Reviewerly/exHarmony huggingface dataset:
DESENVOLVIMENTO DE APLICATIVO COM REALIDADE AUMENTADA PARA O ENSINO DE DESENHO TÉCNICO, ELEMENTOS DE MÁQUINAS E PROJETOS MECÂNICOS

Augmented Reality (AR) is an interactive technology that introduces virtual objects or images into the real world.This technology can be used in applications for smartphones and tablets, without a computer with high processing power.AR is a suitable technology to be applied in the educational field.The user interact with the object of study, stimulating their interest and learning.This article presents the development of an application compatible with different operational systems for smartphones, to assist teaching technical design, machine elements and mechanical projects.The application can be used at different moments for learning: either in pre-preparation for classes, during classes or after them, to review the contents studied or to perform exercises.This project is a partnership between Federal University of ABC (UFABC) and Centro Universitário FEI.In each institution, two scientific initiation projects were approved and are under development and tests.
Knowledge-Based Information Retrieval System for Construction Industry : Applying Knowledge Management Concept in Small and Medium Enterprise

Knowledge management (KM) is the process of making the best use of knowledge and information within an organization to achieve business objectives.  It allows people to access and apply the most appropriate knowledge when it is needed and supports learning. Knowledge-Based Information Retrieval System is one of the tools built for small and medium enterprises (SMEs), especially for construction companies that need to manage information in their abundance of explicit documents that are dependent to the employees’ tacit knowledge.  Through this system, the employees can share their explicit and implicit knowledge in the knowledge management system, which is developed with the concept of knowledge-based information retrieval.  The development of this system will ease employees’ work in the company in terms of retrieving information, sharing knowledge, leveraging knowledge management, and so on.  The main function of the Knowledge-Based Information Retrieval (KBIT) system is to enable users to search for files related to project or tender, or files needed at that time by project name or by location.  Besides that, users can view the files and scroll the documents through the system interface.  This system will bring a significant impact to the users, who consist of the administrator and employees.  In addition to that, it will improve the business process and ensure efficiency in the company’s business value chain.  The important contribution of this system is in terms of reducing waste of time to search for files.
Training Deep Neural Networks with Constrained Learning Parameters

Today's deep learning models are primarily trained on CPUs and GPUs. Although these models tend to have low error, they consume high power and utilize large amount of memory owing to double precision floating point learning parameters. Beyond the Moore's law, a significant portion of deep learning tasks would run on edge computing systems, which will form an indispensable part of the entire computation fabric. Subsequently, training deep learning models for such systems will have to be tailored and adopted to generate models that have the following desirable characteristics: low error, low memory, and low power. We believe that deep neural networks (DNNs), where learning parameters are constrained to have a set of finite discrete values, running on neuromorphic computing systems would be instrumental for intelligent edge computing systems having these desirable characteristics. To this extent, we propose the Combinatorial Neural Network Training Algorithm (CoNNTrA), that leverages a coordinate gradient descent-based approach for training deep learning models with finite discrete learning parameters. Next, we elaborate on the theoretical underpinnings and evaluate the computational complexity of CoNNTrA. As a proof of concept, we use CoNNTrA to train deep learning models with ternary learning parameters on the MNIST, Iris and ImageNet data sets and compare their performance to the same models trained using Backpropagation. We use following performance metrics for the comparison: (i) Training error; (ii) Validation error; (iii) Memory usage; and (iv) Training time. Our results indicate that CoNNTrA models use 32x less memory and have errors at par with the Backpropagation models.
Developing Technological Competence of Students in Russian Language Classes

At the same time, the methods of forming the technological competence of future teachers are described. The key indicators of technology are standardization, unification of production processes and their implementation under certain conditions. It should be noted, technological ideas in didactics and pedagogy cannot be called a new concept. Technology in the pedagogical process is a set of psychological methods, pedagogical methods, didactic conditions and other indicators aimed at meeting the needs of the country in the field of higher professional education and the needs of the society.
Reinvestigating Entrepreneurship Financing and Poverty Eradication in Nigeria: Any Difference from the Case of Small and Medium Scale Enterprises?

This study focuses on examination of the relationship between entrepreneurship financing from the perspective of Small and Medium Scale Enterprises (SMEs) and poverty eradication in Nigeria from 1990 to 2018. Data were collected from the Central Bank of Nigeria Statistical Bulletin and World Development Indicators. Consequently, Autoregressive Distributed Lagged, Bounds test and Error Correction Model techniques were utilized to address the objective of the study. Agriculture and forestry financing did not eradicate poverty in the short run in Nigeria. However, the aggregate commercial banks` SMEs financing, the manufacturing and food processing business financing contributed to poverty eradication in in the short run and long run respectively. But broad money supply contributed to poverty eradication in the short run only. However, this study recommends that any time the goal of the policy makers is to eradicate poverty in Nigeria, entrepreneurship financing with reference to manufacturing and food processing should be embarked upon since it has the capacity to alleviate poverty in the both short run in the country. Similarly, the Central Bank of Nigeria should implement appropriate policies that will increase commercial banks’ lending towards agricultural sub sector.
Professional Development Of The Teacher's Personality In The System Of Secondary Special Vocational Education

In the article, the role of a teacher as a receiver and conductor of professional knowledge as a professional development of a teacher in the system of secondary special and professional education is not only to systematize different information flows to form a holistic approach to informing and obtaining information. Creation of conditions for interest in self-education using communication technologies, one of the aspects of such interest is the implementation of innovative approaches to professional development of the teacher.
UPAYA MENINGKATKAN AKTIVITAS DAN HASIL BELAJAR MATEMATIKA DENGAN METODE TUTOR SEBAYA PADA MATERI MATRIKS

The purpose of this study was to improve the activities and learning outcomes of students of class XI MIA 3 in SMA Negeri 1 Sekadau in the academic year 2019/2020 on matrix material using peer tutoring methods. This type of research is a classroom action research carried out in 2 cycles, each cycle consisting of 4 stages of planning, action implementation, observation and reflection. Retrieval of data through a collection using a collection of observations and learning outcomes are obtained by providing a final test at the end of each cycle. The results of observations for student activity during pre-cycle 67.81% in the first cycle to 76.88% and in the second cycle to 86.46% means that there is an increase of 17.82%. Mathematical learning outcomes from the first cycle increased by 41.00%, while from the first cycle to the second cycle increased by 14.46%. So it can be removed by using the peer tutoring method to increase the activity and learning outcomes of students of class XI MIA 3 of SMA Negeri 1 Sekadau in the academic year 2019/2020 on matrix material.Keywords: activities, learning outcomes of peer tutoring methods, matrix
"Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groups

WhatsApp is the most popular messaging app in the world. Due to its popularity, WhatsApp has become a powerful and cheap tool for political campaigning being widely used during the 2019 Indian general election, where it was used to connect to the voters on a large scale. Along with the campaigning, there have been reports that WhatsApp has also become a breeding ground for harmful speech against various protected groups and religious minorities. Many such messages attempt to instil fear among the population about a specific (minority) community. According to research on inter-group conflict, such `fear speech' messages could have a lasting impact and might lead to real offline violence. In this paper, we perform the first large scale study on fear speech across thousands of public WhatsApp groups discussing politics in India. We curate a new dataset and try to characterize fear speech from this dataset. We observe that users writing fear speech messages use various events and symbols to create the illusion of fear among the reader about a target community. We build models to classify fear speech and observe that current state-of-the-art NLP models do not perform well at this task. Fear speech messages tend to spread faster and could potentially go undetected by classifiers built to detect traditional toxic speech due to their low toxic nature. Finally, using a novel methodology to target users with Facebook ads, we conduct a survey among the users of these WhatsApp groups to understand the types of users who consume and share fear speech. We believe that this work opens up new research questions that are very different from tackling hate speech which the research community has been traditionally involved in.
Efficient Management and Optimization of Very Large Machine Learning Dataset for Question Answering

Question answering strategies lean almost exclusively on deep neural network computations nowadays. Managing a large set of input data (questions, answers, full documents, metadata) in several forms suitable as the first layer of a selected network architecture can be a non-trivial task. In this paper, we present the details and evaluation of preparing a rich dataset of more than 13 thousand question-answer pairs with more than 6,500 full documents. We show, how a Python-optimized database in a network environment was utilized to offer fast responses based on the 26 GiB database of input data. A global hyperparameter optimization process with controlled running of thousands of evaluation experiments to reach a near-optimum setup of the learning process is also explicated.
THE DESTINY OF PHILOSOPHY IN THE DIGITAL AGE

Philosophy and Humanities, their place and role in the society and culture of the digital epoch are analyzed in the article. The position of philosophy in the modern educational system is considered. The typical setting of nowadays for the formation of a "one-dimensional person" - a narrow-profiled specialist - is criticized. The forecasts for the future of humanitarian education and philosophy of the leading theoreticians and experts of the digital era are cited. It’s concluded that the role of philosophy in the digital age is to create the new life-affirming meanings and preserve previous worldview standards that have confirmed their validity and effectiveness throughout history.
Unpacking Information Bottlenecks: Surrogate Objectives for Deep Learning

The Information Bottleneck principle offers both a mechanism to explain how deep neural networks train and generalize, as well as a regularized objective with which to train models. However, multiple competing objectives are proposed in the literature, and the information-theoretic quantities used in these objectives are difficult to compute for large deep neural networks, which in turn limits their use as a training objective. In this work, we review these quantities, compare and unify previously proposed objectives, which allows us to develop surrogate objectives more friendly to optimization without relying on cumbersome tools such as density estimation. We find that these surrogate objectives allow us to apply the information bottleneck to modern neural network architectures. We demonstrate our insights on MNIST, CIFAR-10 and ImageNette with modern DNN architectures (ResNets).
A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction

Joint entity and relation extraction framework constructs a unified model to perform entity recognition and relation extraction simultaneously, which can exploit the dependency between the two tasks to mitigate the error propagation problem suffered by the pipeline model. Current efforts on joint entity and relation extraction focus on enhancing the interaction between entity recognition and relation extraction through parameter sharing, joint decoding, or other ad-hoc tricks (e.g., modeled as a semi-Markov decision process, cast as a multi-round reading comprehension task). However, there are still two issues on the table. First, the interaction utilized by most methods is still weak and uni-directional, which is unable to model the mutual dependency between the two tasks. Second, relation triggers are ignored by most methods, which can help explain why humans would extract a relation in the sentence. They're essential for relation extraction but overlooked. To this end, we present a Trigger-Sense Memory Flow Framework (TriMF) for joint entity and relation extraction. We build a memory module to remember category representations learned in entity recognition and relation extraction tasks. And based on it, we design a multi-level memory flow attention mechanism to enhance the bi-directional interaction between entity recognition and relation extraction. Moreover, without any human annotations, our model can enhance relation trigger information in a sentence through a trigger sensor module, which improves the model performance and makes model predictions with better interpretation. Experiment results show that our proposed framework achieves state-of-the-art results by improves the relation F1 to 52.44% (+3.2%) on SciERC, 66.49% (+4.9%) on ACE05, 72.35% (+0.6%) on CoNLL04 and 80.66% (+2.3%) on ADE.
Prototyping process in education and science

Current article shows the prototyping process, types of prototyping models, best practices of prototyping, limitations of prototypes, advantages, and disadvantages of prototyping, as well as differences between software and hardware prototyping. Prototyping infrastructures are also shown here. In this case, three examples from Riga Technical University are offered. This publication demonstrates the authors' opinions only, and it cannot be regarded as the European Union's official position. The paper is developed in a frame of the project “ERASMUS+ Capacity-building in the Field of Higher Education 2019 Call for Proposals. Development of practically-oriented student-centered education in the field of modeling of Cyber-Physical Systems, CybPhys. 609557-EPP-1-2019-1-LV-EPPKA2-CBHE-JP - ERASMUS+ CBHE. The article is also devised for students of applied physic and electrical engineering specialties. The article can be useful for students and professionals focusing on innovation management issues.
Subject Guides: Extended Reality Space: Extended Reality Space

Virtual Reality (VR), Augmented Reality (AR), & Mixed Reality (MR), when taken beyond the bounds is referred to as Extended Reality (XR). This Space is for Research, Development, and Experimentation.
The 9th RailTopoModel conference took place on 22 May 2018 in Paris – UIC Communications

The 9th RailTopoModel conference took place in Paris, with contributions and participants from institutions, railway companies, software development companies, universities, and other…
Subject Guides: Extended Reality Space: High-End VR Headsets Available

Virtual Reality (VR), Augmented Reality (AR), & Mixed Reality (MR), when taken beyond the bounds is referred to as Extended Reality (XR). This Space is for Research, Development, and Experimentation.
Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples

This work describes simple and efficient algorithms for interactively learning non-binary concepts in the learning from random counter-examples (LRC) model. Here, learning takes place from random counter-examples that the learner receives in response to their proper equivalence queries. In this context, the learning time is defined as the number of counter-examples needed by the learner to identify the target concept. Such learning is particularly suited for online ranking, classification, clustering, etc., where machine learning models must be used before they are fully trained. We provide two simple LRC algorithms, deterministic and randomized, for exactly learning non-binary target concepts for any concept class $H$. We show that both of these algorithms have an $\mathcal{O}(\log{}|H|)$ asymptotically optimal average learning time. This solves an open problem on the existence of an efficient LRC randomized algorithm while simplifying and generalizing previous results. We also show that the expected learning time of any arbitrary LRC algorithm can be upper bounded by $\mathcal{O}(\frac{1}{\epsilon}\log{\frac{|H|}{\delta}})$, where $\epsilon$ and $\delta$ are the allowed learning error and failure probability respectively. This shows that LRC interactive learning is at least as efficient as non-interactive Probably Approximately Correct (PAC) learning. Our simulations show that in practice, these algorithms outperform their theoretical bounds.
Smart Auto Insurance: High Resolution, Dynamic, Privacy-Driven, Telematic Insurance

Data driven approaches to problem solving are, in many regards, the holy grail of evidence backed decision making. Using first-party empirical data to analyze behavior and establish predictions yields us the ability to base in-depth analyses on particular individuals and reduce our dependence on generalizations. Modern mobile and embedded devices provide a wealth of sensors and means for collecting and tracking individualized data. Applying these assets to the realm of insurance (which is a statistically backed endeavor at heart) is certainly nothing new; yet doing so in a way that is privacy-driven and secure has not been a central focus of implementers. Existing data-driven insurance technologies require a certain level of trust in the data tracking agency (i.e. insurer) to not misuse, mishandle, or over-collect user data. Smart contracts and blockchain technology provide us an opportunity to re-balance these systems such that the blockchain itself is a trusted agent which both insurers and the insured can confide in. We propose a "Smart Auto Insurance" system that minimizes data sharing while simultaneously providing quality-of-life improvements to both sides. Furthermore, we use a simple game theoretical argument to show that the clients using such a system are disincentivized from behaving adversarially.
DIDACTIC PROJECTING AS A PRECONDITION FOR BUILDING UP OF THE TEACHING EXPERIENCE AMONG PROSPECTIVE ENGINEERS-EDUCATORS

The article deals with projecting that is an important component of the formation of a new pedagogical ideology - lifelong learning. Projecting as an educational function of the pedagogical system, becomes an important factor in human success, generating new ideas and continuous updating of outdated knowledge. It is analyzed that the practice of projecting is so widespread that in a relatively short period of time within the pedagogical system project types of work at different levels of government are actively implemented. In particular, teachers project educational programs, pedagogical technologies, including educational, upbringing, development subsystems. Thus, the preparation of teachers for design activities becomes relevant, and this in turn determines the understanding of the productive application of projecting logic, the formation of technologies for such activities. Project activity is multifaceted and multifunctional, as it allows to form both engineering skills and abilities, as well as psychological and pedagogical. The psychological basis of didactic projecting is the readiness of the individual to constantly improve their competence, mobility of professional functions. In fact, it is a question of training at the university of an engineer-teacher-professional, competent in the main spheres of pedagogical activity. Training of vocational training establishment students, that is aimed at developing the projecting activity related knowledge, skills, and abilities among prospective engineer-educators is supposed to essentially scale up the vocational training quality with high education establishment graduates, to foster development of their professional mobility and competitiveness in the labor market. Hence it is necessary to ensure high level of professional competence among the graduates, which, in turn, is supposed to provide for involvement of each student into high-quality educational and learning; to provide for building up the core research activity features with prospecting professionals, for building up the skills to gain individually up-front knowledge and put it efficiently into practice. The so called educational projecting, being an option for the implementation of high-quality education concept, fully complies with the said requirements.
Dark Web Marketplaces and COVID-19: Before the Vaccine

The COVID-19 pandemic has reshaped the demand for goods and services worldwide. The combination of a public health emergency, economic distress, and misinformation-driven panic have pushed customers and vendors towards the shadow economy. In particular, dark web marketplaces (DWMs), commercial websites accessible via free software, have gained significant popularity. Here, we analyse 851,199 listings extracted from 30 DWMs between January 1, 2020 and November 16, 2020. We identify 788 listings directly related to COVID-19 products and monitor the temporal evolution of product categories including Personal Protective Equipment (PPE), medicines (e.g., hydroxyclorochine), and medical frauds. Finally, we compare trends in their temporal evolution with variations in public attention, as measured by Twitter posts and Wikipedia page visits. We reveal how the online shadow economy has evolved during the COVID-19 pandemic and highlight the importance of a continuous monitoring of DWMs, especially now that real vaccines are available and in short supply. We anticipate our analysis will be of interest both to researchers and public agencies focused on the protection of public health.
Analysis and Design of E-Commerce Information Systems Using the User Centered Design Method at PT. Pure Healthy Nature

PT.Murni Alam Sehat is one of company sell medical device and medicine by prescription or without doctorâ€™s prescription. The problem in marketing department in this company do not have promotion media for offer the products to consumer, no online sell, and order only by phone. To solve the problem in marketing department is build up e-commerce system. E-commerce system use Unifed Modelling Languange (UML); Activity Diagram, Use Case Diagram, Sequence Diagram, and Class Diagram. User Centered Design (UCD) as design method. Program language for e-commerce system is PHP and MySQL for database. The result of research is a e-commerce system website base to help search information product, company could get report easier, consumer can do purchase by online everywhere and everytime.