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The slide method for ABO blood group typing is commonly used for mobile blood donation and field applications because the test is easy, cost-effective, can be completed in a few minutes and requires a small volume of reagents. However, the reaction must be observed by an individual with expertise within 2 min; otherwise, drying of the reagent will give a false positive result. Moreover, the blood typing reagents must be stored at 4 °C. The present study aimed to create a paper-based device for ABO and RhD blood typing and combine an optical answer sheet reading concept to read and interpret the results with Android smartphones. The invention of this device involved the use of simple filter paper and conjugate pads that were treated with anti-A, -B and -D antibodies. Blood type can be visually identified from the detection zone at the end of the filter paper. An Android smart phone was designed to read the detection zone, interpret the data and subsequently report the results on the user's smartphone. A helpful color chart was also designed for blood typing interpretation by the naked eye. The use of smartphones can reduce human error in data reading and interpretation. In conclusion, ABO and RhD typing with paper-based devices using a smartphone interpretation may provide further advantages for home-based users, mobile blood donation sites and field applications. [Display omitted] • ABO and RhD blood group can be visually identified from paper-based device with 100% accuracy. • Optical answer sheet reading concept was applied for interpretation by smartphone. • A helpful color chart can be used for blood typing interpretation by the naked eye. • Portable paper-based device for ABO and RhD typing could reduce the effect of human errors and the number of technicians needed. • Portable paper-based device for ABO and RhD typing may provide further advantage for field applications. [ABSTRACT FROM AUTHOR]

Monitoring aspects of human performance during various activities has recently become a highly investigated research area. Many new commercial products are available now to monitor human physical activity or responses while performing activities ranging from playing sports, to driving, and even sleeping. However, monitoring cognitive performance biomarkers, such as neuropeptides, is still an emerging field due to the complicated sample collection and processing, as well as the need for a clinical lab to perform analysis. Enzyme-linked immunosorbent assays (ELISAs) provide specific detection of biomolecules with high sensitivity (picomolar concentrations). Even with the advantage of high sensitivity, most ELISAs need to be performed in a laboratory setting and require around 6 h to complete. Transitioning this assay to a platform where it reduces cost, shortens assay time, and is able to be performed outside a lab is invaluable. Recently developed paper diagnostics provide an inexpensive platform on which to perform ELISAs; however, the major limiting factor for moving out of the laboratory environment is the measurement and analysis instrumentation. Using something as simple as a digital camera or camera-enabled Windows- or Android-based tablets, we are able to image paper-based ELISAs (P-ELISAs), perform image analysis, and produce response curves with high correlation to target biomolecule concentration in the 10 pM range. Neuropeptide Y detection was performed. Additionally, silver enhancement of Au NPs conjugated with IgG antibodies showed a concentration-dependent response to IgG, thus eliminating the need for an enzyme–substrate system. Automated image analysis and quantification of antigen concentrations are able to be performed on Windows- and Android-based mobile platforms. [ABSTRACT FROM AUTHOR]

In this work, an Android application for measurement of nitrite concentration and pH determination in combination with a low-cost paper-based microfluidic device is presented. The application uses seven sensing areas, containing the corresponding immobilized reagents, to produce selective color changes when a sample solution is placed in the sampling area. Under controlled conditions of light, using the flash of the smartphone as a light source, the image captured with the built-in camera is processed using a customized algorithm for multidetection of the colored sensing areas. The developed image-processing allows reducing the influence of the light source and the positioning of the microfluidic device in the picture. Then, the H (hue) and S (saturation) coordinates of the HSV color space are extracted and related to pH and nitrite concentration, respectively. A complete characterization of the sensing elements has been carried out as well as a full description of the image analysis for detection. The results show good use of a mobile phone as an analytical instrument. For the pH, the resolution obtained is 0.04 units of pH, 0.09 of accuracy, and a mean squared error of 0.167. With regard to nitrite, 0.51% at 4.0 mg L-1 of resolution and 0.52 mg L-1 as the limit of detection was achieved. [ABSTRACT FROM AUTHOR]

The ubiquitous adoption of Android devices has unfortunately brought a surge in malware threats, compromising user data, privacy concerns, and financial and device integrity, to name a few. To combat this, numerous efforts have explored automated botnet detection mechanisms, with anomaly-based approaches leveraging machine learning (ML) gaining attraction due to their signature-agnostic nature. However, the problem lies in devising accurate ML models which capture the ever evolving landscape of malwares by effectively leveraging all the possible features from Android application packages (APKs).This paper delved into this domain by proposing, implementing, and evaluating an image-based Android malware detection (AMD) framework that harnessed the power of feature hybridization. The core idea of this framework was the conversion of text-based data extracted from Android APKs into grayscale images. The novelty aspect of this work lied in the unique image feature extraction strategies and their subsequent hybridization to achieve accurate malware classification using ML models. More specifically, four distinct feature extraction methodologies, namely, Texture and histogram of oriented gradients (HOG) from spatial domain, and discrete wavelet transform (DWT) and Gabor from the frequency domain were employed to hybridize the features for improved malware identification. To this end, three image-based datasets, namely, Dex, Manifest, and Composite, derived from the information security centre of excellence (ISCX) Android Malware dataset, were leveraged to evaluate the optimal data source for botnet classification. Popular ML classifiers, including naive Bayes (NB), multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were employed for the classification task. The experimental results demonstrated the efficacy of the proposed framework, achieving a peak classification accuracy of 93.03% and recall of 97.1% for the RF classifier using the Manifest dataset and a combination of Texture and HOG features. These findings validate the proof-of-concept and provide valuable insights for researchers exploring ML/deep learning (DL) approaches in the domain of AMD. [ABSTRACT FROM AUTHOR]

This paper uses a cascade-connected fuzzy-PI controller to control the position and speed of a differential drive and four-wheel drive of an autonomous mobile robot for optimal path planning. The angular speed information obtained from the encoder of each motor and the instantaneous position and angle information of the robot were calculated. The angle and position error between the reference points and these values is applied to the fuzzy logic controller as an input signal. The robot angular and linear speed data obtained from the fuzzy logic output were converted into reference speed values with kinematic equations to be applied to the motors. The speed controls of the motors were carried out with a PI controller based on these reference values. The study was performed both as a simulation in the MATLAB program and experimentally in the laboratory environment for one and more reference coordinates. In the experimental study, reference values were sent to the robot via Bluetooth with the Android application designed. At the same time, the instant data of the robot was also collected on the Android device through the same application. These data collected in Excel format were transferred to the computer via e-mail and the graphics were drawn in the MATLAB program. When the results were examined, it was seen that both speed and position control were successfully implemented with the fuzzy-PI controller for optimum path planning of the robot. [ABSTRACT FROM AUTHOR]

• SMS Observer, an Android dynamic analysis framework to analyze SMS-based malware. • Allow an app in an emulator to send and receive SMS to and from a real smartphone. • Maintains the integrity of system logs for further analysis on the server side. • Figures how to detect an app executed in an emulator and to prevent such evasion. • More effective in detecting SMS-based malware than existing frameworks. Nowadays smartphones become an indispensable tool in many people's everyday life that makes themselves attractive targets for attackers. Among various malware targeting at smartphones, SMS-based malware is one of the most notorious ones. Though a number of Android dynamic analysis frameworks have been proposed to analyze SMS-based malware, most of these frameworks or some Android tools, such as Google Android Emulator, do not support an app or malware to send SMS messages to a real smartphone; hence, security researchers cannot use them directly to analyze the behavior of SMS-based malware. In our previous work, SMS Helper , we designed an application layer tool to allow an app or malware in an Android emulator to send and receive SMS messages to or from a real smartphone. Based on SMS Helper, this paper proposes an Android dynamic analysis framework, called SMS Observer , to assist security researchers to analyze SMS-based malware. SMS Observer integrates SMS Helper into it as a client agent, meanwhile, and it maintains the integrity of system logs. This paper also figures out a way to detect whether an app is executed in an emulator and describes how to use SMS Observer to prevent such evasion. Experimental results using real-world malware samples show SMS Observer is much more effective in detecting SMS-related behavior of SMS-based malware than existing frameworks, such as Google Android Emulator, Andrubis, CopperDroid, and DroidBox. SMS Observer can analyze sophisticated SMS-based malware samples and provide a comprehensive view of malicious behavior. [ABSTRACT FROM AUTHOR]

Soon after the pandemic inflicted by Covid-19 was declared, different organizations and smart application development companies suggested that artificial intelligence could be an useful tool in helping the medical staff monitoring the virus spread for a better understanding of its behavior. This paper presents a smart application CovidWatch developed for users real-time warning regarding the possibility of contracting the Sars-CoV-2 virus from an infected person. For this purpose, the developed application uses the Bluetooth module of the user's device in order to check the social distance and to determine the possible contacts. The developed application has a number of four modules (the expert system (ES), the data retrieval, the real-time information and warning and the interface), that work on Android devices. Using the artificial intelligence techniques (knowledge-based systems), the application has a knowledge base populated with user's data, the user's time of contact and distance and the users identified as being contacts. If a user warns the android application that he has been infected with Covid-19, it further triggers the expert system inference engine, allowing the knowledge to be inferred. Based on the implemented rules, the inference engine analyses the data regarding the users that were in contact with an infected person. If those meet the conditions taken into consideration for declaring a person as being contact (a certain distance and time) they receive in real time a warning message and some advice regarding how it is recommended to act. The main advantage of android application is that it provides real-time warning regarding the possibility or probability of being infected with Covid-19, warning that can be deactivated only after the user has confirmed that the took note of this. A set of CovidWatch application simulation results is also presented taking into consideration different scenarios. [ABSTRACT FROM AUTHOR]

The number of mobile applications downloaded recently from Google stores increase steadily. It is believed that mobile applications market revenues will grow fast and receive more attention from software developers. Even though, hardware technologies and operating systems have made a big contribution to improve the battery capacity, power consumption is one of the most important and crucial non-functional properties of mobile applications. Many approaches have been dedicated for analyzing and optimizing the power consumption at different levels. This paper proposes a method for analyzing energy leaks of Android application at the design phase. First, it introduces a formalization of an event-based power consumption model. After that, the paper presents a method to translate this model to Event-B notations. Based on the target model, it is able to check if the application leads to energy leaks or violates the power consumption constraints based on formal proofs. Finally, the motivating examples are shown for the illustration purpose. [ABSTRACT FROM AUTHOR]

With the advent of smart phones, the popularity of free Android applications has risen rapidly. This has led to malicious Android apps being involuntarily installed, which violate the user privacy or conduct attack. Malware detection on Android platforms therefore is a growing concern because of the undesirable similarity between malicious behavior and benign behavior, which can lead to slow detection, and allow compromises to persist for comparatively long periods of time in infected phones. The contributions of this paper are first a multiple dimensional, kernel feature-based framework and feature weight-based detection (WBD) designed to categorize and comprehend the characteristics of Android malware and benign apps. Furthermore, our software agent is orchestrated and implemented for the data collection and storage to scan thousands of benign and malicious apps automatically. We examine 112 kernel attributes of executing the task data structure in the Android system and evaluate the detection accuracy with a number of datasets of various dimensions. We find that memory- and signal-related features contribute to more precise classification than schedule-related and other descriptors of task states listed in our paper. Particularly, memory-related features provide fine-grain classification policies for preserving higher classification precision than the signal-related and others. Furthermore, we study and evaluate 80 newly infected attributes of the Android kernel task structure, prioritizing the 70 features of most significance based on dimensional reduction to optimize the efficiency of high-dimensional classification. Our second contribution is that our experiments demonstrate that, as compared to existing techniques with a short list of task structure features (16 or 32 features), our method can achieve 94%-98% accuracy and 2%–7% false positive rate, while detecting malware apps with reduced-dimensional features that adequately abbreviate online malware detections and advance offline malware inspections. [ABSTRACT FROM AUTHOR]

The paper presents two mobile robot solutions on crawler belts and wheels equipped with a robotic arm. The robot on tracks can be controlled from distance, being able to move in environments unknown or dangerous to humans. It is controlled by computer applications installed on a smartphone device with Android operating system. This paper proposes a specific approach to allow a human user to interact in a natural way with the mobile robot in performing ordinary tasks that require handling of small objects and moving them on an uneven terrain. A solution is provided for the interaction between the human operator and the mobile robot being very important for achieving a simple, flexible and in the same time efficient interference. [ABSTRACT FROM AUTHOR]

Analyzing the runtime behaviors of Android apps is crucial for malware detection. In this paper, we attempt to learn the behavior level features of an app from function calls. The challenges of this task are twofold. First, the absence of function attributes hinders the understanding of app behaviors. Second, the graphical representation of function calls cannot be directly processed by classical machine learning algorithms. In this paper, we develop two methods to overcome these challenges. Based on function embedding, we first propose the concept of enhanced function call graphs (E-FCGs) to characterize app runtime behaviors. We then develop a Graph Convolutional Network (GCN) based algorithm to obtain vector representations of E-FCGs. Extensive experiments show that the features learned by our method can achieve surprisingly high detection performance on a variety of classifiers (e.g., LR, DT, SVM, KNN, RF, MLP and CNN), significantly outperforming the traditional static features. [ABSTRACT FROM AUTHOR]

Mobile banking, shopping, and in-app purchases utilize persistent authentication states for access to sensitive data. One-shot authentication permits access for a fixed time period. For instance, a username/password-based authentication allows a user access to all the shopping and payments data in the Amazon shopping app. Traditional user passwords and lock screens are easily compromised. Snooping attacks--observing an unsuspecting user entering passwords--and smudge attacks--examining touchscreen finger oil residue--enable compromised user authentication. Mobile device interactions provide robust human and device identity data. Such biometrics enhance authentication. In this paper, behavioral attributes during user input constitute the password. Adversary password reproduction difficulty increases since pure observation is insufficient. Current mobile continuous authentication schemes use, among others, touchscreen-swipe interactions or keyboard input timing. Many of these methods require cumbersome training or intrusive authentication. Software keyboard interactions provide a consistent biometric data stream. We develop biometric profiles using touch pressure, location, and timing. New interactions authenticate against a profile using a divergence measure. In our limited user-device data sets, the classification achieves virtually perfect accuracy. [ABSTRACT FROM AUTHOR]

Conceived as a theory of competitive harm, self-preferencing has been at the core of recent European landmark cases (e.g., Google Android, Google Shopping). In the context of EU competition law, beyond the anticompetitive leveraging effect, self-preferencing may lead to vertical and horizontal exclusionary abuses, encourage exploitation abuses, and generate economic dependence abuses. In this paper, we aim at characterizing the various forms of self-preferencing, investigating platforms' capacity and incentives to do so through their dual role, by shedding light on the economic assessment of these practices in an effects-based approach. We analyze the different options for remedies in this context, by insisting on their necessity, adequacy, and proportionality. [ABSTRACT FROM AUTHOR]

In today's world it is impossible to do without the use of various communication systems. They are now the integral part of our lives. Almost every modern person uses one or another system of information exchange with other people. Mobile platforms are most often used for information exchange. The vast majority are platforms such as Android, iOS and Windows Phone. Information transmitted from one user to another can often be confidential or restricted. So, the appropriate methods of information protection should be used for this purpose. Today, information security researchers focus on improving the performance of cryptographic algorithms and the primitives on which they are based. Nevertheless, the issues of building of integrated security systems for instant messaging systems remain insufficiently considered. This paper is devoted to the development of a comprehensive application protection system for instant messaging. Any IM application consists of three main parts. The first component is the program code executed on a mobile platform. The second part is the communication channel. The last part is the program code executed on the server side. This paper discusses each of the main parts for building a comprehensive application protection system for instant messaging. The choice of certain components for building a security system is substantiated, the schemes of operation of the program code executed on the Android mobile platform are given, the choice of forms of transmission of user information through the communication channel is substantiated. Recommendations for choosing a communication channel and ensuring its security are given. The schemes of the server side of the application are also given. The choice of certain hashes functions, functions of input information checks, approaches to the implementation of the protection system, storage system and processing of user information on the server side is substantiated. The developed application also shows some shortcomings of the mobile platform Android in terms of information security. It is shown how attackers can take advantage of these shortcomings, and suggestions to prevent their use are provided. The results of the work can be used to protect information in practical systems. [ABSTRACT FROM AUTHOR]

Traditional testing methods for mobile apps focus on detecting execution errors. However, the evolution of mobile networks toward 5G will require additional support for app developers to also ensure good performance and user experience. Manual testing in a number of scenarios is not enough to satisfy the expectations of the apps' end users. This paper presents the testing framework developed in the TRIANGLE project (https://www.triangle-project.eu/), which integrates a complete mobile network testbed to test, benchmark and certify mobile apps. In this paper, we focus on a recent extension of the TRIANGLE framework that uses model-based testing based on model checking to support the automatic generation of user interactions. We introduce the complete testing framework and the basis of the model-based extension. Finally, we use the testing framework to evaluate the performance of the ExoPlayer app in different network scenarios. ExoPlayer is a video streaming app for Android that implements different adaptive streaming protocols. [ABSTRACT FROM AUTHOR]

The paper analyzes several key technologies of data monitoring and quality control system based on the Internet of Things technology from the perspective of system integration, including Web database access, real-time display of dynamic data, dynamic drawing of curves, etc., and gives technical difficulties. Routes and improvements have solved the problems of the system in terms of real time and security. The Zigbee wireless sensor network is used as the data acquisition end, and the server receives the data collected by the wireless sensor network through the serial communication to connect the CC2530 wireless communication module. At the same time, the paper combines the actual needs of users, and designs the server system according to the software engineering development process. While realizing the normal PC browser access to the server, the Android application of the mobile terminal is developed and the mobile is realized. In terminal access to the server, users can access the server by accessing computers on the network and mobile phones with Android systems. Viewing the running status of the device, authorized users can also make judgments based on real-time data and can send control commands and adjust the environment. [ABSTRACT FROM AUTHOR]

With the rapid development of smart devices, mobile phones have permeated many aspects of our life. Unfortunately, their widespread popularization attracted endless attacks that are serious threats for users. As the mobile system with the largest market share, Android has already become the hardest hit for years. To Detect Android Malware by ORGB Anlysis, in this paper, we present DAMBA, a novel prototype system based on a C/S architecture. DAMBA extracts the static and dynamic features of apps. For further analyses, we propose TANMAD algorithm, a two-step Android malware detection algorithm, which reduces the range of possible malware families, and then utilizes subgraph isomorphism matching for malware detection. The key novelty of this paper is the modeling of object reference information by constructing directed graphs, which is called object reference graph birthmarks (ORGB). To achieve better efficiency and accuracy, in this paper, we present several optimization strategies for hybrid analysis. DAMBA is evaluated on a large real-world dataset of 2239 malicious and 1000 popular benign apps. The detection accuracy reaches 100% in most cases, and the average detection time is less than 5 s. Experimental results show that DAMBA outperforms the well-known detector, McAfee, which is based on signature recognition. In addition, DAMBA is demonstrated to resist the known malware attacks and their variants efficiently, as well as malware that uses obfuscation techniques. [ABSTRACT FROM AUTHOR]

Speed, as one of the basic parameters of the traffic flow, has a great importance in terms of efficiency (level of service, travel time) and safety (number of traffic accidents, consequences and risk of accident occurrence). Speed management includes a series of measures that are applied in order to achieve the balance between safety and efficiency in the traffic flow. One of the most important measures for achieving this goal is to define a credible speed limit, i.e. the speed limit that would allow maximization of the capacity and level of service of the observed section, or minimization of users' costs and the risk of traffic accident occurrence. Considering the fact that improper and unadjusted speed causes a number of significant negative effects, the aim of this paper is to carry out the analysis of the credibility of the existing speed limits, i.e. to determine the factors that impact the speed on two-lane sections of valley roads. In order to achieve this, field research in real-world conditions was carried out using the floating vehicle method and an Android phone application. This paper analyzed the speed on the cross-section (time-mean speed). However, it also analyzed the traffic flow speeds on the entire homogeneous impact segment on the basis of automated traffic counters (micro level), as well as on the macro level, i.e. along the six analyzed sections of two-lane rural valley roads (space-mean speed). Also, empirical research determined the speed limit values on the selected sections and free flow speeds on the basis of the Linear Model and by applying the HCS software. Within the speed analysis, the values of operating speeds were determined based on analytical models and methods. In order to obtain a clearer image of the credibility of the existing speed limits, an analysis of the speed limit violations according to classes was carried out. [ABSTRACT FROM AUTHOR]

The presented paper describes the creation of a mobile application module for showing a current menu at Mendel University cafeterias. Nowadays, the students and the university staff have the menu accessible through a static webpage. The presented solution serves as a dynamic presentation of the current menu for all Mendel University in Brno cafeterias. Most cafeteria menu applications use the REST architectures for the server backend. The contribution of the paper is in usage of the Google Firebase Realtime database as a server backend. Unlike in traditional REST architecture, the data e.g. the food rating is instantaneously changed and the users have an up to date information. Part of the presented solution is also a mobile application module for both Android and iOS where university students and staff can see the current cafeterias menus. [ABSTRACT FROM AUTHOR]

Android malware uses anti-reverse analysis and APK shelling technology, which leads to the failure of the classification method based on decompiled features and the reduction of the classification accuracy based on single file features. Moreover, the lack of samples in some families of Android malware makes the classification model based on sample learning ineffective. To solve the above problems, this paper proposes a two-layer general framework for Android malware classification and adversarial training named GMADV, which enhances classifier performance through adversarial training. In the sample classification layer, based on the transformation method of the Markov model, it is proposed for the first time to convert the three files in the APK into RGB Markov images, and use VGG13 to automatically extract features and classification; In the variant amplification layer, the idea of "regression for generation" is firstly proposed, and GMM-GAN based on Gaussian process is designed to amplify the diversity of samples within the family. The experimental results show that RGB Markov images have better classification performance than grayscale images. On the three datasets, the classification effect after amplification has been improved to varying degrees, and all F1_Score reaches 95 %. Compared with other methods, GMADV has stronger family sample amplification ability and greater adversarial intensity. [ABSTRACT FROM AUTHOR]

The emergence of the covid19 pandemic has forced many countries to be more focused on the technologies that can save human life. A lot of efforts have focused on containment and mitigation actions with varying degrees of success. However, lack of attention given to administering urgent requests for convalescent plasma blood as part of treatment for critical covid19 patients although the passive administration of antibodies from a person who has convalesced may be an effective therapy for those patients who have yet to develop their antibody response. Hence, interest in the use of convalescent plasma has been rising, not only commenced by medical professionals but also among patients' family members, especially for urgent requests. This paper proposed timelinebased posts to tackle the urgent-request issue that sort based on first come first serve priority. From this timeline, the people in search of donors may find donors' information and location quickly. Furthermore, we added a timeline page, search function, a virtual assistant (chatbot) that automatically shares posts to social media without exiting the app, etc. These features promote the hit to the right users and their respond faster than before. The previous study reported from the 40 PSARS patients undergoing ribavirin and methylprednisolone treatment, 19 were administered with convalescent plasma, whereas 21 were given an additional dose of methylprednisolone. The group of patients who were treated with plasma therapy showed better recovery and reduced mortality. With this situation, we hope our contribution in the form of an Android app can assist those in need at the right time and save more lives. Moreover, this app can be referred to as a model for other comparable apps. [ABSTRACT FROM AUTHOR]

Many Android apps employ WebView, a component that enables the display of web content in the apps without redirecting users to web browser apps. However, WebView might also be used for cyberattacks. Moreover, to the best of our knowledge, although some countermeasures based on access control have been reported for attacks exploiting WebView, no mechanism for monitoring web access via WebView has been proposed and no analysis results focusing on web access via WebView are available. In consideration of this limitation, we propose a web access monitoring mechanism for Android WebView to analyze web access via WebView and clarify attacks exploiting WebView. In this paper, we present the design and implementation of this mechanism by modifying Chromium WebView without any modifications to the Android framework or Linux kernel. The evaluation results of the performance achieved on introducing the proposed mechanism are also presented here. Moreover, the result of threat analysis of displaying a fake virus alert while browsing websites on Android is discussed to demonstrate the effectiveness of the proposed mechanism. [ABSTRACT FROM AUTHOR]

Today's rapid evolution in information and communication technologies affects all sectors, including education, and has a positive impact. As a result, teachers need to use technology effectively and keep up with innovation to meet the needs of the next generation. The college music educational system was always an important part of higher education in China, and the corresponding music curriculum system has gradually been established. This curriculum system has been instrumental in the systematization and standardization of China's music industry, and it has produced a large number of outstanding musical talents for the country. The goal of this research is to discover advanced and dependable teaching methods. With the growing popularity of mobile intelligent terminal devices and the expanding application of the Android platform, using a mobile intelligent terminal for university courses' learning has become a more powerful technical feature. Through the development of an intelligent learning application using the Android intelligent platform, students can practice exercises and evaluate themselves, allowing them to analyze their weak points based on their evaluation value, whether in class or after class, and then facilitate their learning. In this paper, I present my original perspectives and proposals on music education in colleges and universities, based on literature, analysis, and study, as well as my years of practical experience. To assure stability of the suggested framework, scalability, and sustainability, I have used the Model View Controller (MVC) architecture. This framework is based on the mobile client of Android that teaches and queries college music remotely and controls smart music. According to the experimental data, online music teaching has a greater learning effect on music skills and enhances traditional music performance by 25%. In terms of increasing interest in musical courses, this online college teaching information platform has the ability to raise 74% of students' awareness. [ABSTRACT FROM AUTHOR]

Video streaming abuses range from copyright infringement and piracy to child sexual abuse and murder. This paper performs proactive forensics of Twitch video streaming activities on Android devices. We designed our experiments using a methodology based on black‐box testing principles and conducted them on Android devices using a forensic framework. The results of our experiments suggest that forensic investigators can extract evidences of streams broadcast, shared, and watched by the user, and associate them with the Twitch account of the user. We detail all the recoverable artifacts of forensic significance, provide mechanisms to extract them based on the identified anchors, and interpret their significance in forensic investigations. [ABSTRACT FROM AUTHOR]

The easy accessibility and simplicity of Short Message Services (SMS) have made it attractive to malicious users thereby incurring unnecessary costing on the mobile users and the Network providers' resources. The aim of this paper is to identify and review existing state of the art methodology for SMS spam based on some certain metrics: AI methods and techniques, approaches and deployed environment and the overall acceptability of existing SMS applications. This study explored eleven databases which include IEEE, Science Direct, Springer, Wiley, ACM, DBLP, Emerald, SU, Sage, Google Scholar, and Taylor and Francis, a total number of 1198 publications were found. Several screening criteria were conducted for relevant papers such as duplicate removal, removal based on irrelevancy, abstract eligibility based on the removal of papers with ambiguity (undefined methodology). Finally, 83 papers were identified for depth analysis and relevance. A quantitative evaluation was conducted on the selected studies using seven search strategies (SS): source, methods/ techniques, AI approach, architecture, status, datasets and SMS spam mobile applications. A Quantitative Analysis (QA) was conducted on the selected studies and the result based on existing methodology for classification shows that machine learning gave the highest result with 49% with algorithms such as Bayesian and support vector machines showing highest usage. Unlike statistical analysis with 39% and evolutionary algorithms gave 12%. However, the QA for feature selection methods shows that more studies utilized document frequency, term frequency and n-grams techniques for effective features selection process. Result based on existing approaches for content-based, non-content and hybrid approaches is 83%, 5%, and 12% respectively. The QA based on architecture shows that 25% of existing solutions are deployed on the client side, 19% on server-side, 6% collaborative and 50% unspecified. This survey was able to identify the status of existing SMS spam research as 35% of existing study was based on proposed new methods using existing algorithms and 29% based on only evaluation of existing algorithms, 20% was based on proposed methods only. This study concludes with very interesting findings which shows that the majority of existing SMS spam filtering solutions are still between the "Proposed" status or "Proposed and Evaluated" status. In addition, the taxonomy of existing state of the art methodologies is developed and it is concluded that 8.23% of Android users actually utilize this existing SMS anti-spam applications. Our study also concludes that there is a need for researchers to exploit all security methods and algorithm to secure SMS thus enhancing further classification in other short message platforms. A new English SMS spam dataset is also generated for future research efforts in Text mining, Tele-marketing for reducing global spam activities. • A comprehensive review of Soft Techniques in SMS Spam Classification (upto May'19). • Overview of the most exploited methods used in feature selection & classification. • Taxonomy of existing state of the art methodologies is developed. • User's acceptability of SMS spam applications on Android App store was evaluated. [ABSTRACT FROM AUTHOR]

It is expected that in the next decade, majority of world population will be living in cities. Better public services and infrastructures in the city are needed to cope with the booming population. City vehicles that cruising for parking have indirectly causing traffic, making one harder to travel around the city. Thus, a smart parking system can certainly lays the foundation to build a smart city. This paper proposed a cost-effective IoT smart parking system to monitor city parking space and provide real-time parking information to drivers. Moreover, instead of the conventional approach that uses embedded sensors to detect vehicles in the parking area, camera image and machine vision technology are used to obtain the parking status. In the prototype, twenty outdoor parking lots are covered using a 5 megapixel camera connected to Raspberry Pi 3 installed at the 5th floor of the nearby building. Machine vision in this project that involved motion tracking and Canny edge detection are programmed in Python 2 using OpenCV technology. Corresponding data is uploaded to an IoT platform called Ubidots for possible monitoring activity. An Android mobile application is designed for user to download real-time data of parking information. This paper introduces a low cost smart parking system with the overall detection accuracy of 96.40%. Also, the mobile application allows users to alert other car owners for any emergency incidents and double parking blockage. The developed system can provide a platform for users to search for empty car parking with ease and reduce the traffic issues such as illegal double parking especially in the urban area. [ABSTRACT FROM AUTHOR]

The symptoms of the same tomato disease in different stages are obviously different, and different diseases show some similarities. Traditional pattern recognition methods can not reflect the dynamic changes of the pathological characterization, and the practicability is poor. To solve this problem, this paper proposed a Multi-Scale AlexNet recognition model for mobile platform based on convolutional neural network (CNN), and implemented a tomato leaf disease image recognition system for agricultural workers based on Android. Many parameters and large memory utilization of traditional AlexNet model are unfit for mobile platform, this paper adjusted the network structure of the traditional model by removing the local response normalization(LRN) layer, modifying the full connection layer, setting up different convolution kernel extraction features, designed a multi- scale recognition model based on the AlexNet. The model consists of 6 layers. It can optimize the training time and memory utilization and achieve high precision. After removing the LRN layer, there was a 30% decrease in running time. Extending the single convolution kernel into multi-scale (1 × 1,3 × 3,5 × 5,7 × 7) convolution kernel then fused at the first layer, removing full connection layer 6 and 7, and taking the place of global average pooling layer, then the model size was only 30.2 M. The forward propagation rate (FRP) and backward propagation rate (BPR) were reduced, and the global average pooling is better than the global maximum pooling on recognition accuracy. So the Multi-Scale AlexNet model used global average pooling in the 5th layer. In image preprocessing phase, in order to avoiding over fitting of the trained model caused by the unbalanced distribution of sample numbers, we had zoomed, flipped, color jittering, add noise and rotated the original pictures of dataset randomly to get the augmented dataset, and used 70% of the pictures as the train dataset and the rest as the validation dataset(20%) and test dataset(10%). These pictures were quantized to 224 × 224 dpi for Multi-Scale AlexNet training, and the original dataset and augmented dataset were used to train models. In order to validate the performance of the proposed model, comparative tests were done between Multi-Scale AlexNet and traditional pattern recognition method. It repeated 600 tests. The results showed that the CNN model achieved 92.7%, the high average recognition accuracy of each disease and each disease in the early, middle and late stages. Compared with the other CNN Net model(MobileNet, SequeezeNet, LeNet-5), the Multi-Scale AlexNet achieved the highest recognition accuracy, and reached 95.8% on the late stage disease dataset, but the SequeezeNet model used less memory. The MobileNet and SequeezeNet model reached lower recognition accuracy on the middle and late stages dataset, that because their convolution size was small. The recognition system was implemented on Android platform, and then test was done on field dataset. The results showed that the average recognition accuracy was 89.2%, its less value was due to the complex background of image. Then the system can meet the requirements of disease image recognition on mobile platform in production practice. The research results provide a method for disease image recognition based on convolution neural network, and provide a reference for automatic identification of crop diseases and engineering applications. [ABSTRACT FROM AUTHOR]

A combined thread-paper microfluidic device (μTPAD) is presented for the determination of glucose in blood. The device is designed to include all the analytical operations needed: red blood cell separation, conditioning, enzymatic recognition, and colorimetric transduction. The signal is captured with a smartphone or tablet working in video mode and processed by custom Android-based software in real-time. The automatic detection of the region of interest on the thread allows for the use of either initial rate or equilibrium signal as analytical parameters. The time needed for analysis is 12 s using initial rate, and 100 s using the equilibrium measurement with a LOD of 48 μM and 12 μM, respectively, and a precision around 7%. The μTPAD allows a rapid determination of glucose in real samples using only 3 μL of whole blood. • A μTPAD for the determination of glucose in whole blood using 3 μL of sample has been developed. • The μTPAD include analytical operations such as RBC separation, sample conditioning, enzymatic recognition and transduction. • An app detects automatically the ROI on the thread allowing the use of initial rate or equilibrium signal as parameter. [ABSTRACT FROM AUTHOR]

Automated testing of Android apps is essential for app users, app developers, and market maintainer communities alike. Given the widespread adoption of Android and the specificities of its development model, the literature has proposed various testing approaches for ensuring that not only functional requirements but also nonfunctional requirements are satisfied. In this paper, we aim at providing a clear overview of the state-of-the-art works around the topic of Android app testing, in an attempt to highlight the main trends, pinpoint the main methodologies applied, and enumerate the challenges faced by the Android testing approaches as well as the directions where the community effort is still needed. To this end, we conduct a systematic literature review during which we eventually identified 103 relevant research papers published in leading conferences and journals until 2016. Our thorough examination of the relevant literature has led to several findings and highlighted the challenges that Android testing researchers should strive to address in the future. After that, we further propose a few concrete research directions where testing approaches are needed to solve recurrent issues in app updates, continuous increases of app sizes, as well as the Android ecosystem fragmentation. [ABSTRACT FROM AUTHOR]

Several two-sided platforms base their business model on collecting user data, which not only is used for advertisements that generate revenue, but also improve the underlying algorithm that forms the core of any virtual platform. In such markets, big data generates network effects that sustain the market position of the dominant player. Further, scope in data adds a crucial competitive advantage to the advertisement-driven business model. The paper argues that by cutting the supply of user data to its competitors, a dominant player can successfully restrict its competitors from gaining critical mass (in terms of both scale and scope) that is crucial to stay viable in a competitive market. The literature on the competition assessment of data-driven markets has predominantly been theoretical hitherto. This paper presents the competition assessment of two recent cases—European Commission's decision against Google in the Android licensing case, and Bundeskartellamt's (German Federal Cartel Office) action against Facebook— in their technological and economic context to ascertain foreclosure. While Google's practices resulted in foreclosure, the technological and economic context in Bundeskartellamt's case against Facebook does not present a convincing theory of foreclosure. The paper also draws common lessons from these cases that can guide the competition assessment in similar circumstances. The paper, therefore, contributes to the scant academic literature on the exclusionary conduct in data-driven markets from a practical standpoint. [ABSTRACT FROM AUTHOR]

Abstract Smartphone use, especially the Android platform, has already got 80% market shares, due to an aforementioned [ where ?] report, it becomes an attacker's primary objective. There is a growing number of storing private data onto smart phones and low safety defense measures, attackers can use multiple ways to launch and attack user's smartphones. (e.g. Using different coding style to confuse the malware detecting software). Existing Android malware detection methods use multiple features, like safety sensor API, system call, control flow structure and data information flow, then also machine learning to check whether its malware or not. These features provide app's unique property and limitation, that is to say, from some perspectives it might suit for some specific attack, but wouldn't suit for others. Nowadays most malware detection methods use only one of the aforementioned features, and these methods mostly analyze to detect code, but facing the malware code confusion and zero-day attacks, the aforementioned feature's extraction method may cause wrong judgement. So, it's necessary to design an effective technique analysis to prevent malware. In this paper, we use the importance of words from an apk, because of code confusion, some malware attackers only rename variables. If using general static analysis cannot judge correctly, then we use these importance values to go through our proposed method to generate an image, finally use a convolutional neural network to decide whether the apk file is malware or not. Highlights • This paper presented a method that can detect Android malwares more conveniently than using general static analysis. • Using convolutional neural network to analyze whether an apk file is a malware or not. • We convert an apk file to java code which is used to generate images by using two algorithms. [ABSTRACT FROM AUTHOR]

Robots have been designed to take over people's activities that involve repeated and high physical effort. As they are equipped with increasingly powerful computing units, the robots have begun to be programmed and used for complex tasks, involving more decision making and a more accurate mechanical control. In particular, humanoid robots are used for various assistance or human interaction applications. Among medical applications with humanoid robots, one of the most complex is that of therapy for children with psychiatric and neurodevelopmental disorders. Thus, the use of robots is motivated by several factors that increase therapeutic efficiency: children are attracted to toys, but also to technology, predictability of a device / robot's actions facilitates the learning process, etc. The difficulty of this therapy is given by the need to use a complex robot, whose architecture needs to develop motor / gestural, verbal and even cognitive skills. In this paper, we will present the details of implementation on the Nao robot platform of behavioral therapy programs for children with Autistic Spectrum Disorder (ASD) used in a specialized clinic. In the first part of this paper, some representative papers from the literature address the problem of the use of robots in the therapy of children with ASD. In the second part, the technical capabilities of the Nao robot are presented. Nao is a humanoid, with a height of 58 cm, which can perform complex movements using the hands and feet and is equipped with numerous sensors that facilitate human-robot interaction: tactile sensors, proximity sensors, video cameras, microphones, etc. The third part is dedicated to presenting the Nao robot programming and configuration methods and the solutions chosen to implement the application. Also, we describeour Android application that allows the therapist to control the robot throughout all stages of therapy: choosing the program from therapy, choosing / changing the requirement and the item, give rewards, etc. In the last part, some conclusions and perspectives of these research activities are outlined. [ABSTRACT FROM AUTHOR]

In recent years, mobile malware has become one of the most important threats to the development of mobile Internet. Effective prevention and control of malicious applications concern the healthy development of the mobile Internet industry and the vital interests of the vast number of mobile terminal users. However, due to many new characteristics of mobile intelligent terminals, such as storing personal privacy data, the traditional software security technology cannot be applied to mobile applications directly. Therefore, the security detection for mobile applications is of great significance. In this paper, we proposed a simple-Dalvik intermediate language-based method to detect the malicious mobile applications. In this method, we first reduce the 218 instructions in the Dalvik instruction set to a simpler set, SDIL, through simplification and optimization. By using SDIL, we can effectively refine the instruction features and maintain the control relationships of the source program. After that, we use an improved MOSS algorithm to detect malicious mobile applications. Our experimental results show that the method proposed in this paper greatly improves the detection efficiency of malicious mobile applications and maintains good accuracy. [ABSTRACT FROM AUTHOR]

Based on the whole process of malware detection in the Android system. this paper compared and analyzed the status and progress of the research at home and abroad, introduced the source and function of the standardized data samples from the point of view of sample acquisition, and expounded the principles to be followed in the feature selection. This paper compared and analyzed the advantages and disadvantages of various detection methods from the point of view of detection methods, and summed up and summarized the selection methods of feature data sets and the evaluation methods of experimental results. Finally, it prospected the research and development direction of the future Android malware detection method. [ABSTRACT FROM AUTHOR]

In the IoT environment, all devices are connected to each other, and mobile device is considered as key device. But hacking into mobile devices is increasing rapidly with the increase in mobile device users. As the market share of Android OS increases, hacking of mobile devices has focused on Android devices. Although there are many security solutions for mobile devices, they are fragmentary for mobile threats; that is, they are solutions for only several threats rather than comprehensive solutions. There is hence a limit to protecting user’s and company’s data stored or used on mobile devices from various types of hacking. To address this, we propose a mobile device protection technology based on domain isolation. Virtualization technology has emerged to increase CPU utilization in server-class PCs and to run various OSs in one system. As these virtualization technologies become lightweight, they are beginning to be applied to embedded devices. In this paper, we applied this lightweight embedded virtualization technology to mobile devices to divide mobile devices into two areas. Therefore, users can have hidden area from hacker attack in addition to Android OS area which can use same as existing mobile device. There is a hardware-based mobile security solution using an secure element, but this has to be reflected in the manufacturing process of the mobile device. However, since the domain separation technology using the virtualization, proposed in this paper, is a software solution, it has an advantage that it can be applied to a device that is already in use. In addition, to protect the hidden area, application authentication/authorization and user authentication technology were applied. And we use white-box cryptography to get root of trust of the key which is used for secure storage and data encryption/decryption. We believe this is a fundamental solution for protecting the mobile device users from hacking. We implemented and tested various mobile applications operating on a mobile device that incorporates our proposed structure based on domain isolation. There is some performance degradation caused by the domain separation, but it is negligible. According to https://www.wired.com/insights/2012/11/mobile-supercomputers/, the chips for mobile phones have evolved and mobile phones will soon become supercomputers. In this case, the addition of virtualization to the mobile device will have less impact on the computing power of the mobile device, and data protection stored in mobile devices and secure execution environment of security programs will become more important issues. Therefore, our TeeMo structure is a necessary technology to protect mobile device users. [ABSTRACT FROM AUTHOR]

The leaking of sensitive data on Android mobile device poses a serious threat to users, and the unscrupulous attack violates the privacy of users. Therefore, an effective Android malware detection system is necessary. However, detecting the attack is challenging due to the similarity of the permissions in malware with those seen in benign applications. This paper aims to evaluate the effectiveness of the machine learning approach for detecting Android malware. In this paper, we applied the bio-inspired algorithm as a feature optimization approach for selecting reliable permission features that able to identify malware attacks. A static analysis technique with machine learning classifier is developed from the permission features noted in the Android mobile device for detecting the malware applications. This technique shows that the use of Android permissions is a potential feature for malware detection. The study compares the bio-inspired algorithm [particle swarm optimization (PSO)] and the evolutionary computation with information gain to find the best features optimization in selecting features. The features were optimized from 378 to 11 by using bio-inspired algorithm: particle swarm optimization (PSO). The evaluation utilizes 5000 Drebin malware samples and 3500 benign samples. In recognizing the Android malware, it appears that AdaBoost is able to achieve good detection accuracy with a true positive rate value of 95.6%, using Android permissions. The results show that particle swarm optimization (PSO) is the best feature optimization approach for selecting features. [ABSTRACT FROM AUTHOR]

Malware detection from the smartphone has become a challenging issue for academicians and researchers. In this research paper, we applied five distinct machine learning algorithms and three different ensemble methods to develop a model for detecting malware from an Android-based smartphone. In this study, we proposed a framework that helps in selecting the right sets of the feature with an aim to improve the performance of the malware detection models. The proposed malware detection framework is then validated by considering two distinct performance parameters, i.e., accuracy and F-measure as a benchmark to detect malware from real-world apps. We performed an empirical study on thirty different categories of Android apps. The experimental data set consists of 1,94,659 benign apps and 67,538 malware apps that are collected from different promised repositories. Empirical results reveal that the models developed by using the proposed feature selection framework are able to detect more malware-infected apps when compared to all extracted feature sets. Moreover, the malware detection model build by using nonlinear ensemble decision tree forest (NDTF) approach is achieved a detection rate of 98.8%. In addition to that, the proposed malware detection framework is more effective in detecting malware-infected apps as compared to different anti-virus scanners and different frameworks or approaches developed in the literature. [ABSTRACT FROM AUTHOR]

According to the World Health Organization, cardiovascular diseases are the leading cause of death worldwide. For prevention, diagnosis and treatment of heart disease, a medical examination known as electrocardiogram is required, the exam records the electrical activity of the heart and is acquired through a device called an electrocardiograph. In the same way, there is a growing motivation towards the development of new technologies to monitor health and ensure the general well-being of the population, which is speeded up through the rise and advancement of mobile devices. This paper presents the design and implementation of an electrocardiograph which allows the graphic display of the electrocardiographic signal on a mobile device with Android operating system and has an interface to a personal computer where the signals obtained are deployed, processed and analyzed. To build the device, an evolutionaryincremental methodology was followed. The functioning of the system was evaluated in the detection of arrhythmias and acute myocardial infarction; achieving performance indicators of TPR = 87.50% for signals with arrhythmias and TPR = 92.59% for signals with infarction. In this way, information can be captured, processed, parameterized, transmitted, stored in integral health computer systems and used to perform diagnostics by remote specialists; profiling, this system, as alternatives for the diagnosis, care and monitoring of people who have heart problems. [ABSTRACT FROM AUTHOR]

In this paper, time spent and the repetition of using the Social Network Sites (SNS) in Android applications are investigated. In this approach, we seek to raise the awareness and limit, but not eliminate the repeated uses of SNS, by introducing AndroidTrack. This AndroidTrack is an android application that was designed to monitor and apply valid experimental studies in order to improve the impacts of social media on Iraqi users. Data generated from the app were aggregated and updated periodically at Google Firebase Real-time Database. The statistical factor analysis (FA) was presented as a result of the user’s interactions. [ABSTRACT FROM AUTHOR]

The widespread use of smartphones in recent years has led to a significant rise in the sophistication and number of Android malicious applications (apps) targeting smartphone users. Android-based smartphones attract attackers more than other smartphones due to the popularity and open source features of the Android environment. Malware apps pose a potential threat to the security of Android-based smartphones because of the storage of confidential and private information. To improve the classification efficiency of Android malicious apps, this paper proposes a hybrid approach for the classification of Android malware by integrating the fuzzy C-means clustering (FCM) algorithm with the light gradient boosting machine (LightGBM). First, the fuzzy clustering method is utilized for generating highly significant clusters of the Android apps' permissions that reflect the certain characteristics of the Android apps. The primary goal of using fuzzy clustering is to produce new features by gathering Android app permissions with related patterns together in clusters. Second, the LightGBM is employed to take the app's permissions and their clusters resulting from FCM as inputs and outputs the classification result as a malware or goodware app after the training. The advantages of using LightGBM are its high learning efficiency and precise classification. The significance of the proposed approach is illustrated by conducting several experiments using a well-known dataset containing benign and malware Android apps. The results of the experiments show that the suggested approach outperforms the other approaches and attains the highest accuracy (94.63%), area under the curve (AUC) (98.74%) and precision (97.70%). [ABSTRACT FROM AUTHOR]

This research paper presents MLDroid—a web-based framework—which helps to detect malware from Android devices. Due to increase in the popularity of Android devices, malware developers develop malware on daily basis to threaten the system integrity and user's privacy. The proposed framework detects malware from Android apps by performing its dynamic analysis. To detect malware from real-world apps, we trained our proposed framework by selecting features which are gained by implementing feature selection approaches. Further, these selected features help to build a model by considering different machine learning algorithms. Experiment was performed on 5,00,000 plus Android apps. Empirical result reveals that model developed by considering all the four distinct machine learning algorithms parallelly (i.e., deep learning algorithm, farthest first clustering, Y-MLP and nonlinear ensemble decision tree forest approach) and rough set analysis as a feature subset selection algorithm achieved the highest detection rate of 98.8% to detect malware from real-world apps. [ABSTRACT FROM AUTHOR]