Reviewerly/exHarmony huggingface dataset:
Smart Attendance System Using Machine Learning Algorithms

In face recognition, the difficult task is to detect the face from the existing database, and the problems may arise due to bad image quality, bad lighting, or the person not facing the camera, etc. The face of the same person will change for different expressions. If the person's age changes, the size, and color of the person may also change, which makes face recognition more difficult. In this paper, the proposed system android application is used for face recognition for attendance system. The proposed system can be used in colleges and schools for attendance systems. The human face will be recognized from the image uploaded to the server of that particular class; this can be done by using an algorithm. Here we are using the HAAR cascade classifier to identify the face by removing noise and crop the face in the image. Then, at that point the HOG (Histogram Of Gradients) calculation is utilized to extricate the features from the detected face. In the last step, we use SVM (Support Vector Machine) classifier to identify the person from our database. When the person is found in the image, then the attendance will be automatically allocated in the excel sheet of that particular class.
Ericsson announces new education and youth empowerment commitments

Corporate Philanthropy ReportVolume 37, Issue 3 p. 3-3 News Briefs Ericsson announces new education and youth empowerment commitments First published: 10 February 2022 https://doi.org/10.1002/cprt.31193Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume37, Issue3March 2022Pages 3-3 RelatedInformation
One-Class Classification Approach in Accelerometer-Based Remote Monitoring of Physical Activities for Healthcare Applications

Human activity recognition (HAR) enables numerous application scenarios in ambient assisted living thanks to IoT integration. Healthcare is one of the most prominent use cases of HAR serving individuals who suffer from aging or disabilities as well as healthy people. Remote monitoring of daily activities within home environment may offer assistance in tracking adherence of patients to therapeutical procedures such as exercise monitoring. Within an IoT framework, the type of sensors used influences usability of the HAR system. Accelerometers introduce a noninvasive sensing option as opposed to cameras which intrude into users’ privacy. Recognition of target activities when they are performed among other activities brings about challenges. Typical multi-class classification employed in such recognition tasks necessitates training data collection for all activity types which can be encountered in the prediction stage. Due to unlimited variety of daily living activities, the number of activity classes for which training data should be collected is infinitely many. Expressing the recognition problem in terms of one-class classification (OCC) architecture can aid in resolving this bottleneck. In this chapter, we propose an OCC-based HAR architecture with IoT integration. In our OCC scheme, we utilize artificial data generation (ADG) to generate training data for the negative class based on the target class. In the proposed model, the target class is the only class for which training data are collected. The OCC scheme enables recognizing the target class when the other class is represented with artificially generated data. We present the results of an experimental study for our OCC model on a dataset consisting of ambulatory and static activities.
MONITOR: a multi-domain machine learning approach to predicting in-hospital mortality

Christina C. Guerrier, Salvatore J. D’Acunto, Guillaume P. L. Labilloy, Rhemar A. Esma, Heather A. Kendall, Daniel A. Norez, Jennifer N. Fishe
Application of blockchain technology in automobile ecosystem

Aiming at the challenges of opaque transaction data and lack of mutual trust among participants in the service ecosystem of the automobile industry, a de-trust system model based on blockchain smart contract technology is constructed, which takes the after-sales prepaid products from an automobile manufacturer as the research object. Under the scenario of prepaid products in automotive industry, this model systematically discusses the roles of participants, business processes and smart contract solution in the blockchain for the first time. The proposed model can not only enrich the application scenarios of consortium blockchain but also be extended to more cross industry ecosystems.
Psychotes Papi Kostick Web-Based Online

Technological progress has experienced very rapid development. Technology that has developed began to slowly replace the conventional way of becoming more modern, especially in terms of psychological tests. The psychological test known as a psychological test is a test that is carried out to measure aspects of the individual psychologically. One of these psychological tests is the PAPI Kostick Test which is an inventory report of a person's personality in a work environment. The online PAPI Kostick test was developed using HTML, CSS, JavaScript, and PHP. Application testing was conducted to obtain correlation in terms of appearance, comfort, convenience, satisfaction, and long-term use.
A Comparative Analysis of Euclidean-Support Vector Machine

Support vector machines (SVMs) are a class of machine learning algorithms which use kernel functions to map data into feature space, where a separating hyperplane can be computed to classify the data. Generally, SVMs are reliant on the selection of kernel function and parameters, which can severely downgrade the performance of the classifier if mishandled. In this paper, we compare the performance of the Euclidean-SVM (ESVM), that have low dependency on kernel selection, with conventional SVM. The comparison are conducted with two different datasets. Results show that the ESVM has higher performance consistency as compared to SVM.
Aplikasi Quizizz Menumbuhkan Minat Belajar Matematika Siswa Kelas 2 SD Muhammadiyah 18 Surabaya di Era Pandemi Covid-19

This study aims to (1) describe the growth of students' learning interest in mathematics learning (2) knowing the students' learning outcomes after applying daily assessment questions through quizizz application. Quizizz application is an application that is easy to access can mealaui Mobile or laptop. Quizizz application can add science and also in this learning process students will not be saturated because they can learn while playing. This type of research is qualitative research. The respondents were grade 2 students at SD Muhammadiyah 18 Surabaya. Data obtained through such as; (1) quizzes of learners' response to the interest of learners in the ease of accessing quizizz applications, 2) the reach of learning objectives through quizizz applications, 3) the availability of facilities in learning using quizizz applications. The result of this study is that students' interest in online learning through quizziz application is very good with a percentage of 94% and the understanding of materials in math lessons has been good at 86.6%. While the device or facilities used for online learning such as smartphones, laptops, internet quota or wifi are also good at 80,59%. Students' interest in using quizizz apps on math learning is effective. The conclusion is that learning through quizizz application can foster students' learning interest.
Document Classification with Termolator for COVID-19 Literature

The COVID-19 pandemic has led to a multiplicity of research publications related to various aspects of coronavirus. Research topics range from COVID-19 transmission mechanisms to the public health response of various countries, and the publications need to be categorized for easier and more efficient access to resources. This paper explores various machine learning-based document classification techniques to categorize COVID-19 related literature. We integrate a novel terminology dictionary with machine learning models to study the dictionary's impact on the effectiveness of various classification techniques. We report a slight boost to F1 scores as a result of our modifications.
Fostering Integrated Governance Quality through Technology Penetration: Thresholds of Democracy in Sub-Saharan Africa

In the quest for the attainment of democracy with its fully unleashed potentials, the role of information and communication technology (ICT) is integral within this current knowledge economy disposition. The study explores the effect of mobile technology penetration on governance quality from the unconditional and marginal effects of mobile phones and diverse democracy indicators. The analysis is carried out by applying the instrumental variables (IV) Tobit regression to the data to examine the relationship among the variables of interest with a view to handling possible endogeneity issues in the empirical model. The study finds that weak democracy is detrimental to the effect of mobile phone penetration on integrated governance quality and that the higher the mobile phone penetration, the lower the weak democracy quality in SSA. The study concludes by recommending efforts and policies to be enacted and implemented such as the enhancement of mobile technology for concise quality governance.
The Network Distance Learning System Aided by Computer Information and Online Video Technology

With the continuous development and progress of computer network technology, big data mining has been vigorously applied in various industries. The informatization of teaching and educational administration is an important part of the informatization of college education. The system designed in this paper adopts a three-tier structure development model to build a curriculum resource and teaching management system based on .NET technology as the core system plan. The article explores the realization of curriculum resources and teaching management platform solutions through WEB technology. The system revolves around the teaching resources and activities involved in the whole process of teacher&#x0027;s course teaching, and realizes the teaching plan, syllabus, teaching schedule, experiment arrangement, learning resources (multimedia resources, courseware, teaching and learning templates and tools, cases, Documents, student work) and other resource information on the Internet publishing, inquiries, browsing and updating. In addition, extracurricular supplementary teaching activities between teachers and students can be carried out through online Q&#x0026;A, teacher-student exchanges and special discussions.
Naive Bayesian Classifier Based Semi-supervised Learning for Matching Ontologies

The evolution of Semantic Web (SW) depends on the increasing number of ontologies it contains. However, the existing ontologies are diverse in structure and content, because their design standards are different. In order to ensure their knowledge sharing, the correspondences between different ontology entities should be determined, which is called ontology matching. Currently, various ontology matching techniques have been proposed, which makes use of different strategies or methods to improve the quality of alignment. Being enlightened by the success of Semi-supervised Learning (SL) in ontology matching domain, this paper further proposes aN aive Bayesian Classifier (NBC) based SL, and use it to obtain high-quality alignment. In particular, our approach first models the ontology matching issue as a binary classification problem; then the Positive and Unlabeled (PU) training set and the negative examples that are determined by it is constructed, which is used to train the NBC; and finally, with the learned knowledge, the complete ontology alignment is obtained. The testing cases used in the experiment are provided by Ontology Alignment Evolution Initiative (OAEI). Comparing with the existing ontology matching techniques, the results show that the effectiveness of our method.
Hyperlocal Covert Communication using FM Radio Waves

In this paper, a proposal is presented to put light into the possibility of using FM radio waves for hyperlocal covert communication. In this proposal, FM radio waves are used to send secret information through phase encoding of audio signal. This kind of information hiding is called steganography. The data to be hidden is encrypted and inserted by modulating phase value of audio signal. Phase coding is being used instead of Spread Spectrum Encoding or Least Significant Bit(LSB) based techniques for covert communication using FM radio waves. It is observed that the performance of the proposed algorithm is good.
Multi-Model Convolution: An Innovative Machine Learning Approach for Sign Language Recognition

Hearing loss impairs DHH (Deaf and Hard of Hearing) people's language abilities and speech development. It is always recommended to perform early detection and begin training in early childhood. This study investigates a real-time sign language recognition system by developing a profitable machine learning technique. The proposed system makes use of the DCNN (Deep Convolutional Neural Network) model. The trained model is further transformed into an useful real-time application by using computer vision to accept real-time input for DCNN. The optimal CNN architecture for the proposed DCNN is selected from a multitude of alternatives, and it is then modified and tuned to make the model as modest as possible. It extracted spatial as well as temporal features from data. The innovative concept of multi-model convolution is built for accurate recognition of real-time signs and showcased in the implemented potential improvements section of the paper. With this novel technique, the model's accuracy is increased to 83%, ensuring that every ASL character is accurately detected.
Problems of integration of classroom and extracurricular independent work of students

In the context of the total digitalization of the education system, free access to information, the development of distance and mixed forms of education, it is the integration of classroom and extracurricular work of students that allows creating conditions for the activation of creative activity of students, for stimulating their responsibility and hard work, as well as the intensification of informal interaction between students and teachers. In the innovative experience of higher education, there is a certain practice of integrating classroom and extracurricular independent work of students, which today requires deep analysis and systematization. The purpose of the study is to identify and systematize the main directions of integration of classroom and extracurricular work of students in modern higher education. Research methods that were applied are analysis and generalization of scientific and pedagogical literature on the problem, comparison, systematization. The results of the study. Depending on the degree of interrelation of the integrated structures, the following models of integration of classroom and extracurricular independent work of students are distinguished: traditional; the “flipped learning” model, which assumes the organization of traditional classroom and extracurricular independent work "on the contrary"; the project-research model, which assumes the implementation of a closed cycle of productive activities by students – from the idea, design to the implementation of the project and reflection. System integration is achieved through the use of an interconnected sequence of educational tasks: operational – as mini-projects of educational activity; tactical, corresponding to supra-situational activity – larger educational projects, where students can already set their own goals, actively apply their knowledge in various subjects in practice; strategic – tasks of the creative level, corresponding to the creative activity of the individual. Conclusions and recommendations. In the modern educational process, extracurricular independent work of students is increasingly acquiring the status of the main form of organizing learning process. Strengthening the role of independent work requires a new view on the integration of classroom and extracurricular independent work of students. Teachers should make greater use of the opportunities of educational-research projects and the "flipped classroom" technology as modern means and forms of convergence of classroom and extracurricular work of students. The results of the research can be used in the process of designing curricula, work programs of disciplines, individual forms of organizing independent classroom and extracurricular work of students.
MirrorForge: Rapid Prototyping of Complex Mirrors for Camera and Projector Systems

From small tangibles to large tabletops, mirrors with complex geometries can be invaluable tools to reflect or form light for cameras and projectors. While many fabrication techniques are available for prototyping physical or electronic components, creating mirrors requires manual computation and industrial manufacturing equipment and is therefore considerably slower and more expensive. We propose a technique for fabricating mirror surfaces based on thermoplastics sheets with a laminated metallization layer and 3D-printed fixtures for bending or vacuum forming. Given our toolchain, a simulation of mirrors for cameras and projectors by rendering the CAD model is possible and allows to fabricate and evaluate design iterations quickly, making the process reasonably accessible for research. Finally, we show two prototypes for tangible interfaces based on our mirrors for projection and camera-based interaction, discussing advantages and limitations.
Language to language Translation using GRU method

Current state of the art translation systems for speech to speech rely heavily on a text representation for the interpretation. By transcoding speech to text we lose important information about the characteristics of the voice like the emotion, pitch and accent. The thesis examine the likelihood of using an GRU neural network model to translate speech to speech without the requirement of a text representation that's by translating using the raw audio data directly so as to persevere the characteristics of the voice that otherwise stray within the text transcoding a part of the interpretation process. As a part of the research we create an information set of phrases suitable for speech to speech translation tasks. The thesis leads to a signal of concept system which requires scaling the underlying deep neural network so as to figure better.
Efficient FPGA-based ECDSA Verification Engine For Permissioned Blockchains

Permissioned blockchain platforms heavily depend on cryptography to provide a layer of trust within the blockchain network, thus verification of cryptographic signatures often becomes the bottleneck. ECDSA is the most commonly used cryptographic scheme in permissioned blockchains. In this work, we propose an efficient implementation of ECDSA signature verification on FPGA, in order to improve performance of permissioned blockchains that aim to use FPGA-based hardware accelerators. We propose several optimizations for modular arithmetic (e.g., custom multipliers and fast modular reduction) and point arithmetic (e.g., reduced number of point double and addition operations, and optimal width NAF representation). Based on these optimized modular and point arithmetic modules, we propose an ECDSA verification engine that can be used by any application for fast verification of signatures. We further optimize our ECDSA verification engine for Hyperledger Fabric (one of the most widely used blockchain platforms) by moving carefully selected operations to a precomputation block, thus simplifying the critical path of ECDSA signature verification. Our ECDSA engine running at 250MHz on Xilinx Alveo U250 accelerator card can perform a verification in 760 microseconds with a throughput of 1,315 verifications/second, which is ~2.5 times faster than state-of-the-art FPGA-based implementations. Our Hyperledger Fabric-specific ECDSA engine can perform a verification in 368 microseconds, and 2,717 verifications/second. With 10 engines, Hyperledger Fabric can achieve a throughput of 7,520 transactions/second.
Fundamentals of Face Recognition with IoT

In today’s society, security is a major priority. As an innovative technology, IOT (Internet of Things) can be utilised in conjunction with facial recognition to make our mission of ensuring smart security simpler, easier, and foolproof. Face recognition refers to the process of recognising and verifying people’s faces. Advertising, security, payment, healthcare, criminal identification, and banking are among the areas where face recognition has made a significant impact. Face recognition entails a few phases, the first of which is processing or detecting an image, or vice versa, followed by feature extraction, dimensional reduction techniques, similarity measurement, and finally, image classification. The final stage in the effective implementation of a face recognition system is feature extraction. In this chapter, we reviewed many state-of-the-art methodologies over the last few eras, as well as a comparison of different feature extraction methods combined with IoT. In the chapter, a complete process flow of face recognition is proposed. Different dimensional reduction techniques are applied to face images, through which more detailed features of the image are extracted. Furthermore, machine learning techniques are applied to this extracted data, which in turn gives accurate results. The chapter basically emphasises how using the different algorithms and techniques integrated with IoT applications can be more effective for face detection and recognition.
Recursive and Non-Recursive Generalized Least-Squares Methods for Estimation of Time Series Models with Exogenous Variables

We present in this paper two generalized least-squares (GLS) methods for estimating regression coefficients of time series models with exogenous variables. The non-recursive GLS method is a generalization of the GLS method suggested by Cochrane and Orcutt (1949). The proposed GLS method consists of a sequence of four linear regressions. A first regression is fitted and provides residuals. These residuals are modeled as an autoregressive process and are used in a second regression (or autoregression) for obtaining estimators of autoregressive coefficients. These estimators are used to generate transformed endogenous and exogenous variables. A third regression makes use of the lagged values of these transformed variables to estimate the regression coefficients. The estimators of the regression coefficients are used to determine the true residuals which are modeled as an ARMA process which is finally used for obtaining the estimators of autoregressive and moving average parameters. The second GLS method is a recursive version of the first GLS method where the estimators are updated at each time point on receipt of the additional observations. The Simulation results based on different model structures with varying numbers of observations are used to compare the performance of our methods with that of exact maximum likelihood (EML) estimates.