Popular Applications Research Articles

What Is the Draw of the Metaverse? Personality Correlates of Zepeto Use Motives and Their Associations With Psychological Well-Being.

Despite the hype surrounding the metaverse, there is scant empirical research that examines who uses the service, for what specific purposes, and with what consequences. Based on a survey of current Zepeto users (N = 200), a popular metaverse application that enables people to create avatars and socialize while exploring the virtual spaces, we investigated (a) the key motives of Zepeto use, (b) how Big Five personality traits predict specific motives of Zepeto use, and (c) how specific motives of Zepeto use are associated with users' psychological well-being. Overall, users were largely driven by the desire to explore the virtual world and enjoy unique experiences, but such a tendency was stronger among those higher on openness and agreeableness. Extroverts were more likely to use Zepeto for functional purposes, while those higher on neuroticism turned to Zepeto to escape from reality. As for psychological consequences, while those using Zepeto for functional and escaping purposes reported higher levels of loneliness, those who used Zepeto for social and experiential goals were less lonely. The experiential and escape motives predicted perceived social support in the opposite directions. Moreover, by comparing Zepeto users' responses with those of non-users (N = 200), we found that (a) non-users overestimated users' motives of Zepeto use, especially social and escape motives, (b) Zepeto users were higher on extraversion and openness than non-users, and (c) users reported higher levels of loneliness than non-users with no significant difference in perceived social support. Implications of the findings and future directions are discussed.

Toward Interpretable Graph Tensor Convolution Neural Network for Code Semantics Embedding

Intelligent deep learning-based models have made significant progress for automated source code semantics embedding, and current research works mainly leverage natural language-based methods and graph-based methods. However, natural language-based methods do not capture the rich semantic structural information of source code, and graph-based methods do not utilize rich distant information of source code due to the high cost of message-passing steps. In this paper, we propose a novel interpretable model, called graph tensor convolution neural network (GTCN), to generate accurate code embedding, which is capable of comprehensively capturing the distant information of code sequences and rich code semantics structural information. Firstly, we propose to utilize a high-dimensional tensor to integrate various heterogeneous code graphs with node sequence features, such as control flow, data flow. Secondly, inspired by the current advantages of graph-based deep learning and efficient tensor computations, we propose a novel interpretable graph tensor convolution neural network for learning accurate code semantic embedding from the code graph tensor. Finally, we evaluate three popular applications on the GTCN model, variable misuse detection, source code prediction, and vulnerability detection. Compared with current state-of-the-art methods, our model achieves higher scores with respect to the top-1 accuracy while costing less training time.

Gender Preference towards the Choice of Video Streaming Service Providers among Youth in Covid-19

Media content providers, through video streaming services have revolutionized the way of providing entertainment to its viewers. They have not only changed time, date and venue of our content consumption but has also upsurged new media watching habits. The study was conducted to study the preference and reasons of preferring one video streaming service provider over others electronic platforms during the spread of Covid-19. 286 youth within the age group of 15-24 formed the sample of the study and online survey was conducted to gather their responses due to the Covid restrictions. The data was further tabulated and analysed with the help of t-test through SPSS. The research concluded Hotstar and Netflix to be the preferred medium for the males whereas Voot was the most popular application amongst females. Pricing was found to be the least satisfying criterion among both the categories whereas quality, language and variety showed varied response.

Model of rules for malicious input parameters detection


 
 
 This article is devoted to detection of advanced techniques of malicious input parameter injections and web application firewall (WAF) bypass. The authors have proposed a hierarchical model for detection rules definition, which allows to edit different fragments separately. This model has been implemented with the usage of Backus-Naur form and ANTLR4 (generator of parsers and lexers). The solution has been tested using some popular web application scanners. The testing environment has been created with Python3. The results of research have been compared with the corresponding ones for the existing open source solution – libinjection. The main accent has been made to SQL injcetions and Cross-Site Scripting attacks.
 
 

A comparison of the accuracy of mushroom identification applications using digital photographs

Objective To compare the accuracy of three popular mushroom identification software applications in identifying mushrooms involved in exposures reported to the Victorian Poisons Information Centre and Royal Botanic Gardens Victoria. Background Over the past 10 years, an increasing number of software applications have been developed for use on smart phones and tablet devices to identify mushrooms. We have observed an increase in poisonings after incorrect identification of poisonous species as edible, using these applications. Design We compared the accuracy of three iPhone™ and Android™ mushroom identification applications: Picture Mushroom (Next Vision Limited©), Mushroom Identificator (Pierre Semedard©), and iNaturalist (iNaturalist, California Academy of Sciences©). Each app was tested independently by three researchers using digital photographs of 78 specimens sent to the Victorian Poisons Information Centre and Royal Botanic Gardens Victoria over a two-year period, 2020–2021. Mushroom identification was confirmed by an expert mycologist. For each app, individual and combined results were compared. Results Picture Mushroom was the most accurate of the three apps and correctly identified 49% (95% CI [0–100]) of specimens, compared with Mushroom Identificator (35% [15–56]) and iNaturalist (35% [0–76]). Picture Mushroom correctly identified 44% of poisonous mushrooms [0–95], compared with Mushroom Identificator (30% [1–58]) and iNaturalist (40% [0–84), but Mushroom Identificator identified more specimens of Amanita phalloides correctly (67%), compared to Picture Mushroom (60%) and iNaturalist (27%). Amanita phalloides was falsely identified, twice by Picture Mushroom and once by iNaturalist. Conclusions Mushroom identification applications may be useful future tools to assist clinical toxicologists and the general public in the accurate identification of mushrooms species but, at present, are not reliable enough to exclude exposure to potentially poisonous mushrooms when used alone.

Model development and calibration of two-dimensional population balance model for twin-screw wet granulation based on particle size distribution and porosity

Population balance models (PBMs) have been widely studied for the simulation of continuous twin-screw wet granulation. Due to the limited data availability and computational efforts, one-dimensional PBM is the most popular application to the pharmaceutical industry. While multi-dimensional PBMs can predict multiple granule quality attributes, model development and calibration has been the big challenges. This paper presents a two-dimensional compartmental PBM which compute granule size and porosity. Granulation experiments were performed for two formulations and two process settings, followed by granule characterizations. In PBM development, consolidation was newly introduced, whereas the aggregation and breakage kernels were based on the previous model. Calibration results proved that the model can simulate granule size and porosity simultaneously for different formulations and process settings. Some cases show porosity increase that needs to be considered in the future. Further experiments and model development can increase the applicability of the two-dimensional PBM for the pharmaceutical industry.

A Comparative Study of Intelligent Online e-Learning Systems

In recent decades there has been extremely rapid development in the area of Information Technology (IT), with different IT applications altering significantly as well. Online teaching and learning is one of the most popular IT applications. The primary objective of this essay is to examine and then compare the different e-learning designs. Some recommendations on the limits identified based on the analytical comparative analyses of these different designs have been offered as context of this research. This study highlights a number of research and design difficulties to enhance smart online learning systems to offer the cultural elements of online classrooms with a productive effect. The study also highlights the concerns that need to be addressed.

Utilization TikTok application for learning oral English

The development of technologies has revolutionized English language communication competence. Social media have swept the world within a short period of time. Teenagers are inevitably enthralled at all ages. Since TikTok is the most popular social application inside and outside China, supplying many English-speaking short videos benefits learning English as the Foreign Language (EFL)students by improving their sensitivity to English. This paper utilizes a questionnaire to investigate the effectiveness of applying TikTok to oral English learning from student’s point of view. The expected findings show students are eager to improve their competence in speaking English. Meanwhile, students categorize the existing problems of oral English education into three kinds: Pedagogy, teaching materials, and pronunciation. More than 70% of students think taking advantage of TikTok helps settle these existing problems. Therefore, teachers are suggested to combine useful short videos on TikTok with the speaking missions in English-speaking classes.

The Night Albums: Visibility and the Ephemeral Photograph

The Night Albums: Visibility and the Ephemeral Photograph

No Seeing is Also Believing: Electromagnetic-Emission-Based Application Guessing Attacks via Smartphones

Mobile devices have emerged as the most popular platforms to access information. However, they have also become a major concern of privacy violation and previous researches have demonstrated various approaches to infer user privacy based on mobile devices. In this paper, we study the electromagnetic (EM) emission of a laptop that could be harvested by a commercial-off-the-shelf (COTS) mobile device, e.g., a smartphone. We propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagAttack</monospace> , which exploits the electromagnetic side channel of a laptop to guess user activities, i.e., application launching and application operation. The key insight of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagAttack</monospace> is that applications are discrepant in essence due to the different compositions of instructions, which can be reflected on the CPU power consumption, and thus the corresponding EM emissions. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagAttack</monospace> is challenging since that EM signals are noisy due to the dynamics of applications and the limited sampling rate of the built-in magnetometers in COTS mobile devices. We overcome these challenges and convert noisy coarse-grained EM signals to robust fine-grained features. We implement <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagAttack</monospace> on both an iOS and an Android smartphone without any hardware modification, and evaluate its performance with 30 popular applications, 30 YouTube videos, and 50 top websites in China. The results demonstrate that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagAttack</monospace> can recognize aforementioned 30 applications with an average accuracy of 98.6 percent, and identify which video out of the 30 candidates being played with an average accuracy of 97.5 percent and visiting which website among the 50 candidates with an average accuracy of 90.4 percent.

Mechanism, Tools and Techniques to Mitigate Distributed Denial of Service Attacks

Abstract: Attacks such as Distributed Denial of Service (DDoS) continue to menace the Internet. Attackers are able to use larger bandwidths because they concentrate on application layers. In order to create novel prevention strategies, it is crucial to understand the nature of distributed denial of service assaults. One of the most upsetting types of attacks on the Internet today is the distributed denial of service (DDoS) attack. When fundamental Internet infrastructure and services, such as the Domain Name System, are targeted or misused, such attacks become far more powerful and deadly (DNS). The DNS is a key component of the Internet's core infrastructure, and it plays a significant role in supporting many popular Internet applications like e-mail, VoIP, etc. in addition to translating human-readable names into IP addresses. Attacks such as Distributed Denial of Service (DDoS) continue to menace the Internet. Attackers are able to use larger bandwidths because they concentrate on application layers. In order to create novel prevention strategies, it is crucial to understand the nature of distributed denial of service assaults. The development of proactive preventative techniques is a crucial area of study, just like in other security-related fields. This essay examines recently released preventative measures. Additionally, it emphasizes the cloud era and how its systems are safeguarded against DDoS assaults

The Role of E-Satisfaction on Repurchase and E-Wom Intention on The Costumers of Food Products By Local Micro and Small Businesses on The Digital Platforms

Despite the increasing popularity of online food delivery services, extant studies have not looked at factors that contribute to customers’ willingness to repurchase and recommend food products by local micro and small businesses to other app users. Meanwhile, the topic is essential given that food products from local micro and small businesses face direct competition with well-known products from large corporations on digital platforms. Therefore, this study aims to examine the role of food quality, price fairness and perceived value on e-satisfaction and the role of e-satisfaction on the repurchase and e-WOM intention of the customers of food products by local micro and small businesses. Using data from 100 customers of local micro and small businesses on the popular food delivery applications in Pekalongan, Indonesia and employing Partial Least Square (PLS) in our data analysis, our results show that food quality and price fairness have a significant effect on e-satisfaction. In addition, our findings show that e-satisfaction has a positive and significant effect on repurchase intention, but it shows a non-significant positive effect on e-WOM intention. Overall, our findings suggest that on digital platforms, gaining customer satisfaction about the product's quality and the price's fairness may not be enough, as it does not indicate a significant relationship with the willingness of the customers to recommend it to other app users. Keywords: food quality, price fairness, perceived value, e-satisfaction, repurchase intention, e-WOM intention

Multipath transmission aware ABR algorithm for SVC HAS

Adaptive video streaming over Hypertext Transfer Protocol (HTTP) is one of the most popular applications that have been used on the Internet in the last decade. In such applications, clients change the quality of the received video throughout the streaming session with respect to current network conditions. In these systems, the use of scalable coded video is one of the alternatives that can obtain video files encoded at different qualities. In this paper, we propose an adaptive bitrate (ABR) algorithm that aims to improve the quality of not only future segments but also the segments in the buffer. The quality of the already buffered segments is improved by downloading additional layers of the SVC-coded video, hence, preventing network resources from being wasted while increasing Quality of Experience (QoE). We use multiple paths between the server and the clients for transferring the video packets. In such systems, the ABR algorithm should determine which segment and layer should be requested from which path and when. The proposed ABR algorithm selects a group of segments to increase the quality of the buffered segments and for future segments, and selects paths for each segment in this group. Simulation results show that clients achieve higher QoE by using our approach when compared with conventional ABR algorithms. Furthermore, we show that using multiple paths cannot guarantee that QoE is maximized unless the end-system is aware of multipath transmission.

Effectiveness and safety of Inclisiran in hyperlipidemia treatment: An overview of systematic reviews.

This paper aimed to comprehensively evaluate the effectiveness and safety of Inclisiran in treating hyperlipidemia through an overview of systematic reviews (SRs). The Cochrane Library, EMBASE, PubMed, CNKI, WANGFANG database, VIP database, ClinicalTrials.gov, and ICRT were searched electronically to collect SRs and meta-analysis of Inclisiran in hyperlipidemia treatment from the establishment of the database till May 2022. Two researchers independently screened the relevant literature, then the assessment of multiple systematic reviews tool was made into assess the methodological quality of the included studies. Data extracted were used to perform the study through RevMan5.3 software. The grading of recommendations assessment, development, and evaluation tool was used to grade the quality of the evidence of the outcomes included in the SRs. Prospero ID: CRD 42022326845. A total of 10 relevant SRs were included, involving 7 randomized controlled trials. The assessment results of the assessment of multiple systematic reviews tool suggested that the quality of the SRs included needed to be improved. The reduced level of low-density lipoprotein cholesterol of the experimental group was lower than the control group, and the difference in the amount of effectiveness was statistically significant (MD = -50.13, 95%CI: -56.2 to -44.06, P < .00001). The grading of recommendations assessment, development, and evaluation results showed that out of 27 outcomes, 8 were high-quality, 3 were of medium quality, 6 were of low quality, and 10 were of the most inferior quality. 300mg Inclisiran with 2 injections a year has the best therapeutic effect, which can significantly reduce low-density lipoprotein cholesterol and total cholesterol, and increase high-density lipoprotein cholesterol levels in patients with hyperlipidemia. Inclisiran has a favorable safety profile, with no significant difference in the incidence of adverse reactions compared to a placebo. Most of the adverse effects were associated with the reaction on the injection site.

Improving Speaking Skills through Tiktok Application: An Endevour of Utilizing Social Media in Higher Education

TikTok is one of the popular applications that contain short-video sharing. Every year, the number of users grows up in every country. They use TikTok not only as the media for entertainment and business but also as the media for learning. The aim of this research is to describe the effect of the TikTok application to improve students’ speaking skills. This research belongs to quasi-experimental research with group pre-post-test design involving 32 students in Diploma of Midwifery Program Universitas Muhammadiyah Kudus as the population. The sampling technique is total sampling with the experimental group (16 students) and control group (16 students). To know the students’ speaking skills, there are four components that must be measured, such as vocabulary, fluency, pronunciation, and comprehension. Moreover, the students’ score has been classified into five categories, they are very good, good, medium, low, and failed. After treatment, the score of speaking ability is improved for the experimental group. The vocabulary increases 45,6%, fluency increases 40,6%, pronunciation increases 31,6%, and comprehension increases 32,8% from the pre-test score. TikTok is a useful application that not only improves speaking ability but also improves the students’ creativity, motivation, and vocabulary.If the students have wrong in speaking, they not only get the academic punishment but also digital punishment.

MHealth Apps Targeting Obesity and Overweight in Young People: App Review and Analysis.

Overweight and obesity have been linked to several serious health problems and medical conditions. With more than a quarter of the young population having weight problems, the impacts of overweight and obesity on this age group are particularly critical. Mobile health (mHealth) apps that support and encourage positive health behaviors have the potential to achieve better health outcomes. These apps represent a unique opportunity for young people (age range 10-24 years), for whom mobile phones are an indispensable part of their everyday living. However, despite the potential of mHealth apps for improved engagement in health interventions, user adherence to these health interventions in the long term is low. The aims of this research were to (1) review and analyze mHealth apps targeting obesity and overweight and (2) propose guidelines for the inclusion of user interface design patterns (UIDPs) in the development of mHealth apps for obese young people that maximizes the impact and retention of behavior change techniques (BCTs). A search for apps was conducted in Google Play Store using the following search string: ["best weight loss app for obese teens 2020"] OR ["obesity applications for teens"] OR ["popular weight loss applications"]. The most popular apps available in both Google Play and Apple App Store that fulfilled the requirements within the inclusion criteria were selected for further analysis. The designs of 17 mHealth apps were analyzed for the inclusion of BCTs supported by various UIDPs. Based on the results of the analysis, BCT-UI design guidelines were developed. The usability of the guidelines was presented using a prototype app. The results of our analysis showed that only half of the BCTs are implemented in the reviewed apps, with a subset of those BCTs being supported by UIDPs. Based on these findings, we propose design guidelines that associate the BCTs with UIDPs. The focus of our guidelines is the implementation of BCTs using design patterns that are impactful for the young people demographics. The UIDPs are classified into 6 categories, with each BCT having one or more design patterns appropriate for its implementation. The applicability of the proposed guidelines is presented by mock-ups of the mHealth app "Morphe," intended for young people (age range 10-24 years). The presented use cases showcase the 5 main functionalities of Morphe: learn, challenge, statistics, social interaction, and settings. The app analysis results showed that the implementation of BCTs using UIDPs is underutilized. The purposed guidelines will help developers in designing mHealth apps for young people that are easy to use and support behavior change. Future steps involve the development and deployment of the Morphe app and the validation of its usability and effectiveness.

Comparative Study and Analysis of Forensic Artifacts of WhatsApp and Telegram on Android Devices

Numerous instant messaging are available for mobile devices as a cheaper alternative over operator-based text messaging via SMS. Furthermore, instant messaging allow the user to exchange textual messages, images, audio, and videos. However, the ease offered by instant messaging also had a negative impact including making instant messaging as a criminal land. This paper focuses on conducting forensic data analysis of two popular instant messaging applications on Android smartphones; WhatsApp and Telegram. In this analysis, we use open-source tools and software applied on non-rooted Android devices. By using the result, an analyst will be able to read, and reconstruct the chronology of the messages and the list of contact and also know the difference in data structures obtained from these two instant messaging applications.

Cost-Profiling Microservice Applications Using an APM Stack

Understanding how the different parts of a cloud-native application contribute to its operating expenses is an important step towards optimizing this cost. However, with the adoption and rollout of microservice architectures, the gathering of the necessary data becomes much more involved and nuanced due to the distributed and heterogeneous nature of these architectures. Existing solutions for this purpose are either closed-source and proprietary or focus only on the infrastructural footprint of the applications. In response to that, in this work, we present a cost-profiling solution aimed at Kubernetes-based microservice applications, building on a popular open-source application performance monitoring (APM) stack. By means of a case study with a data engineering company, we demonstrate how our proposed solution can provide deeper insights into the cost profile of the various application components and drive informed decision-making in managing the deployment of the application.

Meta Analysis on Yield Effect of Biochar for Staple Crops in China

In order to quantitatively evaluate the impact of biochar on the yield of staple crops in China, a total of 866 pairs of data from 116 published studies were collected. Meta analysis was used to quantitatively analyze the impact of biochar on the yield of staple crops in China and its influencing factors. Meanwhile, a structural equation model (SEM) was constructed to further explain the interaction among the factors. The results showed that compared with no biochar application, biochar application could improve the physical and chemical properties of the soil in the main grain fields and increase the yield of the main grain crops, with an average yield increase rate of 8.77%. Among them, when the pH of biochar was 7-8, the average yield increase rate was the highest, reaching 26.49%. When C/N<60, the average yield increase rate was 13.73%, which was significantly higher than that of C/N≥60. Applying biochar to acidic or neutral soil could give full play to its yield-increasing effect. When the amount of carbon applied was 10-20 t·hm-2, the average yield increase rate of wheat and corn was the highest. The average yield of rice was the highest when the amount of carbon was 15-25 t·hm-2. However, the yield-increasing rates of rice with different carbon application levels were similar, so it is possible to consider losing part of the yield and give consideration to the economic benefits by appropriate reduction. In addition, the yield-increasing effect of biochar decreased with the increase in application years, and generally the yield-increasing effect was not significant after three years. The SEM showed that the application amount of biochar not only directly affected the yield of staple crops but also indirectly affected the yield of staple crops by improving soil fertility, whereas biochar C/N and pH only affected the yield of staple crops by improving soil fertility. Therefore, in the future application of biochar, priority should be given to applying it to acidic or neutral soil with low fertility, and its C/N and pH should be controlled below 60 and 7-8, respectively. Soil conditions, biochar characteristics, and field management measures should be comprehensively considered to maximize the yield of staple crops. This research can provide scientific basis and theoretical basis for the popularization and rational application of biochar.

Signature identification and user activity analysis on WhatsApp Web through network data

WhatsApp messenger is a popular instant messaging application that employs end-to-end encryption for communication. WhatsApp Web is the browser-based implementation of WhatsApp messenger. Users of WhatsApp communicate securely using SSL protocol. Encryption and use of common port for communication by multiple applications poses challenge in traffic classification for application identification. It is highly needed to analyze the network traffic for the purpose of QoS, Intrusion Detection and application specific traffic classification. In this paper, we have done traffic analysis on the network packets captured through data transfer in whatsApp web. In the result, we have explored the user activities such as message texting, contact sharing, voice message, location sharing, media transfer and status viewing. Packet level traffic analysis of user activities reveal patterns in the encrypted SSL communication. This pattern is identified across SSL packet lengths for WhatsApp media transfer and voice message communication. Other important features WhatsApp is the ability to view the status of the message being sent. We have identified the read and unread message status in these data packets by exposing signatures in the network layer. These signatures are identified with the help of the SSL lengths in the TLS header information of WhatsApp Web network traffic traces. Various other information on WhatsApp traffic presented in our study is relevant to the version of WhatsApp Web v0.3.2386. Also in this work machine learning based classifier is trained to classify the encrypted malicious, normal network traffic. From the results it is clear that SVM classifier gives the highest accuracy of 0.9666.