Location-Based Internationalization and Localization With Mobile Computing

The Android system operates on many smartphones in many locales. Websites and web tools have their own requirements in day-to-day life. To reach the maximum users, the app and website should handle all the resources such as text strings, functions, layouts, graphics, and any other static data that the app/website needs. It requires internationalization and localization of the website and app to support multiple languages. The basic idea of this chapter is to present an approach for localizing the Android application according to the location data that the app received from the device, but many users do not allow the “access location” feature so this approach will be a dead end in this case. The authors have proposed some other techniques to achieve this feature of localization and internationalization by implementing the “choose language” service so that the app can itself optimize its content and translate it into the user's native language.

Why Does Privacy Paradox Exist?

In this exploratory study, the main aim was to find, ‘why do people disclose information when they are concerned about their privacy?’. The reasons that provide a plausible explanation to the privacy paradox have been conjectural. From the analysis of the eighteen in-depth interviews using grounded theory, themes were then conceptualized. We found rational and irrational explanations in terms of cognitive biases and heuristics that explain the privacy paradox among mobile users. We figured out some reasons in this context of mobile computing which were not emphasized earlier in the privacy paradox literature such as Peanut Effect, Fear of Missing Out- FoMo, Learned Helplessness, and Neophiliac Personality. These results add to the privacy paradox discourse and provide implications for smartphone users for making privacy-related decisions more consciously rather than inconsiderately disclosing information. Also, the results would help marketers and policymakers design nudges and choice architectures that consider privacy decision-making hurdles.

Computer Mathematical Modeling Based on the Improved Genetic Algorithm and Mobile Computing

In order to change the problem of data redundancy in a genetic algorithm, this paper proposes a computer mathematical model based on the combination of an improved genetic algorithm and mobile computing. Combined with the least square method, MATLAB software is used to solve the equations, determine the range of parameters, and solve the estimation parameter range and identification problems. The improved genetic algorithm combined with mobile computing and least square method to establish a mathematical model greatly increased the individual search space and increased the operation rate of 90% compared to the basic genetic algorithm or mobile computing. The results show that the improved genetic algorithm and mobile computing have a certain ability to identify the optimal solution and greatly improve the work efficiency.

Mobile Computing as a Support to Teach the Concept of Function

We make a proposal to teach the concept of function using mobile computing. This proposal is based on research that has support in education theories such as Constructivism and Problem Solving Learning. In the first part we show the difficulties that undergraduate Computer Science students have in the first semester of Engineering while working with that mathematical concept. And application to be used by the students anywhere and anytime in mobile devices is designed that process data and is intended to show them the concept of function in a problem solving situation. The proposed activities are part of the education methodology used in the research and within this paper we show the diagnostic questionnaire as one of the methodological tools and its results supporting the designed activities and its application to mobile devices

Prediction of Mineralization Prospects Based on Geological Semantic Model and Mobile Computer Machine Learning

Mineral resources are indispensable in the development of human society and are the foundation of national economic development. As the prospecting target shifts from outcrop ore to concealed ore, from shallow to deep, the difficulty of prospecting becomes more and more difficult. Therefore, the prediction of mineralization prospects is of great significance. This paper is aimed at completing the prediction of mineralization prospects by constructing geological semantic models and using mobile computer learning to improve the accuracy of prediction of mineralization prospects and expanding the application of semantic mobile computing. We use five different semantic relations to build a semantic knowledge library, realize semantic retrieval, complete information extraction of geological text data, and study mineral profiles. Through the distributed database of mobile computing, the association rules and random forest algorithm are used to describe the characteristics of minerals and the ore-controlling elements, find the association rules, and finally combine the geological and mineral data of the area and use the random forest algorithm to realize the prospect of mineralization district forecast. The geological semantic model constructed in the article uses the knowledge library for associative search to achieve an accuracy rate of 87.9% and a recall rate of 96.5%. The retrieval effect is much higher than that of traditional keyword retrieval methods. The maximum value of the posterior result of the mineralization prospect is 0.9027, the average value is 0.0421, and the standard deviation is 0.1069. The picture is brighter, and the probability of mineralization is higher.