Reviewerly/exHarmony huggingface dataset:
The Role of Black-Shale Strata in the Formation of the Natalka and Pavlik Gold Deposits (Yana–Kolyma Orogenic Belt)

Abstract —Data on geochemistry, distribution of ore and rare-earth elements and precious metals, and micromineralogy are presented. The objects of study are late Permian sedimentary and volcanosedimentary deposits of the Tikhonya Brook (Atkan (P3at) and Omchak (P3om) formations) and hydrothermally metamorphosed rocks of the Natalka and Pavlik gold deposits of the Omchak ore–placer cluster. Analysis of the deposit ores showed enrichment in chalcophile trace elements Au, Ag, As, W, and Sb relative to their average contents in the upper crust and the host Permian rocks. The high contents of W and Bi in the ores suggest the participation of a magmatic fluid. The absence of abnormal contents of Ni, Co, Sb, Mo, Cr, and Se indicates the redeposition of these elements from ore-bearing rocks, without their input by ore-forming fluids, which is confirmed by the isotopic composition of sulfide sulfur and the characteristics of carbonaceous ore material. The formation of deposits proceeded with a change in REE contents. All objects show similar trace-element patterns: The rocks are enriched in LREE and lack a Ce anomaly. The identical REE patterns of ores reflect their inheritance from unaltered late Permian deposits. It has been established that the ores formed under different redox conditions, mainly with the participation of a relatively oxidized fluid enriched in LREE of the hydrothermal system NaCl–H2O, with domination of Cl over F. The studies have shown that the host carbonaceous sedimentary complexes, which served as additional sources of precious and associated metals, played a crucial role in the formation of the Natalka and Pavlik gold deposits. Some of the ore elements in the unaltered deposits form their own minerals.
Security Analysis of Ripple Consensus

The Ripple network is one of the most prominent blockchain platforms and its native XRP token currently has one of the highest cryptocurrency market capitalizations. The Ripple consensus protocol powers this network and is generally considered to a Byzantine fault-tolerant agreement protocol, which can reach consensus in the presence of faulty or malicious nodes. In contrast to traditional Byzantine agreement protocols, there is no global knowledge of all participating nodes in Ripple consensus; instead, each node declares a list of other nodes that it trusts and from which it considers votes.Previous work has brought up concerns about the liveness and safety of the consensus protocol under the general assumptions stated initially by Ripple, and there is currently no appropriate understanding of its workings and its properties in the literature. This paper closes this gap and makes two contributions. It first provides a detailed, abstract description of the protocol, which has been derived from the source code. Second, the paper points out that the abstract protocol may violate safety and liveness in several simple executions under relatively benign network assumptions.
Hybrid data compression using fuzzy logic and Huffman coding in secure IOT

Research in the area of Internet of things (IoT) and cloud computing has gained a considerable attention in today's world of information technology (IT). Data compression and security are increasingly appreciated due to their imperative roles in online data sharing and transfer in multimedia networks. Hence, image compression is focused on reducing data redundancy and saving memory and transmission bandwidth. Because of the increased entropy of coded images, it is difficult to reduce the amount of lossless information after encryption using the existing methods. Accordingly, using the hybrid Huffman coding algorithm, it is possible to guarantee the security and verification of information. Therefore, an efficient compression method is proposed that combines fuzzy logic with Huffman coding. In this method, coding is based on Hoffman code, which is statistically independent and defines fuzzy logic-based weighting functions for the frequency of the existing symbols in data to generate efficient code for compression. Fuzzy logic is then coded to provide a secure information mapping on the information codes. In this proposed system, the key information is coded using the special key of that information and the data is embedded using the data encryption key and data in the receiver are decoded using the same key. Here, different compression techniques are compared with Huffman coding. The simulation is performed under matlab2017b software. The results show that a good compression rate is achieved, there is no significant difference between the decoded and original images and compression ratio in the image is increased to over 40%.
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator

Gradient estimation in models with discrete latent variables is a challenging problem, because the simplest unbiased estimators tend to have high variance. To counteract this, modern estimators either introduce bias, rely on multiple function evaluations, or use learned, input-dependent baselines. Thus, there is a need for estimators that require minimal tuning, are computationally cheap, and have low mean squared error. In this paper, we show that the variance of the straight-through variant of the popular Gumbel-Softmax estimator can be reduced through Rao-Blackwellization without increasing the number of function evaluations. This provably reduces the mean squared error. We empirically demonstrate that this leads to variance reduction, faster convergence, and generally improved performance in two unsupervised latent variable models.
Continuing education potential to form research competence of students

Abstract The constantly changing labor market requires the formation of specialists with a desire for self-improvement, capable of moving from one type of activity to another, perhaps not related to the previous one. This task can be called a priority in the field of professional education, which dictates its relevance for mathematics education. When setting the formulated problem and solving it, special attention should be paid to recognizing the importance of the formation and development of the educational potential of the creative activity of students. The article presents an analysis of the possibilities of a continuous education system, put forward theoretical provisions and pedagogical conditions for the effective formation of research skills of students within this system. The characteristic of the structural-content model of the formation of the research competence of future teachers (or specialists of other professions requiring the formation of research competencies) in the system of three-level education is given on the example of studying one of the directions of discrete mathematics.
Computer Generated Colorized Image Forgery Detection using VLAD Encoding and SVM

With the development of multimedia technology, more and more applications used in digital image processing, tampering with digital images is become easier. This paper proposes a new approach for image detection based on color tampering by encoding multiple color channels features based on VLAD without embedding watermarks. We have counted multiple sets of common color channels in computer vision, choosing the most suitable combination of color channels, and then using VLAD to encode these selected features, finally to train a SVM model by encoded features, we also found that when a lot of classes image were mixed together cannot be will detected, so we adopted deep learning to train a ResNet of classification, and first to classify our dataset, in comparison with state-of-theart method, various experiments result prove that the proposed approach achieves better performance in computer generated colorized image forgery detection.
Asymptotic Behaviour of the Empirical Distance Covariance for Dependent Data

Abstract We give two asymptotic results for the empirical distance covariance on separable metric spaces without any iid assumption on the samples. In particular, we show the almost sure convergence of the empirical distance covariance for any measure with finite first moments, provided that the samples form a strictly stationary and ergodic process. We further give a result concerning the asymptotic distribution of the empirical distance covariance under the assumption of absolute regularity of the samples and extend these results to certain types of pseudometric spaces. In the process, we derive a general theorem concerning the asymptotic distribution of degenerate V-statistics of order 2 under a strong mixing condition.
WITHDRAWN: Empirical analysis of security enabled cloud computing strategy using attribute based encryption scheme

In this modern world, there is a huge requirement to convert each and every record into digital format and store those records in a safe place. The concept of storing such huge records in digital format over the system is complex to maintain and needs sufficient storage. It needs to be recovered with Remote Storage Servers, in which the data can be stored over there instead of maintaining the data on local server. The concept of using such cloud servers again need to concern with two complex issues, one is called data maintenance and the other one is security. The data maintenance scheme is the most important concern to deal with cloud and remote server systems, which needs to be properly allocated and scheduled for storing the digital records over the server. The data maintenance scheme follows duplication elimination strategies, in which the system allows the user to upload the unique documents. The data uploaded by the user is cross-verified by the Duplication-Avoidance Scheme over the proposed system and once the data is not matched with any of the content presented already into the server means it will be preceded for further storage, otherwise the data cannot be allowed to push into the server. The second concern is Security, in which the data or records maintained into the remote server need to be secured with proper crypto mechanisms. In present industry there are multiple intruders and attackers available to hack and attack the data maintained into the server. All these are possible for intruders only when the data content is weak in security norms. So, that the proposed approach used an advanced encryption strategy called Attribute Based Encryption Scheme (ABES). This proposed approach encrypts the data based on user attributes and dynamically generated security codes.
Automate Lithological Classification of the Amotape Tahuin Metamorphic Complex in Ecuador using Random Forest and a Multi-Sensor Satellite Imagery Approach

In this paper, a methodological approach to classify lithology features using random forest and multisensor satellite imagery is presented. The incorporation of remote sensing techniques into regional geology mapping is important in order to make possible the implementation of monitoring strategies aimed to enhance national capabilities to provide information for natural disasters management, sustainable exploitation of natural resources, etc. In this context, the use of both optical/infrared and multi-wavelength synthetic aperture radar satellite data was evaluated in order to train a random forest algorithm, taking as reference field data, to generate a lithological map. Preliminary results have shown the capability of this approach to lithological classification with accuracies values higher than 80% in some conditions to map sedimentary, igneous, and metamorphic lithological classes.
Development of a Generalized Voice-Controlled Human-Robot Interface: One Automatic Speech Recognition System for All Robots

Automatic speech recognition (ASR) technology has been used by researchers to develop voice-controlled robots. However, the usual pattern is to develop an ASR interface that works specifically with one particular robot. Generalized ASRs, ones that can communicate with multiple robots, are needed. A generalized ASR allows an operator to control several robots that are operated by different programming languages at the same time via a single interface. This paper describes the development and an initial user study of a generalized ASR interface using a commercially available ASR software. The user study provided initial validation of the interface's functionality using a variety of commands. Results indicated a reasonably high input flexibility; the interface successfully recognized 85% of 1200 spoken commands. It also demonstrated output flexibility; the generalized ASR fed the robot a sequence of keystrokes that formed any syntax required to trigger its task programs. Implications for human-robot interaction are discussed.
Automatically Generating Websites from Hand-drawn Mockups

Designers often use physical hand-drawn mockups to convey their ideas to stakeholders.Unfortunately, these sketches do not depict the exact final look and feel of web pages, and communication errors will often occur, resulting in prototypes that do not reflect the stakeholder's vision.Multiple suggestions exist to tackle this problem, mainly in the translation of visual mockups to prototypes.Some authors propose end-to-end solutions by directly generating the final code from a single (black-box) Deep Neural Network.Others propose the use of object detectors, providing more control over the acquired elements but missing out on the mockup's layout.Our approach provides a real-time solution that explores: (1) how to achieve a large variety of sketches that would look indistinguishable from something a human would draw, (2) a pipeline that clearly separates the different responsibilities of extracting and constructing the hierarchical structure of a web mockup, (3) a methodology to segment and extract containers from mockups, (4) the usage of in-sketch annotations to provide more flexibility and control over the generated artifacts, and (5) an assessment of the synthetic dataset impact in the ability to recognize diagrams actually drawn by humans.We start by presenting an algorithm that is capable of generating synthetic mockups.We trained our model (N=8400, Epochs=400) and subsequently fine-tuned it (N=74, Epochs=100) using real human-made diagrams.We accomplished a mAP of 95.37%, with 90% of the tests taking less than 430ms on modest commodity hardware (≈ 2.3fps).We further provide an ablation study with well-known object detectors to evaluate the synthetic dataset in isolation, showing that the generator achieves a mAP score of 95%, ≈1.5× higher than training using hand-drawn mockups alone.
Learning from deep model via exploring local targets

Deep neural networks often have huge number of parameters, which posts challenges in deployment in application scenarios with limited memory and computation capacity. Knowledge distillation is one approach to derive compact models from bigger ones. However, it has been observed that a converged heavy teacher model is strongly constrained for learning a compact student network and could make the optimization subject to poor local optima. In this paper, we propose proKT, a new model-agnostic method by projecting the supervision signals of a teacher model into the student's parameter space. Such projection is implemented by decomposing the training objective into local intermediate targets with approximate mirror descent technique. The proposed method could be less sensitive with the quirks during optimization which could result in a better local optima. Experiments on both image and text datasets show that our proposed proKT consistently achieves the state-of-the-art performance comparing to all existing knowledge distillation methods.
Research on the application of computer technology in the construction and management of “double qualification” teachers in undergraduate colleges and Universities under the background of combination of production and education

Abstract At present, the national economic industrial chain has gradually approached the normalization period of industrial structure improvement, industrial transformation and industrial upgrading and efficiency. With the support of computer skills, the project of integration of production and teaching puts forward higher requirements for the construction and management of double qualification teachers. Around the main line of the new situation of the integration of production and education, some experts put forward a series of sound teacher training system and path[1]. It has been proved that the construction of a team of double qualification teachers who can combine theoretical knowledge and practical ability is of great help to the construction of the foundation for the cultivation of applied talents.
ASSESSMENT OF READING COMPREHENSION LEVELS AMONG GRADE 11 SENIOR HIGH SCHOOL STUDENTS: TOWARDS THE DEVELOPMENT OF PROPOSED K-12 CONTEXT APPROPRIATE INSTRUCTIONAL TOOL

The reading comprehension is a basic requirement when the students enter in the universities or colleges or decide to work in the industry. Therefore, it is necessary for the teachers to give as many opportunities as possible by providing them with wide range of effective and useful materials in reading as well as to develop students reading habit and enhance their reading techniques. Students nowadays are observed having a great dilemma when it comes to comprehending a text. Reading comprehension is said to be the heart and goal of reading since the purpose of it is to gather meaning from the text. A good reader must always see to it that reading materials is properly understood. Most students can easily read a text but cannot comprehend its meaning. The study assesses the levels of reading comprehension of grade 11 General Academic Strand Students of Buting Senior High School towards the development of K-12 Context Appropriate Instructional Tool. This study sought to answer the following questions: What are the students reading profile? What are the levels of reading comprehension the respondents? Is there significant difference on the levels of reading comprehension of the two sections? And What K-12 instructional toolcan be proposed? The data was calculated using simple statistical tool of percentage, weighted mean and T-test. Students reading profile reveals that they read few times a week with 11-20 minutes reading time, read only one to five books during free time and they use electronic sources when reading. Students sometimes prefer to read the materials they want to read. With regards to reading techniques, they sometime make predictions, guessesand set goals when reading, however, students rarely summarize the story. There is no significant difference on students preference in reading habit and reading techniques between the two sections. The results of readingcomprehension levels:70% are outstanding in literal level 55% are satisfactory in inferential level and 42% are fair in critical-evaluative level. There is no significant difference on the levels of Reading Comprehension between the two sections. The study recommends instructional tools must be constructed which will be beneficial to the students with low levels of reading comprehension.
Deploy-able Privacy Preserving Collaborative ML

In the data-driven world, emerging technologies like the Internet of Things (IoT) and other crowd-sourced data sources like mobile devices etc. generate a tremendous volume of decentralized data that needs to be analyzed for obtaining useful insights, necessary for reliable decision making. Although the overall data is rich, contributors of such kind of data are reluctant to share their own data due to serious concerns regarding protection of their privacy; while those interested in harvesting the data are constrained by the limited computational resources available with each participant. In this paper, we propose an end-to-end algorithm that puts in coalescence the mechanism of learning collaboratively in a decentralized fashion, using Federated Learning, while preserving differential privacy of each participating client, which are typically conceived as resource-constrained edge devices. We have developed the proposed infrastructure and analyzed its performance from the standpoint of a machine learning task using standard metrics. We observed that the collaborative learning framework actually increases prediction capabilities in comparison to a centrally trained model (by 1-2%), without having to share data amongst the participants, while strong guarantees on privacy (ϵ, δ) can be provided with some compromise on performance (about 2-4%). Additionally, quantization of the model for deployment on edge devices do not degrade its capability, whilst enhancing the overall system efficiency.
Paleoenvironmental reconstruction of gold-bearing BIF from the Archean Cuiabá deposit based on petrographic and geochemical studies

In the world-class Cuiabá gold deposit, located in the Archean Rio das Velhas greenstone belt in Brazil, metamorphosed carbonate-dominated banded iron formations represent the main lithology hosting gold mineralization. The deposit's stratigraphy consists of a basal metavolcanic unit with carbonaceous metasedimentary rocks, followed by a layer of gold-bearing BIF and thin layer of carbonaceous metapelite, a upper metavolcanic unit, and turbiditic metapelites associated with metavolcanoclastic rocks. The BIF is essentially composed of fine-grained quartz and siderite, with variable contents of carbonaceous matter. It is assumed that these chemical sedimentary rocks, classified as Algoma-type BIF, have been formed in the deep-sea environment, relatively close to volcanic spreading centers. This hypothesis is supported by the BIF's geochemical signature, which in general presents a positive anomaly of La (La/La*paas = 0.64–3.43), Eu (Eu/Eu*paas = 1.27–4.05) and Y, reflecting characteristics inherited from seawater and high-temperature hydrothermal fluids (>250 °C). In addition, the commonly observed superchondritic Y/Ho ratio values reinforce the influence of seawater on the rock's geochemistry. The absence of Ce anomalies concerns the oxygenation conditions of seawater, indicating low oxygen availability. The low content of Al2O3 (<0,27%) and other immobile elements, typical of detrital sediments, corroborate the interpretation that these rocks have been deposited in a deep marine environment with low siliciclastic influence. Subordinately, ferruginous metachert, BIF with magnetite and BIF with silicates are also present in the Cuiabá gold deposit. This diversity may be related to specific changes in the basin during the deposition of ferruginous chemical sedimentary rocks - changes that may indicate, for example. variations in the detritic support or availability of carbonaceous matter. Furthermore, geochemical studies from the lower and upper mafic metavolcanic units of the Cuiabá gold deposit helped to constrain and provide a broader understanding of the depositional basin's geotectonic context. Consequently, a genetic model that includes the paleoenvironment of this sequence is proposed. Both rocks of the lower and upper units have very similar mineralogical composition, but register significant geochemical differences. The lower unit is a calc-alkaline basalt/andesite from a continental arc, interpreted as having been developed during the Rio das Velhas II event (2800-2760 Ma), while the upper unit is defined as basalt with a transitional affinity to tholeiitic, possibly corresponding to an ocean floor volcanism.