Privacy Rating of Mobile Applications Based on Crowdsourcing and Machine Learning

With the advent of the 5G network era, the convenience of mobile smartphones has become increasingly prominent, the use of mobile applications has become wider and wider, and the number of mobile applications. However, the privacy of mobile applications and the security of users' privacy information are worrying. This article aims to study the ratings of data and machine learning on the privacy security of mobile applications, and uses the experiments in this article to conduct data collection, data analysis, and summary research. This paper experimentally establishes a machine learning model to realize the prediction of privacy scores of Android applications. The establishment of this model is based on the intent of using sensitive permissions in the application and related metadata. It is to create a regression function that can implement the mapping of applications to score . Experimental data shows that the feature vector prediction model can uniquely be used to represent the actual usage and scheme of a system's specific permissions for the application.

Privacy Rating of Mobile Applications Based on Crowdsourcing and Machine Learning

With the advent of the 5G network era, the convenience of mobile smartphones has become increasingly prominent, the use of mobile applications has become wider and wider, and the number of mobile applications. However, the privacy of mobile applications and the security of users' privacy information are worrying. This article aims to study the ratings of data and machine learning on the privacy security of mobile applications, and uses the experiments in this article to conduct data collection, data analysis, and summary research. This paper experimentally establishes a machine learning model to realize the prediction of privacy scores of Android applications. The establishment of this model is based on the intent of using sensitive permissions in the application and related metadata. It is to create a regression function that can implement the mapping of applications to score . Experimental data shows that the feature vector prediction model can uniquely be used to represent the actual usage and scheme of a system's specific permissions for the application.

Active Learning Based on Crowdsourced Data

The paper proposes a crowdsourcing-based approach for annotated data acquisition and means to support Active Learning training approach. In the proposed solution, aimed at data engineers, the knowledge of the crowd serves as an oracle that is able to judge whether the given sample is informative or not. The proposed solution reduces the amount of work needed to annotate large sets of data. Furthermore, it allows a perpetual increase in the trained network quality by the inclusion of new samples, gathered after network deployment. The paper also discusses means of limiting network training times, especially in the post-deployment stage, where the size of the training set can increase dramatically. This is done by the introduction of the fourth set composed of samples gather during network actual usage.

Towards a cashless society: Use of electronic payment devices among generation Z

Nowadays, transactions on e-commerce platforms (e-payment) utilizing a credit card are popular. Using credit cards for electronic purchases over the Internet is much different from offline purchases in traditional stores; only online transactions do not include either physical credit card or a signature. The e-payment has become a common mode of payment for online transactions made. It is an electronic billing system that gives clients the ability to make payments using the Internet. The objective of this paper was to analyze the associations among continuance intention e-payment, effort expectancy, facilitating conditions, performance expectancy, social influence, and actual usage of e-payment. The data was tested empirically on data collected from 667 Generation Z e-payment users in Malaysia. The results found that facilitating conditions, performance expectancy, and social influence impacted the actual usage of e-payment. Surprisingly, effort expectancy was not significantly associated with the actual e-payment usage. The findings of this study have several implications for managers and point the way towards future research. No prior empirical study has investigated the role of the Unified Theory of Acceptance and Use of Technology model on e-payment usage among Generation Z in Malaysia to the best of the authors’ knowledge. These results provide valuable contributions that can help decision-makers formulate or adjust their strategies associated with e-payments.

Learning Technologies among Academics in the United Arab Emirates: One Academic Year since COVID-19

This research aims to investigate the adoption of learning technologies among academics in higher education institutes within the United Arab Emirates (UAE) after completing one academic year teaching through a hybrid technique due to the Coronavirus (COVID-19) pandemic. The researchers propose a validated framework that integrates the Technology Acceptance Model (TAM), Social Cognition Theory (SCT), and Innovation Diffusion Theory (IDT). The research was conducted using the quantitative method, where 170 academics from 22 higher education institutes in the UAE completed an online survey. The researchers found that the adoption of learning technologies depends on the individual academics and the higher education institutes; therefore, the researchers suggest building a more effective strategy to accelerate adoption. Additionally, the researchers also found that the academics’ perceptions of the usefulness of learning technologies do not affect their behavior while utilizing them, and that the behavioral intention does not affect the actual usage of the learning technologies.

The Investigation of Employer Adoption of Human Resource Information Systems at University Using TAM

The new conditions that arise as a result of the transformation of the information society are reflected in many areas, such as the changes in human resources management, especially in the context of human resource information systems (HRIS). HRIS have become an important element in increasing organizational performance and productivity. The primary aim of this chapter is to investigate the employers' adoption for human resource information systems (HRIS) in higher education by using technology acceptance model (TAM). The results of the study revealed that perceived usefulness and perceived ease of use has an important role of employers' attitudes towards using, behavioral intention to use the system, and actual usage of HRIS. A survey was administered, and a sample of 155 academic and administrative personnel at Isparta Süleyman Demirel University were analyzed using SmartPLS version 3.2.7.

COOPERATION AS CORNERSTONE OF VILLAGE ECONOMY TO EMPOWER SOCIETY DURING COVID-19 PANDEMIC ACCEPTANCE OF TECHNOLOGY INNOVATION (ATI) IN JOMBANG, INDONESIA

According to Undang-Undang (UU) No. 25 of 1992, a cooperative is a business entity composed of individuals or cooperative legal entities. In Indonesia, cooperatives have successfully become an organization occupying a special position in the structure of the national economy. The existence of cooperatives is expected to have various positive effects on the Indonesian economy. The application of technological development in cooperatives can be aimed at the business development of cooperatives to encourage the Indonesian economy. In the rapid technological development, it is very helpful for various economic participants to establish communications quickly and cheaply.Existing technologies can support the revitalization of cooperatives in Indonesia by providing applications. The research method used in this study is non probability sampling on Surabaya and Jombang. The data processing analysis is using simple and multiple regression with SPSS 22.0 software. The results of this study that there are seven hypotheses which are five hypotheses are accepted and two hypotheses are rejected. The accepted hypotheses are perceived value has a positive and significant effect on actual usage with the coefficient regression is 0.556, compatibility has a positive and significant effect on perceived value with the coefficient regression is 0.531, perceived ease of use has a positive and significant effect on perceived value with the coefficient regression is 0.230, compatibility has a positive and significant effect on actual usage with the coefficient regression is 0.219, and perceive usefulness has a positive and significant effect on perceived value with the coefficient regression is 0.196. On the other hand two hypotheses were rejected are perceived ease of use has a positive and significant effect on actual usage with the coefficient regression is 0.124 and perceived usefulness has a positive and significant effect on actual usage with the coefficient regression is 0.041.

Application of lean manufacturing technology in a milk manufacturing company

This work presents the application of lean management principle in the area of waste elimination in a powder milk plant of a production company. The existing manufacturing procedure for the company’s plant was evaluated, and a lean manufacturing technique was developed for the plant. To achieve this, a time series analysis was carried out on the econometric data obtained, and a future forecast predicting losses and usage was predicted based on the practised manufacturing procedures. The econo­metric linear transfer function technique applied to the actual usage and actual loss data obtained filtered the waste in the production process and generated forecast values for actual loss and usage. This revealed that there had been poor manufacturing practices in the factory. The findings from this research can be used as a guide to managers on wastage control in a production system.