Common Users Research Articles

The status of body mass index on heart rate recovery in young adults: literature review

The development of modern adaptation increases the prevalence of obesity with a characteristic of fat hoarding especially in young adults. Young adults are the most common users of gadgets, and they have sedentary lifestyle. Body Mass index (BMI) is the ratio of weight in kilogram (kg) and height that has been squared in meter (m) and becomes the easiest parameter to measure excess fat. Excessive fat accumulation can affect vagal reflex, causing an impact in Heart Rate Recovery (HRR). Delayed HRR or ≤ 12 bpm increases the risk of arrhythmia and sudden cardiac death. The purpose of this review is to determine the status of BMI on HRR in young adults. The searching for article reviews used electronic database with several databases namely PubMed, Ebscohost, Proquest, and Google scholar resulted nine eligible articles. Overweight decreased HRR and delaying vagal reactivation which increased the risk of death caused by sympathetic nerve dominance which increased the risk of heart attack. Based on the review result, the profile of anthropometry can describe the fitness status of the cardiovascular system.

Proliferation of Digital Payments in India: A Pathway to Cashless Economy

The goal of this article is to learn about the demographic characteristics that influence the rise of digital payments in India, compare the various means of digital payments, and examine the challenges that consumers experience. The popularity of digital payments has skyrocketed in recent years, especially since demonetization. This article can be taken into consideration, as it focuses on SDG 1 – Poverty. The rise of digital transactions will aid in the creation of a cashless economy, which will help to reduce black money, corruption, and tax evasion while also increasing transparency in all transactions. This study is based on primary data acquired from 189 respondents via a questionnaire survey utilizing a suitable sampling approach. Fear of security threats was discovered to be one of the causes for non-use of digital payments, according to this study. People with a higher level of education were more aware of digital payments, and the majority of people began utilizing them after the demonetization. Transaction failure was the most common issue users encountered, and security risks included fake calls, redirection to another website, etc. It was also established that the majority of users are likely to suggest digital.

THE FORM AND FUNCTION OF REDUPLICATION IN ENGLISH AND URDU LANGUAGES: A CROSS LINGUIST STUDY

The main concentration of this work is to look at the forms and functions of morphological doubling in syntax and it aims at to look at the functions and roles of double questions morphemes of Urdu and to make comparative analysis of Urdu and English languages. There are different types of doublings in English language e.g. adverbs doublings, semantic doubling but question doubling does not exist in English language because it is the unique feature of Urdu language only. Qualitative analysis with purposive sampling approach, with the framework of Lexical Functional grammar (LFG).through the concept of F-Structure from (LFG) theory, double morphemes will be analyzed. For this purpose, data will be collected through document analysis, standard books, articles, published researches, dictionaries and data from common users. 100 sentences, consisting of double morpheme, will be selected and same were put into F-Structure to explore their lexical structure to show the semantic functions in both languages. Simultaneously, the sentences were also analyzed syntactically in order to find out their appropriateness. The study may provide a good level of confidence to develop Urdu National Language processing (UNLP) and enlighten the phenomena that morphological doubling in Urdu used for multiple semantic functions to explore human notions and feelings as compare to any other South Asian Language because Urdu is the only language with per gram. and study will also explore the functional and dynamic aspects of LFG. Keywords: Reduplication, LFG, syntax, Double interrogative morphemes.

Clinical pattern of synthetic cannabinoids users in Upper Egypt: cross-sectional study

BackgroundThere is an expanding use of new psychoactive substances containing synthetic cannabinoids in the last years. This study was conducted to identify the epidemiologic data of acute and chronic toxicity by synthetic cannabinoids in Upper Egypt patients.ResultsAll cases included in the presenting study were fifty males. Most users of synthetic cannabinoids were in the adolescence and middle age group (15–< 35) representing 68%. Curiosity was the most common motivator for using synthetic cannabinoids. Alteration of perception was reported in 68% of subjects after synthetic cannabinoids use. Additionally, dizziness, loss of consciousness, convulsion, and panic attacks were also reported. Cardiovascular adverse effects experienced by users were palpitations (76%) and chest pain (12%). Half of included subjects (50%) reported financial problems and about one-third (32%) got involved in domestic violence. Abnormal routine laboratory findings that were found in included cases were in the form of 12% anemia, 10% leukocytosis, and 6% leucopenia. Also, liver and kidney functions were elevated in 8% and 4% of the cases, respectively. While 22% and 4% of cases were positive for hepatitis C and HIV respectively.ConclusionsThis study can be concluded that adolescence are the most common users of SCs; neuro-psychiatric and cardiovascular side effects were the most experienced by subjects. Violence in many forms, especially domestic violence, was associated with synthetic cannabinoids abuse.Trial registrationRegistered in clinical trial under name syntheticcannabinoidsAssiut and ID NCT03866941 and URL.

Characterization of working and mobile phone usage distances in common users of electronic devices and computers

AIM: To analyse and characterize the pattern of working and mobile phone usage distances for common users of electronic devices and computers. METHODS: Transversal descriptive study consisting of a screening campaign evaluating the visual status of different professionals working with computers or electronic devices more than 2h per day. A total of 100 healthy patients with a mean age of 41.2 (range: 20-62) years were revised and subdivided according to job subgroups. A battery of screening tests was performed to all participants in a single session at the working environment of each of them, including measurement of the working distance (WD), arm length, and mobile phone usage distances (MPUD). RESULTS: The WD was significantly longer than MPUD (61.22±12.35 vs 32.22±6.35 cm; P&lt;0.001). Likewise, MPUD was significantly shorter than the arm length (74.44±4.65 cm; P&lt;0.001). A total of 4% (4/100) and 91% (91/100) of patients had a WD and MPUD of 40 cm or below, respectively. A weak although statistically significant correlation was found between WD and MPUD (r=0.387, P&lt;0.001) as well as between WD and arm length (r=0.260, P=0.009). Statistically significant differences were detected among job subgroups in WD (P=0.021), being longer for militaries compared to university researchers and lecturers (P=0.025). CONCLUSION: Working and mobile phone usage distances vary significantly among common users of computers and digital devices, although the use of mobile phones tends to be performed at significantly closer distances. This parameter should be considered when planning any type of optical correction, especially for presbyopia.

Promoting product idea contribution of heterogeneous users for product improvement in online innovation communities

ABSTRACT Collaboration between firms and product users has been widely practiced to enhance the efficiency and quality of product development. We consider a collaborative product improvement setting where a sponsoring firm and product users jointly create or promote product design ideas through firm-sponsored online user innovation communities (FOUICs). Using a differential-game framework, we study a benchmark case in which product users involved in FOUICs are homogeneous in their innovative contribution capacities and propensities. We then incorporate users’ heterogeneity such that product users are distinguished into lead users and common users. Our analytical result shows that the equilibrium effort levels and utilities of both the sponsoring firm and product users may increase or decrease with a change in the output elasticities of product design ideas or in the cost elasticities of the effort levels. Moreover, lead users’ equilibrium effort level and utility are more sensitive to the elasticity parameters of product users’ effort than those of common users, and lead users obtain a higher total incentive reward than that of common users. Last, we compare the benchmark model and the extended model to identify the condition under which one model outperforms the other one for both the sponsoring firm and product users.

MinerAlert: an hybrid approach for web mining detection

The introduction of new memory-based crypto-mining techniques and the rise of new web technologies like WebAssembly, made the use of browsers for crypto-currencies mining more and more convenient and popular. That, in turn, originated a new form of computer piracy, called cryptojacking, which is rapidly gaining ground on the web. A cryptojacking site exploits its visitors’ hardware resources to secretly mine crypto-currencies. This paper analyzes current web-based cryptojacking detection methods in order to propose a novel hybrid strategy. Current detection methods are found to require either considerable computer administration skills or execution privileges usually not available to common users. In this view, a method, named MinerAlert, has been designed and proposed, aiming at detecting in real-time sites performing cryptojacking. To address the limitations of current methods, the method implementation has been achieved through a browser extension. The present paper describes the method’s details and its implementation. It also reports the experimental results of its utilization, showing its positive performances in terms of ease of use, successful detections and speed.

Analysis Model of Personality and Psychological Characteristics of Network Users under High-Pressure Working Environment

Based on the prediction of social network psychological indicators, this paper studies the analysis model of network users’ personality and psychological characteristics under a high-pressure working environment. Through the analysis of five common network users’ personalities, the characteristics of personality psychology are extracted. Combined with the high-pressure working environment, this paper analyzes the characteristics of network users’ personalities and psychology, collects the characteristics of network users’ personality themes, and analyzes the characteristics of the dynamic extended dictionary. In order to ensure the accuracy of feature analysis, the author’s LDA algorithm is used to predict the network short text psychological index of network users under a high-pressure working environment. The experimental results show that the model designed in this paper is better than the traditional method in practical application.

PG-explorer: Resource Description Framework data exploration with property graphs

The Resource Description Framework (RDF) has been widely applied to represent and exchange domain information because of its machine-readable characteristic. With a huge amount of RDF data available, retrieving RDF data is essential, so that many RDF query approaches have been developed. But many traditional approaches generally require users to know RDF model and query language, and this seriously prevents a large number of common non-expert users from obtaining information in RDF datasets. In this paper, we propose an approach that users can explore massive RDF datasets by interactively manipulating property graphs. Our approach provides users with a series of operations in interactively constructing property graphs to describe their query intents. To efficiently explore massive RDF datasets, we convert RDF data into property graphs for storage. This can greatly reduce the size of massive RDF datasets and more importantly the converted property graphs can instruct users to understand the underlying structure of RDF datasets, which is very useful in users’ construction of their query property graphs. The constructed query property graphs are finally transformed into expressions with the query language of graph databases. With high-performance graph databases as query engines, we developed an RDF data exploration system – PG-Explorer. Through experiments over real-world datasets, we evaluated the effectiveness and superiority of our approach. The user study demonstrates that the proposed approach simplifies users’ exploration of RDF data and can satisfy their exploration needs.

Eye Movement Recognition based Human Machine Interaction System

Abstract: The sector of human computer access has been undergoing a new revolution lately. While other companies have and are still spending millions to develop highly visually appealing GUIs and state-of–art interaction systems for the common users since the inception of desktops, the development of interaction systems for the disabled has taken a kick start recently. This Eye gaze system is a control system for people with naturally challenged which can run system with their eyes. Where we all are blessed to operate the computer with ease using our hands. But in case of paralytic patients with no mobility and speech even though their brains and vision are functional, they can’t utilize their intelligence and stay unemployed. Thus, our eye gaze system helps in providing solution to this problem. Keywords: Interaction systems, Eye Gaze system, Speech synthesize

BDS near real-time maneuver detection based on triple-differenced phase observations

The BeiDou Navigation Satellite System (BDS) geostationary earth orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites need frequent orbital maneuvers to keep them in position due to various perturbations. However, common users cannot obtain the exact maneuver times, which may yield a decline in the real-time service performance of the BDS. To solve this problem, we presented a near real-time maneuver detection method for BDS based on triple-differenced phase observations, which can automatically detect the exact start and end times of satellite orbital maneuvers. In this method, the carrier-phase triple-differences residuals are firstly computed using the precise station coordinates, broadcast ephemeris and dual-frequency observations of ground tracking stations. Afterward, the residuals are be modeled using linear regression to identify maneuver times. The observations from the BDS Experimental Tracking Stations (BETS) and the reference time announced by the Beijing Satellite Navigation Center (BSNC) from Day of year (DOY) 100 to DOY 300, 2017 are used to evaluate the performance of the proposed method. The detected maneuver periods are consistent with the announced ones, except the detected start times lag 42 min and end times advance 23 min on average. Then, the detected times are further validated by dynamic precise orbit determination (POD) after the maneuver. The phase residuals and 3D RMS of the orbit differences of maneuvered satellites proved that the accuracy of detected maneuver times is better than 5-minute. At last, the kinematic precise point positioning (PPP) validation is implemented to evaluate the effect of more accurate GEO and IGSO satellites to BDS positioning. Results show that both the convergence and accuracy of BDS PPP are improved by adding one satellite that has ended maneuvering.

College Enquiry Chat Bot System with Text to Speech

Chat bots are intelligent systems that interpret and react to users' questions in their native language. In a conversation, the chat bot reacts in the same way as a human would. It functions as a virtual assistant, and its accuracy is assessed by determining a correlation between user questions and chat bot responses. For a better user experience, the implemented Chat bot has two modes: text mode and audio mode. It provides an interactive approach of answering through voice messages when in audio mode. There is a long line at the inquiry window during the Institute's Academic Admission procedure. Even more challenging is the situation for parents who live in various cities, states, and nations. The purpose of this system is to give students and parents a place to ask questions and get answers via easy English language text messages or audio commands. Instead of queuing at an information desk to ask questions about the admissions process, students and parents will collaborate with a bot. Artificial intelligence (AI) and natural language processing (NLP) algorithms are used to create chatbots, which are intelligent systems. It effectively interacts with users and responds to their questions. Organizations, government groups, and non-profit associations are the most common users of dialogue/conversation operators. These conversational experts work for a wide range of businesses, from small start-ups to large corporations. There are a variety of code-based and interface-based chatbot development platforms available on the market.

Creating RESTful APIs over SPARQL endpoints using RAMOSE

Semantic Web technologies are widely used for storing RDF data and making them available on the Web through SPARQL endpoints, queryable using the SPARQL query language. While the use of SPARQL endpoints is strongly supported by Semantic Web experts, it hinders broader use of RDF data by common Web users, engineers and developers unfamiliar with Semantic Web technologies, who normally rely on Web RESTful APIs for querying Web-available data and creating applications over them. To solve this problem, we have developed RAMOSE, a generic tool developed in Python to create REST APIs over SPARQL endpoints. Through the creation of source-specific textual configuration files, RAMOSE enables the querying of SPARQL endpoints via simple Web RESTful API calls that return either JSON or CSV-formatted data, thus hiding all the intrinsic complexities of SPARQL and RDF from common Web users. We provide evidence that the use of RAMOSE to provide REST API access to RDF data within OpenCitations triplestores is beneficial in terms of the number of queries made by external users of such RDF data using the RAMOSE API, compared with the direct access via the SPARQL endpoint. Our findings show the importance for suppliers of RDF data of having an alternative API access service, which enables its use by those with no (or little) experience in Semantic Web technologies and the SPARQL query language. RAMOSE can be used both to query any SPARQL endpoint and to query any other Web API, and thus it represents an easy generic technical solution for service providers who wish to create an API service to access Linked Data stored as RDF in a triplestore.

Inference for Optimal Differential Privacy Procedures for Frequency Tables

When releasing data to the public, a vital concern is the risk of exposing personal information of the individuals who have contributed to the data set. Many mechanisms have been proposed to protect individual privacy, though less attention has been dedicated to practically conducting valid inferences on the altered privacy-protected data sets. For frequency tables, the privacy-protection-oriented perturbations often lead to negative cell counts. Releasing such tables can undermine users’ confidence in the usefulness of such data sets. This paper focuses on releasing one-way frequency tables. We recommend an optimal mechanism that satisfies ϵ-differential privacy (DP) without suffering from having negative cell counts. The procedure is optimal in the sense that the expected utility is maximized under a given privacy constraint. Valid inference procedures for testing goodness-of-fit are also developed for the DP privacy-protected data. In particular, we propose a de-biased test statistic for the optimal procedure and derive its asymptotic distribution. In addition, we also introduce testing procedures for the commonly used Laplace and Gaussian mechanisms, which provide a good finite sample approximation for the null distributions. Moreover, the decaying rate requirements for the privacy regime are provided for the inference procedures to be valid. We further consider common users’ practices such as merging related or neighboring cells or integrating statistical information obtained across different data sources and derive valid testing procedures when these operations occur. Simulation studies show that our inference results hold well even when the sample size is relatively small. Comparisons with the current field standards, including the Laplace, the Gaussian (both with/without post-processing of replacing negative cell counts with zeros), and the Binomial-Beta McClure-Reiter mechanisms, are carried out. In the end, we apply our method to the National Center for Early Development and Learning’s (NCEDL) multi-state studies data to demonstrate its practical applicability.

Aligning Dynamic Social Networks: An Optimization over Dynamic Graph Autoencoder

Social network alignment, aligning different social networks on their common users, is receiving dramatic attentions from both academic and industry. All existing studies consider the social network to be static and neglect its inherent dynamics. In fact, the dynamics of social networks contain the discriminative pattern of an individual, which can be leveraged to facilitate social network alignment. Hence, we for the first time propose to study the problem of aligning dynamic social networks. Towards this end, we propose a novel Dynamic Graph autoencoder based dynamic social network Alignment approach, referred to as DGA, unfolding the fruitful dynamics of social networks for user alignment. However, it faces challenges in both modeling and optimization: (1) To model the intra-network dynamics, we design a novel dynamic graph autoencoder to learn user embeddings with complex network dynamics. (2) To model the inter-network alignment, we design a unified optimization framework over proposed dynamic graph autoencoders, constructing a common subspace for user alignment across different networks. (3) To address this optimization problem, we design an effective alternating algorithm with solid theoretical guarantees. We conduct extensive experiments on real-world datasets and show that the proposed approach substantially outperforms the state-of-the-art methods.

Identifying legitimate user in DDoS attack using Petri net

The effective construction of systems and that are embedded in an automated or organisational context is a major difficulty in modern research. These systems are built so that customers, manufacturers, users, and better understand of a system and how it can be installed, used, altered, and enhanced with the help of the model. Petri nets are used to represent and investigate discrete systems such as running in parallel, web - based, concurrent, and non-deterministic systems, among others. It has a strong mathematical foundation and also the ability to model in a graphical form. With its significant development and advancements in recent years, network has pervaded every part of our lives. As a result of this process, a growing number of critical network security issues have arisen, including network intrusion issues.The aim of this article is to present an approach to develop and verify a method for modeling of network attacks either at server side or client side. Moreover the goal is to prove that the method enables one to design models resembling the same behaviour of attacks that support the detection process of selected network attacks and facilitate the application of countermeasures. The most common network threat targeting end users and servers are caused by DDoS. The DDoS detection process can be performed either by understanding the properties of attacks or analysing its behavioural models. The proposed approach to modeling of attacks is based on Petri nets. The designed framework is designed detecting DDoS attack at the server side and mitigates the network attacks. The system's major elements are detection and packet classification based on the confidence index value. The CI value helps to detect untrusted network attacks and prioritize the attacks. An algorithm for detecting untrusted network attack is designed and modelled using Petri nets.

Face Inverse Rendering via Hierarchical Decoupling.

Previous face inverse rendering methods often require synthetic data with ground truth and/or professional equipment like a lighting stage. However, a model trained on synthetic data or using pre-defined lighting priors is typically unable to generalize well for real-world situations, due to the gap between synthetic data/lighting priors and real data. Furthermore, for common users, the professional equipment and skill make the task expensive and complex. In this paper, we propose a deep learning framework to disentangle face images in the wild into their corresponding albedo, normal, and lighting components. Specifically, a decomposition network is built with a hierarchical subdivision strategy, which takes image pairs captured from arbitrary viewpoints as input. In this way, our approach can greatly mitigate the pressure from data preparation, and significantly broaden the applicability of face inverse rendering. Extensive experiments are conducted to demonstrate the efficacy of our design, and show its superior performance in face relighting over other state-of-the-art alternatives. Our code is available at https://github.com/AutoHDR/HD-Net.git.

Blockchain and Cloud to Overcome the Problems of Buyer and Seller Watermarking Protocols

Copyright protection of digital content has become a problem not only for web content providers but also for ordinary web users who like to publish their digital contents on social or user generated content platforms. Among the possible solutions to such a problem, digital watermarking, in conjunction with watermarking protocols, appears to be a valid alternative to current DRM (digital rights management) systems. In fact, watermarking based solutions insert perceptually invisible copyright information into the copies of contents published or distributed on the web in order to track them. Such insertions are carried out according to the watermarking protocols, which have evolved over the years from the classic “buyer and seller” paradigm into a simpler and versatile “buyer friendly” and “mediated” approach. However, such an approach cannot exploit the new technologies that characterize the current Internet. This paper presents a new watermarking protocol able to adapt the “buyer friendly” and “mediated” approach to the use of innovative technologies such as cloud platforms and blockchain. In this way, (1) content providers and common web users can take advantage of the computing and storage resources made available by cloud platforms; (2) the involvement of trusted third parties in the protocols can be reduced by using blockchain without complicating the protection scheme. In fact, these two goals make the protocol particularly suited for the current Internet.

Pre- and post-home visit behaviors after using after-hours house call (AHHC) medical services: a questionnaire-based survey in Tokyo, Japan

BackgroundAfter-hours house call (AHHC) medical services have been implemented in Japan to reduce ambulance use, as well as overcrowding at the emergency department (ED). Examining the pre-and post-home visit behaviors of those using AHHC medical services will provide insights into the usefulness of these services and help develop strategies to reduce ED visits and ambulance use further.MethodsThis questionnaire-based study used data from anonymized medical records and internet-based questionnaires completed by patients who used AHHC medical services in Tokyo, Japan, between January 1 and December 31, 2019. The questionnaire comprised two questions: (1) What action would the patient have taken in the absence of AHHC services and (2) what action was taken within 3 days following the use of the AHHC services. In addition, following home consultations, AHHC doctors classified the patient’s illness severity as mild (treatable with over-the-counter medications), moderate (requires hospital or clinic visit), or severe (requires ambulance transportation).ResultsOf the 15,787 patients who used AHHC medical services during the study period, 2128 completed the questionnaire (13.5% response rate). Individuals aged ≤15 years and 16–64 years were the most common users of AHHC services (≤15 years, 71.4%; 16–64 years, 26.8%). Before using the AHHC service, 46.4% of the total respondents reported that they would have visited an ED had AHHC services not been available (≤15 years, 47.8%; 16–64 years, 42.8%; ≥65 years, 43.6%). The proportion of patients originally planning to call an ambulance was higher among those in the older age groups (≤15 years, 1.1%; 16–64 years, 6.0%; ≥65 years, 20.5%). After using the AHHC services, most patients (68.1%) did not visit a hospital within 3 days; however, the proportion of patients who visited an ED and called an ambulance within 3 days increased with the severity of illness.ConclusionsIncreasing AHHC medical services awareness among older adults and patients assessed as having severe illnesses regularly availing of AHHC services may help reduce ED visits and ambulance use.

Efficient Retrieval of Data Using Semantic Search Engine Based on NLP and RDF

With the evolution of Web 3.0, the traditional algorithm of searching Web 2.0 would become obsolete and underperform in retrieving the precise and accurate information from the growing semantic web. It is very reasonable to presume that common users might not possess any understanding of the ontology used in the knowledge base or SPARQL query. Therefore, providing easy access of this enormous knowledge base to all level of users is challenging. The ability for all level of users to effortlessly formulate structure query such as SPARQL is very diverse. In this paper, semantic web based search methodology is proposed which converts user query in natural language into SPARQL query, which could be directed to domain ontology based knowledge base. Each query word is further mapped to the relevant concept or relations in ontology. Score is assigned to each mapping to find out the best possible mapping for the query generation. Mapping with highest score are taken into consideration along with interrogative or other function to finally formulate the user query into SPARQL query. If there is no search result retrieved from the knowledge base, then instead of returning null to the user, the query is further directed to the Web 3.0. The top “k” documents are considered to further converting them into RDF format using Text2Onto tool and the corpus of semantically structured web documents is build. Alongside, semantic crawl agent is used to get &lt;Subject-Predicate-Object&gt; set from the semantic wiki. The Term Frequency Matrix and Co-occurrence Matrix are applied on the corpus following by singular Value decomposition (SVD) to find the results relevant for the user query. The result evaluations proved that the proposed system is efficient in terms of execution time, precision, recall and f-measures.