Reviewerly/exHarmony huggingface dataset:
Morphological and Microchemical Evaluation of Gold Along the Upper River Rafin Gora from Parts of the Proterozoic Kushaka Schist Belts in Minna North Central Nigeria

Placer gold is widely distributed in the Kushaka schist belt of northern Nigeria, with scarce lode gold discovery because of intense weathering. This study uses the morphology and microchemistry of the alluvial gold found as regoliths in stream sediments from the Upper River Rafin Gora, which is part of the Proterozoic Kushaka schist belt of northwestern Nigeria. The gold grains were studied to constrain the nature of the source mineralization. Panning, SEM and EMPA were used in studying morphology and alloy composition of the placer gold grains. Au fineness was higher at the rim with significantly less Ag content. The gold grains have a fineness value of between 90.24 to 100.87 in the core and 98.96 to 101.50 while Ag is between 9.42 to 16.20(wt%) at the core and 0.42 to 1.60(wt%) in the rim. The SEM results depict angular to sub-rounded surface textures with grooves, pits and cavities filled with clays. These morphological attributes indicate proximity to the source. Pyrite inclusions in the gold grains indicate a hydrothermal gold veining system. The gold in this study is alloyed with Ag and from its composition. Single source mineralization is been deduced for this study and the gold falls within the epithermal orogenic deposits according to Townley 2003 classification.
Protection of Personal Health Data During the SARS_COV19 Pandemic in Tunisia

In the Tunisian context, and during the SARS_COV19 pandemic, the authors proposed to the Hospital Centers of the Ministry of Health in Tunisia as well as the Institut of Pasteur a set of e-health projects based on artificial intelligence. During the IT development process, they had many problems related to the processing of health data: insufficiency and lack of health data for the development of innovative and intelligent applications, the health system having no digitalization, the need for law application of personal health data even in the context of scientific research. Moreover, the risk of data leaks, non-compliance with the law and cyber attacks are increased by a greater use of personal devices, a common circulation of data, and sensitive computer systems that are becoming more accessible outside hospitals' networks. In this chapter, the authors present the process that has been applied to guarantee the protection of personal health data through three different projects and explain the steps for this process implementation.
Deep Shape Representation with Sharp Feature Preservation

We present a novel implicit neural representation to reconstruct CAD models from point clouds in high quality. Our method first extracts sharp edge points from input points by a sharp feature detection network and then recovers implicit surfaces preserving the sharp features of ground-truth models. The sharp feature detection network uses a U-Net structure for feature encoding, and the attention module is introduced to improve the accuracy in CAD models. This detection network is lightweighted and accepts input points with arbitrary numbers. Afterwards, we propose an MLP-based network to train implicit representation from input points with its extracted sharp points. A two-stage training process is proposed, and the loss functions are designed for each stage to ensure that the sharp features of the input points are learned while the surface details are fitted. Comparing our method with other SOTA methods in the ABC dataset, our method is significantly superior to the existing nonlearning and learning 3D reconstruction methods in terms of surface approximation quality and sharp feature preservation. Moreover, we can gain spline representations from learned shapes for CAM as the application of our method.
Graphic Novel Development as A Reading Media for Increasing Literacy Skill Of 8th Grade Junior High School Students

This research believes that junior high school students in eighth grade need suitable and interesting reading material to develop their literacy skills which one way to achieve it is to design graphic novels of learning material on it. This research was designed in the form of design and development (D&amp;D) model proposed by Richey and Klein (2007). The development model used in this research was Richey and Klein's (2007) ADDE model. The result was a graphic novel as an attractive literacy media that was developed for assisting eighth grade junior high school students’ literacy skills. Based on the content validity judgment, the graphic novel was highly relevant to the twelve criteria of a good graphic novel with minor revision. The quality judgment was utilized to determine whether or not the graphic novel was already qualified to serve as a good literacy source by using expert judgment. There were two expert judges and twelve users who took role in judging the quality of the graphic novel. Based on the quality judgment, the graphic novel was categorized as excellent media. Thus, the graphic novel was categorized as excellent media for assisting eighth grade students’ literacy skills
Fall Recognition Based on Human Skeleton in Video

With the increase of age, the elderly often fall, which seriously threatens their lives. Investigations and studies have shown that timely assistance after a fall can reduce the risk of death. So this paper proposes a fall recognition method based on the human skeleton in the video. First, the video data set is passed through the human pose estimation algorithm to obtain a new video data set containing human skeleton information and annotated. Then build a two-class model (falls and non-falls) based on the three-dimensional convolutional neural network to train and test the fall behavior model. The experimental results show that the method has high accuracy and recall rate and has good application value.
Awareness and Use Pattern of E-ShodhSindhu Resources: A Case Study of Sri Venkateswara University Library, Andhra Pradesh

Library Consortia is a network for the purchasing and accessing e-information to provide immediate access to greater resources for users of individual libraries. The e-ShodhSindhu is the Consortium for Higher Education E-Resources merging three consortia, i.e. UGC-INFONET Digital Library Consortium, INDEST-AICTE Consortium and N-LIST in December 2015. The e-ShodhSindhu is meant to supply current and depository access to E-Resources i.e. E-books, e-journals, e-journal archives, on-line databases to its member establishments at a negotiated rate of subscription. The purpose of this paper is to find out user awareness and usage of e-ShodhSindhu Digital Library Consortium by Faculty Members and Research Scholars of Sri Venkateswara University. A structured questionnaire was designed and randomly distributed to 350 respondents for data collection, among the faculty members and research scholars. Out of which, 274 filled-in questionnaires were returned. The study reveals that most of the respondents are aware and use digital library consortium but there is a need to conduct more awareness/training programs for enhancing the use of e-ShodhSindhu Digital Library Consortium.
Comprehensibility Analysis and Assessment of Academic Texts

Written communication can be affected by low comprehensibility of documents. In this work, we present a computational-linguistic analysis, evaluation and feedback for academic documents based on three comprehensibility measures: Connectivity, Dispersion, and Comprehensibility Burden. These measures are explored and validated in a document collection of undergraduate and graduate levels, ranging from technician to doctoral. This preliminary exploration also provides parameters to assess new texts, i.e. writings in progress (drafts). A visualization feedback during the assessment process is proposed and illustrated, aimed to understand and resolve detected deficiencies. Reported results set the basis to reach an online comprehensibility assessment application.
Modeling Students’ Learning Performance and Their Attitudes to Mobile Learning

The paper presents an exploration of the role of mobile technology for the realization of personalized learning and for improvement the students’ learning performance within an intelligent educational environment. The two of the created predictive models show the patterns and anomalies in students’ learning behavior and their learning performance. The utilized supervised machine learning algorithms: Random Forest, ID3, Naïve Bayes, Deep learning, k-NN are evaluated, and the results points out that the most suitable algorithms for solving these classification tasks are decision tree-based Random Forest and ID3. A multi-layer perceptron is used for predicting the students’ group learning performance at whole.
A Brief Introduction to Recurrent Neural Networks

In the last chapter, we looked at convolutional neural networks (CNNs). Another network architecture that is widely used (for example, in natural language processing) is the recurrent one. Networks with this architecture are called recurrent neural networks, or RNNs. This chapter is a superficial description of how RNNs work, with one small application that should help you better understand their inner workings. A full explanation of RNNs would require multiple books, so the goal of this chapter is to give you a very basic understanding of how they work. It’s useful for machine learning engineers to have at least a basic understanding of RNNs. I discuss only the very basic components of RNNs to elucidate the very fundamental aspects. I hope you find it useful. At the end of the chapter, I suggest further reading in case you find the subject interesting and want to better understand RNNs.
On $$\psi $$-Lattices in Modular $$(\varphi ,\Gamma )$$-Modules

Let $$F/{\mathbb Q}_p$$ be a finite field extension, let k be a finite field extension of the residue field of F. Generalizing the $$\psi $$ -lattices which Colmez constructed in étale $$(\varphi ,\Gamma )$$ -modules over $$k[[t]][t^{-1}]$$ , we define, study and exemplify $$\psi $$ -lattices in étale $$(\varphi ,\Gamma )$$ -modules over $$k[[t_1,\ldots ,t_d]][\prod _it_i^{-1}]$$ for arbitrary $$d\in {\mathbb N}$$ .
Topic Analysis of Superconductivity Literature by Semantic Non-negative Matrix Factorization

We analyze a corpus consisting of more than 17,000 abstracts in the general field of superconductivity, extracted from the arXiv – an online repository of scientific articles. We utilize a recently developed topic modeling method called SeNMFk, extending the standard Non-negative Matrix Factorization (NMF) methods by incorporating the semantic structure of the text, and adding a robust system for determining the number of topics. With SeNMFk, we were able to extract coherent topics validated by human experts. From these topics, a few are relatively general and cover broad concepts, while the majority can be precisely mapped to particular scientific effects or measurement techniques. The topics also differ by ubiquity, with only three topics prevalent in almost 40% of the abstract, while each specific topic tends to dominate a small subset of the abstracts. These results demonstrate the ability of SeNMFk to produce a layered and nuanced analysis of large scientific corpora.
Remote Sensing of Listvenite Rock for Kaymaz Gold Deposit, Eskişehir-Turkey

Kaymaz Gold Deposit (KGD) is an economically important gold deposit in Eskişehir, Central Anatolia, Turkey. The deposit occurred in listvenite rock, an alteration product of serpentine in Tavşanlı Zone, located at the south of İzmir-Ankara-Erzincan Suture Zone. In this study, we studied the alteration that occurred in the KGD by petrographic examination and XRD analyses. We used FTIR (Fourier-Transform Infrared) spectroscopy and studied the spectral absorption properties of field samples. The absorbance and reflectance patterns of the samples were used to derive mineral maps using ASTER and Sentinel-2 images. The argillized zone is mapped using ASTER band ratio images 4/6, 4/9, 7/6, and RGB composite images. In addition, the 11/2 and 10/11 band ratios of Sentinel-2 were used to map Fe-oxide containing listvenite and serpentinite, respectively. The images of band rations and derived RGBs discriminated the altered rocks and the metamorphic and ultramafic rocks that occurred around the open-pit mine and can be used to explore new deposits in similar geological settings. The results are verified and confirmed in the field, which showed that the ore zone is highly silicified with significant Fe-oxide content.
BERT Model-Based Approach for Detecting Racism and Xenophobia on Twitter Data

The large amount of data generated on social networks makes the task of moderating textual content written by users complex and impossible to do manually. One of the most prominent problems on social networks is racism and xenophobia. Although there are studies of predictive models that make use of natural language processing techniques to detect racist or xenophobic texts, a lack of these has been observed in the Spanish language. In this paper we present a solution based on deep learning models and, more specifically, models based on transfer learning to detect racist and xenophobic messages in Spanish. For this purpose, a dataset obtained from the social network Twitter has been created using data mining techniques and, after a preprocessing, it has been labelled into racist messages and non-racist messages. The trained models are based on BERT and were called BETO and mBERT. Promising results were obtained showing 85.14% accuracy in the best performing model.
Deployment Settings for the Integration of Blockchain Networks in Learning Factories

Currently discussed blockchain applications go beyond the field of cryptocurrencies. Industry and research increasingly discuss new concepts for industrial use cases such as in supply chain management or manufacturing. To meet the challenges for the implementation of blockchain in existing industrial systems, it needs skilled engineers and blockchain experts. However, there is a lack of educational concepts and industry-like test environments to educate students in the field of blockchain technologies. Learning factories provide an infrastructure that is promising to enable education and research in this field. Therefore, this contribution discusses how learning factories can support conceptualization and implementation of blockchain-based systems. It proposes possible deployment settings and presents a blockchain network based on embedded systems that is integrated in an existing learning factory at Technische Universit¨at Braunschweig. This setting supports the investigation of smart contract design. The developed application allows for the tracing of product-specific process parameters and further analysis. The contribution concludes with an outlook for using the presented implementation for further research and transferring the results to academic education.
A Planning Domain Definition Language Generator, Interpreter, and Knowledge Base for Efficient Automated Planning

The Planning Domain Definition Language (PDDL) successfully encodes classical planning tasks by easily describing objects, actions, and states in many planning domains. PDDL also describes domains, but they include only predefined sets of actions that can solve problems in a finite set of states. Indeed, the PDDL structure disables the processing of single predicates and operators. As a consequence, they cannot be arbitrarily composed to model new domains. To overcome these limitations, we propose a domain-independent, general-purpose knowledge design and task planning system based on the combination of a PDDL generator and interpreter and a Knowledge Base. The former builds planning data structures, where every object is a PDDL token independent of its original domain. It also allows merging these objects to formulate new PDDL domains and problems, ensuring consistency and validity of generated definitions. Their resolution is based on a powerful object-based reasoning instead of an inefficient lexical-based one. The latter contains the necessary relationships and representations to allow data storing and reusability. Their combination enables the storage, interpretation, and reuse of planning data, resulting in integration between the planning process and description logic reasoning. The overall system guarantees a flexible adaptation of the computed planning domains to changing environmental conditions, agent capabilities, and assigned tasks, promoting effective sharing and reuse of domain knowledge across different systems and applications.
<i>EnConVis</i>: A Unified Framework for Ensemble Contour Visualization

Ensemble simulation is a crucial method to handle potential uncertainty in modern simulation and has been widely applied in many disciplines. Many ensemble contour visualization methods have been introduced to facilitate ensemble data analysis. On the basis of deep exploration and summarization of existing techniques and domain requirements, we propose a unified framework of ensemble contour visualization, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EnConVis (Ensemble Contour Visualization)</i> , which systematically combines state-of-the-art methods. We model ensemble contour visualization as a four-step pipeline consisting of four essential procedures: member filtering, point-wise modeling, uncertainty band extraction, and visual mapping. For each of the four essential procedures, we compare different methods they use, analyze their pros and cons, highlight research gaps, and attempt to fill them. Specifically, we add Kernel Density Estimation in the point-wise modeling procedure and multi-layer extraction in the uncertainty band extraction procedure. This step shows the ensemble data’s details accurately and provides abstract levels. We also analyze existing methods from a global perspective. We investigate their mechanisms and compare their effects, on the basis of which, we offer selection guidelines for them. From the overall perspective of this framework, we find choices and combinations that have not been tried before, which can be well compensated by our method. Synthetic data and real-world data are leveraged to verify the efficacy of our method. Domain experts’ feedback suggests that our approach helps them better understand ensemble data analysis.
THE IMPACT OF THE CURRENT PANDEMIC ON THE DETENTION CENTERS’ POPULATION AND THE DIFFERENT ROLE OF THE GOVERNEMENT, THE JUDICIARY AND THE PRESS: AN ITALIAN PERSPECTIVE

the current pandemic has dramatically urged every State to grant the preparedness of its detention system to protect from the risk of contagion its population, whose members are obliged to live in close proximity and whose clerks are mostly excluded from forms of smart or remote working. Patently such features expose the detention environment to develop a high risk of contagion of Covid 19 should adeguate prevention measures not be adopted and this argument was recently sustained by the Italian judges who placed under home arrest some Mafia associates who developed critical health conditions while being in custody under the most restrictive detention regime provided by the Italian legislation (so called art. 41 bis Ord. Pen.). The echo of such judicial decisions, that were perceived as a disguised release in favour of perpetrators of some of the most heinous crimes recognized by the Italian criminal code, determined a severe reaction both from the victims’ families and from the press and ultimately led the government to intervene in order to reform the decision making process. This scenario therefore suggested a reflection upon the urgency of finding even in times of pandemic an adequate balance between the rule of law and the respect of the fundamental rights of the detained people, which is constantly pursued by judges through decisions which should be made accessible to public opinion, in order to prevent dangerous social conflicts expecially in areas where the traumatic memory of the effects of the committed crime is dramatically still alive.
Parallel Functional Architectures Within a Single Dendritic Tree

The input-output transformation of individual neurons is a key building block of neural circuit dynamics. While previous models of this transformation vary widely in their complexity, they all describe the underlying functional architecture as unitary, performing a single type of elementary computation, either once or cascaded multiple times. Here, we show that the input-output transformation of CA1 pyramidal cells is instead best captured by two distinct functional architectures operating in parallel. We used statistically principled methods to fit flexible, yet interpretable models of the transformation of spatiotemporal patterns of input spikes into the somatic "output'' voltage, and to automatically select among alternative functional architectures. The contribution of NMDA-nonlinearities was accurately captured by a single static and global nonlinearity. In contrast, dendritic Na+ spikes required a functional architecture with multiple dynamic nonlinearities and clustered connectivity. These two architectures incorporated distinct morphological and biophysical properties of the neuron and its synaptic organization.
A Whale’s Tail - Finding the Right Whale in an Uncertain World

Abstract Explainable machine learning and uncertainty quantification have emerged as promising approaches to check the suitability and understand the decision process of a data-driven model, to learn new insights from data, but also to get more information about the quality of a specific observation. In particular, heatmapping techniques that indicate the sensitivity of image regions are routinely used in image analysis and interpretation. In this paper, we consider a landmark-based approach to generate heatmaps that help derive sensitivity and uncertainty information for an application in marine science to support the monitoring of whales. Single whale identification is important to monitor the migration of whales, to avoid double counting of individuals and to reach more accurate population estimates. Here, we specifically explore the use of fluke landmarks learned as attention maps for local feature extraction and without other supervision than the whale IDs. These individual fluke landmarks are then used jointly to predict the whale ID. With this model, we use several techniques to estimate the sensitivity and uncertainty as a function of the consensus level and stability of localisation among the landmarks. For our experiments, we use images of humpback whale flukes provided by the Kaggle Challenge “Humpback Whale Identification” and compare our results to those of a whale expert.
Learning Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimization

Deep reinforcement learning algorithms can perform poorly in real-world tasks due to the discrepancy between source and target environments. This discrepancy is commonly viewed as the disturbance in transition dynamics. Many existing algorithms learn robust policies by modeling the disturbance and applying it to source environments during training, which usually requires prior knowledge about the disturbance and control of simulators. However, these algorithms can fail in scenarios where the disturbance from target environments is unknown or is intractable to model in simulators. To tackle this problem, we propose a novel model-free actor-critic algorithm---namely, state-conservative policy optimization (SCPO)---to learn robust policies without modeling the disturbance in advance. Specifically, SCPO reduces the disturbance in transition dynamics to that in state space and then approximates it by a simple gradient-based regularizer. The appealing features of SCPO include that it is simple to implement and does not require additional knowledge about the disturbance or specially designed simulators. Experiments in several robot control tasks demonstrate that SCPO learns robust policies against the disturbance in transition dynamics.