Reviewerly/exHarmony huggingface dataset:
The transitions from mildly explosive to caldera-forming eruptions at Colli Albani volcano (Italy)

&amp;lt;p&amp;gt;The Colli Albani volcano is an ultrapotassic caldera complex located 30 km to the SE of Rome and has displayed a wide range of eruptive behaviors, ranging from effusive activity to highly explosive and large volume eruptions (up to 63 km&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt; dense rock equivalent per eruption) despite its mafic nature.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We combine physical volcanology, petrology, and geochemistry to focus on the mildly explosive to effusive products of two sections (Tuscolo and Artemisio) which are located on opposite sides of the main caldera and stratigraphically between the last large ignimbrite, Villa Senni. The target of this study is to identify the processes responsible for the transition from the smaller explosions to the larger caldera-forming ignimbrite eruptions, and eventually trace how the magmatic system rebuilds in the interim.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Whole rock analyses, mineral chemistry, and petrography of fall deposits from both field localities are compared with an existing dataset for the Villa Senni ignimbrites. We will use unsupervised and supervised machine learning approaches to identify similarities and differences between large caldera-forming eruptions and mild-explosive to effusive activity and identify the processes modulating the transition between these two behaviours.&amp;lt;/p&amp;gt;
Study Guides: Public Communication: Plan Your Search

This guide will help Public Communications students to navigate the key resources and develop core information literacy skills. A Planning your Search page template: generic, multidisciplinary
Exploring Device Capabilities with Calls

With all of the ideas we have already covered in the book, and will still cover in later chapters, it is easy to delve headlong into all kinds of application development that only deal with the great software platform Android provides. It is so easy, in fact, that often you overlook one of the other tremendous opportunities you have as an Android developer: working with the Android hardware as well.
Research Guides: Borrowing Books: Check out, Renew, Fines & FAQ's

Here's how to find the book you need, place a hold on it if you so desire, request a book through Interlibrary Loans, check out a book from a participating library with TexShare, and to request that books be shipped to MCC sites away from the main campus.
Blueprint - HOMESET Sdn Bhd : "HOMETRASH‟ / Aisa Ezzatulmirza Sapari

HomeSet Sdn Bhd located in Jalan Prima Utama, Johor that focus on the production of smart dustbin which called as „homeTrash‟. „homeTrash‟ is an electronic machine to an improve normal dustbin by elevating it to be smart using sensors for garbage and logics. It functions to minimize human interferences in collection and disposal of trash. The company was started on January 2020 and focusing to targeted customer with certain stage of age especially for adults in range of 20 to 40, focusing to household and people who work in the offices especially to customer with middle-class income. As the beginning for the business, we will concentrate in peninsular Malaysia. Marketing of the product will be made through media social, online stores, banner, flyers and placement in the stores. „homeTrash‟ is a new product in Malaysia with combination of technology also design concept of decoration. The simple and small design make the product is easy to be place as a decoration where „homeTrash‟ has variation of colour that has a unique, streamlined design and also stainless steel. The selling of „homeTrash‟ is expected to start at the beginning of year 2021. The prediction of the total market size for three years is consistent by 45 %, where it will rise more in the future, although the market share and sales will grow by at least 4% for within three years. As agreed in our agreement, every partner contributed a certain amount of capital. We agreed that Aisa Ezzatulmirza Binti Sapari as a General Manager, Syafiqah Nureen Ezaty Binti Waharudin as Administrative Manager, Saidatul Afifah Binti Afpandi as the Marketing Manager, Alya Qistina Binti Hisyam as an Operational Manager, Zawani Najiah Binti Rasli as the Financial Manager and Nurzahirah binti Mohd Yussof as Sales Manager. Each partner has its own obligations where all partners are expected to plan and manage the department's operations wisely.
WEB APP: String Similarity Search - A Hash-based Approach

During this Project, we study string similarity search based on edit distance that is supported by many database management systems like Oracle and PostgreSQL. Given the edit distance, ed(s, t), between two strings, s and t, the string similarity search is to search out each string t in a string database D which is almost like a query string s such that ed(s, t) = t for a given threshold t. Within the literature, most existing work takes a filter-and-verify approach, where the filter step is introduced to reduce the high verification cost of 2 strings by utilizing an index engineered offline for D. The two up-to-date approaches are prefix filtering and native filtering. We have a tendency to propose 2 new hash- primarily based labeling techniques, named OX label and XX label, for string similarity search. We have a tendency to assign a hash-label, H s , to a string s, and prune the dissimilar strings by comparing 2 hash-labels, H s and H t , for two strings s and t within the filter step. The key idea is to take the dissimilar bit- patterns between 2 hash-labels.Our hash-based mostly approaches achieve high efficiency, and keep its index size and index construction time one order of magnitude smaller than the present approaches in our experiment at the same time.
Multi-Target Stance Detection Based on GRU-PWV-CNN Network Model

To uncover opinions on different people and events from text on the internet, stance detection must be performed, which requires an algorithm to mine stance tags for different targets (people or events). Some text contain multiple targets, and the content describing different targets is related, which results in poor stance detection performances. Therefore, stance detection for such data is defined as multi-target stance detection. To address this issues, a network model composed of a gated recurrent unit, a position weight vector, and a convolutional neural network (GRU-PWV-CNN) is proposed. First, the bidirectional GRU (Bi-GRU) is employed to extract the unstructured features, and a position-weight vector is designed to represent the correlation between every word and the given target. Next, these two forms of information are fused and transmitted to a CNN to complete the secondary extraction of features. Finally, a softmax function is used to carry out the final classification. A multi-target stance detection corpus for the 2016 US election was used to compare the performances of our method and other methods, including the Seq2Seq and AH-LSTM. The experimental results showed that the proposed method achieved well and had a 2.82% improvement in the macro-averaged F1-score.
Greedy Dynamic Blocking for Rumour Detection on Live Twitter Using Machine Learning

We propose a community multi-Trends assessment grouping way to deal with train notion classifiers for numerous tweets at the same time. In our methodology, the assessment data in various tweets is shared to prepare more exact and vigorous estimation classifiers for each Trends when marked information is scant. In particular, we decay the opinion classifier of each Trends into two segments, a worldwide one and a Trends-explicit one. Various customer surveys of subjects are currently accessible on the Internet. Naturally distinguishes the significant parts of themes from online shopper surveys. The significant item angles are recognized dependent on two perceptions. With the point of arranging patterns from the get-go. This would permit to give a separated subset of patterns to end clients. We investigate and explore different avenues regarding a bunch of direct language-autonomous highlights dependent on the social spread of patterns to classify them into the presented typology. Our strategy gives an effective method to precisely arrange moving points without need of outer information, empowering news associations to find breaking news progressively, or to rapidly recognize viral images that may improve promoting choices, among others. The examination of social highlights additionally uncovers designs related with each sort of pattern, for example, tweets about continuous occasions being more limited the same number of were likely sent from cell phones, or images having more retweets starting from a couple of innovators. The worldwide model can catch the overall conclusion information and is shared by different tweets. The Trends-explicit Greedy and Dynamic Blocking Algorithms model can catch the particular assessment articulations in each Trend. Likewise, we remove Trends-explicit feeling information from both marked and unlabeled examples in each Trend and use it to improve the learning of Trends-explicit notion classifiers.
Correction to: Impedance spectroscopy and investigation of conduction mechanism in reduced graphene/CuFe2O4 nanocomposites

A correction to this paper has been published: https://doi.org/10.1007/s00339-021-04613-7
The Rise of Virtual Commerce: An Abstract

Advances in digital technologies continue to transform the way that organisations conduct business, and none has been more widely anticipated than Virtual Reality. Numerous academics and practitioners in a variety of industries have identified valuable opportunities that come with implementing Virtual Reality technologies into various contexts. Stakeholders are actively experimenting with Virtual Reality implementation to achieve objectives, and recently Virtual Reality technologies have been applied in business environments to aid virtual commerce. This research implements a conceptual approach to clearly define virtual commerce and the Virtual Reality ecosystem. Literature review aided the process of examining and understanding the larger virtual reality environment in which the different stakeholders operate and how value is created by and through the different elements in order to drive the business-to-consumer virtual reality market. As such, it helped determine the scope as well as the mapping and analysis of the current state of the Virtual Reality market and the central value-adding activities that different stakeholders undertake in the Virtual Reality industry. After evaluating the literature and the market, the researchers were able to clearly define the Virtual Reality ecosystem’s architecture, which was visualized and turned into a strategic analytical and decision support tool. As the Virtual Reality market grows, matures and becomes less fragmented, adoption across industries is expected to speed up and Virtual Reality will eventually gain a prominent place within the global economy. This brings about changes and implications regarding the ways in which commerce will be conducted. With several online retailers already at the forefront of commodifying virtual shopping, and an even bigger part implementing it as a marketing tool for business, the technology can be expected to become a valuable asset in enhancing and personalizing the online shopping experience. The paper closes by discussing future developments and research implications for theory and practice.
Data Generation Methods for Multi-Label Images of LEGO Bricks

Data collection and annotation have proven to be a bottleneck for computer vision applications. When faced with the task of data creation, alternative methods to traditional data collection should be considered, as time and cost may be reduced signif- icantly. We introduce three novel datasets for multi- label classification purposes on LEGO bricks: a traditionally collected dataset, a rendered dataset, and a dataset with pasted cutouts of LEGO bricks. We investigate the accuracy of a ResNet classi- fier tested on real data, but trained on the different datasets. This research seeks to provide both in- sight into future dataset creation of LEGO bricks, as well as act as an advisor for general multi-label dataset creation. Our findings indicate that the tra- ditionally collected dataset is prone to overfitting due to speedups used during collection, with 90% accuracy during training but 19% during testing. Furthermore, synthetic data techniques are appli- cable to multi-label LEGO classification but need improvement, with accuracy ranging from 34% to 45%.
Perceptions on the educational impact of COVID-19: Analysis of a TECNOEDU Webinar

The work we present is based on the experience of the knowledge transfer activities of the Mapfre Foundation Chair for Technology and Education, consisting of three webinars where the speakers are education professionals and researchers in the field of Educational Technology. In the public we have had university, Undergraduate and Postgraduate students and education professionals of different educational levels. Among the attendees, a questionnaire has been disseminated to know their impression of the situation experienced during confinement by the COVID-19 pandemic and the prospects that they predict on education in relation to the use of technology. We have performed a quantitative analysis of dichotomous and qualitative variables using an exploratory coding system, at both times, during and after the pandemic. We have also investigated the possible relationships between codes, through a co-occurrence analysis. The main results describe that most people have had sufficient resources to face the abrupt change to online teaching, although it has involved a high emotional effort. Regarding the future post-COVID-19, the people surveyed consider that, after the pandemic is over, greater methodological and didactic flexibility will be advocated both in the offer of itineraries in the subjects and in the offer of academic degrees.
Design Problems of Bridge Pile-Soil Interaction Based on Machine Learning

Pile foundations are usually buried deep underground and form an interactive whole with the surrounding soil. The pile foundation is mainly to bear vertical forces. Its main function is to improve the bearing capacity of the foundation and effectively control the settlement of the foundation and the foundation. In order to understand the working performance and interaction law of the pile-soil interaction system to carry out reasonable in-situ tests and correct numerical simulation analysis, it is necessary to conduct an in-depth theoretical analysis of the vertical bearing characteristics of the pile foundation. This paper analyzes the process of bridge pile-soil interaction based on machine learning algorithms. The analysis includes the different classification methods of pile foundations, the load transfer mechanism of the pile-soil system, the exertion mechanism of pile side resistance, the exertion mechanism of pile tip resistance, and the factors affecting the vertical bearing capacity of a single pile. The research results have a certain effect on the bridge pile-soil design.
Цифровая терминология: подходы к определению понятия «робот» и «робототехника»

The paper provides an assessment of the definitions of robot and robotics available in the scientific literature, with the author's position on their relevance, comprehensiveness and relevance. It is established that there are several approaches to the definition and interpretation of the categories under consideration. Most scientists proceed from their own vision of the object of research, while relying on the existing authoritative points of view in this field. However, the constant development of digital technologies entails the need to form a new conceptual and the categorical apparatus, which should be based on definitions that define the fundamental foundations of a particular scientific concept. The author's definition of the concepts robot and robotics is proposed.
Perancangan Aplikasi Peta Digital (CarryMap) Guna Optimalisasi Pengelolaan Data Di BPP Kecamatan Sinjai Utara Kabupatesn Sinjai Provinsi Sulawesi Selatan

Abstrack &#x0D; Data as a source of information is very important to support the developmentof sustainable agriculture. The obstacle that is experienced the most by the Department of Agriculture is the availability of information systems of agricultural data that are not up to date. CarryMap is an application that is used to display a land distribution mapand also information without enclose data in the form of shape files or the other data.This study aims to 1) describe the existence of agricultural potential data in the BPP of North Sinjai,Sinjai; 2) describe the mechanism of data collection used in North Sinjai; 3)design an application of digital map for data management existed in North Sinjaito make the application becomes more effective; 4)describe the implementation of the digital map application based on geographic information systems of North Sinjai; 5) describe the use of data as the information of agricultural development. This study is a descriptive study with a qualitative approach. The main techniques in collecting data are document study and in-depth interviews. The results showed that the dataof agricultural potential was still not organizedwell because the mechanism of data collection was still done manually. The application design went through several stages but the implementation of the application design was well responded by the agricultural extensions. The use of data as a source of information on agricultural development must have a potential data of land resourcein detail.&#x0D; Keywords : CarryMap, Data, Digital Map
A Web-Based Virtual Research Environment for Marine Data

&amp;lt;p&amp;gt;Like most areas of research, the marine sciences are undergoing an increased use of observational data from a multitude of sensors. As it is cumbersome to download, combine and process the increasing volume of data on the individual researcher's desktop computer, many areas of research turn to web- and cloud-based platforms. In the scope of the SeaDataCloud project, such a platform is being developed together with the EUDAT consortium.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The SeaDataCloud Virtual Research Environment (VRE) is designed to give researchers access to popular processing and visualization tools and to commonly used marine datasets of the SeaDataNet community. Some key aspects such as user authentication, hosting input and output data, are based on EUDAT services, with the perspective of integration into EOSC at a later stage.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The technical infrastructure is provided by five large EUDAT computing centres across Europe, where operational environments are heterogeneous and spatially far apart. The processing tools (pre-existing as desktop versions) are developed by various institutions of the SeaDataNet community. While some of the services interact with users via command line and can comfortably be exposed as JupyterNotebooks, many of them are very visual (e.g. user interaction with a map) and rely heavily on graphical user interfaces.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this presentation, we will address some of the issues we encountered while building an integrated service out of the individual applications, and present our approaches to deal with them.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Heterogeneity in operational environments and dependencies is easily overcome by using Docker containers. Leveraging processing resources all across Europe is the most challenging part as yet. Containers are easily deployed anywhere in Europe, but the heavy dependence on (potentially shared) input data, and the possibility that the same data may be used by various services at the same time or in quick succession means that data synchronization across Europe has to take place at some point of the process. Designing a synchronization mechanism that does this without conflicts or inconsistencies, or coming up with a distribution scheme that minimizes the synchronization problem is not trivial.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Further issues came up during the adaptation of existing applications for server-based operation. This includes topics such as containerization, user authentication and authorization and other security measures, but also the locking of files, permissions on shared file systems and exploitation of increased hardware resources.&amp;lt;/p&amp;gt;
CML: Chronic Myeloid Leukemia Detection Using Particle Swarm Optimization and Fuzzy C-Means Clustering

Leukemia is a blood cancer caused by a rise in the number of white blood cells in your body. Those white blood cells crowd out the red blood cells and platelets that your body needs to be healthy. The extra white blood cells don’t work right. However, the macroscopic examination of human blood necessitates various microscopic techniques, including color imaging, segmentation, and clustering, to distinguish patients with the disease. The white blood cell count can be higher than normal and prevent the immune system from functioning properly. The most reliable method to detect leukemia is with the assistance of a microscope. Automation of this form of Leukemia diagnosis is required because fewer and more expensive precise tests are required than the above. Blood slides don't reveal the findings because of hematologists' combined experience and tiredness, and those results can vary significantly from one PSO to another. As it is reasonably inexpensive and effective, blood-stained histopathology imaging to test for leukemia is a low-cost and innovative choice. The fuzzy-c segmentation coefficients of this paper are compared to those for image segmentation k. Using the GLCM to separate color characteristics from photographs performs better precision than using Fuzzy-classifications. Fuzzy's consistency level is 96%, while sharpness is 83% CNN is mainly a television outlet. The whole project has been done using the MATLAB toolkit.
The importance of introducing innovative educational technologies in social development of society

The analysis of pedagogical research conducted in the country shows that the technologicalization of the educational process is closely linked with innovative ideas that reflect the status of pedagogical innovations. Improving the effectiveness of education and the introduction of innovative developments in the field of science will serve to increase the intellectual potential of consumer students and learners. This article scientifically examines the importance of the introduction of innovative educational technologies in the social development of society.
LibGuides: Library Induction: Home

A hub for everything you need to be introduced to the library. Click on the menu to explore further.
Grouping Words with Semantic Diversity

Deep Learning-based NLP systems can be sensitive to unseen tokens and hard to learn with high-dimensional inputs, which critically hinder learning generalization.We introduce an approach by grouping input words based on their semantic diversity to simplify input language representation with low ambiguity.Since the semantically diverse words reside in different contexts, we are able to substitute words with their groups and still distinguish word meanings relying on their contexts.We design several algorithms that compute diverse groupings based on random sampling, geometric distances, and entropy maximization, and we prove formal guarantees for the entropy-based algorithms.Experimental results show that our methods generalize NLP models and demonstrate enhanced accuracy on POS tagging and LM tasks and significant improvements on medium-scale machine translation tasks, up to +6.5 BLEU points.Our source code is available at https://github.com/abdulrafae/dg.