Mobile Operating System Research Articles

Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality

Mobile robots in industry are commonly used in warehouses and factories. To achieve the highest production rate, requirements for path planning algorithms have caused researchers to pay significant attention to this problem. The artificial potential field algorithm, which is a local path planning algorithm, has been previously modified to obtain higher smoothness of path, to solve the stagnation problem and to jump off the local minimum. The last itemized problem is taken into account in this paper—local minimum avoidance. Most of the modifications of artificial potential field algorithms focus on a mechanism to jump off a local minimum when robots stagnate. From the efficiency point of view, the mobile robot should bypass the local minimum instead of jumping off it. This paper proposes a novel artificial potential field supported by augmented reality to bypass the upcoming local minimum. The algorithm predicts the upcoming local minimum, and then the mobile robot’s perception is augmented to bypass it. The proposed method allows the generation of shorter paths compared with jumping-off techniques, due to lack of stagnation in a local minimum. This method was experimentally verified using a Husarion ROSbot 2.0 PRO mobile robot and Robot Operating System in a laboratory environment.

Comparative Analysis of Native and Cross-Platform iOS Application Development

Abstract Nowadays, mobile applications are developed for more and more areas, providing great help for our everyday lives. When designing a mobile application, the first important decision to make is to choose the targeted platform. Is it only phone or tablet as well? Should the app run on Android or iOS, or should it be available on both mobile operating systems? In the latter case, besides the native development environments, it is worth considering a cross-platform development environment to write the software. This study investigates both the development and performance aspects of some possibilities for iOS application development, namely, native iOS development in Xcode, Xamarin.iOS, and Xamarin.Forms frameworks.

What and How long: Prediction of Mobile App Engagement

User engagement is crucial to the long-term success of a mobile app. Several metrics, such as dwell time, have been used for measuring user engagement. However, how to effectively predict user engagement in the context of mobile apps is still an open research question. For example, do the mobile usage contexts (e.g., time of day) in which users access mobile apps impact their dwell time? Answers to such questions could help mobile operating system and publishers to optimize advertising and service placement. In this article, we first conduct an empirical study for assessing how user characteristics, temporal features, and the short/long-term contexts contribute to gains in predicting users’ app dwell time on the population level. The comprehensive analysis is conducted on large app usage logs collected through a mobile advertising company. The dataset covers more than 12K anonymous users and 1.3 million log events. Based on the analysis, we further investigate a novel mobile app engagement prediction problem—can we predict simultaneously what app the user will use next and how long he/she will stay on that app? We propose several strategies for this joint prediction problem and demonstrate that our model can improve the performance significantly when compared with the state-of-the-art baselines. Our work can help mobile system developers in designing a better and more engagement-aware mobile app user experience.

A Comparative Analysis of Mobile Application Development Approaches

Over the last decade, there has been a significant increase in the development of mobile applications. The performance of the developed applications depends largely on the development approaches. There are two widely used approaches: (1) native, where the application is targeted and developed for a specific platform, (2) crossplatform, where the developed application runs on multiple platforms. This paper aims to address the question of which approach should be used in various scenarios. We have performed a detailed comparison of the two approaches by developing a mobile app using both approaches. Experiments are performed using Android and iOS, the two most well-known mobile Operating System. The criteria of deciding the best approach include performance, usability and support. Our results show that both approaches are viable depending on the requirements and type of the application to be developed, with native having an edge. Guidelines are presented at the end to help the developers in choosing the best approach. The fundamental differences and advantages of each approach are discussed.

A Noninterference Model for Mobile OS Information Flow Control and Its Policy Verification

Mobile operating systems such as Android are facing serious security risk. First, they have a large number of users and store a large number of users’ private data, which have become major targets of network attack; second, their openness leads to high security risks; third, their coarse-grained static permission control mechanism leads to a large number of privacy leaks. Recent decentralized information flow control (DIFC) operating systems such as Asbestos, HiStar, and Flume dynamically adjust the label of each process. Asbestos contains inherent covert channels due to this implicit label adjustment. The others close these covert channels through the use of explicit label change, but this impedes communication and increases performance overhead. We present an enhanced implicit label change model (EILCM) for mobile operating systems that can close the known covert channel in these models with implicit label change and supports dynamic constraints on tags for separation of duty. We also formally analyze the reasons why EILCM can close the known covert channels and prove that abstract EILCM systems have the security property of noninterference with declassification by virtue of the model checker tool FDR. We also prove that the problem of EILCM policy verification is NP-complete and propose a backtrack-based search algorithm to solve the problem. Experiments are presented to show that the algorithm is effective.

Android vs. IOS: a comparative analysis over mobile operator infrastructures based on crowdsourced mobile dataset

User equipment (UE)’s operating system (OS) and category types are important factors that are affecting the end-user performance in a given mobile network operator (MNO)’s infrastructure. For this reason, fair and statistically accurate observed network performance differences of UE’s OSs based on category types, MNOs or locations can be of interest for mobile telecommunication ecosystem players. This paper’s focus is on performance comparisons of UE OSs (including Android, IOS (iPhone Operating System) and Windows phones) over different UE categories, MNOs and locations based on previously collected end-to-end nationwide crowd-sourced data measurements in Turkey. The analysis results performed in this paper uses statistical comparisons of unpaired observations due to imbalance between number of observations between all OSs and yield insight on how the mobile OS types’ network performances differ using some important Key Performance Indicators (KPIs) such as downlink (DL) speed, latency, jitter and packet loss (PL). The outcome of the analysis indicate that Android devices perform better in terms of DL speed among all MNOs, whereas IOS devices are better in terms of latency values. On the other hand depending on the UE category, the performances of MNOs may vary when IOS and Android OSs are compared based on different KPIs. Additionally, IOS has shown better performance than Android in large geographical areas of Turkey. Finally, the business aspects of performing the proposed statistical OS comparisons from the perspectives of OS developers, MNOs, device manufacturers, and end-users are highlighted.

Deep Learning Based Android Anomaly Detection Using a Combination of Vulnerabilities Dataset

As the leading mobile phone operating system, Android is an attractive target for malicious applications trying to exploit the system’s security vulnerabilities. Although several approaches have been proposed in the research literature for the detection of Android malwares, many of them suffer from issues such as small training datasets, there are few features (most studies are limited to permissions) that ultimately affect their performance. In order to address these issues, we propose an approach combining advanced machine learning techniques and Android vulnerabilities taken from the AndroVul dataset, which contains a novel combination of features for three different vulnerability levels, including dangerous permissions, code smells, and AndroBugs vulnerabilities. Our approach relies on that dataset to train Deep Learning (DL) and Support Vector Machine (SVM) models for the detection of Android malware. Our results show that both models are capable of detecting malware encoded in Android APK files with about 99% accuracy, which is better than the current state-of-the-art approaches.

Development and Implementation of Java Game Engine Based on Network Information Technology

With the development of computer technology and network technology, various digital learning methods have seriously impacted the traditional education and teaching mode, among which gameplay learning represented by teaching games has attracted wide attention of educators. It is considered to be the most effective way to stimulate learners’ learning motivation. Mobile game is an important part of mobile phone value-added service. The research of mobile game engine has extremely important application value for the development of mobile phone game. This paper mainly studies how to realize a high-performance mobile game engine. Through the analysis of mobile phone operating system, mobile game market, mobile phone game user needs, the technology selection and main function modules of the engine are determined. Through the research of java technology, the technical architecture of engine server and client is determined. Performance and algorithm optimization are the focus of this paper, because performance issues are the biggest bottleneck in developing games on java phones.

Accuracy of the SWAY Mobile Cognitive Assessment Application.

BackgroundMobile electronic devices have become integral tools in addressing the need for portable assessment of cognitive function following neurocognitive/motor injury. SWAY Medical, Inc., has employed mobile device motion-based technology in the SWAY Cognitive Assessment (SWAY CA) application to assess cognitive function.PurposeThe purpose of this study was to assess whether the SWAY CA application (reaction time, impulse control and inspective time) was able to reliably operate on different mobile devices and operating systems (iOS, Android). The study further sought to assess the validity of the SWAY CA application against the FDA approved ImPACT QT mobile device application.Study DesignOriginal Research, observational study of validity.Methods88 healthy, young adults, 18 to 48 years (mean= 22.09 ± sd=4.47 years) completed four, randomized and counter-balanced, reaction time tests (2- SWAY RT, 2- ImPACT QT) using different operating systems (iOS, Android) of 4 randomly assigned mobile devices.ResultsANOVAs reported the SWAY CA application (reaction time, impulse control, inspection time) operated reliably with iPhone 6S, Samsung Galaxy S9, and iPad Pro 5 mobile devices (p > 0.05), respectively. Google Pixel 3 reliability with SWAY CA application remains undetermined. SWAY CA simple reaction motion measures were in agreement (r = -0.46 to 0.22, p ≤ 0.05) with several ImPACT QT reaction time measures. SWAY CA impulse control and inspection time measures are weakly correlated (r = -0.25 to -0.46, p ≤ 0.05) with five ImPACT QT reaction time measures.ConclusionThe motion-based SWAY CA mobile device application appears to reliably operate when being administered on different mobile devices and software operating systems. Furthermore, the SWAY CA application appears to be comparable to the ImPACT QT and serve as a valid tool for assessing reaction time measures.Level of EvidenceLevel 2b (observational study of validity).

MALWARE PROTECTION SYSTEM ON ANDROID

Abstract: At the moment of writing this report (august 2018), the Android has a worldwide market share of 76.88%, being in an ascending slope since 2009, with an increase of 4.14% since the same period on last year and leaving its rival iOS far behind, at 20.38%[1]. Also, Google announced in 2017 that more than 2 billion users are monthly active on devices. These 2 facts make this OS the most important one on the mobile devices and not only smartphones, as nowadays even the smartwatches are using Android (named Wear) being in continuously improving the experience to be as close as the one on phones[2].
 
 [1] Mobile Operating System Market Share Worldwide, http://gs.statcounter.com/os-marketshare/mobile/worldwide
 [2] The best Wear OS smartwatches: LG, Fossil, Huawei, Polar and more, https://www. wareable.com/smartwatches/ best-android-watch.

Machine learning based approach to analyze file meta data for smart phone file triage

With the rapid increase in mobile phone storage capacity and penetration, digital forensic investigators face a significant challenge in quickly identifying relevant examinable files within a plethora of uninteresting OS and application files extracted by forensic tools. This challenge can have serious adverse effects in time critical cases, and can also result in increasing case backlog. A possible solution for this issue is to prioritize digital artifacts. This is referred to as triage. Several digital forensic triage methodologies based on classical automation techniques such as block hash and regular expression matching have been proposed. However, such techniques suffer from the significant limitation of requiring users to know and hardcode data templates and relations of interest. In literature, more flexible machine learning based approaches have been proposed to classify whether a mobile device, rather than a mobile device artifact, is of interest or not based on its usage metrics and file-system metadata. Also, recently an approach has been proposed and tested in triaging data generated and extracted from a computer-based operating system. However, this approach did not cover smart mobile operating system, and it did not consider key steps such as feature engineering, feature selection, and hyper-parameter tuning. Hence, in this paper, we propose a comprehensive machine learning based solutions with features extracted from file metadata to identify possible smart phone files of interest that should be examined. A range of classification algorithms are tested and their performance compared. Our classification models were trained and tested on a dataset consisting of the metadata of nearly 2 million files extracted from devices running Android OS and linked to real terrorism cases. The use of real case data allows obtaining realistic results, and restricting the operating system and case type helps narrow the experimentation scope enough to provide a proof of concept. Through our experiments, a best classifier is also identified.

On the Effect of k Values and Distance Metrics in KNN Algorithm for Android Malware Detection

There is a remarkable increase in mobile device usage in recent years. The Android operating system is by far the most preferred open-source mobile operating system around the world. Besides, the Android operating system is preferred in many devices on the Internet of Things (IoT) devices are used in many areas of daily life. Smart cities, smart environment, health, home automation, agriculture, and livestock are some of the usage areas. Health is one of the most frequently used areas. Since the Android operating system is both the widely used operating system and open-source, the vast majority of malware released on the market is now designed for Android platforms. Therefore, devices using the Android operating system are under serious threat. In this study, a system that detects malware on Android operating systems based on machine learning is proposed. Besides, feature vectors are created with permissions that have an important place in the security of the Android operating system. Feature vectors created using the k-nearest neighbor algorithm (KNN), one of the machine learning techniques, are given as input to this algorithm, and a classification of malicious software and benign software is provided. In the KNN algorithm, the k value and the distance metric used to find the closest sample directly affect the classification performance. In addition, the study examining the parameters of the KNN algorithm in detail in permission-based studies is limited. For this reason, the performance of the malware detection system is presented comparatively using five different k values and five different distance metrics under different data sets. When the results are examined, it is observed that higher classification performances are obtained when values such as 1, 3 are given to k and metrics such as Euclidean and Minkowski are chosen instead of the Chebyshev distance metric.

Effectiveness of Type of Feedback and Frequency on Digital Game-Based L2 Vocabulary Acquisition

The frequency of word exposure in teaching materials, along with corrective feedback, has often been identified as a powerful variable in the learning of vocabulary in a foreign language. The effect of the number of times an action is presented as accurate in digital game-based language learning scenarios (i.e., knowledge of correct response [KCR] feedback) however remains under-investigated. This study aims to analyse the learning of L2 mobile operating system terminology by a group of students of English as a foreign language by using the data collected by the video game The Conference Interpreter in order to identify the predictive strength of term frequency (times a term is shown), KCR feedback (times a correct answer is provided), and corrective feedback (times a term is shown as incorrect) regarding short and long-term L2 vocabulary learning. The regression analysis performed showed that the factor ‘KCR feedback' may be a positive predictor of short-term vocabulary learning, whereas the other factors seemed to have no statistical effect at any significant level.

Penerapan Kamus Kalimat Bahasa Bali Untuk Wisatawan

The application of Natural Language Processing (NLP) in the Balinese sentence dictionary is an application that can provide information about the meaning of Indonesian to Balinese translation using the words contained in the Indonesian pocket dictionary, the results or meanings obtained are based on the application of Natural Language Processing (NLP) in accordance with the stipulated provisions. This application does not translate per word but per sentence. The application of NLP in the Balinese sentence dictionary serves to provide convenience for tourists or users who are used to communicate with the Balinese people and understand the Balinese language itself. In addition, it can provide time efficiency to users because this application is built offline on the Android mobile operating system, so that it can be accessed anywhere and anytime. In its implementation, the waterfall method is used and the output of this application is Indonesian sentences to Balinese sentences. And in the development of this application used tools with UML (Unified Modelling Processing) which consists of use case diagrams, activity diagrams, sequential diagrams, statechart diagrams and class diagrams. Keywords: Dictionary, Natural Language Processing (NLP), Balinese

Behavioral Host-Based Intrusion Detection and Prevention System for Android

Intrusion Detection System (IDS) is vital to protect smartphones from about to happen security breach and make sure user privacy. Android is the most popular mobile Operating System (OS), holding many markets share. Android malware detection has received important concentration, existing solutions typically rely on performing resource intensive analysis on a server, assuming an uninterrupted link between the device and the server. In this paper, we propose a behavior Host-based IDS (HIDS) by using permissions incorporating arithmetical and ML algorithms. The benefit of our proposed IDS is two folds. First, it is completely independent and runs on the smartphone device, without need any link to a server. Second, it requires only training dataset consisting of some of examples from both benign and malicious datasets for tuning. though, in put into practice, collecting malicious examples is exciting since its important infecting the device and collecting many of samples in order to characterize the malware’s behavior and the labelling has to be done. The evaluation outcome show that the proposed IDS gives a very hopeful accuracy.

Intelligent Tour System

The Smart tour app and website presents category classification of mobile travel applications accessible at the moment for tourists in application stores for most popular mobile operation systems (Android). The most interesting category is "Travel Guides" that combines "Information Resources" and "Location-Based Services" category. The information is given by the users who visited various places and hotels and also will rate the places and hotels. According to rating other user will decide whether to visit the place or not and customize his tour. The objective of the travel and tourism management system project is to develop a system that automate the process and activities of travel and the purpose is to design a system using which can perform all the operation related to travelling.

Maintaining a level playing field when Big Tech disrupts the financial services sector

ABSTRACT Google, Apple, Facebook and Amazon (the “GAFAs”) have been slow to disrupt the financial services sector, but they are likely to do so in the coming years by using their control of important customer access points such as mobile operating systems, search engines, app stores, and marketplaces. This paper discusses these issues in the context of competition law enforcement and the emerging UK and EU regulatory regimes aiming to curb the GAFAs’ market power. The new rules can ensure that consumers will benefit from the innovations of the GAFAs and others without suffering the long-run effects of their further accumulation of market power. The new rules can ensure that the GAFAs do not benefit from an asymmetry of regulatory obligations compared to their financial services competitors, and that the GAFAs cannot leverage their market power from core activities into financial services whereby their financial services competitors are hindered in reacting.

Access Android Device Using The FatRat and Metasploit

At present, smartphones are widely used for both business and personal purposes. As we all know that android is the popular mobile operating system. Like Windows operating system vulnerability the android has also vulnerability. And on the basis of these vulnerabilities, an attacker can obtain a user’s privacy data. But one possible way to avoid accessing of system and network i.e. penetration testing
 This paper describes penetration testing, Kali Linux tools such as Metasploit and TheFatRat. These tools have proved to be effective in Android exploitation. For example, by using TheFatRat, create a payload using msfvenom. Furthermore, the Payload creates a backdoor to access the system, using Metasploit, which exploits the android device and finds the vulnerability and, according to vulnerability, access the victim's system.

Privacy by Default, Abuse by Design: EU Competition Concerns About Apple's New App Tracking Policy

With the most recent updates of its mobile operating systems, in April 2021 Apple implemented a new global App Tracking Transparency policy. The policy obliges app developers to display an additional (Apple-designed) prompt to request permission from end users for the developer to “track” the user, even when the user has already consented to the sharing of its data through the developer’s own consent tool. Apple presents the new policy as a step to enhance privacy. Others, including the authors of this article, see it as a “bombshell for third-party mobile ad tech” that, together with Google’s disabling of third-party cookies, only entrenches the data supremacy of Apple and Google, and forecloses data-based competition and consumer choice across the entire Apple ecosystem. These concerns in fact have already triggered an investigation by the French Autorite de la concurrence and a complaint of eight media and tech associations before the German Bundeskartellamt. From the standpoint of EU law, this article describes the relevant conduct and technicalities, its impact on end users’ data privacy and on competition, and how both interests may be weighed considering the existing legislation.

Maintaining a Level Playing Field When Big Tech Disrupts the Financial Services Sector

Google, Apple, Facebook and Amazon (the “GAFAs”) have been slow to disrupt the financial services sector, but they are likely to do so in the coming years. The precise nature of the disruption and the appropriate regulatory response is not clear-cut, but the GAFAs’ advantages will stem from their control of the main customer access points, such as mobile operating systems, search engine results pages, app stores, and marketplaces. This paper discusses the GAFAs’ anticipated disruption in the financial services sector in the context of competition law enforcement and the emerging regulatory regimes in the EU and UK that seek to curb the GAFAs' market power. If the new rules are drafted carefully, consumers can benefit from the innovations of the GAFAs and others without suffering the long-run effects of their further accumulation of market power. The new rules should ensure that the GAFAs do not benefit from an asymmetry of regulatory obligations whereby they are not subject to the same rules as their financial services competitors. The GAFAs’ should not be able to leverage their market power from core activities into financial services such that their financial services competitors are hindered in reacting to the GAFAs’ competitive threat.