Reviewerly/exHarmony huggingface dataset:
Interface Sampling and Indicator Minerals for Detecting the Footprint of the Lancefield North Gold Deposit under the Permian Glacial Cover in Western Australia

Areas under a thick Permian glacial cover in Western Australia formed as glaciers gouged fresh bedrock and deposited diamictites in disconnected valleys and basins. These areas now present the greatest challenge for mineral exploration in the northeast Yilgarn Craton. At the Lancefield North gold prospect, in the southern part of the Duketon Greenstone Belt, Permian diamictites on average 40 m thick cover unweathered basalt hosting gold mineralization. The basal Permian diamictites consist of fresh, very poorly sorted, angular to rounded, pebble- to boulder-sized, polymictic clasts supported by a matrix of coarse-grained sand and mud. The framework and matrix are cemented by calcite, dolomite, chlorite, and pyrite. These diamictites are stable under alkaline and reducing conditions below the water table. Detrital; fresh sulfides; gold; and opaque oxides, such as pyrite, pyrrhotite, chalcopyrite, sphalerite, arsenopyrite, gersdorffite, cobaltite, pentlandite, scheelite and galena, chromite, ilmenite, and magnetite, are identified in the framework and matrix of the fresh diamictites, and these are identical to those in the primary gold mineralization. Weathering of diamictites and oxidation of detrital and diagenetic sulfides above the water table produced several Fe- and Mn-rich redox fronts and secondary chalcocite and bornite. Interface sampling across the Archean–Permian unconformity shows Au, As, Zn, Ni, Co, and Cd anomalism over the mineralization compared to the background. However, these elements are low in concentration in the redox fronts, where Fe is correlated with As, Cu, Mo, and Sb and Mn is correlated with Co, Ni, and Ba. Gold shows elevated levels in the fresh basal diamictites and decreases in the weathered diamictites over the mineralization. A sampling at or near the Archean–Permian unconformity (interface sampling) only delineates gold mineralization, with no hydromorphic dispersion halo beyond the peripheries. At the Lancefield North prospect, the detrital indicator sulfides are mechanically dispersed up to 500 m to the east of the mineralization in the direction of ice flow. This dispersal distance is controlled by the rough topography of the Archean–Permian unconformity, and it may be greater, but the estimation of the actual distance of transport is limited by the distribution of drill hole locations.
Educational Policies During the Lockdown: Measures in Spain after Covid-19

The pandemic has disrupted students’ lives, learning, and well-being worldwide and exacerbated existing disparities in education. Countries have unevenly followed policy recommendations to ensure education by non-governmental agencies, and in some cases, political and economic ideology has directly influenced the decisions taken, Spain being a case in point. The instructions and regulations published in April 2020 in Spain are analysed and compared in order to regulate the end of the school year, its evaluation, and the start of the new year, given the situation of suspension of classes during and the confinement of the Spanish population decreed due to the Covid-19 pandemic. The 20 documents published by the Autonomous Communities of Spain are subjected to critical discourse analysis. Their approaches and the aspects they highlight or ignore are examined to identify the different models of education that each region defends in times of crisis. There are significant differences between conservative and progressive regions, the latter being more inclined to implement the recommendations of non-governmental organisations.
Face Verification with Challenging Imposters and Diversified Demographics

Face verification aims to distinguish between genuine and imposter pairs of faces, which include the same or different identities, respectively. The performance reported in recent years gives the impression that the task is practically solved. Here, we revisit the problem and argue that existing evaluation datasets were built using two oversimplifying design choices. First, the usual identity selection to form imposter pairs is not challenging enough because, in practice, verification is needed to detect challenging imposters. Second, the underlying demographics of existing datasets are often insufficient to account for the wide diversity of facial characteristics of people from across the world. To mitigate these limitations, we introduce the F aV CI2D dataset. Imposter pairs are challenging because they include visually similar faces selected from a large pool of demographically diversified identities. The dataset also includes metadata related to gender, country and age to facilitate fine-grained analysis of results. F aV CI2D is generated from freely distributable resources. Experiments with state-of-the-art deep models that provide nearly 100% performance on existing datasets show a significant performance drop for F aV CI2D, confirming our starting hypothesis. Equally important, we analyze legal and ethical challenges which appeared in recent years and hindered the development of face analysis research. We introduce a series of design choices which address these challenges and make the dataset constitution and usage more sustainable and fairer. F aV CI2D is available at https://github.com/AIMultimediaLab/FaVCI2D-Face-Verification-with-Challenging-Imposters-and-Diversified-Demographics.
Real-time identification of frequency-hopping millimeter-wave signals using photonic time stretch and reservoir computing

Real-time identification of frequency-hopping millimeter-wave (mm-wave) signals is a real challenge, due to the high demand for detection bandwidth and processing speed. In this paper, we propose and demonstrate a novel microwave photonic approach to identifying frequency hopping of mm-wave signals based on the concepts of photonic time stretch (PTS) and Reservoir Computing (RC). The PTS scheme allows the modulated signal to be slowed down hence reducing the required detection bandwidth. The developed RC model offers unique features such as being more efficient and time saving in temporal data pattern classification than traditional methods. According to the simulation, RC can recognize the hopping instants and give a precise hop timing estimation in real-time.
Multi-views reinforced LSTM for video-based action recognition

Recently, the long short-term memory network (LSTM) and attention mechanism have greatly boosted the research of video-based action recognition. For this task, feature extraction especially temporal feature extraction is essential. However, most studies focus on improving the temporal feature extraction ability of the model, ignoring the lack of temporal information in the input. To alleviate the issue above, we propose multi-views reinforced LSTM (MR-LSTM). First, we propose an innovative feature extractor named multi-views temporal feature extractor (MTFE) to extract multi-views temporal features from RGB frames in different views. Secondly, we propose multi-views reinforced attention (MRA) mechanism, which utilizes multi-views features to enrich the temporal information in the input of LSTM. MTFE and MRA mechanisms alleviate the lack of temporal information in the input of LSTM. Equipped with the modules above, LSTM can extract more discriminative temporal features. Finally, we propose non-fair fusion strategy to obtain more discriminative fusion features that are beneficial for classification. The ablation experiment demonstrates the effectiveness of all proposed modules. In comprehensive experiments on UCF101 and HMDB51 datasets, our architecture performs competitively against state-of-the-art methods.
Approximate Inference for Neural Probabilistic Logic Programming

DeepProbLog is a neural-symbolic framework that integrates probabilistic logic programming and neural networks. It is realized by providing an interface between the probabilistic logic and the neural networks. Inference in probabilistic neural symbolic methods is hard, since it combines logical theorem proving with probabilistic inference and neural network evaluation. In this work, we make the inference more efficient by extending an approximate inference algorithm from the field of statistical-relational AI. Instead of considering all possible proofs for a certain query, the system searches for the best proof. However, training a DeepProbLog model using approximate inference introduces additional challenges, as the best proof is unknown at the start of training which can lead to convergence towards a local optimum. To be able to apply DeepProbLog on larger tasks, we propose: 1) a method for approximate inference using an A*-like search, called DPLA* 2) an exploration strategy for proving in a neural-symbolic setting, and 3) a parametric heuristic to guide the proof search. We empirically evaluate the performance and scalability of the new approach, and also compare the resulting approach to other neural-symbolic systems. The experiments show that DPLA* achieves a speed up of up to 2-3 orders of magnitude in some cases.
A DIGITALIZATION AND INDUSTRY 4.0

The innovations brought with the use of computers and communication technologies cause various and significant changes in every aspect of our lives.Industry 4.0 offers a wide range of processing, communication and production capabilities. Along with Industry 4.0, digitalization, data classification and conversion algorithms and software, also known as digitalization, are also developing with giant steps. With the process of digitalization, digital products such as computers, printers and scanners are the most important units of the enterprises and have started to transfer the traditional accounting place to smart accounting. Days of accounting transactions as we know them, are now behind. The software, which was prepared for answering, started to use automatic answering system in communication with the customer. Today's Big Data concept is a help finding a solution instead of being scary.Research studies were carried out with the development of a digitalization software in the field of intelligent accounting. Firstly, the developed software is scanned with the Flexi Capture SDK software and the document type is determined. Thus, digitizing andconverting the data into a suitable format saves a great deal of time and effort. The performance of companies using the advantages of traditional accounting and smart accounting was measured. The results of our research proved the success and performance of the developed software in the best way.
Virtual Technologies as a Means of Involving Students of a Pedagogical University in the Educational Process

The article discusses virtual technologies as a way to increase the involvement of students of a pedagogical university in the educational process. The pedagogical experiment was carried out on the basis of the Faculty of Pedagogy and Psychology of the Ulyanovsk State Pedagogical University named after I.N. Ulyanov. The conducted empirical research has shown the effectiveness of the used virtual technologies when preparing students. The article describes the Virtual Pedagogical Simulator, its structure and application in the educational process of a pedagogical university in order to increase the involvement of students in the educational process.
Motorcycle Safety System With Esp8266 Nodemcu As Engine Control

Motorcycles are still the vehicle that is most in demand by the Indonesian people as a means of transportation, but motorcycles are very vulnerable to theft or burglary due to inadequate security systems. The current security system is only a handlebar lock and remote. So it is necessary to have a vehicle security tool against criminal acts, especially theft. The method used in this study is Research and Development, covering potential and problem stages, data collection, product design, design validation, design revision, product testing, product revision, usage trials, potential problems, mass products. The result of the research is a motorcycle safety system device using a relay as engine control. With this security system, users can feel safe from theft
Graph-based machine learning improves just-in-time defect prediction

The increasing complexity of today's software requires the contribution of thousands of developers. This complex collaboration structure makes developers more likely to introduce defect-prone changes that lead to software faults. Determining when these defect-prone changes are introduced has proven challenging, and using traditional machine learning (ML) methods to make these determinations seems to have reached a plateau. In this work, we build contribution graphs consisting of developers and source files to capture the nuanced complexity of changes required to build software. By leveraging these contribution graphs, our research shows the potential of using graph-based ML to improve Just-In-Time (JIT) defect prediction. We hypothesize that features extracted from the contribution graphs may be better predictors of defect-prone changes than intrinsic features derived from software characteristics. We corroborate our hypothesis using graph-based ML for classifying edges that represent defect-prone changes. This new framing of the JIT defect prediction problem leads to remarkably better results. We test our approach on 14 open-source projects and show that our best model can predict whether or not a code change will lead to a defect with an F1 score as high as 77.55% and a Matthews correlation coefficient (MCC) as high as 53.16%. This represents a 152% higher F1 score and a 3% higher MCC over the state-of-the-art JIT defect prediction. We describe limitations, open challenges, and how this method can be used for operational JIT defect prediction.
Assessing Open-Ended Human-Computer Collaboration Systems: Applying a Hallmarks Approach

There is a growing desire to create computer systems that can collaborate with humans on complex, open-ended activities. These activities typically have no set completion criteria and frequently involve multimodal communication, extensive world knowledge, creativity, and building structures or compositions through multiple steps. Because these systems differ from question and answer (Q&A) systems, chatbots, and simple task-oriented assistants, new methods for evaluating such collaborative computer systems are needed. Here, we present a set of criteria for evaluating these systems, called Hallmarks of Human-Machine Collaboration. The Hallmarks build on the success of heuristic evaluation used by the user interface community and past evaluation techniques used in the spoken language and chatbot communities. They consist of observable characteristics indicative of successful collaborative communication, grouped into eight high-level properties: robustness; habitability; mutual contribution of meaningful content; context-awareness; consistent human engagement; provision of rationale; use of elementary concepts to teach and learn new concepts; and successful collaboration. We present examples of how we used these Hallmarks in the DARPA Communicating with Computers (CwC) program to evaluate diverse activities, including story and music generation, interactive building with blocks, and exploration of molecular mechanisms in cancer. We used the Hallmarks as guides for developers and as diagnostics, assessing systems with the Hallmarks to identify strengths and opportunities for improvement using logs from user studies, surveying the human partner, third-party review of creative products, and direct tests. Informal feedback from CwC technology developers indicates that the use of the Hallmarks for program evaluation helped guide development. The Hallmarks also made it possible to identify areas of progress and major gaps in developing systems where the machine is an equal, creative partner.
Higher Education Evaluation System Based on Factor Analysis

In this era, higher education has entered a stage of rapid development, and the degree of popularization has continued to increase.In this case, a healthy and sustainable system is particularly important.The optimal weight equation of the second-level index was established by the projection tracking method, and the particle swarm algorithm was used to solve it and the projection tracking evaluation model was obtained[1]. In addition, a K-means clustering model was established, and three levels of evaluation criteria were obtained: general, good and excellent. In the end, an evaluation system that can assess the health of any country’s higher education system has been formed. Then divide the country through three principles.
Comparative Evaluation of Deep Learning-Based Sar-Optical Image Matching Approaches

The automatic matching of corresponding pixels in SAR and optical remote sensing imagery has been an active field of research for many years. While early approaches were usually based on the measurement of image similarity by signal-based measures or hand-crafted image features, more recent matching techniques make use of deep learning. Since the different approaches proposed in the literature are usually trained and evaluated on specific, individual datasets, i.e. with unique input data and target label criteria, a direct comparison has not yet been possible. With this paper, we intend to close that gap by providing the first comparative evaluation of different state-of-the-art deep learning-based SAR-optical image matching approaches.
Prospecting Potential of the Yanjingou Gold Deposit in the East Kunlun Orogen, NW China: Evidence from Primary Halo Geochemistry and In Situ Pyrite Thermoelectricity

The Wulonggou Au district in the East Kunlun Orogen is one of the most important Au producing regions in China. The Yanjingou Au deposit occurs within a shear zone in the northeastern Wulonggou Au district. Based on detailed field investigations, geochemical data for the primary halo, and in situ thermoelectric data for pyrite, the following key results were obtained: (1) the Yanjingou Au deposit has the fractured-altered-rock type gold mineralization that is arsenopyrite-rich; (2) elemental correlations and cluster analysis show that Au and As are the most diagnostic elements; (3) geochemical data for the primary halo indicate the deposit is a shallow supra-ore halo ore body; and (4) in situ pyrite thermoelectric data show that the proportion of P-type pyrite is &gt;80% and the detachment rate is 50%, which can be inferred that the location of the ore body is shallow. Based on our data, we present a mineralization prediction model for the ore body. The Yanjingou Au deposit has a good mineralization and high prospecting potential, with at least half of the ore body being concealed at depth, which has important scientific guiding significance for the breakthrough of prospecting and exploration.
RelationRS: Relationship Representation Network for Object Detection in Aerial Images

Object detection is a basic and important task in the field of aerial image processing and has gained much attention in computer vision. However, previous aerial image object-detection approaches have insufficient use of scene semantic information between different regions of large-scale aerial images. In addition, complex background and scale changes make it difficult to improve detection accuracy. To address these issues, we propose a relationship representation network for object detection in aerial images (RelationRS): (1) Firstly, multi-scale features are fused and enhanced by a dual relationship module (DRM) with conditional convolution. The dual relationship module learns the potential relationship between features of different scales and learns the relationship between different scenes from different patches in a same iteration. In addition, the dual relationship module dynamically generates parameters to guide the fusion of multi-scale features. (2) Secondly, the bridging visual representations module (BVR) is introduced into the field of aerial images to improve the object detection effect in images with complex backgrounds. Experiments with a publicly available object detection dataset for aerial images demonstrate that the proposed RelationRS achieves a state-of-the-art detection performance.
Comparison of Image segmentation, HOG and CNN Techniques for the Animal Detection using Thermography Images in Automobile Applications

Animal Vehicle Collision is an inviolability concern that comes with the cost of both humankind and animals. It has popularly resulted in millions of deer-vehicle collisions claims and fatalities. The only way to prevent the above-saddened statics is to drive wildlife safely away from roadways due to morbidity and injuries. This paper undrapes the optimal comparative study between edge-based image segmentation and CNN-HOG for self-acting animal detection. As the fatal crashes peaks during night-time, night vision image detection is focused on this paper with the mounted camera in the vehicle. Edge-based image segmentation is applied to the intelligent animal detection system to demonstrate the prowess of animal detection. The intelligent system processes thermographic images and feature extractions used for the object existence prediction. Deer is the overly populated animal and most commonly spotted animal used as the subject of detection in this research. The animal detection is done using the Histogram of Oriented Gradient (HOG) transform, whereas optimization is demonstrated using image segmentation. Image segmentation helps in precise animal detection by extending the continuity of the images, which is crucial for image processing during detection. The results vividly conclude the contribution of image segmentation accuracy to the existing HOG-based intelligent system with 91% accuracy using the wide roadsides of San Antonio, TX, in the USA.
PENERAPAN MEDIA KARTU KATA BERGAMBAR UNTUK MENINGKATKAN KEMAMPUAN MEMBACA PERMULAAN DAN DAYA INGAT ANAK

The purpose of this study was to describe the pictorial word card media in learning in order to improve the beginning reading skills and memory of B1 group children in Amanah Kindergarten, South Bengkulu. The research method used was collaborative CAR, which means that researchers with class teachers and peers jointly carry out learning to improve the quality or learning outcomes. The subjects in this study were 13 children in group B1 TK Amanah Bengkulu Selatan. observation sheet. Data analysis used the average, percentage and t test. The results showed that the application of pictorial word card media could improve children's beginning reading skills with the overall percentage achievement for Cycle I 70.5% with developing categories as expected, Cycle II 83.6 % with very well developed categories, and 92.6% in Cycle III with very well developed categories and children's memory abilities with the overall percentage achievement for Cycle I with developing categories according to expectations of 69.2%, Cycle II 85.2% with categories developing very well, and Cycle III 89.7% with very well developed categories
Implementation of Software Requirements Specification (SRS) For Village Websites

As one of the efforts of the Village to convey information to the community, the village provides all information to the public and others through the website as a medium of information. Software Requirements Specifications (SRS) are documents that describe all the requirements that must be met by the software. The document is made by the software developer (developer) after collecting information from prospective software users (users). The method used in this research is to use the method of observation and interviews on the subdistrict of to collect data and find information related to this research. With this research, it is hoped that it can be an input for the website developer of the village in the future so that the website of the village can be able to meet the needs of the community as a medium of information. The results of this study indicate that the village website requires development in the online service features section with a system design that has been made by the author based on the Software Requirement Specifications (SRS).