Reviewerly/exHarmony huggingface dataset:
Metallogeny of Manto-type Copper Deposits of Iran: A Possible Link to the Evaporitic basins

&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Abstract&amp;lt;/strong&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Iran is one of the most significant producers of copper in the world and hosts varieties of copper deposits, including porphyry Cu-Mo, vein-type, volcanogenic massive sulfide (VMS), sediment-hosted stratabound copper (SSC), Manto-type, and skarn.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Manto-type deposits are the second producer of copper in Iran, mostly hosted in basalt, basalt-andesite to andesite volcanic rocks. There are more than 40 Manto-type copper deposits and occurrences in Iran, such as Mari, Abbas-Abad, Vorezg, Robat, Simakan, and Sorkho, and most of them are economically deposits. Most of these deposits occur in Eocene volcanic rocks, and a small amount of them (such as KeshtMahki, Hassanabad, Khorjan, and Simakan) are hosted in the Early Cretaceous volcanic rocks that mainly concentrated in the Saveh-Yazd (in the Urumieh-Dokhtar magmatic belt), Qazvin-Zanjan, Sabzevar-Neishabour, Semnan-Shahroud volcanic zones, and eastern Iran.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The stratabound sulfide ores in these Manto-type copper deposits include chalcocite, chalcopyrite, and bornite, associated with covellite, malachite, atacamite, chrysocolla, and minor azurite in the oxidized and supergene ore zones. Sulfide mineralization usually occurs as a replacement in pyrites and feldspars, vein and veinless, and breccia, which is accompanied by carbonatization, propylitic, and minor sericite alterations. Geological and geochemical data indicate that most of these deposits formed within plate failed continental rift and back-arc extensional environments related to the subduction of the oceanic crust of neo-Tethys beneath the Iranian Plateau.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Furthermore, the temporal and spatial distribution of these deposits in terms of time shows a close relationship with evaporitic basins. This phenomenon suggests a genetic relationship between the formation of Manto-type deposits and the circulation of brines from adjacent evaporitic basins in shallow extensional tectonic environments.&amp;lt;/p&amp;gt;
Hazards Scientist David Applegate Nominated for USGS Director

If confirmed, Applegate would fill a yearlong vacancy at the head of the agency.
Developing a Positive Mindset to Entrepreneurship and Social Activity through the Innovative Learning System (InnoSchool)

The concept of entrepreneurship as a competence is very important for achieving smart, sustainable, and inclusive growth in the context of European policies. The employment growth, reducing poverty or social exclusion, personal and career development largely depends on the rise of entrepreneurship. Promoting entrepreneurship for sustainable development should start at school age when is the best time for development of the entrepreneurial mindset and bringing up social activity. The article summarizes observations and analysis of the results of the Innovative Learning System (InnoSchool). This system copes with the weak entrepreneurial culture and low engagement of young people towards social innovation and entrepreneurship. The training system uses intensive learning interaction between direct participants, students and teachers on one hand, and all concerned parties from school, local and regional authorities, business, educational institutions and non-governmental organisations on the other hand. The result is a change in the attitude of high school students regarding entrepreneurship, motivation and dedication to social responsibilities, which would have a positive impact on their future professional career.
Adria-Eurasia collision front: insights from GNSS time series in NE Italy

&amp;lt;p&amp;gt;North-Eastern Italy is a region of particular interest in tectonics because it is located on the northernmost edge of the convergent margin between Eurasia and the Adria microplate with consequences on the regional deformation and seismicity. The FReDNet (Friuli Venezia Giulia Deformation Network) GNSS network was established in 2002 to monitor the crustal deformation in NE-Italy and it is currently counting 19 permanent GNSS stations. In order to place the regional deformation in a broader tectonic context, we processed the data from FReDNet and other geodetic networks covering northern Italy and surrounding areas (including some sites in Slovenia and Austria) in the period 2002-2021. We used the GAMIT-GLOBK software ver10.71 to process multi-satellite data and to calculate the position and velocity for each station. We processed the whole dataset by using Galileo and G100 CINECA HPC clusters.&amp;amp;#160;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, we will show the processing strategies and analyze the GNSS time-series of NE-Italy stations, as well as the outcoming deformation field. The preliminary results&amp;amp;#160; confirm the decrease in the velocity module from the Friuli plain toward the Alps, suggesting a possible deformation accrual in the latter.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This research was supported by OGS and CINECA under HPC-TRES program award number 2020-11. We acknowledge the CINECA award under the ISCRA initiative, for the availability of high performance computing resources and support (IscraC IsC83_GPSIT).&amp;lt;/p&amp;gt;
Increasing The Level Of Formation Of Educational Motivation And Independence Of Students

Interest in learning activities and in the learning process itself, understanding of the learning material can only be formed by using active, creative teaching methods. The research considers the use of practice-oriented tasks in teaching disciplines of the natural science cycle. In order to form and activate the interest of students in the study of natural sciences, it is necessary to model production problem situations that are solved with the help of universal educational actions obtained in the classroom The study distinguished two personal qualities of students, which prevail over others when considering the learning process; they are educational motivation and cognitive independence. It is necessary not only to form these qualities, but also to activate them in every possible way in the learning process for the successful mastering of knowledge and skills that will be useful to the future specialist in his professional activities. The results of the study confirmed that the use of practice-oriented tasks in the study of the disciplines of the natural science cycle has a positive effect on the formation and activation of students' learning motivation and it contributes to an increase in the level of the cognitive independence in training future specialists, that are necessary for the development of modern agricultural production. This fact will allow solving many personnel problems in the agro-industrial complex in the future.
Internet of Things: Survey and Challenges in Education Sector

IoT has been gradually bringing a sea of technological changes in our daily lives, which in turn helps to making our life simpler and more comfortable, though various technologies and applications. There is innumerable use- fulness of IoT applications into all the domains including medical, manufacturing, industrial, transportation, education, governance, mining, habitat etc. The future is Internet of Things, which will transform the real world objects into intelligent virtual objects. The IoT aims to unify everything in our world under a common infrastructure, giving us not only control of things around us, but also keeping us informed of the state of the things. In Light of this, present study addresses IoT concepts through systematic review of scholarly research papers, corporate white papers, professional discussions with experts and online databases. Moreover this research article focuses on definitions, geneses, basic requirements, characteristics and aliases of Internet of Things. The main objective of this paper is to provide an overview of Internet of Things, working, and vital technologies and their usages in our daily life and mainly challenges in Education Sector.
Laser Scanning Based depth sensing and displaying for augmented reality

Fashionable Augmented Reality Smartglasses require ultra-compact high performance display solutions of high brightness and extremely low power consumption. Laser Beam Scanning (LBS) offers some advantages over other microdisplay technologies particularly with respect to brightness, contrast, size, weight and power consumption. A biaxial piezoelectric MEMS scanning mirror that is operated in resonance in a miniature vacuum environment consequently minimizes power consumption and also allows reducing projector size to a minimum. MEMS mirror based laser scanning is not only the key to stylish AR smartglasses it also enables building extremely compact depth sensing cameras that are the basis for enabling interactivity – not only in the Metaverse
How do students choose a college: Is it because of the cost of education or the image of the college?

The increasing competition between universities makes every university must always take strategic steps, in attracting the interest of prospective students. This study aims to analyze the effect of the cost of education and the image of the university on student decisions to choose higher education institutions at the XYZ Islamic University. This study uses a quantitative approach with an explanatory research type. The research sample consisted of 388 respondents and was statistically processed using multiple linear regression analysis. The results showed that the cost of education and the image of the university had an effect on students' decisions to choose higher education institutions. These findings suggest that higher education institutions should set competitive tuition fees as well as image. Several studies have shown that in general the cost of education and the image of a higher education is a consideration for prospective students in choosing a university. It is important for institutions in managing these two things to attract prospective students to choose certain universities.
Real-time Detection and Avoidance of Obstacles in the Path of Autonomous Vehicles Using Monocular RGB Camera

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;In this paper, we present an end-to-end real-time detection and collision avoidance framework in an autonomous vehicle using a monocular RGB camera. The proposed system is able to run on embedded hardware in the vehicle to perform real-time detection of small objects. RetinaNet architecture with ResNet50 backbone is used to develop the object detection model using RGB images. A quantized version of the object detection inference model is implemented in the vehicle using NVIDIA Jetson AGX Xavier. A geometric method is used to estimate the distance to the detected object which is forwarded to a MicroAutoBox device that implements the control system of the vehicle and is responsible for maneuvering around the detected objects. The pipeline is implemented on a passenger vehicle and demonstrated in challenging conditions using different obstacles on a predefined set of waypoints. Our results show that the system is capable of detecting objects that appear in an image area as small as 20×30 pixels in a 1280×720 image and can run at a speed of 24 frames per second (FPS) on the embedded device in the vehicle. A data analyzer is also employed to visualize the real-time performance of the system.&lt;/div&gt;&lt;/div&gt;
Research of Camouflage Evaluation Based on Human Visual Attention Mechanism

In order to evaluate effectiveness of target camouflage objectively and scientifically, a new model of human visual attention mechanism is established to calculate the locations and shifts of the attention focus. The model of similarity between the target and background regions is established to quantitatively evaluate the camouflage effect. The relation of the shift of the attention focus, the similarity and target detection probability is obtained by using a BP neural network. Application shows that this method can search target automatically and measure target detection probability precisely, and has advantages such as high operability and operability.
Universities, Equality and the Neo-Liberal City

Equality in the City - Imaginaries of the Smart Future; Interdisciplinary collection exploring cities and urban spaces in the context of technological and digital innovation. An approachable discussion of the issues surrounding smart digital futures and the disruptive potential of smart technologies in our cities; issues of change, design, austerity, ownership, citizenship and equality. 29col and 3b&amp;w.
Modelling the irrigation water demand through integration of irrigation scheme with NASA-Land Information System Framework (LISF) &amp;#160;in India

&amp;lt;p&amp;gt;The current study provides the irrigation water estimate based on incorporation of satellite-derived irrigation scheme and crop datasets into the NASA-Land Information System Framework (LISF) in India. NOAH 3.3 land surface model within NASA-LISF was run at 0.05-degree resolution for nine years from 2011 to 2019. The irrigation scheme accurately captures the seasonality and the two growing seasons that is December-March and August-November. The MODIS leaf area index product helps to regulate the seasonality and estimated irrigation amount and timing is based on 50% depletion of soil moisture at the field capacity in the rootzone. The results show that the evapotranspiration (ET) and latent heat flux (LE) have increased significantly in the cropped region with improvement in correlation with the MODIS ET and LE products. The study also shows an improvement in soil moisture simulation at the test sites (Varanasi and Gujarat). Besides, successfully demonstrating the irrigation timing and quantity, the present study can also be relevant to hydrological and energy fluxes studies of areas that still lack proper quantification of agricultural practices utilizing irrigation.&amp;lt;/p&amp;gt;
Exploiting Don't-Care Information in Neural Network Learning

In this paper, we present a novel neural network architecture called M-net, which exploits the don’t-care information in training multilayer feedforward neural networks. Our method takes advantage of the user’s prior knowledge as well as the neural network’s ability to learn from examples. The user’s prior knowledge is encoded in the form of don’t-care inputs to reduce the number of training patterns required to represent a function. We derive the learning rule of M-net in the context of error backpropagation, and demonstrate its use on the priority decoding problem. Compared with conventional backpropagation networks, M-net drastically reduces the learning time while achieving superior quality.
Application of image hash algorithm in copyright protection system

Because of the sensitivity of traditional hash algorithms to original data, it is not suitable for image content authentication. In this paper, we analyze and compare image hashing algorithms, realized a difference-value hashing algorithm (dHash), and apply the dHash algorithm to a blockchain-based image copyright protection system. dHash algorithm determines the similarity of images to ensure the uniqueness of image copyright.The test shows that the dHash algorithm has high accuracy and faster speed of images with Gaussian noise.
Modulation Scheme Classification in Cognitive Radio Networks Using the Long Short Term Memory (LSTM) of Deep Learning

Cognitive Radio (CR) system has been adopted for efficient utilization of radio frequency spectrum. The classification of signal modulation schemes is one of the main characteristics of the CR for appropriate demodulation of sensed signals. However, conventional Modulation Classification (MC) techniques require extensive extraction of signal features, which is not often guaranteed. Thus, Deep Learning (DL) has been seen as a promising solution to this drawback in MC. This paper proposes a DL-based MC technique using the Long Short-Term Memory (LSTM) network architecture. The proposed LSTM model was trained on M-ary Phase Shift Keying (MPSK) and M-ary Quadrature Amplitude Modulation (MQAM) signal types. The LSTM directly learns the features of a given modulation scheme of a signal sample during training. The signal samples were generated via computer simulation for Signal-to-Noise Ratios (SNRs) from -10 dB to 20 dB with an interval of 5 dB over flat fading channel and Additive white Gaussian noise (AWGN). Simulation results show that the proposed LSTM model achieves an average classification accuracy of 95% at SNRs above 0 dB.
One-Way Flow System Water Quality Modelling of Terantang Reclamation Channel

Abstract The implementation of reclamation process in wetland has encountered some obstacles, such as the occurrence of pyrite oxidation, sedimentation, and accumulation of toxic materials, especially the concentration of Fe (iron) in the channel. The condition of the reclamation channel which is influenced by tidal events and environmental factors causes the dynamics of Fe concentration to be strongly influenced by hydrodynamic aspects and its interaction with dissolved and suspended particulates. An accurate model will be useful as a tool to evaluate alternative management of water systems to overcome the problem of Fe concentration accumulation in the tidal wetland reclamation channel. Fe fate and transport were modeled using a numerical model of hydrodynamics and water quality EFDC (Environmental Fluid Dynamic Code). The partition process separates the metal fraction into two phases (dissolved and solid), which is related to the value of the partition coefficient (Kp). The application of the partition process is considered to have a significant influence on the accuracy of the estimated Fe concentration. The simulation results of the model with the partition coefficient approach have better accuracy than the conventional model which tends to be over-predicted compared to the observation data. Simulation of one-way flow system shows a decrease of Fe concentration in the channel by an average of 52.9% compared to two-ways flow system. The application of one-way flow system is possible through the placement of flap gates that will direct the flow to the drainage channels.
Utilization of K-Medoids Algorithm for Klustering of Oil Palm Sprouts

Palm oil is still a prima donna commodity in the plantation sector and as a major foreign exchange earner to date. Research and development of this commodity is very important to maintain Indonesia's position as the largest palm oil producing country in the world. The purpose of this study was to analyze what internal and external factors are the strengths, weaknesses, opportunities and threats for marketing oil palm sprouts in PPKS Marihat. To analyze what are the priority strategies to be implemented for the marketing of sprouts at PPKS Marihat. The research method used is the K-Medoids clustering algorithm by selecting the sprout data in order to determine the best quality of sprouts. Based on the results of research using the K-Medoids algorithm with manual calculations and testing, the same results were obtained, namely cluster 1 with very good sprouts category had 7 members, cluster 2 with good sprouts category had 12 members and cluster 3 with poor sprouts category had 7 members. . Testing data on Rapid Miner using the K-Medoids algorithm can display 3 classes with an accuracy percentage of 100%. So it can be concluded that the K-Medoids algorithm can be used for clustering oil palm sprouts at PPKS Marihat.
An Automated Reasoning System for Multi-Agent Cooperation

We present an automated reasoning system for multi-agent knowledge and time, which is a kind of multi modal logics, incorporating common knowledge and implicit knowledge. Such knowledge is essential when considering reasoning, planning and cooperating problem solving in distributed and dynamically changing environments. We first consider how to represent such common knowledge and implicit knowledge based on modal logic. We then describe how to use the framework of the possible-world semantics to capture the notations of common knowledge and implicit knowledge. The reasoning procedure of our system is based on a so-called semantic method, that is, a formula expressing multi-agent knowledge and time is first translated, according to the possible world semantics, into the set of corresponding first-order clauses. We then check the satisfiability of the set of translated first-order clauses by a general purpose first-order theorem proof procedure ME (the model elimination) ([10]), augmented with the capabilities of handling inequality predicates introduced by the translation. We apply the idea of theory resolution for reasoning about inequalities efficiently. We also show some experimental results of our automated reasoning system.
EXPERIENCES IN EIT DIGITAL MASTER SCHOOL JOINT DEGREE IN TIMES OF COVID

Joint degrees provide extra challenges over other programs. There are multiple reasons for that like moving to a different country (or continent, as well as living far away from friends and family, not mentioning others. Because of these, applying for a joint degree is always a hard decision, moreover, Covid-19 introduced some new difficulties and made the already existing ones even harder.European Institute of Innovation & Technology (EIT) Digital Master School consists of two years master's programme at two different European universities. The programs are Information Technology (IT) related, however, every curriculum includes 30% of innovation and entrepreneurship studies. This provides enough knowledge to create a startup from scratch and make it a successful company. The program also offers extra events like a summer school, and a kick-off which helps people in networking, getting to know teachers and other students from the different universities who take part in EIT Digital. Converting all of these events to an online platform can be very challenging for both the organizers and the participants. Organizers had to find a way to involve attendees in the activities while participants had to interact with others and solve problems from their homes. Many things could have gone wrong during this: internet issues, software issues, device issues, time-zone issues, etc. Sometimes these issues could not be fixed easily, but inside of the EIT Digital community, even under the given circumstances, people did everything to make each other feel comfortable and involved. Online platforms made it possible to reach out to people who would not be able to attend an in-person event. Inviting widely acknowledged presenters to give a talk about their area of expertise is a great way to give students a role model, especially when they are in lack of social inputs. The results are self-explanatory, over 90% of students find a job in a related field to their studies within 6 months.
Generating Custom Datasets with Multi Generative Adversarial Networks

Object detection and data collection from custom targets suffer from certain problems, which inherently occur in deep learning networks owing to problems such as difficulty of collection and data bias.Therefore, in this study, we proposed the Multi-GAN framework for generating augmented datasets.This framework comprises two parts: the first part generates data that reflect various textures related to decep learning based on deep convolutional GAN (DCGAN) and Wasserstein GAN (WGAN) structures.The second part provides multiple resolutions based on super-resolution GAN (SRGAN).Here, this paper presents efficient dataset construction methods along with a conventional augmentation method called manipulation technique.Through the experiments, which were based on average precision, conducted on the collected and augmented datasets, the proposed frameworkdemonstrated to improve detection accuracy.Additionally, we confirmed that the multi-GAN framework is superior with respect to efficiency to data collection.