Reviewerly/exHarmony huggingface dataset:
Learning Neural Representations and Local Embedding for Nonlinear Dimensionality Mapping

There are M neural modules, respectively determining individual scopes of M data supports also as their neighboring relations. Neighboring relations among data supports are associated with edges of a graph. Derived from training observations, the graph configuration describes neighboring relations among data supports. All neural modules are further extended for NDR mapping. The extension simply equips every neural module with a posterior weight that represents the image of the centroid of a correspondent data support. The image of each centroid and pictures of its neighboring centroids following the property of locally nonlinear embedding induce nonlinear constraints for optimizing all posterior weights.This work explores neural approximation for nonlinear dimensionality mapping supported internal representations of graph-organized regular data supports. Given training observations are assumed as a sample from a high-dimensional space with an embedding low-dimensional manifold. An approximating function consisting of adaptable built-in parameters is optimized subject to given training observations by the proposed learning process, and verified for transformation of novel testing observations to pictures within the low-dimensional output space. Optimized internal representations sketch graph-organized supports of distributed data clusters and their representative images within the output space. On the idea, the approximating function is in a position to work for testing without reserving original massive training observations. The neural approximating model contains multiple modules. Each activates a non-zero output for mapping in response to an input inside its correspondent local support. Graph-organized data supports have lateral interconnections for representing neighboring relations, inferring the minimal path between centroids of any two data supports, and proposing distance constraints for mapping all centroids to pictures within the output space. Following the distance-preserving principle, this work proposes Levenberg-Marquardt learning for optimizing images of centroids within the output space subject to given distance constraints, and further develops local embedding constraints for mapping during execution phase. Numerical simulations show the proposed neural approximation effective and reliable for nonlinear dimensionality reduction mapping.
Prácticas pedagógicas: una mirada a la formación de estudiantes en condición desplazamiento del colegio básico Santa María de la Cruz

Faced with the theme of this article, the following was taken into account: educational inclusion, reflection on pedagogical practice and training, and this is how the qualitative approach was used during the preparation of this work. In such a way that it was sought to explain the pedagogical practices from the teaching task, which made it necessary to elaborate a search in specialized databases and bibliographic files. Where there is a finding regarding reasonable and curricular adjustments in terms of the needs of the displaced population, such as: daily food, educational offer and acquisition of basic services (water and electricity). Therefore, it was established that the work of conventional pedagogical practice is not enough, since there are difficulties of a family and economic nature within the displaced population. For this, the permanent dialogue that the learning event in the individual has in common was implemented, since finally it is learned from a series of constant events in the school environment, where not only do the dynamics of daily living converge within a learning space, but also the experiences and difficulties within the family. In order to conclude, within the work with students in displacement condition, empathy is necessary, the flexibility of the contents and methods to obtain the required knowledge, where it is hoped to promote changes from the social, family and school scopes, providing an adaptation to the current environment, to improve coexistence and assimilate pedagogical practices.
Secured IoT Based Health Monitoring System

Health Monitoring is the pivotal part of human well being. In Today’s world of automation the field of biomedicine is no longer a distant part. Application of Technology has gained its importance in the field of biomedicine. It has improved the total process of medication. Earlier detection of diseases is possible through continuous monitoring of the patient. Health Monitoring has gained its importance from the past decades. Health monitoring systems are about to revolutionize the life of human by fast detection and real time monitoring of the patients. Continuous Monitoring of the patient is essential for earlier detection of diseases and taking the preventive measures possible. Continuous Monitoring of the patients by doctors is not always possible. To overcome this problem an IoT based continuous monitoring system is proposed in this paper where the data i.e.; the patient parameters are collected from the patient using the sensors and the data that is collected is processed at the processor. The processed data is updated to the cloud. Utilization of IoT innovations has arranged doctors and patients for connecting them to restorative regions in medicinal services environment. In this method the information collected by the wearable devices i.e.; sensors in real time are stored in database which connects doctors and people at the time of an emergency for the right information. Mostly, the health care systems concentrate on integrity and confidentiality of the data. Data Privacy, Data Integrity, Data Freshness, Authentication and Anonymity are the security requirements in IoT based health care systems. Cryptography is used to provide confidentiality to the data. In this paper we propose an Advance Encryption Standard Algorithm (AES) to provide security to the data that is updated into the cloud to finish the required prerequisites.
LibGuides: Summer Reading .... In Person AND Virtually 2021: Online Presentation Ideas (crafts, STEM and more!)

So you find yourself unable to hold your traditional summer reading program. Here are some ideas that you can use Ideas to use to present to your library patrons, digitally.
Implementation of Virtual National Digital Service Assistant Using Dialog Flow

I have studied for almost four years at my University. Here I have gained a lot ofknowledge, from which I found my destiny and work with that in the future. I found myfavorite subject Artificial Intelligence from my University and found it as a place to workon this topic at Hypertag Solutions Ltd. I learned from here how to work in corporate life.This company helps to build my professional career one step forward.Technology is increasing day by day and every moment new technology was invented.There is a competition among companies who work with technology. So there is achallenge to hold the local and international market value. That’s why I need to lot of workskills which instructed me Hypertag Solutions Ltd. I learned from here about ArtificialIntelligence, Chatbot-Framework, IoT, and Python programming language, which I canuse to solve real-life problem-solving.At the end of my internship period, I can say that it helped me to increase my skill, as aresult, I can solve any problem to build Chatbot. So, I avowedly want to thank myuniversity and Hypertag Solutions Ltd.
IoT, AI, and Blockchain: Catalysts for Digital Transformation

Blockchain is an emerging technology that can radically improve transactions security at banking, supply chain, and other transaction networks. It's estimated that Blockchain will generate $3.1 trillion in new business value by 2030. Essentially, it provides the basis for a dynamic distributed ledger that can be applied to save time when recording transactions between parties, remove costs associated with intermediaries, and reduce risks of fraud and tampering. This book explores the fundamentals and applications of Blockchain technology. Readers will learn about the decentralized peer-to-peer network, distributed ledger, and the trust model that defines Blockchain technology. They will also be introduced to the basic components of Blockchain (transaction, block, block header, and the chain), its operations (hashing, verification, validation, and consensus model), underlying algorithms, and essentials of trust (hard fork and soft fork). Private and public Blockchain networks similar to Bitcoin and Ethereum will be introduced, as will concepts of Smart Contracts, Proof of Work and Proof of Stack, and cryptocurrency including Facebook's Libra will be elucidated. Also, the book will address the relationship between Blockchain technology, Internet of Things (IoT), Artificial Intelligence (AI), Cybersecurity, Digital Transformation and Quantum Computing. Readers will understand the inner workings and applications of this disruptive technology and its potential impact on all aspects of the business world and society. A look at the future trends of Blockchain Technology will be presented in the book.
Think Before You Click: Follow the Basic Hygiene During Financial Transactions

]Financial scams have a long history. A long time ago, even in 300 BC, Kautilya in his famous book named “Arthashastra” gave a description of frauds. His exposition on forty ways of embezzlement bears the characteristics of some of the modern day financial frauds. The integration of diverse technological applications, coupled with the fast growth of online users in today’s digitized era, have created increased opportunities for the commission of financial scams. The Australian Competition and Consumer Commission states that one of the best ways to combat financial frauds and scams is to stay one step ahead of the fraudsters by being aware of scams. This article is also an effort to educate the general public on various fraudulent schemes with a view to empowering them to be aware of such scams.
LibGuides: Quick Guide to Getting Started in a Remote Environment: The Online Environment

Quick overview of some recommended practices for working/studying remotely. How to set-up and office environment, manage interruptions, best browsers, etc.. How best to manage in an online environment.
A geophysical analysis of the conceição river region quadrilátero ferrífero, based on field, petrographic, aerial images and airborne data

EnglishThe Quadrilatero Ferrifero region, located in the southeast portion of the Sao Francisco Craton, is one of the main metallogenic provinces in Brazil. Fieldwork, petrography, high-resolution airborne geophysics (magnetic and gamma-ray spectrometry data), and aerial images allowed us to produce a new map at the 1:25,000 scale, with important contributions in the lithotypes detailing, understanding of the geological structures and relationship between the different stratigraphic units. Interpretation of airborne geophysical data integrated with field structural and lithological observations were successfully employed in the creation ofthe litho-structural framework in a poorly exposed Proterozoic and Archean terrain. Airborne gamma-ray spectrometry data aided in the mapping process in areas with regolith cover including erosional ridges. The magnetic total derivative image revealed regional and local structures. In addition, our work details the units of occurrence of Rio das Velhas and Minas Supergroup. The aerial coverage of the Minda and Santa Quiteria formations strongly increased, as well as the area of the Caue Formation was better defined. The new geological map provides many improvements over the pre-existing maps. New lithological facies and structures were identified and others become more visible and lithological boundaries are refined or confirmed portuguesA regiao do Quadrilatero Ferrifero, sudeste do Craton de Sao Francisco, e uma das principais provincias metalogenicas do Brasil. Trabalho de campo, petrografia, geofisica aerea de alta resolucao (dados de espectrometria magnetica e de raios gama) e imagens aereas nos permitiram produzir um novo mapa naescala 1: 25.000, com importantes contribuicoes no detalhamento de litotipos, no entendimento das estruturas geologicas e nas relacoes entre as diferentes unidades estratigraficas. A interpretacao de dados geofisicos aereos, integrados a observacoes estruturais e litologicas de campo, foi empregada com sucesso na criacao da estrutura lito-estrutural em um terreno Proterozoico e Arqueano pouco exposto. Dados de espectrometria de raios gama auxiliaram no processo de mapeamento em areas com extensa cobertura de regolito, incluindo cinturoes erosivos. A imagem derivada total magnetica revelou estruturas regionais e locais. Alem disso, este trabalho detalha as unidades de ocorrencia dos Supergrupos Rio das Velhas e Minas. A cobertura aerea das formacoes Minda e Santa Quiteria aumentou fortemente, assim como a area da Formacao Caue foi melhor definida. Novas facies e estruturas litologicas foram identificadas e outras se tornam mais visiveis e os limites litologicos sao refinados e/ou confirmados
Two-stage structure aware image inpainting based on generative adversarial networks.

In recent years, the image inpainting technology based on deep learning has made remarkable progress, which can better complete the complex image inpainting task compared with traditional methods. However, most of the existing methods can not generate reasonable structure and fine texture details at the same time. To solve this problem, in this paper we propose a two-stage image inpainting method with structure awareness based on Generative Adversarial Networks, which divides the inpainting process into two sub tasks, namely, image structure generation and image content generation. In the former stage, the network generates the structural information of the missing area; while in the latter stage, the network uses this structural information as a prior, and combines the existing texture and color information to complete the image. Extensive experiments are conducted to evaluate the performance of our proposed method on Places2, CelebA and Paris Streetview datasets. The experimental results show the superior performance of the proposed method compared with other state-of-the-art methods qualitatively and quantitatively.
Scalable Graph Neural Network Training

Graph Neural Networks (GNNs) are a new and increasingly popular family of deep neural network architectures to perform learning on graphs. Training them efficiently is challenging due to the irregular nature of graph data. The problem becomes even more challenging when scaling to large graphs that exceed the capacity of single devices. Standard approaches to distributed DNN training, such as data and model parallelism, do not directly apply to GNNs. Instead, two different approaches have emerged in the literature: whole-graph and sample-based training. In this paper, we review and compare the two approaches. Scalability is challenging with both approaches, but we make a case that research should focus on sample-based training since it is a more promising approach. Finally, we review recent systems supporting sample-based training.
Adult Education and Full-time Professionals' Problem Solving Skills: Insights From the Survey of Adult Skills

Sponsored by OECD, PIAAC represents the first attemptto assess adult problem solving in technology-rich environments(PS-TRE) on an international scale that is comparablecross-culturally and cross-nationally. The objectives of this studyare to study (1) the distributions of PS-TRE proficiency scoresacross 14 selected countries and (2) within each country, theassociations between PS-TRE proficiency scores and the differentformats of adult education and training (AET) participation. Usingdata on full-time professionals (at least 25 years old) from thesecountries, propensity score weighting was applied to estimate theassociations between the different formats of AET participation andtheir PS-TRE proficiency scores. To place these estimates incontext, parallel analyses were conducted – one with the sample offull-time associates in the 14 selected countries and the otherwith full-time professionals’ Literacy and Numeracy proficiencyscores as measured by PIAAC. The results showed that aftercontrolling for socio-demographic background, occupationalcategories, use of key information-processing skills (both at homeand at work), as well as use of generic workplace skills, noconsistent pattern was found across the 14 selected countries. Atthe individual country level, scattered significant relationshipswere identified. For example, in Denmark, both formats of AETparticipation (vs. None) are significantly and positivelyassociated with full-time professionals’ PS-TRE proficiency scoresand their probability of scoring in the top quartile of the PS-TREdistribution (p < .01). While in the United States, Formal AET(vs. None) is significantly and positively associated withfull-time associates’ PS-TRE proficiency scores and theirprobability of scoring in the top quartile of the PS-TREdistribution (p < .01). The variations in relationships betweenthe different formats of AET participation and working adults’skills proficiency across domains and samples indicate thenecessity of conducting qualitative research on AET programs inindividual countries. Furthermore, to provide recommendationstailored to the specific needs of each country, a fine-grainedclassification of AET programs based on the OECD guideline wassuggested.
Comparison of LSTM and GRU in Video Activity Recognition Using Transfer Learning and Time Distributed Layer in Keras

With the advancements in Deep Learning, many feats are achievable today that can be considered technologically appealing and also very useful in real life. Our paper aims to recognize human activity in videos using Deep Learning and compare Gated Recurrent Unit (GRU) efficiency with Long Short Term Memory (LSTM) and various transfer learning models to determine an optimal and simplistic action recognition mode. Taking video as input, we convert it into sequences of frames, process each frame through a Convolutional Neural Network (CNN), and connect the entire sequence, using a time distributed layer, to LSTM or a GRU. To achieve this, we train our model on UCF101 and HMDB51, two large video activity datasets. UCF101 has 101 action classes that are widespread across various activities, and HMDB51 has 51 action classes that are more focused on humans' movement.Activity recognition is beneficial as it can help us refine many parts of society, especially post-pandemic. This project is our attempt at activity recognition and analysis on a couple of methods that can hopefully be helpful.
Face Mask Detection using Mobile Net V2

Face Mask Detection is an amazing field of study, during the times, we need to ensure proper mask wearing to avoid the infections and reduce the case load on the medical facilities. In this report we discuss various ideas and algorithms to detect the face mask by taking a look at various papers. The algorithms discussed in this paper are already being used for face detection and mask detection. This model will be used to detect face masks and ensure proper mask wearing.
MODEL KERANGKA PEMIKIRAN SEBAGAI UPAYA KELAYAKAN TULISAN TUGAS AKHIR

Final Task Writing is the most important part of learning terminals whose initial understanding is to attempt to fully explain the integrated topic area among the topics chosen, why do they?and the formulation and purpose written. Unfortunately, not many final assignments succeededin making background and topic issues diusungnya well, in part because some of the problemsinclude the vagueness and ridicule, partly due to copy-paste factors of certain paragraphs ofwriting so that the flow of paragraphs to be less good , partly due to lack of equipped with thebasis of several reasons on the release (less diligent reading libraries). The method used is touse qualitative descriptive for the needs of exposure feasibility analysis and design ofinformation systems. Referring to the background and the methods used then generated anunderstanding strategy in the model that is expected to provide benefits, to help realize the easeand feasibility of a decent final tome.
A Metaknowledge Framework for the Training of Trainers Using Smart Mobile Learning Applications

Smart mobile learning (SML) is evolving due to the availability of devices, advancement in technologies (artificial intelligence and big data analytics), and a pressing need from users for a change in their learning environment. SML has impacted many facets of learning and one of it is the training of trainers (ToT). This chapter presents a state-of-the-art SML framework for the training of trainers - called the SCALED framework. The SCALED framework allows trainers to plan their training based on metaknowledge related to stakeholders, competencies, affordances, learners, evaluation and design intentions. The chapter presents two case studies, one related to SDG4 and the other to SDG3, both addressing the needs of developing countries in the ToT and how the trainers collaborate with their partners in reaching the goals of their programs. The case studies provide excellent instances of how metaknowledge surfaced using the SCALED framework.
LibGuides: 855XCFall2020-Dr.SuttonEcoliteracy: Young Adult Titles

This is a guide to the Poudre River Library District's new Ecoliteracy Collection, developed due to the generosity of a local patron.
Incremental multi-view object detection from a moving camera.

Object detection in a single image is a challenging problem due to clutters, occlusions, and a large variety of viewing locations. This task can benefit from integrating multi-frame information captured by a moving camera. In this paper, we propose a method to increment object detection scores extracted from multiple frames captured from different viewpoints. For each frame, we run an efficient end-to-end object detector that outputs object bounding boxes, each of which is associated with the scores of categories and poses. The scores of detected objects are then stored in grid locations in 3D space. After observing multiple frames, the object scores stored in each grid location are integrated based on the best object pose hypothesis. This strategy requires the consistency of object categories and poses among multiple frames, and thus it significantly suppresses miss detections. The performance of the proposed method is evaluated on our newly created multi-class object dataset captured in robot simulation and real environments, as well as on a public benchmark dataset.
Soil Movement Analysis Using Geospatial Information System Method in Gunuang Padang Area Batang Arau Village

Abstract Batang arau village is an area that has an area around Gunung Padang. Padang Mountain is a small hill in the south of the Padang city which is directly adjacent to the Indian Ocean. Based on existing history, this area has experienced landslides which caused property losses and the loss of the lives of residents in that location. As a tourist area and a fairly densely populated residential area, knowing the areas prone to land movement is a must, so that people understand the potential for land movement that exists in the location where they live. Geospatial information system is a method that can be used to map the soil vulnerability conditions in the research area. Based on the soil movement model developed by Puslittanak in 2004, 6 supporting maps are needed to produce a map of soil movement around the research area, namely, regional geological maps, rainfall maps, geomorphological maps, soil type maps, and rock type maps. The six maps are then combined into one to produce a soil vulnerability map in the research location. Based on the results of research, the area of Batang arau subdistrict is included in areas with medium-very high soil movement vulnerability. Areas that are included in residential areas are in the medium vulnerability category, while areas that are included in the tourism object of Siti Nurbaya are in the very high vulnerability category.