Reviewerly/exHarmony huggingface dataset:
NEW GEOLOGICAL DATA ON ORLEA MINING FIELD, ROŞIA MONTANĂ AU-AG EPITHERMAL DEPOSIT, APUSENI MOUNTAINS, ROMANIA

New geological data on the Orlea mining field, Roșia Montană ore deposit, Romania were obtained by fieldwork carried out in the underground level +730 m, where three types of mineralized structures hosted by the Vent Breccia formation were studied, i.e., (i) flatly dipping vein with rhodochrosite gangue, (ii) steeply dipping tectonic breccia dyke, and (iii) steeply dipping base metal vein, respectively. The Vent Breccia is a polymictic matrix-supported breccia with sedimentary (clay, sandstone), volcanic (dacite), and metamorphic (quartzite, garnet micaschist) clasts. Four types of hydrothermal alterations were identified, i.e., (i) K-metasomatosis (adularia I); (ii) phyllic alteration (sericite); (iii) silicification; and (iv) potassic alteration (adularia II). The ore mineral assemblage consists of electrum, pyrite, chalcopyrite, sphalerite, galena, and minor arsenopyrite and pyrrhotite. The higher-grade ore body is the intermediate-sulfidation flatly dipping vein with rhodochrosite gangue grading up to 101g/t Au. The tectonic breccia dyke is a low-sulfidation ore body illustrating the evolution from early banded vein structure to late/final open-space clast supported tectonic breccia dyke.
Student Data Privacy and Automatic Textbook Billing

The textbook market in U.S. higher education is changing. In recent years, publishers have developed an automatic billing model, in which colleges and universities negotiate deals with publishers to provide ebooks and courseware to students, folding the cost into student fees. This model is commonly known as "inclusive access." Because it offers students first-day access to course materials - important to student success - as well as some savings over full-priced standard textbooks, it is becoming popular with faculty and administrators. But textbook publishers are promoting these plans for another reason: The data they can collect with digital materials opens a lucrative new market, allowing them to diversify into analytics services.
A Cross-age Kinship Verification Scheme Using Face Age Transfer Model

As the amount of face images collected from diverse devices such as smartphones, CCTVs and high-definition cameras is rapidly increasing, various face-based applications such as finding missing family members, social media analysis and genealogical research have attracted much attention. To effectively implement such applications, a technique called kinship verification can be used. Kinship verification determines the genetic relationship between people through facial image analysis. Recently, as convolutional neural network (CNN) shows good performance in image processing, many CCN-based kinship verification methods have been proposed. However, they suffered from problems such as insufficient labeled data to train deep learning models, and poor validation accuracy when the age difference between people was large. To alleviate these problems, in this paper, we propose a cross-age kinship verification scheme using a face age transfer model. To prove the effectiveness of the proposed scheme, we conducted several comparative experiments with other models, and we confirmed that the verification accuracy and f1 score of our proposed method improved.
Taking It to the Next Level

Let me first congratulate you on having come this far. You’ve worked through all the chapters in this book, so you’re now an expert in generating visuals using Charticulator. It’s always been an implicit understanding that the reason you’ve invested your time and attention in learning how to use Charticulator is so you can design visuals that tell the story of your data, uncompromisingly and unequivocally and that can truly inform and persuade. This has been the overriding objective through everything you have learned about this remarkable chart generating software.
Performance comparison of induced seismicity forecasting models with existing datasets

&amp;lt;p&amp;gt;Within the workflow of Adaptive Traffic Light System, it is important to evaluate the performance of different induced seismicity forecasting models in order to properly weight the forecasts during seismic hazard calculation. In this respect, we propose a standardize test bench approach capable of comparing outputs&amp;amp;#8217; models (in terms of&amp;amp;#160;seismicity rate) and their uncertainties in real time.&amp;amp;#160;We test this approach using different models that are trained using existing datasets from geothermal exploration campaigns. Notably, we use two statistical models that link injection volumetric rate to seismicity rate&amp;amp;#160;with the difference that a Bayesian approach (EM1_BH) additionally adds epistemic uncertainty to the aleatoric uncertainty introduced in a purely frequentist approach (EM1_MLE), one pressure-based seismicity model&amp;amp;#160;(HM0_CAPS)&amp;amp;#160;based on 1D analytical solution for linear pore-fluid diffusion and finally one hybrid&amp;amp;#160;1D&amp;amp;#160;model that includes a physic-based module for linear and non-linear pore-fluid diffusion linked to a&amp;amp;#160;stochastic model for seismicity generation using a seed approach&amp;amp;#160;(HM0_SEED and HM1_SEED).&amp;amp;#160;By using these different models and their uncertainties in our numerical investigations, we show the robustness of the proposed testbench approach.&amp;lt;/p&amp;gt;
DNN based Speaker Meta Information Estimation using Privacy-Preserving Speech Data

There has been concerns about how speech data is collected and shared in the real world since human speech itself has personally identifiable information about the speaker and speech is available to reliably estimate speaker meta information. In this paper, we explore three different types of methods for DNN based speaker meta information estimation and compare the estimation results between the original speech and the anonymized speech. We used McAdam's coefficient-based signal processing technique to make the anonymized speech and privacy-preserving data. Experiments derived using TIMIT dataset show a slight degradation in performance of anonymized speech against the original. Experiments reveal that the model employing both DNN based embedding and voice anonymization can achieve comparable performance to the model using the original speech.
Design and Development of Video-Conferencing System with Sign Language Detection and Voice Recognition using Machine Learning Techniques

Communication is the process of sending and receiving messages through verbal or non-verbal means, including speech or oral methods, writing or gestures and behaviour. In this modern and fast-paced world communication is now easier and more accessible to everyone whether it is audio or video communication. But this comes with some challenges, when we talk about a country or country like India, people speak a thousand languages and learning all languages is not possible, so with the help of voice to a text recognition system, supported by mechanical learning it is possible to transform human language and actions into an internationally accepted language like English. Thus it can be understood by all. Language is a correspondence medium. It's hard to speak with individuals who talk an assortment of dialects. Individuals with incapacities and others with extraordinary requirements Text correspondence is trying for individuals with dyslexia. Address right now, text and interpretation applications are accessible disconnected substances are accessible This is a report about a program that takes sound and changes it to message, as well as some other language, eminently Indian, is utilized to interpret their material dialects. It utilizes a direct Translator API. Speaking with the Deaf (deaf/mute) is a primary task in our society today; this may be due to the truth that their way of communique (symptoms or gestures) requires an interpreter at all times. The conversion of symbols into text may be executed by the use of system mastering algorithms. This project pursuits to construct video conference systems that can guide signal language discovery and translation. Powerful and advanced machine gaining knowledge of fashions with the right statistics could be used to obtain excessive efficiency and accuracy.
Determination of eruption source parameters of the 2011-2013 and February 2021 Etna&amp;#8217;s paroxysms using multi-sensor strategies.

&amp;lt;p&amp;gt;The determination of Eruptive Source Parameters (ESPs) is crucial especially for very active volcanoes whose eruptive intensity can vary significantly. In this aim, new strategies are being developed to determine in near real time the total erupted mass (TEM), total grain-size distribution (TGSD) and plume height from ground sampling and remote sensing methods. Since 2011, Etna volcano has produced about 100 paroxysmal episodes characterized by the emission of fountain-fed tephra plumes whose heights reached up to 15 km (above sea level). In this work, we present multi-sensor strategies based on data acquired by the complementary set of remote sensing systems available at Etna. In fact, multi-sensor strategies may help to refine and assess the uncertainty of ESP estimates made by individual sensors, which can present various limitations such as narrow field of views (e.g., visible imagery) and/or low temporal resolution (e.g., satellite-based infrared).&amp;amp;#160;First, we show how the combination between tephra-fallout deposit and satellite-based estimates, along with numerical modelling, can help to refine estimates of TEM and TGSD, especially for weak explosive eruption such as the 29 August 2011 paroxysm. We use the model TEPHRA2 and compute synthetic data of ground accumulation to successfully fill significant sampling gaps in the tephra-fallout deposits. Moreover, we find that the Rosin-Rammler equation can be used to inform on missing part of the TGSD, including the tail of very fine ash also detected by satellite-based platforms. Additionally, we compare all estimates of Mass Eruption Rates, Plume height and grain-size distributions made by all available methods including Doppler radar detection, visible and infrared imagery, infrasound arrays, gravimetric signals and tephra-fallout deposit sampling. Accordingly, based on each sensor limitation and capacities, we obtain new constraints on ESP estimates acquired during several paroxysms between 2011-2013 and February 2021. We also bring new insights into the differences and complementarities that exist between the available remote sensing methods, especially in the case of future eruptive events at Mount Etna.&amp;lt;/p&amp;gt;
Crimes of Communication: The Implications of Australian Espionage Law for Global Media

Espionage has emerged as a leading national security threat for the digital age. Far from traditional wartime spy tactics, espionage now includes actors—including journalists—accessing and publishing sensitive information online to a global audience. This threat must be addressed; however, overbroad espionage laws have the capacity to criminalize legitimate journalism and chill free expression. This article examines the implications of Australia’s expansive 2018 counterespionage framework for foreign media. It argues that this broad suite of offenses creates a complex risk environment for global media reporting on issues that impact Australia’s national interest or foreign relations. These risks are exacerbated for media organizations owned or controlled by foreign governments and their journalists, sources, and associates. We consider whether the practical and political likelihood of extraterritorial enforcement alleviates the potential impact of the laws and argue for targeted reform to protect press freedom on a global scale.
Peer Review Statement

Abstract All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Single Anonymous • Conference submission management system: Morressier • Number of submissions received: 82 • Number of submissions sent for review: 82 • Number of submissions accepted: 60 • Acceptance Rate (Submissions Accepted / Submissions Received × 100): 73.2 • Average number of reviews per paper: 2 • Total number of reviewers involved: 12 • Contact person for queries: Name: Ahmad Marzuki Email: amarzuki@mipa.uns.acid Affiliation: Universitas Sebelas Maret
Product Innovation: Job Hunting Guru Application for Students’ Self Assessment

The DUE50032 Communicative English III course is one of the required courses for all polytechnic students. Topic 2: Job Hunting Mechanics is the specific topic (in the course) emphasised in the creation of this innovation material. This innovative material, Job Hunting Guru, was created to aid course lecturers in ensuring that the assessment (coursework) for job hunting mechanics is conducted properly and evenly to all students. The usage of smartphone applications has become a widespread channel utilised in all forms of training, as well as one of the key themes in the construction of polytechnic educational systems. It can convey helpful and vital content while also serving as one of the delivery materials in integrated teaching and learning sessions or online. It is strengthened further by increasing student involvement in smartphone application usage and encouraging active learning sessions through the combination of smartphone application and polytechnic system-provided e-learning platform. Course reviewers will learn how to hunt for a job, prepare for a job interview, and analyse rubrics for simulated suitable employment by comparing students as a reference point in this Job Hunting Guru Application. When students must achieve specific criteria in order to pass a job interview simulation assessment, this standard-based assessment approach is acceptable. Furthermore, this application provides as an additional guidance and reference for pupils when performing self-assessment.
A Traffic Sign Detection System Linking Hypothesis Tests and Deep Learning Networks

In this paper, we propose a computationally efficient method for traffic sign detection. Our methodology uses deep learning networks introduced for semantic image segmentation and classification of region of interests generated by image segmentation. The region of interests are detected by segmenting the road scene image dataset and hypothesis tests are applied to accept or reject the probability of being a traffic sign in the detected region of interest. For hypothesis tests, color feature, and the location of the traffic sign with respect to the segmented road information is used. The main contribution of our method is the introduction of hypothesis tests that mutually couple two deep learning networks trained by well-known datasets publicly available for benchmarking purposes. To test and evaluate our traffic sign detection system, we use the German traffic sign detection benchmark dataset with a large set of traffic signs and during its training, The cityscapes dataset offering labeled urban scenes is also leveraged by the traffic sign detection system. Our experimental results illustrate the effectiveness performance metrics are reached at 90.81%, 94.76% and 92.74% in precision, recall and F-measure, respectively. The runtime cost is around 0.4[Formula: see text]s for an image on an ordinary laptop computer.
INSTILLING CULTURAL VALUES IN HIGHER SCHOOL STUDENTS THROUGH TEACHING UNESCO WORLD HERITAGE

E. Mayorova1, E. Temnova 2 1Moscow State University of Management (RUSSIAN FEDERATION) 2Moscow State Institute of International Relations MGIMO University (RUSSIAN FEDERATION)
Social Distancing Detector Using Deep Learning

The paper presents a methodology for social distancing detection using deep learning to evaluate the distance between people to mitigate the impact of this coronavirus pandemic. The detection tool was developed to alert people to maintain a safe distance with each other by evaluating a video feed. The video frame from the camera was used as input, and the open-source object detection pre-trained model based on the YOLOv3 algorithm was employed for pedestrian detection. Later, the video frame was transformed into top-down view for distance measurement from the 2D plane. The distance between people can be estimated and any noncompliant pair of people in the display will be indicated with a red frame and red line. The proposed method was validated on a pre-recorded video of pedestrians walking on the street. The result shows that the proposed method is able to determine the social distancing measures between multiple people in the video. The developed technique can be further developed as a detection tool in real time application.
Mathematical models of wireless local communication networks and methods of calculation of probability-time characteristics

802.11 standartı və CSMA/CA protokolu əsasında qurulmuş simsiz lokal rabitə şəbəkələrinə baxılır. Baxılan şəbəkələr üzrə verilənlərin ötürülməsi zam- anı yaranan fiziki və ehtimal prosesləri nəzərə almaqla z-çevirməsi əsasında, onların riyazi modeli işlənmışdir. Bu model əsasında bilavasitə birləşmə və gözləmə prinsipli həlledicili əks əlaqəli alqoritmılərdən istifadə etməklə baxılan şəbəkələrin riyazi modelləri və ehtimal-zaman xarakteristikalarının hesablanması metodları işlənmişdir. Açar sözlər: simsiz lokal rabitə şəbəkələr, 802.11 stanrartı, CSMA/CA protokolu, z-çevirməsi, riyazi modellər, ehtimal-zaman xarakteristikaları Famil Huseyn Mammadov Mathematical models of wireless local communication networks and methods of calculation of probability-time characteristics Summary Wireless LANs based on the 802.11 standard and the CSMA/CA protocol are considered. Based on the z-transformation of the data in the networks in question, their mathematical models were developed, taking into account the physical and probabilistic processes that occur during their transmission. On this model, mathematical models and methods for calculating the probability-time characteristics of the networks under consideration were developed using directly connected and expectant decision feedback algorithms. Key words: wireless local area networks, 802.11 standard, CSMA/CA protocol, z-conversion, mathematical models, probability-time characteristics.
CGC: an open-source Python module for geospatial data clustering

&amp;lt;p&amp;gt;With the growing ubiquity of large multi-dimensional geodata cubes, clustering techniques have become essential to extracting patterns and creating insights from data cubes. Aiming to meet this increasing need, we present Clustering Geodata Cubes (CGC): an open-source Python package designed for partitional clustering of geospatial data. CGC provides efficient clustering methods to identify groups of similar data. In contrast to traditional techniques, which act on a single dimension, CGC is able to perform both co-clustering (clustering across two dimensions e.g., spatial and temporal) and tri-clustering (clustering across three dimensions e.g., spatial, temporal, and thematic), as well as of subsequently refining the identified clusters. CGC also entails scalable approaches that suit both small and big datasets. It can be efficiently deployed on a range of computational infrastructures, from single machines to computing clusters. As a case study, we present an analysis of spring onset indicator datasets at continental scale.&amp;lt;/p&amp;gt;
Comparative Analysis of Grid-based Decision Tree and Support Vector Machine for Crime Category Prediction

In this study, a comparative analysis of decision tree and support vector machines combined with grid search method to obtain the best optimal parameters are presented. The work conducted to get the best model in categorizing crimes into Assault, auto theft, break &#x0026; enter, and other (robbery and theft over). The experiment was carried out on the Major Crime Indicators (MCI) dataset (2014-2019) in Azure Machine Learning Studio. The proposed method to develop a model starts from data analysis, followed by data pre-processing, training models, testing models, and evaluate the models. Some data problems found are duplication, missing values, improper format, and unbalanced dataset. The proposed methodology and dataset have not been used in previous works to the best of our knowledge. The performance of two machine learning algorithms is measured based on micro-average precision &#x0026; recall, macro-average precision &#x0026; recall, and overall &#x0026; average accuracy. Grid search helps in determining the hyperparameter values for each model. From the experimental results, the achievement of grid-search and decision tree is outperformed grid search and support vector machine with the percentage almost 100 %.
A Study on Cryptography

With the internet having reached a level that merges with our lives, growing explosively during the last several decades, data security has become a main concern for anyone connected to the web. Data security will ensure that our data is only accessible by the intended receiver and prevents any modification or alteration of data. In order to achieve this level of security, various algorithms and methods have been developed. High-assurance cryptography leverages methods from program verification and cryptography engineering to deliver efficient cryptographic software with machine-checked proofs of memory safety, functional correctness, provable security, and absence of timing leaks. Traditionally, these guarantees are established under a sequential execution semantics. However, this semantics is not aligned with the behavior of modern processors that make use of speculative execution to improve performance. This mismatch, combined with the high-profile Spectre-style attacks that exploit speculative execution, naturally casts doubts on the robustness of high-assurance cryptography guarantees. I Cryptography can be defined as techniques that cipher data, depending on specific algorithms that make the data unreadable to the human eye unless decrypted by algorithms that are predefined by the sender.
A Cross-Camera Pedestrian Retrieval Study

Existing pedestrian retrieval algorithms have many problems, and we explore and optimize the algorithms. Pedestrian retrieval is the process of combining pedestrian detection and pedestrian re-identification. The existing algorithms are designed with an end-to-end framework, and we train pedestrian detection and pedestrian re-identification separately to improve the accuracy of the algorithms separately, and then do the combination during testing. In addition, global features are used to achieve advanced results rather than local features, which are slower to train. And a lightweight pedestrian detector is created, which helps the model to run and compute efficiently. Our approach is evaluated on publicly available datasets, and the results on single-domain and cross-domain scenarios demonstrate the effectiveness of our approach.
A Proactive Approach towards Proving Seamless Access to E-Resources

This presentation illustrated a proactive approach carried out by the Electronic Resources Management (ERM) team at the Arizona State University (ASU) Library, which resulted in a reduction in the volume of tickets from users. The discussion included details about the systematic process of handling various e-resource projects carried out by the team, challenges encountered, and recommendations to assist institutions in providing uninterrupted access of e-resources to their library users.