances up to 700km for events with ML &amp;gt; 3.5. Earthquakes with magnitudes of ML=2.5 are generally detectable for epicentral distances up to at least 200km and contribute approximately 200-300 arrivals per event.&amp;lt;br&amp;gt;As a first step towards a 3D model we present a thorough analysis of the consistency of the automatically determined arrival times, which facilitates a reliable removal of outliers.&amp;amp;#160;&amp;lt;br&amp;gt;Furthermore, we show visualizations of our preliminary tomography model and its resolution.&amp;lt;/p&amp;gt;
From Social to Prosocial Machines: A New Challenge for AI

Throughout the past few years, artificial intelligence (AI) has become increasingly more present in our daily lives. A myriad of settings became the stage for AI applications, such as factories, roads, houses, hospitals and even schools. Given these new contexts, AI-powered machines must now place the human at the centre, and be designed to interact with humans in a natural way: AI is becoming social.
ViLO — A Core Ontology of Vietnamese Legal Documents

Legal ontologies play a key role in various legal applications and have been broadly used by many stakeholders. Innovative systems and ontologies in the law hold potential to conduct legal research. With the needs for legal information management in smart applications, especially for Vietnamese law, it is vitally important to construct core legal ontologies for knowledge representation. This study proposes a core ontology for Vietnamese legal documents which covers general legal domain called as ViLO. The ViLO ontology mainly consists of related institutions of Vietnamese political system, types and structures of legal documents. The method of the NeOn-based collaborations among domain experts and ontology engineers was conducted to build up the ViLO ontology. Through FOCA-based validation results, the proposed method was shown to be effective and efficient. The resulting ontology was demonstrated to be reliable and enriched. The ViLO ontology is supposed to be a basis for further constructions of domain ontologies and artificial intelligence applications in Vietnamese law.
Social Versus Physical Distancing: Analysis of Public Health Messages at the Start of COVID-19 Outbreak in Malaysia Using Natural Language Processing

The study presents an attempt to analyse how social media netizens in Malaysia responded to the calls for “Social Distancing” and “Physical Distancing” as the newly recommended social norm was introduced to the world as a response to the COVID-19 global pandemic. The pandemic drove a sharp increase in social media platforms’ use as a public health communication platform since the first wave of the COVID-19 outbreak in Malaysia in April 2020. We analysed thousands of tweets posted by Malaysians daily between January 2020 and August 2021 to determine public perceptions and interactions patterns. The analysis focused on positive and negative reactions and the interchanges of uses of the recommended terminologies “social distancing” and “physical distancing”. Using linguistic analysis and natural language processing, findings dominantly indicate influences from the multilingual and multicultural values held by Malaysian netizens, as they embrace the concept of distancing as a measure of global public health safety.
Least-squares-based formulation of deep learning: Theory and applications to geoscience data analytics

&amp;lt;p&amp;gt;As a specific family of machine learning algorithms, deep learning (DL), successfully applied to several application areas is a relatively new and novel methodology receiving much attention. The DL has been widely applied to a series of problems including email filtering, image and speech recognition, and language processing, but is only beginning to impact on geoscience problems. On the other hand, the standard least-squares (SLS) theory of linear models has been widely used in many earth science areas. This theory connects the explanatory variables to the predicted ones, called observations, through a linear(ized) model in which the unknowns of this relation are estimated using the least squares method. The design matrix, containing the explanatory variables of a set of objects, is usually linearly related to the predicted variables. There are however applications that the predicted variables are unknown (nonlinear) functions of explanatory variables, and hence such a design matrix is not known a-priori. We present a methodology that formulates the deep learning problem in the least squares framework of the linear models. As a supervised method, a network is trained to construct an appropriate design matrix, an essential element of the linear model. The entries of this design matrix, as nonlinear functions of the explanatory variables, are trained in an iterative manner using the descent optimization methods. Such a design matrix allows to employ the existing knowledge on the least squares theory to the DL applications. A few examples are presented to demonstrate the theory.&amp;lt;/p&amp;gt;
Development of a model for increasing the level of skilled digital technologies in the conditions of personalization of educational trajectories of pupils

The article discusses the issue of mastering end-to-end digital technologies by schoolchildren. The authors analyze the conditions for personalizing the educational trajectories of schoolchildren, which are necessary for mastering end-to-end digital technologies. All elements of the model for increasing the level of proficiency in end-to-end digital technologies are considered: the didactic goal, the content of training, methods and forms of organizing the educational process. The purpose of the article is to present the model developed by the authors for increasing the level of proficiency in end-to-end digital technologies in the context of personalizing the educational trajectories of schoolchildren, to prove its effectiveness. Research methodology and methods. The research methodology is based on a comparative and comparative analysis of the results of experimental work on the implementation of the model in the educational process of an educational organization. Research results, discussion. Comparative analysis of the results of experimental work on the implementation of the model in the educational process of an educational organization showed a significant increase in the level of proficiency in end-to-end digital technologies after the introduction of the model into the educational process of an educational organization. Conclusion. The prospects of the obtained presented data are associated with solving the problem of increasing the level of proficiency in end-to-end digital technologies in the context of personalizing the educational trajectories of schoolchildren. For teachers of schools, centers of additional education working in a digital educational environment, it is necessary to take into account in their work special conditions and principles to increase the level of proficiency in end-to-end digital technologies.
ECFAR: A Rule-Based Collaborative Filtering System Dealing with Evidential Data

Collaborative filtering (CF) is considered as one of the most popular and widely used approaches in recommendation systems. CF makes automatic recommendations based on the similarity between users (user-based) or items (item-based) in the system. In this respect, various machine learning techniques were used to create model-based CF methods. However, most of the previous works do not consider the imperfections in the users’ ratings. Thus, in this paper, we tackled the issue of creating a rule-based CF model dealing with evidential data, i.e., data where imperfection is represented and managed thanks to the belief function theory. We proposed a novel method named ECFAR that learns recommendation rules from a soft rating matrix and uses them to make predictions. To assess the reliability of our method, we conducted various experiments on a real-world data set. The experiments show that our proposed method produces satisfying results compared to existing solutions.
Exploring new experimental possibilities for the investigation of nonlinear mesoscopic elasticity

&amp;lt;p&amp;gt;In rocks, concrete, and a variety of other heterogeneous materials commonly grouped in the &amp;amp;#8220;nonlinear mesoscopic elasticity&amp;amp;#8221; class, dynamic excitation leads to a fast softening of the material, followed by a slower recovery process where the material recovers part of its initial stiffness as a logarithmic function of time. This requires us to exit the convenient framework of time independent elastic properties of the solid Earth.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;These phenomena can be observed during seismic events in affected infrastructure as well as in the subsurface. Since the transient material changes are not restricted to elastic parameters, but also affect hydraulic and electric parameters as well as material strength, as documented for instance by long lasting changes of landslide rates, it is of major interest to characterize the softening and recovery phases. This may help us gain more insight in hazard prediction from both a geological and engineering perspective.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;However, the underlying physics of nonlinear mesoscopic elasticity are not agreed upon, as there is a lack of experiments that would allow us to discriminate between the existing models. We aim to contribute to that goal.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Our experiment is made on an MTS815 testing system, with a 100x50mm sample of dry Bentheim sandstone. The sample is subjected to a series of load and hold cycles in the elastic domain, with strains ranging between 1e-5 and 1e-6, while undergoing a progressive increase in confining pressure.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Active acoustic measurements during those loading cycles, with an array of 15 acoustic sensors, allow us to determine wavespeed changes over the course of the experiment to observe the dynamic softening as well as the recovery process in the sample.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this preliminary experiment, we investigate the capabilities of the testing machine in terms of signal quality, signal stability and lapse time dependent decorrelation of coda waves. In a second phase, we will vary confining pressure, pore pressure and temperature in order to study the influence on the softening and recovery phases.&amp;lt;/p&amp;gt;
Introduction

We provide initial motivation for studying string theory, and give an overview of the structure of this book.
Analysis of heat transfer rate on Bakar Batu as Papua cultural practice

Heat (Q) is the energy transferred between a system and its surroundings by temperature differences. Heat is a physical quantity that is affected by mass (m), specific heat (c), and temperature changes (ΔT). Heat transfer rate (H) is the change in heat in each time unit (H = ΔQ/Δt). This study aimed to determine the heat transfer rate in Bakar Batu as Papua cultural activities. Research on Bakar Batu cultural practice had not been done yet. It was important to determine the heat transfer rate in Bakar Batu because the practice was included in indigenous science. The process included the cooking process of chicken, vegetables, and sweet potatoes. It was begun by heating some stones for 45 minutes. Then the heated stone was transferred to a hole with a diameter of 140 cm and a depth of 54 cm. Chicken, vegetables, and sweet potatoes were also inserted into the holes and covered with leaves that act as insulators. Temperature measurements during the heating process were observed every five minutes with a total duration of 95 minutes. Temperature measurement was carried out using a digital thermometer. The data for calculating the heat transfer rate (H) for 10 minutes was 10.4 J I minute. After that, the H at the 15th to the 55th minute decreased to 5.3 J I minute to 0.1 J I minute. H at the 60th minute to the 95th minute was equal to zero. There was a thermal equilibrium between the foodstuffs and the stone in the last 35 minutes. The linear equation obtained from the linear graph between heat change (ΔT = ΔQ) and the value of H (ΔT / Δt = H) was y = 0.093x + 0.642.
Local-global coupling relationship based low-light image enhancement

Convolutional neural networks have made significant progress in various fields of artificial intelligence. However, the convolution operation which is based on a shared parameter sliding window mechanism only focuses on modeling the local relationship and is insufficient to establish global relationships. Nonetheless, the local and global relationships are both crucial for feature representation. Therefore, this paper concentrates on how to efficiently construct and couple the local and global relationships to mine more abundant feature information and enhance the discriminability of features. To this end, a local-global coupling module is designed, which is composed of four basic components: feature extraction, local relationship branch based on depth-wise convolution (DWConv), global relationship branch based on multi-head self-attention (MHSA), and local-global relationship coupling based on point-wise convolution (PWConv). On this basis, a local-global relationship coupling with encoder-decoder structure neural network is proposed, which can efficaciously model the local-global coupling relationship, strengthen the discriminative ability of feature information, and improve the performance of the model. Extensive experiments on low-light image enhancement datasets demonstrate the effectiveness of the proposed method, which significantly outperforms the state-of-the-art counterparts. Furthermore, the importance and effectiveness of local-global relationship coupling are analyzed through ablation and extended experiments.
Classical Relativistic Strings

Classical strings are introduced through the Nambu–Goto and Polyakov action. D-branes arise through Dirichlet boundary conditions. The conformal gauge and light-cone coordinates are discussed, as well as the relation between symmetries and conservation laws. Explicit solutions to the equations of motion are found for periodic, Neumann, and Dirichlet boundary conditions.
Role of Librarian in Electronic Libraries

The term information electronic library it’s need and importance and takes a stock of some notable efforts being made to initiate information electronic library activities in different parts the world. The aim of electronic library is to explorer and to collect useful knowledge over above faster coping. Search and to distribute. So present paper discuses in electronic library services. It’s meaning, feature, feature, roll of librarians, services of e-libraries, advantages, disadvantages, and objective etc..
Recommending an Insurrection: Facebook and Recommendation Algorithms *

Facebook faced two types of decisions. First, what content or accounts do you remove from your platform? In many ways, this was an easier yet more visible decision, where most platforms had guidelines as to the type of content allowed. In mid-2019, Facebook began allowing algorithms to take down hate speech content automatically, without being first sent to a human reviewer. This software was able to detect 65 percent of the comments that Facebook ultimately determined were hate speech and removed. While Facebook promoted a hands-off approach to let users decide what content should be removed, Facebook took a more active approach when it came to the promotion and recommendation of content and in telling users what to read, watch, or join next. While Facebook attempted to remove individual accounts when the behavior reached a particular threshold, only after January 6 did Facebook realize that the movement had “normalized delegitimization and harm to the norms underpinning democracy”.