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Over the past years, transport mode recognition has become a large field of research. However, flight as a type of transportation has been mostly overlooked. A system for flight detection might be useful for context-aware applications, but more importantly, it can be used to automatically manage airplane mode on smartphones. Smartphones transmit radio frequency signals which could potentially interfere with aircraft systems, and it is therefore important that devices enable airplane mode to avoid this problem. This paper proposes flyDetect, a method for automatic flight mode detection and an embodiment in the form of an app that demonstrates the viability of the method. Thus, the system uses the accelerometer and barometer in an Android smartphone, can detect the start and end of a flight, and notify other apps or systems on the device when this happens. Our evaluation shows that flyDetect meets the requirements set for the solution, and the results are very promising. [ABSTRACT FROM AUTHOR]

This paper demonstrates that it is possible to access a mobile phone's sensors through a web browser without the user's knowledge. Thus mobile phones are vulnerable to side-channel attacks. A software tool was developed to check access to a smartphone's sensors and generate a report with results and recommendations. After reviewing scientific publications on the topic, a technical experiment was conducted to prove that access to various sensors of the examined device can be accessed without the user being aware of it. The experimental results show that Chrome and Samsung Internet browsers, running on Android, are vulnerable to side-channel attacks. The research methods used in this paper were comparative analysis, synthesis and technical experiment. [ABSTRACT FROM AUTHOR]

With the emergence of smartphones, Android has become a widely used mobile operating system. However, it is vulnerable when encountering various types of attacks. Every day, new malware threatens the security of users' devices and private data. Many methods have been proposed to classify malicious applications, utilizing static or dynamic analysis for classification. However, previous methods still suffer from unsatisfactory performance due to two challenges. First, they are unable to address the imbalanced data distribution problem, leading to poor performance for malware families with few members. Second, they are unable to address the zero-day malware (zero-day malware refers to malicious applications that exploit unknown vulnerabilities) classification problem. In this article, we introduce an innovative meta-learning approach for multi-family Android malware classification named Meta-MAMC, which uses meta-learning technology to learn meta-knowledge (i.e., the similarities and differences among different malware families) of few-family samples and combines new sampling algorithms to solve the above challenges. Meta-MAMC integrates (i) the meta-knowledge contained within the dataset to guide models in learning to identify unknown malware; and (ii) more accurate and diverse tasks based on novel sampling strategies, as well as directly adapting meta-learning to a new few-sample and zero-sample task to classify families. We have evaluated Meta-MAMC on two popular datasets and a corpus of real-world Android applications. The results demonstrate its efficacy in accurately classifying malicious applications belonging to certain malware families, even achieving 100% classification in some families. [ABSTRACT FROM AUTHOR]

This paper introduces ESMira, a server and mobile app (Android, iOS) developed for research projects using experience sampling method (ESM) designs. ESMira offers a very simple setup process and ease of use, while being free, decentralized, and open-source (source code is available on GitHub). The ongoing development of ESMira started in early 2019, with a focus on scientific requirements (e.g., informed consent, ethical considerations), data security (e.g., encryption), and data anonymity (e.g., completely anonymous data workflow). ESMira sets itself apart from other platforms by both being free of charge and providing study administrators with full control over study data without the need for specific technological skills (e.g., programming). This means that study administrators can have ESMira running on their own webspace without needing much technical knowledge, allowing them to remain independent from any third-party service. Furthermore, ESMira offers an extensive list of features (e.g., an anonymous built-in chat to contact participants; a reward system that allows participant incentivization without breaching anonymity; live graphical feedback for participants) and can deal with complex study designs (e.g., nested time-based sampling). In this paper, we illustrate the basic structure of ESMira, explain how to set up a new server and create studies, and introduce the platform's basic functionalities. [ABSTRACT FROM AUTHOR]

Heart rate (HR) is vital for medical and healthcare purposes. This study presents an Android-based heart rate measurement system utilizing a single-lead electrocardiogram (ECG). Three electrodes placed on the arm in lead I configuration capture the ECG signals. An AD8232 sensor amplifies the signal, which is then digitized by Arduino Nano and transmitted to an Android device via HC-05 Bluetooth. The Android application processes the ECG data using the Pan-Tompkins algorithm with an optimized threshold coefficient to extract HR information. The system displays the ECG waveform and the calculated HR on the user interface. Our evaluation demonstrates high accuracy with an error rate of only 0.042%, sensitivity of 99.84%, and positive predictive value of 97.06%. This research suggests the potential of this system for convenient and reliable HR monitoring using readily available smartphones. [ABSTRACT FROM AUTHOR]

Researchers have proposed different obfuscation transformations supported by numerous smartphone protection tools (obfuscators and deobfuscators). However, there is a need for a comprehensive study to empirically characterize these tools that belong to different categories of transformations. We propose a property-based framework to systematically classify twenty cutting-edge tools according to their features, analysis type, programming language support, licensing, applied obfuscation transformations, and general technical drawbacks. Our analysis predominantly reveals that very few tools work at the dynamic level, and most tools (which are static-based) work for Java or Java-based ecosystems (e.g., Android). The findings also show that the widespread adoption of renaming transformations is followed by formatting and code injection. In addition, this paper pinpoints the technical shortcomings of each tool; some of these drawbacks are common in static-based analyzers (e.g., resource consumption), and other drawbacks have negative effects on the experiment conducted by students (e.g., a third-party library involved). According to these critical limitations, we provide some timely recommendations for further research. This study can assist not only Android developers and researchers to improve the overall health of their apps but also the managers of computer science and cybersecurity academic programs to embed suitable obfuscation tools in their curricula. [ABSTRACT FROM AUTHOR]

Biology is an abstract, elusive science, one of which is respiratory material. It requires a media that can visualize objects on a two-dimensional book of biology to a three-dimensional one with a learning video. Research aims to develop, describe kal, and describe the teacher's assessment and student response to the results of the product developed. This research and development using model 4D consists of define, design, foresight, and develop ate. The result of this realization is the production of a medium of pocket books and apps downloaded on android smartphone platforms, able to present in real, tangible, three-dimensional objects to respiratory matter. The media was declared worthy by the validator of materials and media experts with an average rating score on final validation at 84.68% and 93.17%. Trials in the field as practical tests were obtained an assessment result from teachers by 85.36%, student responses of small groups and large groups are 86% and 83.65%. Also, it is also noted that a media pocket book and applications using this augmented reality technology are worthy to be used by students in studying the human respiratory system for grade 11th MIPA SMAN 1 Kota Jambi. [ABSTRACT FROM AUTHOR]

This research discusses the development of an Android application through the integration of fingerprint recognition technology for user authentication as a way of improving security in the system. The app serves as an additional security layer, requiring users to verify their identity before gaining access to any installed applications on their Android device. The use of technologically developed devices with fingerprint scanners that have the capacity for analyzing biological traits facilitates a strong user verification process. The process makes use of a fingerprint previously registered by the user to authenticate user legitimacy, hence providing an efficient authentication process. A set of metrics was introduced and verified against a legacy system, proving that the proposed system surpasses the legacy system. This new approach offers the advantage of serving as an alternative to using PIN numbers or pattern unlocking on Android smartphones.

With the surge in the number of people using smartphone devices all over the world, the mobile computing industry has been thriving. There are around 3.6 billion smartphone users worldwide as recorded till 2020 and we expect it to be 4.3 billion by 2023. In India itself, we have 696 million smartphone users, and it is expected to be 875 million by 2023. With the increase in the population going online, online shopping has become very convenient. But we still face issues with shopping online at local stores or booking appointments at local hair salons like hunting for a particular item from the store to store only to find it's unavailable or the shop is closed. Through our application "ShopChain", we improve the local store shopping by providing them the opportunity/platform to get their stores online so they can update the customers with live updates about their store. [ABSTRACT FROM AUTHOR]

The continuously improving performance of mass-market global navigation satellite system (GNSS) chipsets is enabling the prospect of high-precision GNSS positioning for smartphones. Nevertheless, a substantial portion of Android smartphones lack the capability to access raw carrier phase observations. Therefore, this paper introduces a precise code positioning (PCP) method, which utilizes Doppler-smoothed pseudo-range and inter-satellite single-difference methods. For the first time, the results of a quality investigation involving BDS-3 B1C/B2a/B1I, GPS L1/L5, and Galileo E1/E5a observed using smartphones are presented. The results indicated that Xiaomi 11 Lite (Mi11) exhibited a superior satellite data decoding performance compared to Huawei P40 (HP40), but it lagged behind HP40 in terms of satellite tracking. In the static open-sky scenario, the carrier-to-noise ratio (CNR) values were mostly above 25 dB-Hz. Additionally, for B1C/B1I/L1/E1, they were approximately 8 dB-Hz higher than those for B2a/L5/E5a. Second, various PCP models were developed to address ionospheric delay. These models include the IF-P models, which combine traditional dual-frequency IF pseudo-ranges with single-frequency ionosphere-corrected pseudo-ranges using precise ionospheric products, and IFUC models, which rely solely on single-frequency ionosphere-corrected pseudo-ranges. Finally, static and dynamic tests were conducted using datasets collected from various real-world scenarios. The static tests demonstrated that the PCP models could achieve sub-meter-level accuracy in the east (E) and north (N) directions, while achieving meter-level accuracy in the upward (U) direction. Numerically, the root mean square error (RMSE) improvement percentages were approximately 93.8%, 75%, and 82.8% for HP40 in the E, N, and U directions, respectively, in both open-sky and complex scenarios compared to single-point positioning (SPP). In the open-sky scenario, Mi11 showed an average increase of about 85.6%, 87%, and 16% in the E, N, and U directions, respectively, compared to SPP. In complex scenarios, Mi11 exhibited an average increase of roughly 68%, 75.9%, and 90% in the E, N, and U directions, respectively, compared to SPP. Dynamic tests showed that the PCP models only provided an improvement of approximately 10% in the horizontal plane or U direction compared to SPP. The triple-frequency IFUC (IFUC123) model outperforms others due to its lower noise and utilization of multi-frequency pseudo-ranges. The PCP models can enhance smartphone positioning accuracy. [ABSTRACT FROM AUTHOR]

Background: Foreign body ingestion (FBI) represents the most common cause of emergent gastrointestinal endoscopy in children. FBI's management can be quite challenging for physicians because of the variability of the clinical presentation, and the decision tree becomes even more intricate because of patient-specific variables that must be considered in the pediatric age range (e.g., age of patients and neuropsychiatric disorders) in addition to the mere characteristics of the foreign body. We present an application for smartphones designed for pediatricians and pediatric surgeons based on the latest guidelines from the official pediatric societies. The app aims to help physicians manage FBI quickly and properly in children. Materials and methods: The latest pediatric FBI management guidelines were reviewed and summarized. The flow chart we obtained guided the development of a smartphone application. A questionnaire was administered to all pediatric surgeon trainees at our institute to test the feasibility and helpfulness of the application. Results: An app for smartphones was obtained and shared for free on the Google Play Store and Apple Store. The app guides the physician step by step in the diagnostic process, analyzing all patient- and foreign body-specific characteristics. The app consultation ends with a suggestion of the most proper decision to make in terms of further radiological investigations and the indication and timing of endoscopy. A questionnaire administered to trainees proved the app to be useful and easy to use. Conclusion: We developed an app able to help pediatricians and pediatric surgeons manage FBI in children, providing standardized and updated recommendations in a smart and easily available way. [ABSTRACT FROM AUTHOR]

The use of smartphones is increasing rapidly and the malicious intrusions associated with it have become a challenging task that needs to be resolved. A secure and effective technique is needed to prevent breaches and detect malicious applications. Through deep learning methods and neural networks, the earliest detection and classification of malware can be performed. Detection of Android malware is the process to identify malicious attackers and through the classification method of malware, the type is categorized as adware, ransomware, SMS malware, and scareware. Since there were several techniques employed so far for malware detection and classification, there were some limitations like a reduced rate of accuracy and so on. To overcome these limitations, a deep learning-based automated process is employed to identify the malware. In this paper, initially, the datasets are collected, and through the preprocessing method, the duplicate and noisy data are removed to improve accuracy. Then the separated malware and benign dataset from the preprocessing phase is dealt with in feature selection. The reliable features are extracted in this process by Meta-Heuristic Artificial Jellyfish Search Optimizer (MH-AJSO). Further by the process of classification, the type of malware is categorized. The classification method is performed by the proposed Dense Dilated ResNet101 (DDResNet101) classifier. According to the type of malware the breach is prevented and secured on the android device. Although several methods of malware detection are found in the android platform the accuracy is effectively derived in our proposed system. Various performance analysis is performed to compare the robustness of detection. The results show that better accuracy of 98% is achieved in the proposed model with effectiveness for identifying the malware and thereby breaches and intrusion can be prevented. [ABSTRACT FROM AUTHOR]

In social science, health care, digital therapeutics, etc., smartphone data have played important roles to infer users' daily lives. However, smartphone data collection systems could not be used effectively and widely because they did not exploit any Internet of Things (IoT) standards (e.g., oneM2M) and class labeling methods for machine learning (ML) services. Therefore, in this paper, we propose a novel Android IoT lifelog system complying with oneM2M standards to collect various lifelog data in smartphones and provide two manual and automated class labeling methods for inference of users' daily lives. The proposed system consists of an Android IoT client application, an oneM2M-compliant IoT server, and an ML server whose high-level functional architecture was carefully designed to be open, accessible, and internationally recognized in accordance with the oneM2M standards. In particular, we explain implementation details of activity diagrams for the Android IoT client application, the primary component of the proposed system. Experimental results verified that this application could work with the oneM2M-compliant IoT server normally and provide corresponding class labels properly. As an application of the proposed system, we also propose motion inference based on three multi-classML classifiers (i.e., k nearest neighbors, Naive Bayes, and support vector machine) which were created by using only motion and location data (i.e., acceleration force, gyroscope rate of rotation, and speed) and motion class labels (i.e., driving, cycling, running, walking, and stilling). When compared with confusion matrices of the ML classifiers, the k nearest neighbors classifier outperformed the other two overall. Furthermore, we evaluated its output quality by analyzing the receiver operating characteristic (ROC) curves with area under the curve (AUC) values. The AUC values of the ROC curves for all motion classes were more than 0.9, and the macro-average and micro-average ROC curves achieved very high AUC values of 0.96 and 0.99, respectively. [ABSTRACT FROM AUTHOR]

An inter-system model for multi-GNSSs (global navigation satellite systems) makes the interoperability among different GNSS constellations possible. In recent years, inter-system models for geodetic receivers have been extensively studied. However, the precise positioning of smartphones suffers from various problems, and the current research mostly focuses on how to achieve the GNSS ambiguity resolution. Based on the research of receiver channel-dependent bias, in this study, we will research the temporal behaviors of differential inter-system bias (DISB) and implement an inter-system model for smartphones. A representative Huawei P40 (HP40) smartphone was used in the experiments, and the results show the following: (1) For the HP40, the frequencies of Code Division Multiple Access (CDMA) systems are free of receiver channel-dependent phase bias, which provides the chances for further interoperability among these systems. However, the code observations of the HP40 are influenced by receiver channel-dependent code bias; it is therefore suggested to set a large initial standard deviation (STD) value for code observations in the positioning. (2) GPS L1/QZSS L1 and BDS-2 B1I /BDS-3 B1I are free of phase DISB, and there is obvious phase DISB between GPS L1 and Galileo E1; even so, the valuations are sufficiently stable and the STD is close to 0.005 cycles. However, the phase DISB of GPS L1/BDS B1I is unstable. (3) For kinematic positioning, when the stable phase DISB is introduced, a 3–38.9% improvement in the N/E/U directions of the positioning accuracies in the inter-system differencing is achieved compared with the intra-system differencing. [ABSTRACT FROM AUTHOR]

In the last few years, we have witnessed the spread of computing devices getting smaller and smaller (e.g., Smartphones, Smart Devices, Raspberry, etc.), and the production and availability of data getting bigger and bigger. This work presents DLV-EE, a framework based on Answer Set Programming (ASP) for performing declarative reasoning tasks over data-intensive, distributed applications. It relies on the DLV2 system and it features interoperability means for dealing with Big-Data over modern industry-level databases (relational and NoSQL). Furthermore, the work introduces DLV-IoT, an ASP system compatible with "mobile" technologies for enabling advanced reasoning capabilities on smart/IoT devices; eventually, DLV-EE and DLV-IoT via some real-world applications are illustrated as well. [ABSTRACT FROM AUTHOR]

The mobile phones Operating System like android and IOS finds wide use in smartphones and tablets and is thus suitable for home controllers. This project presents a smart home controller that uses WIFI in smartphone devices to control the operation of an automated security door system. The system idea is to use an Android or IOS app that sends commands to Electronic Relay-connected to home WIFI or internet- to control the opening of the door connected to an electric intercom. The Relay module is installed on the Intercom phone and the intercom phone connected to an electric lock which is installed on the door, the Relay Module receives the commands from the smartphone through WIFI, and passes these commands to the microcontroller that controls the opening of the door. The system can be used in various situations where access to an enclosure, doors, or any other thing needs to be open electronically. With the help of smartphone apps that will illustrate later, we can use smartphones to open the door using a smartphone voice assistant. [ABSTRACT FROM AUTHOR]

Background: Implementing automated seizure detection in long-term electroencephalography (EEG) analysis enables the remote monitoring of patients with epilepsy, thereby improving their quality of life. Objective: The objective of this study was to explore an mHealth (mobile health) solution by investigating the feasibility of smartphones for processing large EEG recordings for the remote monitoring of patients with epilepsy. Methods: We developed a mobile app to automatically analyze and classify epileptic seizures using EEG. We used the cross-database model developed in our previous study, incorporating successive decomposition index and matrix determinant as features, adaptive median feature baseline correction for overcoming interdatabase feature variation, and postprocessing-based support vector machine for classification using 5 different EEG databases. The Sezect (Seizure Detect) Android app was built using the Chaquopy software development kit, which uses the Python language in Android Studio. Various durations of EEG signals were tested on different smartphones to check the feasibility of the Sezect app. Results: We observed a sensitivity of 93.5%, a specificity of 97.5%, and a false detection rate of 1.5 per hour for EEG recordings using the Sezect app. The various mobile phones did not differ substantially in processing time, which indicates a range of phone models can be used for implementation. The computational time required to process real-time EEG data via smartphones and the classification results suggests that our mHealth app could be a valuable asset for monitoring patients with epilepsy. Conclusions: Smartphones have multipurpose use in health care, offering tools that can improve the quality of patients' lives. [ABSTRACT FROM AUTHOR]

Android Smartphones are proliferating extensively in the digital world due to their widespread applications in a myriad of fields. The increased popularity of the android platform entices malware developers to design malicious apps to achieve their malevolent intents. Also, static analysis approaches fail to detect run-time behaviors of malicious apps. To address these issues, an optimal unification of static and dynamic features for smartphone security analysis is proposed. The proposed solution exploits both static and dynamic features for generating a highly distinct unified feature vector using graph based cross-diffusion strategy. Further, a unified feature is subjected to the fuzzy-based classification model to distinguish benign and malicious applications. The suggested framework is extensively experimentally validated through both qualitative and quantitative analysis and results are compared with the existing solutions. Performance evaluation over benchmarked datasets from Google Play Store, Drebin, Androzoo, AMD, and CICMalDroid2020 revealed that the suggested solution outperforms state-ofthe-art methods. We achieve average detection accuracy of 98.62% and F1 Score of 0.9916. [ABSTRACT FROM AUTHOR]

Xiaomi Mi8 with a Broadcom BCM47755 chip, an Android smartphone that supports multi-constellation (GPS, GLONASS, Galileo, BeiDou, and QZSS) and dual-frequency (L1/E1 and L5/E5), was launched in May 2018. Unlike previously released smartphones, it was technically expected to provide robust precise positioning with a fast ambiguity resolution, which led many researchers to be overly optimistic about the applicability of high-accuracy techniques such as real-time kinematic (RTK) systems and precise point positioning (PPP) of smartphones. The global navigation satellite system (GNSS) raw measurement quality of Android smartphones is, however, inherently far lower than that of general GNSS receivers due to their structure, which accordingly makes it difficult for them to be realized. Considering inherent limitations of smartphones such as low-quality antenna, frequent cycle slips, and the duty cycle, a practical strategy including L5 measurements, pseudo-range corrections for L5, and a weighting method is proposed in this paper. The results show that the proposed methods of L5 differential GNSS (DGNSS) and Doppler-based filtering can guarantee a positioning accuracy of 1.75 m horizontally and 4.56 m vertically in an Android device, which is comparable to the performance of commercial low-cost receivers. [ABSTRACT FROM AUTHOR]

Researchers have explored multiple Wi-Fi features to estimate user locations in indoor environments in the past decade, such as Received Signal Strength Indication (RSSI), Channel State Information (CSI), Time of Arrival (TOA), and Angle of Arrive (AoA). Fine Time Measurement (FTM) is a protocol standardized by IEEE 802.11-2016, which can estimate the distance between the initiator and the station using Wi-Fi Round-Trip Time (RTT). Promoted by Google, such a protocol has been explored in many mobile localization algorithms, which can provide meter-level positioning accuracy between Wi-Fi RTT-enabled smartphones and access points (APs). However, previous studies have shown that the Wi-Fi RTT measurements are sensitive to environmental changes, which leads to significant errors in the localization algorithms. Such an error usually varies according to different environments and settings. Therefore, this paper investigates the error in Wi-Fi RTT distance measurements by setting multiple experiments with different hardware, motion status, and signal path loss conditions. The experiment results show that four categories of errors are found in RTT distance measurements, including hardware-dependent bias, blocker-dependent bias, fluctuations, and outliers. Comparison and analysis are carried out to illustrate the impact of the different errors on Wi-Fi RTT distance. [ABSTRACT FROM AUTHOR]

Android based teaching material can facilitate students and teacher in the inside and outside classroom learning using smartphone. This study aims to investigate the ways in utilizing construct 2 apps to create android based material to teach early reading for primary students. This study is qualitative research in form of explorative case study which includes students in private and public primary schools at five regencies of Indonesia. The research data is collected using questionnaire and interview. It is analyzed using content analysis. The results of study show that android based material of early reading can be created by utilizing three steps. The conclusion of this study is that android based material of early reading is firstly created by investigating the need of material for primary students. Secondly, it creates the concepts of materials by preparing the material compositions, creating material order, and creating teaching material tools. Thirdly, construct 2 apps is utilized to make the teaching materials based on the concepts by creating parts of learning guide, invitation for praying, materials, evaluation, and games. [ABSTRACT FROM AUTHOR]

Instagram and WhatsApp have become popular social media applications, and the number of active users grows significantly each year. The increased use of Instagram and WhatsApp has increased the number of digital crimes, which are frequently committed by utilizing information obtained and available through the social media accounts of potential victims. Special forensic tools are required for digital crime policing using smartphones. As a result, it is necessary to investigate the functionality of existing forensic tools for processing digital crime cases involving Android phones, particularly for the social media platforms Instagram and WhatsApp. The goal of this study was to evaluate and compare two forensic technologies for obtaining digital evidence from Instagram and WhatsApp using experimental methods. Magnet Axiom discovered 92.31% of all digital evidence, whereas MOBILedit Forensic discovered 79.49% of digital evidence. Using the process of comparing the two study outcomes with forensic technology, Magnet Axiom outperforms MOBILedit Forensic in detecting digital evidence of Instagram and WhatsApp since MOBILedit Forensic cannot restore video data for more than 20 minutes. [ABSTRACT FROM AUTHOR]

Recently, human monkeypox outbreaks have been reported in many countries. According to the reports and studies, quick determination and isolation of infected people are essential to reduce the spread rate. This study presents an Android mobile application that uses deep learning to assist this situation. The application has been developed with Android Studio using Java programming language and Android SDK 12. Video images gathered through the mobile device's camera are dispatched to a deep convolutional neural network that runs on the same device. Camera2 API of the Android platform has been used for camera access and operations. The network then classifies images as positive or negative for monkeypox detection. The network's training has been carried out using skin lesion images of monkeypox-infected people and other skin lesion images. For this purpose, a publicly available dataset and a deep transfer learning approach have been used. All training and testing steps have been applied on Matlab using different pre-trained networks. Then, the network that has the best accuracy has been recreated and trained using TensorFlow. The TensorFlow model has been adapted to mobile devices by converting to the TensorFlow Lite model. The TensorFlow Lite model has been then embedded into the mobile application together with the TensorFlow Lite library for monkeypox detection. The application has been run on three devices successfully. During the run-time, the inference times have been gathered. 197 ms, 91 ms, and 138 ms average inference times have been observed. The presented system allows people with body lesions to quickly make a preliminary diagnosis. Thus, monkeypox-infected people can be encouraged to act rapidly to see an expert for a definitive diagnosis. According to the test results, the system can classify the images with 91.11% accuracy. In addition, the proposed mobile application can be trained for the preliminary diagnosis of other skin diseases. [ABSTRACT FROM AUTHOR]

Limitations of financial record system imply a difficulty for MSME actors in executing the right decisions in their business and accessing credit. This community service activity aims to provide awareness about the importance of financial records for MSMEs, an introduction to digital-based financial recording and financial recording practices for MSMEs. This training which is carried out offline and is attended by MSMEs in Situ Gede Village, West Bogor District, Bogor. This training begins with facilitating a participants’ discussion to gain an initial understanding of digital-based financial reports, then continues with the delivery of material and evaluation in the form of joint practice of making daily financial records of each participants’ businesses using digital application. Achievement on learning outcomes is measured by increasing participants’ skills in using digital financial recording applications (BukuWarung) to carry out financial records. The various features that exist in BukuWarung's digital financial records potentially support MSME actors to arrange their financial reports. Obstacles in implementing activities arise from the level of knowledge on basic accounting and proficiency in using digital technology. Around 80% of participants were able to make financial records using the application, while about 20% were unable to apply or partly due to application installation problems and unsupported devices. For the sustainability of the program, a dialogue platform that facilitates users exchange experiences and information regarding digital financial applications and continuous coaching is important to reduce obstacles that may occur in the future. [ABSTRACT FROM AUTHOR]

Background: The use of Android mobile applications aimed at addressing users' psychological health is increasing. However, no review has been conducted on public Android commercial telepsychological applications in Spain. Method: A systematic search was carried out in the general applications section of Google Play Store based on 20 keywords in Spanish. Applications in Spanish or English that were relevant from the perspective of the search terms were included in the analysis. Results: Of the 1,000 applications retrieved, only 43.59% (337/773) met the inclusion criteria, of which 49.26% (171/337) were Spanish. Most of them were Self-applied treatment applications (52.96%, 29.59%, Spanish; 23.37%, English). A total of 69.44% (37.38% Spanish; 31.95% English) did not indicate the origin of the information and the academic link was anecdotal (3.25%). Conclusions: The low percentage of relevant applications, high prevalence of self-help applications, absence of references on the origin of the information, absence of academic support, and lack of certifications encouraged us to recommend caution to consumers and professionals in their adoption of these applications for the treatment of any psychological disorder. [ABSTRACT FROM AUTHOR]

Motrol is a simple device that satisfies functional requirements necessary for the automatic execution of battery-driven tests and profiling of connected mobile devices. It is specifically a hardware/software platform that allows Dew Computing researchers and developers to automate performance tests on Android-based smartphones. The hardware is based on a NodeMCU Esp8266 microcontroller that runs a firmware for managing the outputs. This software allows enabling/disabling the relays that connect the sockets that power the chargers of up to 4 mobile devices minimizing the need for human intervention. The firmware runs a web server that serves Rest requests from a Rest client with the commands to drive the digital outputs. These digital outputs activate or deactivate the relays to allow current to pass or not to the sockets. Such capability is essential to automate the study of battery behavior on battery-driven devices such as smartphones. Motrol is easy to assemble, knowledge in electronics or programming languages is not necessary, it is constructed with open hardware, and it is cheap, being its total cost ∼USD 30.

The smart phone market is continuously increasing and there are more than 6 billion of smart phone users worldwide with the aid of the 5G technology. Among them Android occupies 87% of the market share. Naturally, the widespread Android smartphones has drawn the attention of the attackers who implement and spread malware. Consequently, currently the number of malware targeting Android mobile phones is ever increasing. Therefore, it is a critical task to find and detect malicious behaviors of malware in a timely manner. However, unfortunately, attackers use a variety of obfuscation techniques for malware to evade or delay detection. When an obfuscation technique such as the class encryption is applied to a malicious application, we cannot obtain any information through a static analysis regarding its malicious behaviors. Hence, we need to rely on the manual, dynamic analysis to find concealed malicious behaviors from obfuscated malware. To avoid malware spreading out in larger scale, we need an automated deobfuscation approach that accurately deobfuscates obfuscated malware so that we can reveal hidden malicious behaviors. In this study, we introduce widely-used obfuscation techniques and propose an effective deobfuscation method, named ARBDroid, for automatically deobfuscating the string encryption, class encryption, and API hiding techniques. Our evaluation results clearly demonstrate that our approach can deobfuscate obfuscated applications based on dynamic analysis results. [ABSTRACT FROM AUTHOR]

Mobile devices, specifically smartphones, have become a necessity in everyday life, as we perform many essential day-to-day tasks using these devices. With the projected increase in mobile devices to 18.22 billion by 2025, the reliance on smartphones will only grow. This demand for smartphones has allowed various companies to start developing their own devices and custom operating systems, each of which puts its own touch on them. In addition, current smartphones have increased processing power, providing users with a computer experience in their pockets. Software developers have taken this opportunity to bridge the gap between personal computers and smartphones by creating the same software for personal computers and mobile devices. Kali Linux is one of the most popular penetration testing tools for desktop use and has been adapted to operate on mobile devices under the name Kali NetHunter. Kali NetHunter has three different versions on mobile platforms that provide various levels of capabilities. Kali NetHunter is just one example in which an application or an operating system applies to a specific niche of users. Highly customized operating systems or applications do not receive the same attention as field research, leaving them unfamiliar to mobile forensic investigators when used maliciously. In this paper, we conducted an exploratory study on the Kali NetHunter Lite application after it was installed and its embedded tools were utilized. Our results show a detailed analysis of the file system and reveal the data from the tests carried out during various phases. Furthermore, the locations of the folders involved in the process were described. [ABSTRACT FROM AUTHOR]

Today, new games are released every day, and the virtual reality market is developing in a similar way to the rapidly growing smartphone ecosystem about 10 years ago. In addition to this, mobile games that take place with smartphones in people’s daily lives can be downloaded to their phones for free, without paying any money, only with an internet connection. In this sense, a mobile game on android has been designed and developed about word games for this study. These word games need to have fast feedback and fast research time to the users and the players. In this context, Directed Acyclic Word Graph (DAWG) has been used and applied for giving fast feedback in the developed game “Kelimetris” in the study. The game “Kelimetris” has been explained in detail step by step with showing its captures, screenshots, UML diagrams and code blocks. In addition, this study has showed – the graphical data structure – DAWG’s efficiency and usability in word-based games on mobile phones on Android. As a result, this study will have had a positive effect on the relationship between data structures and mobile games with the contribution of the developed game “Kelimetris” and the finite state machine DAWG. [ABSTRACT FROM AUTHOR]

Approximately 70% of smartphone users around the world use Android operating system-based smartphones, and malicious apps targeting these Android platforms are constantly increasing. Google has provided "Google Play Protect" to respond to the increasing number of Android targeted malware, preventing malicious apps from being installed on smartphones, but many malicious apps are still normal. It threatens the smartphones of ordinary users registered in the Google Play store by disguising themselves as apps. However, most people rely on antivirus programs to detect malicious apps because the average user needs a great deal of expertise to check for malicious apps. Therefore, in this paper, we propose a method to classify unnecessary malicious permissions of apps by using only the categories and permissions that can be easily confirmed by the app, and to easily detect malicious apps through the classified permissions. The proposed method is compared and analyzed from the viewpoint of undiscovered rate and false positives with the "commercial malicious application detection program", and the performance level is presented. [ABSTRACT FROM AUTHOR]

With the increased use of smartphones over the years, an immense evolution in camera technology and image enhancement applications has been observed. However, this associates significantly greater security concerns with smartphones. Cyber crime offenders often retrieve personal images from second hand smart phones and try to blackmail or harass their former users. Similar situations are often faced by users in undesirable situations like hacking or theft of smartphones. Despite deletion of images by popular image deletion tools, sensitive images can still be recovered by using widely available forensic methods and tools. This paper checks the efficacy of top five image deletion applications namely Shreddit, Android Eraser, Permanent Delete, Sdelete, and Safe Delete in perspective of permanent deletion of images for Android, against National Institute of Standards and Technology's (NIST), Standard for Media Sanitization known as NIST 800-88. The efficiency of the aforementioned deletion applications has been analyzed by using famous image recovery forensic tools; XRY, Autopsy, and DiskDigger. The results of our forensic analysis recovered the deleted images which were ostensibly deleted by the chosen applications. Moreover, all the image locations have been identified in this research which were found to contain images' artifacts. In addition to that, the capability of forensic tools in recovering deleted images has been discussed too. So, this research highlights weaknesses of existing deletion mechanisms. Moreover, based on the conducted forensic analysis and NIST 800-88 standard, the research proposes a model for secure deletion by setting grounds for the development of a secure image deletion application to make permanent deletion of images possible. [ABSTRACT FROM AUTHOR]

The Google Smartphone Decimeter Challenge (GSDC) was a competition held in 2021, where data from a variety of instruments useful for determining a phone's position (signals from GPS satellites, accelerometer readings, gyroscope readings, etc.) using Android smartphones were provided to be processed/assessed in regard to the most accurate determination of the longitude and latitude of user positions. One of the tools that can be utilized to process the GNSS measurements is RTKLIB. RTKLIB is an open-source GNSS processing software tool that can be used with the GNSS measurements, including code, carrier, and doppler measurements, to provide real-time kinematic (RTK), precise point positioning (PPP), and post-processed kinematic (PPK) solutions. In the GSDC, we focused on the PPK capabilities of RTKLIB, as the challenge only required post-processing of past data. Although PPK positioning is expected to provide sub-meter level accuracies, the lower quality of the Android measurements compared to geodetic receivers makes this performance difficult to achieve consistently. Another latent issue is that the original RTKLIB created by Tomoji Takasu is aimed at commercial GNSS receivers rather than smartphones. Therefore, the performance of the original RTKLIB for the GSDC is limited. Consequently, adjustments to both the code-base and the default settings are suggested. When implemented, these changes allowed RTKLIB processing to score 5th place, based on the performance submissions of the prior GSDC competition. Detailed information on what was changed, and the steps to replicate the final results, are presented in the paper. Moreover, the updated code-base, with all the implemented changes, is provided in the public repository. This paper outlines a procedure to optimize the use of RTKLIB for Android smartphone measurements, highlighting the changes needed given the low-quality measurements from the mobile phone platform (relative to the survey grade GNSS receiver), which can be used as a basis point for further optimization for future GSDC competitions. [ABSTRACT FROM AUTHOR]

Android Operating system (OS) has grown into the most predominant smartphone platform due to its flexibility and open source characteristics. Because of its openness, it has become prone to numerous attackers and malware designers who are constantly trying to elicit confidential information by articulating a plethora of attacks through these designed malwares. Detection of these malwares to protect the smartphone is the core function of the smartphone security analysis. This paper proposes a novel traffic-based framework that exploits the network traffic features to detect these malwares. Here, a unified feature (UF) is created by graph-based cross-diffusion of generated order and sparse matrices corresponding to the network traffic features. Generated unified feature is then given to three classifiers to get corresponding classifier scores. The robustness of the suggested framework when evaluated on the standard datasets outperforms contemporary techniques to achieve an average accuracy of 98.74 per cent. [ABSTRACT FROM AUTHOR]

We investigate the feasibility of using built-in GNSS sensors within ubiquitous smartphone devices from a small UAS for the purpose of land remote sensing. We summarize the experimental findings and challenges that need to be resolved in order to perform the GNSS reflectometry (GNSS-R) technique via smartphones. In late 2018, a series of experiments were conducted and designed by integrating two smartphones into a multicopter UAS by attaching them to ground plates to isolate and record both direct and reflected GNSS carrier-to-noise density ratio ($C/N_0$) separately. It was demonstrated that, first, fluctuations of moving GNSS specular reflections are correlated with spatial ground features with appreciable dynamic range and second, radiation pattern of the smartphone's inbuilt antenna has a significant effect on the received signal strength. In 2020, more experiments were conducted to examine the quality of in-built chip and antenna of a smartphone with regard to the GNSS-R approach as well as the consistency of measurements. These follow-up experiments involved, first, placement of the smartphone on a pan-tilt mechanism on a tripod, second, formation flights with smartphone on a gimbal and a high-quality custom-built dual-channel GNSS-R receiver, and, third, flying the UAS at different times of the day on two consecutive days. It was demonstrated that, first, the radiation pattern of the smartphone's GNSS antenna are observed to be highly irregular, but time-invariant, and, second, internal GNSS chip produces observables of sufficient quality, and, third, the fluctuations of the reflected signal are repeatable under the same configuration at different times.

One of the most important indicators of quality of vegetable oils is colour, which can be detected with colorimetric measurements. The determination of colour is traditionally done using colorimeters, spectrometers, tintometers, and other analytical equipment. As an alternative to replace the classical analytical methods, smartphone-based colorimetry using digital image analysis can be used. For colorimetric detection of colour in vegetable oils, a Huawei P30 lite smartphone and android application "Colour Picker" with an image matching algorithm RGB model was used. The image of sample and standard solutions was captured in a polyvinyl chloride box with light-emitting diode (LED) lamps. The aim of the study was to detect the colour of vegetable oils with smartphone-based image analysis. The detected colour of eleven vegetable oils (sea buckthorn, sunflower, rice, macadamia nut, hemp, corn, grape, linseed, rapeseed, olive, and milk thistle oils) was compared with standard solutions of iodine with a concentration range from 0 to 100 mg·100 ml−1. The results show that smartphone-based colorimetry can be used for detection of the colour of vegetable oils and it is possible to compare the colour with standard solutions of iodine. The colour of vegetable oils was expressed as the colour number obtained from an iodine standard solution prepared in deionised water. [ABSTRACT FROM AUTHOR]

In this paper, we present a Smartphone-based Fluorescence Nanoparticle Detector (SPF-NPD) that can be used for identifying biological agents in biomedical applications. The experimental setup consists of an LED light source and an Eppendorf tube holder placed inside a dark chamber with an optimally located slit for aligning the camera of a smartphone. The camera acquires the fluorescence intensity variations in the target liquid sample placed in the Eppendorf tube and passes it to a dedicated android application running in the smartphone. Using the principle of fluorescence-based pathogen detection, the android application detects the pathogens and displays the results within a few seconds. Since, all smartphones are equipped with high-resolution cameras, the proposed SPF-NPD provides a simple and elegant solution for instantaneous detection of fluorescence nano particles and has a great potential for healthcare applications for live detection of pathogens. The intensity measurement in SPF-NPD algorithm uses 5-pixel method, that is, the center pixel followed by four immediate neighbor pixels, because of which, minimal sample quantity is sufficient for precise measurements. We establish the robustness of SPF-NPD through exhaustive experiments with various smartphone cameras having different resolutions ranging from 8 to 20 Megapixels. The results of the proposed SPF-NPD method are validated against those obtained from standard devices such as Perkin-Elmer Picoflor and Perkin-Elmer Enspire. The advantages of the proposed method are highlighted. [ABSTRACT FROM AUTHOR]

In analytical chemistry, one of the most used methods for the determination of chemical substances in colored solutions is spectroscopy. Spectroscopy is a classical method used for quite some time, but by the development of the smart digital world, there is a possibility to replace classical analytical methods with alternative. One of these alternatives is smartphonesbased image analysis on Android or iOS operating systems with god resolution camera systems. The human eyes each wavelength of the visible light can see as a distinctive color. Very important for visible light is the three primary colors, red (R), green (G) and blue (B), which is known as the RGB color model. The aim of the study was determinate the content of TPC with smartphone-based image analysis. As smartphone for colorimetric detection is used Huawei P30 lite. Android application „ColorPicker", with image matching algorithm, were used for detection of total phenolic content (TPC) in vegetable oils. Total phenolic content was expressed as mg x g-1 gallic acid equivalent, and measured by the Folin- Ciocalteu reagent method. For the data comparison and accuracy, spectrophotometer as analytical optical instrument was used. Eleven vegetable oils: sea buckthorn, sunflower, rice, macadamia nut, hemp, corn, grape, linseed, rapeseed, olive and milk thistle were selected for analysis. Calibration solution show very good coefficient of determination for gallic acid respectively, for red color (R) = R² = 0.9954, green color (G) = R² = 0.9934 and blue color (B) =R² = 0.9713. Red and Blue transmitted light have the same or closest coefficient of determination like by classical spectroscopy R² = 0.9997. Results showed that analyses have difference compared with a spectrophotometer in range only from ± 1.7 to 1.2 mg GAE kg-1 oil. The highest concentration of total phenolic content in vegetable oils with smartphone was determinate in hemp and olive oil 182 ± 0.2; 178.7 ± 0.2 mg GAE kg-1 oil, the lowest by rice, grape and macadamia nut oils, respectively 13 ± 0.1, 13.2 ± 0.1 and 12.8 ± 0.1 mg GAE kg-1 oil. Smartphone-based image analysis is suitable for use in determination of the total phenolic content in vegetable oils. [ABSTRACT FROM AUTHOR]

Nowadays, information obtained from mobile phones is often the subject of evidence in front of a court. Forensic analysts often come across smartphones about which they have no prior information. However, they need to extract data from them. The main prerequisite to extract the data is to bypass Android lock screen protection. The HID attack is a promising method to break Android lock screen protection. In many cases, this is the only way how to break the smartphone's nonbiometric lock screen protections on newer Android OS versions. The article contains examples of three non-biometric types of Android smartphone lock screen protections and their computational complexity. The paper describes hardware and software requirements for implementation of HID attack. [ABSTRACT FROM AUTHOR]

Nowadays, due to our everyday stress and current stressful lifestyle, the loss of items appears a frequent issue and may be very inconvenient. In this regard, until the IoT becomes part of everyday life, we can use the software as an efficient tool to assist a person's searching, verifying, and finding lost belongings. This paper presents an Android-based application that we proposed and implemented to help users find lost items. Utilizing this software will enable the subscriber to record his request to the relevant authority. In addition, a special section offers to insert a contact telephone number or email to communicate between the person who found the item and the person who lost it. During testing, among other services, the platform showed its capabilities to register and log users, releasing a lot of information of lost items and automatically forwarding lost-and-found notifications. The paper can be useful for those who deal with the application of information technology. [ABSTRACT FROM AUTHOR]