Reviewerly/exHarmony huggingface dataset:
A Fall Detection Network by 2D/3D Spatio-temporal Joint Models with Tensor Compression on Edge

Falling is ranked highly among the threats in elderly healthcare, which promotes the development of automatic fall detection systems with extensive concern. With the fast development of the Internet of Things (IoT) and Artificial Intelligence (AI), camera vision-based solutions have drawn much attention for single-frame prediction and video understanding on fall detection in the elderly by using Convolutional Neural Network (CNN) and 3D-CNN, respectively. However, these methods hardly supervise the intermediate features with good accurate and efficient performance on edge devices, which makes the system difficult to be applied in practice. This work introduces a fast and lightweight video fall detection network based on a spatio-temporal joint-point model to overcome these hurdles. Instead of detecting fall motion by the traditional CNNs, we propose a Long Short-Term Memory (LSTM) model based on time-series joint-point features extracted from a pose extractor . We also introduce the increasingly mature RGB-D camera and propose 3D pose estimation network to further improve the accuracy of the system. We propose to apply tensor train decomposition on the model to reduce storage and computational consumption so the deployment on edge devices can to realized. Experiments are conducted to verify the proposed framework. For fall detection task, the proposed video fall detection framework achieves a high sensitivity of 98.46% on Multiple Cameras Fall, 100% on UR Fall, and 98.01% on NTU RGB-D 120. For pose estimation task, our 2D model attains 73.3 mAP in the COCO keypoint challenge, which outperforms the OpenPose by 8%. Our 3D model attains 78.6% mAP on NTU RGB-D dataset with 3.6× faster speed than OpenPose.
Implementation of Flutter-based Learning Management System (LMS) at Universitas Andi Djemma Palopo

Learning Management System (LMS) is a tool that is essential to redound an interaction between instructor and the learners and considering the technology headway nowadays (gadget). Therefore, research has been made to develop an LMS application for mobile devices in Universitas Andi Djemma Palopo, especially in the informatics engineering department using the Flutter framework. The method used for this research is R&amp;D which stands for Research and Development with ADDIE Development Model, Analysis, Design, Development, Implementation, and Evaluation. The result of this research is known: 1) This application (LMS) made using the Flutter framework and Firebase database simplifies the development process; 2) This application was made for the informatics engineering department, Universitas Andi Djemma Palopo. This mobile app has several useful features, including making a class, class discussion, making subjects, assignments, and attendance; 3) The LMS implementation uses a questionnaire based on usability and has been obtained with an eligibility percentage of 83.03%. The app has been declared as very feasible based on the eligibility percentage.
IEEE VR 2022

The 2021 VGTC Virtual Reality Best Dissertation Award goes to Mayra Donaji Barrera Machuca, a 2019 graduate from Simon Fraser University, for her dissertation entitled, “Towards More Accurate Immersive 3D Sketching.”. The 2021 VGTC Virtual Reality Best Dissertation Honorable Mention goes to Javad Fotouhi. Dissertation Title: Augmented reality and artificial intelligence in image-guided and robot-assisted interventions.
Developing the Professional Activity of Primary Education Teachers

This article outlines ways for an elementary school teacher to motivate each child from kindergarten through play, identifying their interests, character, and creative abilities, and taking an individual approach. The article shows the ways of deepening the theoretical and practical aspects of science, modern and information and communication technologies in the development of the professional activity of primary school teachers. The professional activity of the pedagogue The professional activity of the teacher is aimed at the formation of the human personality. The article shows how young people who are preparing for the professional career of educators know such features.
E-Vote System Using Blockchain and Artificial Intelligence

In the recent time there has been increase in digital technology which many people are adapting to their daily lives. But there are still some process or events which uses traditional methods or unconventional digital methods. One such event is the Election event. While there are countries which are still using traditional voting system there are also countries which have adapted to digital voting/electoral system. But there has been rise in trust issues against the recent technologies used in electoral system. General elections use the centralized system in which one person or organisation manages whole database and system. Thus, making it possible to tamper with the result of the election according to their way. Thus, to deal with this issue our paper proposes using blockchain technology in the election system which uses a decentralized system in which database is owned and manages by the people who are using it. This can help in nullifying the tampering of votes done by using the centralized system.
Implementasi Text Mining Terhadap Analisis Sentimen Masyarakat Dunia Di Twitter Terhadap Kota Medan Menggunakan K-Fold Cross Validation Dan Naïve Bayes Classifier

The city of Medan is one of the largest cities in Indonesia which has tourist charm, culinary and also supporting facilities and infrastructure that are quite adequate in the city, and of course will be ogled by foreign or foreign tourism. So it is necessary to do research in order to find out how the public sentiment towards tourism in the city of Medan, whether the majority of the world community considers it positive or negative. The response of the world community regarding Medan tourism is obtained from the Application Programming Interface (API) on Twitter because social media has a lot of users in the world. Indonesia has even reached 19.5 million users out of a total of 300 million users worldwide. In this study, one part of the Text Mining algorithm is text preprocessing, the text preprocessing used is case folding, tokenizing, stopwords, and stemming. For the preprocessing stemming, the snowball stemmer algorithm is used, while the analysis of the text data classification uses the Naïve Bayes Classifier algorithm and to partition the data the K-Fold Cross Validation method will be used. The data used in this study is a collection of tweets about tourism in the city of Medan on December 1, 2019 until December 8, 2019. Data is obtained from the Twitter API (Application Programming Interface) as many as 2000 tweets. Extracting data from Twitter via Twitter API (Application Programming Interface) using RStudio as a console for the data retrieval process
Editorial Biologically Learned/Inspired Methods for Sensing, Control, and Decision

The Special Issue aims at collecting new ideas and contributions at the frontier of bridging the gap between biological and engineering systems. Contributions include a wide range of related research topics, from neural computing to adaptive control and cooperative control, from autonomous decision systems to mathematical and computational models, and from neuropsychology-based decision and control to engineering system sensing and control algorithms, as well as applications and case studies of biologically inspired systems. This editorial note provides a brief overview of the accepted articles.
A INSTABILIDADE DA PLACA ATEROSCLERÓTICA E SUAS IMPLICAÇÕES NO INFARTO AGUDO DO MIOCÁRDIO

Objective: To evidence the problems caused by atherosclerosis and its relation to acute myocardial infarction, through the analysis of its pathophysiology and risk factors, in order to alert and prevent future complications, signaling possible forms of treatment, linking lifestyle to this multifactorial pathology.Methods: This is a literature review.Fifteen articles were selected from the LILACS, PUBMED, MEDLINE and SCIELO databases, using the descriptors acute myocardial infarction; atherosclerosis; atherosclerotic plaque; coronary artery disease; cholesterol.We considered studies published between 2015 and 2022.Results: AMI is a cardiovascular disease (CVD), caused mostly by the formation of atherosclerotic plaques.The main risk factors are modifiable, such as hypercholesterolemia, diabetes mellitus, and smoking; and non-modifiable, such as age, genetics, and gender.The accumulation of cholesterol-rich lipoproteins, with LDL-C in sites prone to plaque formation, has been described as the main cause.With the evolution of atherosclerotic plaque an inflammation process occurs, generating immune response that can result in hemodynamic flow obstruction and consequent arterial rupture, leading to AMI.The prevention and treatment strategies are based on: diet, physical activity, smoking cessation, cholesterol dosage, and the aid of specific drugs.Final considerations: The main way to avoid the formation of atherosclerotic plaque, and consequently the evolution to AMI, is in prevention strategies, through the adoption of healthy habits.Thus, it is worth noting that atherosclerosis is a global health problem that requires the awareness of the public sector seeking to encourage routine testing, change of lifestyle habits, which enable prevention and also early diagnosis.
Sistem Pakar Diagnosa Penyakit Autoimun Menggunakan Metode Classical Probability

The expert system for diagnosing autoimmune diseases is a computer-based system that is used as a tool to diagnose autoimmune diseases based on a dynamic knowledge base. Autoimmune disease is a disease that attacks the human immune system which is generally common in women. Autoimmune is a disorder of the immune system due to the failure of the defense of the body's stability. This autoimmune disease causes harm to human organs because it can damage the organs of cells that are still healthy in a person's body. In this case the role of the computer is needed to provide information quickly, precisely, and accurately. One of them is the development of an expert system (Expert System) which is a new breakthrough in the computer world. Expert System is a computer application that provides services for consulting and obtaining solutions to a problem, which works like an expert. This study uses the Classical Probability method which is one method to prove whether a fact is certain or uncertain in the form of a metric that is usually used in expert systems, this study aims to build an application using the Classical Probability method by using the Dreamweaver application as a tool to diagnose autoimmune disease. By making this expert system, it is hoped that it will be useful for many people and can know clearly about Autoimmune disease from its symptoms and solutions, which are displayed in the form of a website using PHP programming with a MySQL database
VOLUNTARIADO NA PANDEMIA DA COVID-19 DESENVOLVIDO NA REDE PUBLICA DE SAÚDE POR ESTUDANTES DO PRIMEIRO ANO DO CURSO DE MEDICINA

Objective: To evidence the problems caused by atherosclerosis and its relation to acute myocardial infarction, through the analysis of its pathophysiology and risk factors, in order to alert and prevent future complications, signaling possible forms of treatment, linking lifestyle to this multifactorial pathology.Methods: This is a literature review.Fifteen articles were selected from the LILACS, PUBMED, MEDLINE and SCIELO databases, using the descriptors acute myocardial infarction; atherosclerosis; atherosclerotic plaque; coronary artery disease; cholesterol.We considered studies published between 2015 and 2022.Results: AMI is a cardiovascular disease (CVD), caused mostly by the formation of atherosclerotic plaques.The main risk factors are modifiable, such as hypercholesterolemia, diabetes mellitus, and smoking; and non-modifiable, such as age, genetics, and gender.The accumulation of cholesterol-rich lipoproteins, with LDL-C in sites prone to plaque formation, has been described as the main cause.With the evolution of atherosclerotic plaque an inflammation process occurs, generating immune response that can result in hemodynamic flow obstruction and consequent arterial rupture, leading to AMI.The prevention and treatment strategies are based on: diet, physical activity, smoking cessation, cholesterol dosage, and the aid of specific drugs.Final considerations: The main way to avoid the formation of atherosclerotic plaque, and consequently the evolution to AMI, is in prevention strategies, through the adoption of healthy habits.Thus, it is worth noting that atherosclerosis is a global health problem that requires the awareness of the public sector seeking to encourage routine testing, change of lifestyle habits, which enable prevention and also early diagnosis.
Conceptualization of an Assessment System to Measure Vocabulary in Science (Project MELVA-S)

This paper aims to report the conceptualization of a web-based Automated Speech Recognition Scoring System, project MELVA-S (Measuring the English Language Vocabulary Acquisition of Latinx Bilingual Students), to measure the science vocabulary of second- and third-grade Latinx students. ELVA (English Learner Vocabulary Acquisition First Iteration) and ELVA-2 (English Learner Vocabulary Acquisition Second Iteration) focused on student’s learning and comprehension on science vocabularies. Both of the iterations are the foundation to build MELVA-S, which intends to measure and evaluate student’s answers with greater accuracy with Machine Learning. As a web-based agent, this system increases satisfaction for both teachers’ and students’ User Experience (UX) from content, design, and engineering perspectives. The project utilized a design-thinking approach and prototyped both the algorithm and the automated system interfaces. Future iterations of ELVA-2 and MELVA-S could consider adopting a Human-Centered Machine Learning approach, implemented with incremental improvements that include evaluation and testing with users, to keep enhancing both usability and functionality of the system for better UX.
A person re-identification network based on multi-granularity feature extraction

The purpose of person re-identification (Re-ID) is to retrieve a person of interest from a set of images taken by multiple cameras. In some current work, simple global features and local features do not allow the model to achieve excellent performance. In this paper, we propose an end-to-end person re-identification network that integrates multi-granularity pedestrian features. Our model contains multiple branching feature extraction modules, specifically, two global feature extraction modules, two auxiliary modules and two attention modules. To enhance the feature extraction capability of the model, we embed an improved parameter-free attention module in the backbone network, which significantly improves the performance. Our comprehensive experiments on the mainstream evaluation datasets of Market-1501, DukeMTMCreid show that our method achieves a more advanced performance that outperforms most existing methods. As an example, on the Market-1501 dataset, with the help of re-ranking(RK) strategy, we get the result of rank-1/mAP=95.8%/94.0% which exceeds most current methods.
EFFECTIVENESS OF STEGO IMAGES PRE-PROCESSING WITH SPECTRAL ANALYSIS METHODS

Early detection of sensitive data leakage during message transmission in communication systems is topical task today. This is complicated by applying of attackers to advanced steganographic methods. Feature of such methods is sensitive information embedding into innocuous (cover) files, such as digital images. This drastically reduces effectiveness of modern stegdetectors based on applying of signature and statistical steganalysis methods. There are proposed several approaches for improving detection accuracy of stegdetectors that are based on image pre-processing (calibration). These methods are aimed at estimation parameters either of stego, or cover images from current analysed image. The first group of calibration methods requires prior information about features of used embedding methods to minimize detection error. In most cases, this information is limited that decrease effectiveness of such calibration methods. The second group of calibration methods is of special interest today due to extensive set of proposed methods for advanced image denoising techniques. Nevertheless, practical usage of such methods requires carefully adjustment of parameters. This restricts fast re-training of stegdetector for revealing stego images formed according to unknown embedding methods.The promising approach for estimation cover image parameters from current (noisy) images is based on applying of novel methods of spectral analysis, namely sparse and redundant representation of signals. Feature of these methods is ability to adjust parameters of basic functions to statistical parameters of analysed set of image. This allows improving effectiveness of stegdetectors without necessity to re-tune theirs parameters for new set if images. The paper is aimed at performance analysis of stego images preprocessing with usage of advanced methods of spectral analysis. The analysis was performed for state-of-the-art HUGO embedding methods by usage of standard ALASKA dataset. Based on obtained results, it was revealed that applying of proposed methods allows improving detection accuracy up to 6 % even in case of absence prior information about used embedding methods and low cover image payload, e.g. less than 10 %. Nevertheless, practical usage of these methods for image calibration requires further improvement of dictionary learning procedure, namely decreasing its computation complexity by processing images with high resolution.
Global Optimization Solution for Dynamic Adaptive 360-Degree Streaming

This paper proposes a global optimization solution that combines different types of perceptual information to further improve transmission efficiency. First, a new rate-distortion (R-D) model is proposed to reflect the characteristics of 360degree video. Second, a globally optimized adaptive bitrate control algorithm is proposed using both the R-D models to adjust the bitrate for each tile in a segment. Finally, a new model parameter updating strategy that is robust to quality variations is proposed to further reduce prediction errors. Comparison results indicate that the proposed method can outperform state-of-the-art methods in terms of various quality of experience (QoE) objectives <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Temporal MTF evaluation of slow motion mode in mobile phones

While slow motion has become a standard feature in mainstream cell phones, a fast approach without relying on specific training datasets to assess slow motion video quality is not available.This manuscript proposes a fast and generalized noreference objective metric based on temporal loss in modulation transfer function (MTF) to evaluate interpolated slow motion mode in mobile phones.First, a standard chart embodying slanted edges is used to capture a slow-motion video.Second, the edge spread function is extracted from a region of interest in individual slow motion frames.Third, the line spread functions and MTFs are calculated.Finally, reference and interpolated frames are identified.Sharpness loss in slow motion mode is quantified by the MTF area difference between minimum interpolated frame and reference frame MTF scores.The proposed approach is evaluated in simulated and experimentally captured slow motion videos.In experiments, slow-motion videos are captured by moving mobile phones mounted on a motorized linear stage apart from the test chart at a constant speed while keeping the test chart still.The proposed MTF scores of several mainstream cell phones are analyzed and compared.
Drawing Up Researcher Terminological Profiles Based on Publications in the Digital Library eLIBRARY.Ru

The terminological profiles of specialists are drawn up based on publications from the digital library eLIBRARY. By averaging the individual terminological profiles, it is possible to draw up a generalized profile (“portrait”) of a small research team (a department, laboratory, or sector). Comparison of individual profiles with the use of proximity measures (for example, a cosine measure) makes it possible to group similar profiles and identify groups of employees who conduct research in the same subject area. This helps determine the research team specialization by means of Text Mining tools without using subjective expert assessments. The results obtained using the profile approach are confirmed by constructing graphs of co-authorship and a graph of terms in the Gephi computer program. The compilation of terminological profiles was also used in the development of personalized scientific activity support systems. This system is intended for helping the user (a specialist in a subject area) in choosing relevant scientific conferences and searching for useful (pertinent as far as possible) publications. For describing text documents, a vector model is used, and the weights of terms are determined by calculating the term occurrence frequency (or the tfc-weighting formula). At the preprocessing stage, the stop-words and rarely encountered words are removed, and lemmatization is carried out. The developed profile approach has been approbated on the example of a small research team specializing in computer science. The terminological profiles were constructed and analyzed, based on which the areas in which the team members specialize have been identified, and a personalized scientific activity support system has been developed, that tracks, in an automated mode, publications in the eLIBRARY in one of the relevant areas (Data Mining).
A General Cost Model in a Cloud Data Center

Cloud Computing is one of the technologies with a rapid development in recent years. This technology provides many services and resources to end users at low prices. Last decade witnessed a growing interest from industry and academia in the study of problems arising in cloud computing. A major condition for the commercial success of a business in cloud data centers is the formulation of a well-defined pricing strategy. Pricing models play a key role in the services purchase decision of the potential cloud customers. On the cloud market there are numerous Cloud Service Providers (CSPs) that offer users various pricing models for storage, databases, memory, and applications. Cloud pricing models are depending on the cloud type: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). The present paper provides an overview on various price models present in the cloud market with price comparisons for the three main CSPs: Amazon, Microsoft, and Google. A general cost model is then proposed for computing the services costs in a Data Center. This model can be used by the manager to make comparisons between the cost of his own Data Center services and the cost of the same CSPs services computed according to the CSPs pricing models. For different types of hardware configurations different electricity consumption is taken into account.