Reviewerly/exHarmony huggingface dataset:
Chapter 10. Subsurface Characterization for Energy Applications

X-ray fluorescence analysis and chemostratigraphy are fundamental tools used to characterize core, core plug, and drill cutting materials that are collected for oil and gas exploration. This chapter provides an overview of the fundamental methods required to conduct analyses and interpret X-ray fluorescence data and will familiarize the user with the development, application, and interpretation of pXRF methods, workflows, and research projects. The basic requirements necessary to clean and prepare core materials, the calibration techniques required to standardize datasets, and the multivariate statistical approaches that can be used to interpret collected datasets are discussed. These tools can be integrated into a robust workflow that will result in consistent elemental concentration datasets that can be interpreted by experienced geochemists looking to better characterize a rock unit of interest.
Systematic Approach to Tuning a Deep CNN Classifying Bharatanatyam Mudras

Computer vision techniques can improve the understanding of classical dance gestures and also open up opportunities for automatically annotating videos. In this paper, we propose an open hand gesture (mudra) dataset for Bharatanatyam, an Indian classical dance and explore it in the context of a deep convolutional neural network (CNN). An exhaustive Bharatanatyam mudra dataset consisting of 15,396 distinct single hand gesture images of 29 classes and 13,035 distinct double hand gesture images of 21 classes were created. A deep convolutional neural network (CNN) was implemented based on the dataset. Various hyperparameters like batch rate, dropout probabilities, epochs and learning rate of this CNN were experimented upon to find optimum values. The role of validation split and the performance of various optimisers were also explored. Using the optimum hyperparameters, a deep CNN was obtained that could accurately classify single hand and double hand gesture images.
Underwater image enhancement and detection based on convolutional DCP and YOLOv5

Underwater image restoration is conducive to better underwater resource detection and information effective trans-mission. However, the light in the complex water body diffusely reflective and the selection absorption of different band light results in blurring and color distortion of underwater image. Therefore, we propose a convolutional Dark Channel Prior (DCP) underwater image recovery algorithm to enhance underwater images. It can do the pre-processing work for the subsequent YOLOv5 object recognition. The enhancement algorithm first performs Commission International Eclairage Lab (CIELAB) equalization of underwater images for color distortion correction. Meanwhile, the underwater image formation parameters are estimated by the minimum convolution region DCP. Then, Contrast Limited Adaptive Histogram Equalization (CLAHE) is per-formed to obtain an enhanced underwater image. Finally, the enhanced underwater image is input to the YOLOv5 model for detection. Experimental results show that the proposed method outperforms state-of-art algorithms in terms of image recovery effect, evaluation quality and detection accuracy.
Statistical grouping of the observation results of the falling number into the interval variation series

Statistical accounting of observations as results of experience is subjected to grouping of the collected data in order to achieve a representative sample. As a rule, statistical grouping of observation results is carried out by presenting distribution series based on a ranking operation. When defining the members of the empirical population in the variational series of distribution, the goal is to reveal the pattern of variation of the trait under study. In the technology of bakery production, a special role is assigned to monitoring the quality of the processed grain and obtained from it flour, including when defects are detected, most of which are violations of the quality standards of washed gluten and the falling number. The purpose of this work was to statistically group the results of observations of the falling number of wheat flour into an interval series for a sample with a reduced enzymatic activity that does not correspond to the upper limit of the quality standard. For the investigated indicator – the falling number, when compiling a discrete and intervaless series, you can only study the characteristics of a dimensional feature, its coefficient of variation and compliance with the standard distribution of a random variable, and when compiling an interval series, also an empirical feature distribution function. The representativeness of the obtained sample with a given probability of 0,95 provides the result when the average value for a larger sample does not go beyond the found confidence interval for the general average. In a practical sense, the obtained results of the analysis of variance are important for the statistical accounting of observations and the compilation of an empirical population of the falling number experimental data with the subsequent presentation of data in distribution series and obtaining an empirical distribution function of the characteristic measured in the experiment.
Blockchain as Commons: Applying Ostrom’s Polycentric Approach to Blockchain Governance

Given the potential benefits of blockchain, it is imperative to incorporate a stable decentralized governance structure capable of promoting network value and reliability through a set of community-agreed rules and behaviors. Political scientist Elinor Ostrom’s polycentric approach to governance of the commons ensures that the community is at the center of the decision-making process that drives an ecosystem’s network effects and its micro- and macro-economic goals. In our design, we envision community members with an equal right to participate in governance tiers, where each participant has the freedom to choose the level of commitment and reward voluntarily. To implement this framework, we contemplate that governors are organized in different layers. We view Ostrom’s eighth principle of “nested enterprise” as the foundation for such a framework. In the context of a blockchain network, “nested enterprise” refers to building responsibilities among governors in nested tiers (“governance tiers”) from the lowest level up to the entire interconnected systems. In this way, governors are organized in a bottom-up ecosystem and connected by their responsibilities and a common goal of governing the commons. It potentially creates a coherent system where governors’ stakes are proportional to their rewards/ incentives – meaning higher stakes are equal to higher incentives. If implemented, the framework will serve as a key catalyst in developing a long-term value-driven relationship between a blockchain-based network and its governing participants. The application of Ostrom’s design principles in a blockchain governance framework has the potential to increase governors’ responsibilities while ensuring long-term, sustainable and transparent management of a blockchain network’s common resources.
PERSPECTIVES OF CASE-STUDY IN TEACHING SOCIAL MEDIA PROMOTION

The article considers the relevance of including the social media promo- tion course in the programs of higher educational institutions and professional develop- ment centers. The article shows the complexity of teaching the subject, which lies in the fact that students cannot apply knowledge in practice after studying the course. Since the market situation and Internet technologies constantly change, it is important that students gain knowledge that they can use for any project. The problem is partially solved by using the case-study method. The article considers the main theoretical points on the basis of a case for a particular company. Thus the students understand the logic of project design and can apply it to other projects.
Effects of Skin-to-Skin Interaction Through Avatars on Users in a VR Environment

With the recent expansion of the effects of the Corona Disaster, research on communication using social VR content has been active from the viewpoints of safety and convenience. It has been reported that synchronizing the movements of an avatar with those of an actual body contributes to a sense of physical possession and spatial awareness. It has also been reported that body contact in nursing homes has the effect of facilitating communication by giving the other person a sense of security. This paper addresses the effect of physical contact via VR avatars. In this study, we collected experimental data on the effect of physical contact through avatars on 44 healthy young men and women through a physiological psychometric experiment. There, we had them converse in a common VR space and evaluated the physiological psychological effects of physical contact during the conversation. As a result of the experiment, nasal skin temperature increased significantly during conversation while shaking hands, compared to conversation without physical contact. We concluded that this biological response was due to the relaxation effect brought about by the physical contact of shaking hands. Visual information, one of the five human senses, has a stronger influence on our perception of the outside world than other sensory information. We showed that visual information of touching or being touched is useful even in a VR environment where there is no physical contact between users.
KEGIATAN LAYANAN KELOMPOK BELAJAR MANDIRI DIMASA PANDEMI

The Covid-19 virus has an impact on all fields including education. All face-to-face activities must be stopped so that educational activities are carried out at home using an online system. This online learning process is switched by students to play at any time regardless of learning, because there is no face-to-face meeting by students and teachers. So that in this online learning, parents hope that there are people to teach their children to learn at home. With the Unisda Thematic KKN, it reduces parents' anxiety in overcoming student laziness
Service recommendation through graph attention network in heterogeneous information networks

Recommending suitable services to users autonomously has become the key to solve the problem of service information overload. Existing recommendation algorithms have some limitations, and discard the side information of the node, or ignore the information of the intermediate node, or omit the feature information of the neighbouring nodes, or not model the pairwise attentive interactions between users and services. To solve the above-mentioned limitations, this paper proposes a service recommendation approach by leveraging the graph attention network (GAT) and co-attention mechanism in heterogeneous information networks (HINs). Specifically, different types of meta-paths are first constructed, and a feature expression is learned for each node in HINs. Then, the feature information of mashups/services is aggregated by the co-attention mechanism. Finally, the multi-layer perceptron (MLP) is applied to recommend suitable services for users. Experiments on a real-world dataset illustrate that the proposed method outperforms other state-of-the-art comparison methods.
Visual Snow Syndrome Improves with Modulation of Resting State Functional MRI Connectivity of Visual Network Following Mindfulness-Based Cognitive Therapy Modified for Visual Symptoms (MBCT-Vision)

Background: Visual Snow Syndrome (VSS) is a neurological disorder of functional network dysregulation, with no published treatment trials to date. We set out to assess the feasibility of Mindfulness-Based Cognitive Therapy for visual symptoms (MBCT-vision) for VSS.Methods: A prospective open-label treatment trial. Primary Outcomes: self-rated symptom questionnaires (baseline vs week-9, week-20) (1) symptom severity and (2) impact of symptoms on daily life. Secondary Outcomes: (1) WHO-5 wellbeing and CORE-10 psychological distress questionnaire scores (baseline vs week-9, week-20); (2) resting-state functional and diffusion MRI (baseline vs week-20); (3) Cross-sectional fMRI (baseline) compared with healthy controls.Findings: 21 participants (14M, median age 30y, SD9.32) recruited. Two dropped.Symptom severity improved: baseline (mean 6.95, SD 1.43) vs week-9 (mean 5.44, SD 2.38, p=0.0286), week-20 (mean 4.37, SD 2.31, p=0.0003).Impact of symptoms improved: baseline (mean 6.21, SD 2.04) vs week-9 (mean 4.11, SD 2.27, p=0.0057), week-20 (mean 2.65, SD 2.48, p=<0.0001).WHO-5 scores improved: baseline (mean 12.6, SD 3.76) vs week-9 (mean 15.9, SD 4.43, p=0.0009), week-20 (mean 16.0, SD 4.82, p=0.0003).CORE-10 scores improved: baseline (mean 15.4, SD 4.98) vs week-9 (mean 13.3, SD 3.74, p=0.0024), week-20 (mean 12.5, SD 4.57, p=0.0015).Within-subjects fMRI analysis (baseline vs week-20): Reduced clusters of VN-related FC within the left lateral occipital cortex (size=82 mL, cluster-level FWE-corrected p-value=0.006) and the left cerebellar lobules VIIb/VIII (size=65mL, cluster-level FWE-corrected p-value=0.02). Increased cluster of DMN-related FC within the precuneus/posterior cingulate cortex (size=69 mL, cluster-level FWE-corrected p-value=0.02).VSS (baseline) compared with healthy controls: reduced VN-related FC within the left lateral occipital cortex (cluster size=118 mL, cluster-level FWE-corrected p-value=0.01).Interpretation: MBCT-vision is a feasible treatment for VSS, with improvements of symptoms and modulation in fMRI functional connectivity of visual networks. Mindfulness interventions can modify brain functional connectivity with potential applications in neurological conditions.Trial Registration Details: ClinicalTrials.gov (Identifier: NCT04184726).Funding Information: Eye Hope; Guy’s and St Thomas’ Trust Charity; Visual Snow Initiative.Declaration of Interests: The authors report no competing interests.Ethics Approval Statement: Written informed consent was obtained from all participants and the appropriate institutional forms have been archived. This study received ethics approval from the National Research Ethics Committee (LondonSouth East, 19/LO/1615).
Fraud on the Crypto Market

Crypto asset trading markets are booming. Traders in the United States presently can buy and sell hundreds of crypto assets on dozens of crypto exchanges, and this trading is expected to further intensify in the coming years. While investors are increasingly turning to crypto asset trading as a vehicle for portfolio appreciation and diversification, the popularization of secondary crypto asset trading risks significant investor harm through increased incidents of fraud. False or misleading statements by crypto asset sponsors or third parties have the prospect of financially impairing traders in crypto asset trading markets, including everyday traders who are ill-equipped to sustain significant investment losses. These significant investment losses will occur alongside and exacerbate any losses that traders may sustain through inherent changes in relatively volatile crypto asset prices. As traders seek judicial redress for their fraud-related injuries, courts will be asked to make doctrinal determinations that will be pivotal to injured traders’ ability to recover. A primary issue that courts will need to confront is whether crypto asset traders can avail themselves of Fraud on the Market in connection with claims asserted under SEC Rule 10b-5 or CFTC Rule 180.1. This Article addresses that question and has as its intended audience not just academics, but also courts, practitioners, and market participants. The Article shows that as a doctrinal matter Fraud on the Market is available in securities and commodities fraud cases involving crypto assets that trade on crypto exchanges, especially in light of the Supreme Court’s decision in Halliburton II, which resolved that Fraud on the Market is predicated on just a generalized notion of market efficiency, rather than a strict financial economic notion of efficiency. Drawing on how courts apply the doctrine to fraud cases involving stock transactions, the Article articulates a framework for how Fraud on the Market should be applied to the crypto asset context and explores methodological issues relevant to the framework’s application in a given crypto asset case.
A proposal for a general-purpose software module to provide contactless operation in a browser environment using deep learning

In this paper, the authors report on the development of creating a software module that can make arbitrary web page have contactless operation feature with neural network model. To obtain hand coordinate data for training, the authors created a special web application with MediaPipe which is an open-source library provided by Google. Thanks to the application, sufficient number of data can be obtained easily. As a result, the authors confirmed that the model can provide excellent UX. We then experimented to evaluate the performance of a software module used the model and confirmed that how accurately it could work with that module.
Using Gamified Interactive Experience to Relieve Psychological Pressure in the Post-pandemic Era

The COVID-19 Pandemic brought the whole society to a standstill, which has more significant psychological pressure on children and adolescents. Governments, companies, and social groups are trying to confront COVID-19 and social distancing in a gamified way. However, due to fear of the virus and uncertainty about the future, even after the Pandemic is well controlled in physical space, people are still reluctant to stop and play in public areas and are afraid to engage with others because of their internal sense of alienation. From the perspective of urban renewal and environmental design, creating a series of micro-scale design interventions in public spaces to relieve psychological pressure has urgency and relevant significance. This paper analyzes the symbiotic relationship between public art installations and communities. Then discovers the characteristics of public installations based on emotional healing. Furthermore, create two design prototypes to demonstrate more vividly how gamified interactive experience could relieve the mental pressure of the surrounding residents and help them gradually adapt to the new normal life.
On Classical Measurement Error within a Bayesian Nonparametric Framework

This paper studies the impact of classical measurement error on a Dependent Dirichlet Process (DDP). Specifically, we study a Simulation-Extrapolation (SIMEX) algorithm, adapted to a nonparametric Bayesian framework, that assesses the impact of measurement error by inducing even further error in the covariate. We illustrate the algorithm via a battery of numerical experiments.
Backdoor Attack Against Deep Learning-Based Autonomous Driving with Fogging

In recent years, deep learning is the main research direction of the autonomous driving industry. And security is the top priority in the research process of autonomous driving. Many studies show that the deep network neural is very vulnerable to be attacked by backdoor during training. The model affected by backdoor performs normally on the clean data, once it encounters the input with trigger designed by us, it will predict incorrect results which may be specially set by the attacker. Based on this, this paper proposes a new method for the vehicle autonomous driving fogging attack by fogging a small part of training data and implanting the back door into the victim model. We demonstrate on three deep neural network models and two data sets, which shows that we have a high attack success rate of 99 When the input image is fogged heavily enough.
Going Beyond Formalisms A Grounded and Embodied Learning Approach to the Design of Pedagogical Statistics Simulations

Computer-based interactive simulations that model the processes of sampling from a population are increasingly being used in data literacy education. However, these simulations are often summarized by graphs designed from the point of view of experts which makes them difficult for novices to grasp. In our ongoing design-based research project, we build and test alternative sampling simulations to the standard ones. Based on a grounded and embodied learning perspective, the core to our design position is that difficult and abstract sampling concepts and processes should: be grounded in familiar objects that are intuitive to interpret, incorporate concrete animations that spontaneously activate learners&rsquo; gestures, and be accompanied by verbal instruction for a deeply integrated learning. Here, we report the results from the initial two phases of our project. In the first iteration, through an online experiment (N=126), we show that superficial perceptual elements in a standard simulation can lead to misinterpretation of concepts. In the second iteration, we pilot test a new grounded simulation with think-aloud interviews (N=9). We reflect on the complementary affordances of visual models, verbal instruction, and learners&rsquo; gestures in fostering integrated and deep understanding of concepts.
Aplikasi Identifikasi Nada Darbuka Dengan Onset Detection, MFCC, Dan KNN

Darbuka is one of the hadrah musical instruments that acts as a marker when the vocals raise or lower the rhythm of the sound. In learning the Darbuka, the trainer needs to check whether the sound produced is correct or not. With the Darbuka tone recognition system, it will be easier for someone to learn hadrah without a coach. The system developed in this study uses onset detection to break the tone pattern. Then each note goes through a feature extraction process using MFCC with parameters of frame length, overlap length, and the number of coefficients. Then the results of feature extraction through a classification process using KNN. Thexresultsxof the system test showxthat the best combination of parameters in the identification of Darbuka tones with a frame length of 20 ms, overlap length of 40%, the number of MFCC coefficients as much as 13 and a value of K = 1 produces a basic tone identification accuracy of 100%, a tone pattern identification accuracy of 30%, and the accuracy of basic tone identification in the tone pattern is 72.67%.
Three-dimensional geological modeling of the Itataia Phosphate-Uranium Deposit (Ceará, Brazil)

The Itataia phosphate-uranium deposit is located in the northwest segment of the Borborema Province in the Ceará Central Domain. The ore mineral is uranium-enriched microcrystalline collophanite apatite that occurs in massive bodies and lenses, stockworks, as well as hydrothermal and karst breccias associated with marble, calc-silicate rocks and gneisses of the Itataia Group. The deformational history of the deposit includes ductile structures developed in the final stages of thrust tectonics linked to the Brasilian orogeny and later brittle phases related to extensional tectonics, which influenced phosphorus and uranium remobilization and the formation of ore bodies. The main uranium anomaly coincides with the upper part of the Serrote da Igreja hill, bordered to the north by an E-W trending subvertical fault scarp that controls a wide valley. Geometric modeling of the geological data of the deposit provides a three-dimensional view of the geometry of ore bodies, distribution of host rock and faults that propagate beneath the surface and control mineralization, as well as the upper and lower limits of surface weathering. The three-dimensional geometric model using the GoCAD software served as a powerful tool for the analysis of the geology of the Itataia deposit, highlighting the geometry of rocks and ore bodies and the processes involved in the genesis of phosphate-uranium mineralization.