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Recently, Android applications have been playing a vital part in the everyday life as several services are offered via mobile applications. Due of its market dominance, Android is more at danger from malicious software, and this threat is growing. The exponential growth of malicious Android apps has made it essential to develop cutting-edge methods for identifying them. Despite the prevalence of a number of security-based approaches in the research, feature selection (FS) methods for Android malware detection methods still have to be developed. In this research, researchers provide a method for distinguishing malicious Android apps from legitimate ones by using a intelligent hyperparameter tuned deep learning based malware detection (IHPT-DLMD). Extraction of features and preliminary data processing are the main functions of the IHPT-DLMD method. The proposed IHPT-DLMD technique initially aims to determine the considerable permissions and API calls using the binary coyote optimization algorithm (BCOA)-based FS technique, which aids to remove the unnecessary features. Besides, bidirectional long short-term memory (Bi-LSTM) model is employed for the detection and classification of Android malware. Finally, the glowworm swarm optimization (GSO) algorithm is applied to optimize the hyperparameters of the BiLSTM model to produce effectual outcomes for Android application classification. This IHPT-DLMD method is checked for quality using a benchmark dataset and evaluated in several ways. The test data demonstrated overall higher performance of the IHPT-DLMD methodology in comparison to the most contemporary methods that are currently in use.

Most of the existing static analysis-based detection methods adopt one or few types of typical static features for avoiding the problem of dimensionality and computational resource consumption. In order to further improve detecting accuracy with reasonable resource consumption, in this paper, a new Android malware detection model based on multiple features with feature selection method and feature vectorization method are proposed. Feature selection method for each type of features reduces the dimensionality of feature set. Weight-based feature vectorization method for API calls, intent and permission is designed to construct feature vector. Co-occurrence matrix-based vectorization method is proposed to vectorize opcode sequence. To demonstrate the effectiveness of our method, we conducted comprehensive experiments with a total of 30,000 samples. Experimental results show that our method outperforms state-of-the-art methods.

Automated GUI testing based on behavioral model is one of the most efficient testing approaches. By mining user usage, test scenarios can be generated based on statistical models such as Markov chain. However, these works require static analysis before starting the exploration which requires too much prerequisites and time.

To address these challenges, we propose a behavioral-based GUI testing approach for mobile applications that achieves faster and higher coverage. The proposed approach does not conduct static analysis. It creates a behavioral model from usage logs by applying a statistical model. The events within the behavioral model are mapped to GUI components in a GUI tree. Finally, it updates the model dynamically to increase the probability of an event that rarely or never occurs when users use the application. The proposed approach was evaluated on four open-source Android applications, and compared with the state-of-the-art tools and manual testing. The main evaluation criteria are code coverage and ability to find errors. The proposed approach performed better than the current state-of-the-art automated testing tools in most aspects.

Because of the widespread usage of Android devices, the Android ecosystem has the highest numbers of users, developers, and app downloads. Researchers find that many Android apps are not sufficiently tested, which may lead to crashes, incorrect behaviors, and security vulnerabilities. Mutation testing is a syntax-based software testing technique that is very effective at designing high-quality tests and evaluating pre-existing tests. Our prior research designed and implemented Android mutation testing technique, and then used experiments to assess its strength. However, the high computational cost of Android mutation testing possibly limits its industrial application. This paper presents an experimental evaluation that investigates redundant mutation operators in Android mutation analysis. While maintaining the test quality, our goal is to reduce the cost by excluding redundant mutation operators or improving their design and implementation. In our evaluation, we first generate mutants and design mutation-adequate tests for each mutation operator. Then, we compute redundancy scores for each pair of mutation operators. Our evaluation results indicate that three operators (AODU, AOIU, and LOI) are redundant in Android mutation analysis. Other three operators (FOB, TVD, and ORL) are very hard to kill. One operator (MDL) needs improvement in its design to eliminate trivial mutants. We also identity subsumption relationships among operators (BWS subsumes BWD, ODL subsumes CDL, COD, and VDL).

Mobile applications create their own security and privacy models through permission-based models. Some applications may request extra permissions that they do not need but may use for suspicious activities. The aim of this study is to identify those spare permissions requested and use this information in the security and privacy approach, which uses static and code analysis together and applies them to the existing datasets; then the results are compared and accuracy level is determined. Classification is made with an accuracy rate of 91.95%.

Users are increasingly relying on smartphones, hence concerns such as mobile app security, privacy, and correctness have become increasingly pressing. Software analysis has been successful in tackling many such concerns, albeit on other platforms, such as desktop and server. To fill this gap, he have developed infrastructural tools that permit a wide range of software analyses for the Android smartphone platform. Developing these tools has required surmounting many challenges unique to the smartphone platform: dealing with input non-determinism in sensor-oriented apps, non-standard control ow, low-overhead yet high-fidelity record-and-replay. Our tools can analyze substantial, widely-popular apps running directly on smartphones, and do not require access to the app’s source code. We will first present two tools (automated exploration, record-and-replay) that increase Android app reliability by allowing apps to be explored automatically, and bugs replayed or isolated. Next, we present several security applications of our infrastructure: a permission evolution study on the Android ecosystem; understanding and quantifying the risk posed by URL accesses in benign and malicious apps; app profiling to summarize app behavior; and Moving Target Defense for thwarting attacks.

In this paper, we present Furhat — a back-projected human-like robot head using state-of-the art facial animation. Three experiments are presented where we investigate how the head might facilitate human–robot face-to-face interaction. First, we investigate how the animated lips increase the intelligibility of the spoken output, and compare this to an animated agent presented on a flat screen, as well as to a human face. Second, we investigate the accuracy of the perception of Furhat's gaze in a setting typical for situated interaction, where Furhat and a human are sitting around a table. The accuracy of the perception of Furhat's gaze is measured depending on eye design, head movement and viewing angle. Third, we investigate the turn-taking accuracy of Furhat in a multi-party interactive setting, as compared to an animated agent on a flat screen. We conclude with some observations from a public setting at a museum, where Furhat interacted with thousands of visitors in a multi-party interaction.

Mobile business ecosystems are based on product innovations and complements created on platforms facilitating transactions between groups of users in a multi-sided market. The purpose of this research is to present a model of success factors (SF) of mobile ecosystems. This research establishes an empirical framework based on the Android ecosystem, which has been analyzed in-depth on firm and ecosystem level, identifying 16 success factors. The main theoretical contribution is a model that identifies SF of platforms, which are related to the identification of the role of users and complementors in increasing innovation success. The model advances research in innovation platforms.

Mobile business ecosystems are based on product innovations and complements created on platforms facilitating transactions between groups of users in a multi-sided market. The purpose of this research is to present a model of success factors (SF) of mobile ecosystems. This research establishes an empirical framework based on the Android ecosystem, which has been analyzed in-depth on firm and ecosystem level, identifying 16 success factors. The main theoretical contribution is a model that identifies SF of platforms, which are related to the identification of the role of users and complementors in increasing innovation success. The model advances research in innovation platforms.

Out-of-band concealment is an effective way of concealing data, but is it feasible on Android? In the paper, we will firstly study the out-of-band concealment and VFS(virtual file system), learn how the file system works, and further figure out whether out-of-band concealment can be implemented on the Android system. After analyzing the process and operations what the file system does when it truncates a file, by a series of experiments, we finally realize the out-of-band concealment on Android system. To improve survivability, we should combine out-of-band concealment with other techniques.

Cardiovascular disease has become one of the most serious diseases which not only endangers human health, but also causes death. The microcirculatory hemodynamic parameters contain abundant cardiovascular information. Through hemodynamic detection, cardiovascular health status can be understood. Results: The data measured by the fingertip pulse wave detection was transmitted to the mobile phone via Bluetooth. The data was obtained at the phone of the Android platform and finally calculated to the relevant microcirculatory hemodynamic parameters. Using this program, users can easily learn about their physical condition through their mobile phones. This equipment allows users to pay attention to their own health at home and improve their health consciousness. Not only that, it also makes the allocation of medical resources more efficient.

This paper firstly discusses the great convenience of the multi-channel video collaboration system based on WebRTC and then leads to the description of the architecture of the system, after that the paper introduces the functions and implementation of the system in detail, and shows how to use the system correctly. Also, the test cases are showed, from which we can draw the conclusion that this multi-channel video collaboration system can apparently bring great efficiency to the law enforcement office.

The monitoring system conducts the remote sampling, data processing and mobile phone monitoring analysis on three important parameters of the water samples including the temperature, dissolved oxygen, and PH values. The STM32F103VET6 with ARM Core-M3 as the core processor is used for sampling and the collected data is sent to the remote server through ATK-SIM900A GPRS module with a 12-bit AD converter, the Android mobile phone users can download the data directly from the server. The system can realize the remote monitoring of field water quality through the Android mobile phone, providing a convenient real-time, continuous monitoring of water quality for the users.

In order to make family life more comfortable, safe, intelligent, good home control system is essential. The ZigBee technology is adopted to set up a home wireless network. The embedded home gateway platform is constructed using ARM Cortex-A9 processor and Android operating system. The Android-based WEB server and client software are developed. Users can log in smart home control system Web server via the Internet or Android phone of installed smart home control client software to control home state and to get the latest home status anytime and anywhere. Meanwhile, in order to improve the measurement accuracy of the system and reduce the false alarm rate, the batch estimate data fusion algorithms based on single sensor are used. Experimental results show that the system can achieve security alarm, indoor environmental testing, home appliances control and intelligent lighting and other functions. The false alarm rate is low.

In order to improve the intelligent level of aquaculture technology, this paper puts forward a remote wireless monitoring system based on ZigBee technology, GPRS technology and Android mobile phone platform. The system is composed of wireless sensor network (WSN), GPRS module, PC server, and Android client. The WSN was set up by CC2530 chips based on ZigBee protocol, to realize the collection of water quality parameters such as the water level, temperature, PH and dissolved oxygen. The GPRS module realizes remote communication between WSN and PC server. Android client communicates with server to monitor the level of water quality. The PID (proportion, integration, differentiation) control is adopted in the control part, the control commands from the android mobile phone is sent to the server, the server again send it to the lower machine to control the water level regulating valve and increasing oxygen pump. After practical testing to the system in Liyang, Jiangsu province, China, temperature measurement accuracy reaches 0.5°C, PH measurement accuracy reaches 0.3, water level control precision can be controlled within ± 3cm, dissolved oxygen control precision can be controlled within ±0.3 mg/L, all the indexes can meet the requirements, this system is very suitable for aquaculture.

Field investigation is an important job for scientists and staff of nature reserve. The traditional method for filed investigation is observing animals or plants outside, measuring related data, filling out paper forms in field, and typing into computer for statistics and analysis. This method is error-prone and inefficient. Intelligent mobile terminals running Android system become popular in recent years. They are portable and powerful. In this paper, we implemented a field investigation system based on Android. With this system, people working in filed can record data easily and store data into database automatically. Meanwhile, this system can integrate tracks with photos, videos and audios to illustrate human activity in detail. This system has been put to use and is welcomed by users from Qinghai Normal University. At the end of this paper, we look forward the future work.

ACFM is a kind of non-destructive testing technique based on electromagnetic induction principle, whose operation is so simple that can rapidly find surface and near surface defects. However, the interface software of ACFM instrument produced at home needed to run on a computer or laptop for further data analysis and waveform display, which was no convenient for special occasions, such as testing of pressure vessel, large entertainment equipment. In order to develop the advantages of the real-time detection of ACFM, this paper proposes an ACFM interface software based on Android, which can be loaded in portable devices running on Android system and can be operated through the touch screen .Just open Wifi to connect wireless probe can start testing.

The most fundamental and important requirement of the tour guide in the tour process is to ensure the safety of tourists. In this paper, a portable guide management system is designed based on RFID technology, the Android software and blue-tooth communication technology. Through this system, the guide can get real-time information if some tourists are left behind, and send text message or dial to those tourists who are left behind immediately. The system reduces the roll-calling time on the tourists, improves the tour guide work efficiency and service quality.

With a high-speed development of the mobile equipment, mobile platform based on Android is gradually developing and many traditional science and technology begin to transform to the mobile equipment. Now miniature and portable spectrometer is becoming one of new research directions, then spectrometer based on mobile platform is an important development object. For the design requirements of the miniature spectrometer data acquisition system, the miniature and high-resolution data acquisition circuit module based on android is designed using linear CCD (TCD1254GFG). STC15 that High-performance embedded MCU is used as the controlling core. A high-speed data transmission circuit with Android USB to UART chip is designed to realize the spectrometer and Android equipment communications. At the same time, we develop a corresponding Android APP of the spectrum acquisition, for processing the spectrum data real-time, such as drawing the spectrogram and accessing spectrum data etc. The Android mobile equipment as the data processing platform will probably replace the traditional method of the data measurement and control based on PC.