In software programs, most of the time, there is a chance for occurrence of faults in general, and exception faults in particular. Localizing those pieces of code that are responsible for a particular fault is one of the most complicated tasks, and it can produce incorrect results if done manually. Semi-automated and fully-automated techniques have been introduced to overcome this issue. However, despite recent advances in fault localization techniques, they are not necessarily applicable to Android applications because of their special characteristics such as context-awareness, use of sensors, being executable on various mobile devices, limited hardware resources, etc. To this aim, in this paper, a semi-automated hybrid method is introduced that combines static and dynamic analyses to localize exception faults in Android applications. Our evaluations of nine open source Android applications of di erent sizes with various exceptions show that the technique proposed in this paper can correctly identify root causes of the occurred exceptions. These results indicate that our proposed approach is e ective in practice in localizing exception faults in Android applications.

Today, mobile phones are one of the first instruments every individual person interacts with. There are lots of mobile applications used by people to achieve their goals. One of the most-used applications is mobile banks. Security in m-bank applications is very important, therefore modern methods of authentication is required. Most of m-bank applications use text passwords which can be stolen by key-loggers. Key-loggers are hidden software to record the keys struck by users. To overcome the key-logging issue, One-Time Passwords are used. They are secure but require additional tools to be used, therefore they cannot be user-friend. Moreover, the voice-based passwords are not secure enough, since they can be heard by other people easily. In other hand, Image-based passwords cannot satisfy users, cause of screen limitation in mobile phones. In this article, a new authentication method is introduced. The password is based on user lip’ s motion which is received via a mobile cellphone camera. The visual information extracted from the user’ s lips movement forms the password. Then the lip motion is tracked to recognize the password by incorporating the lip reading algorithms. The algorithm is based on the Viola-Jones method. It combines the method with a pixel-based approach to segment lips and extract features. After segmenting the lips, some special points of Region of Interests are selected. The information extracted from lips are saved in order to act as algorithm’ s features. In addition, some normalizing methods are considered to normalize the features and prepare themto enter classification phase. In classification step, some known algorithms like Support Vector Machine and K-Nearest Neighbor are applied on features to recognize password and authenticate people. Visual passwords prevent key-loggers from stealing passwords. However, the mobility of a mobile user causes ambient lights to vary in different environments. In this research, a solution is designed to tackle this challenge. Finally a mobile banking application is designed and developed to run on Android mobile phones platform. It incorporates a lip reader which recognizes the passwords in offline mode. The application is independent from the internet connection or a dedicated server. The implemented recognition method has achieved a 70% success rate. In this application a video capture of a letter with 10 frames could be processed in 3. 8 seconds using 628 kilo bytes of memory. These resources are easily available in today’ s mobile phones. Some mobile bank users tested the application to feedback about lip reading password. Most of them were satisfied when using it. They believed the lip reader is more trustable than text passwords and voice-based passwords. In addition, the user-friendliness of it, is a bit more than text password which means that the method can satisfies a mobile bank application user.

Introduction: The widespread use of smartphones has led individuals, companies, and religious institutions in many Islamic countries, including Iran, to launch develop Quranic-themed applications. This study aimed to identify Android-based mobile applications with Quranic themes in Iran, also study their structure and content. Methods: This was a cross-sectional study. The population of the study consisted of all Android apps related to the Holy Quran in Iran. First, by searching Google, Google Play (Google App Store ) and Bazaar (Persian app store) Quranic applications were identified and the applications downloaded, installed and evaluated. The application evaluation tool was a researcher-made checklist. The collected data were analyzed using descriptive statistics. Results: In total, 297 Iranian Quran apps for Android were identified. Most of these apps were design to display the verses of the Quran along with the ability to play their recitation (49. 8%). The use of most Quran apps was completely free (62. 3%). 26. 6% of the applications had not been updated since the initial release. Conclusion: Although most Quran applications for Android in Iran had acceptable usability, the capabilities of these applications were not used. The study of Quran applications in Iran also showed strengths and weaknesses that should be of interest to developers of Quran applications and other similar religious applications in Iran.

Most of the current research on static analysis of Android applications for security vetting either works on Java source code or the Dalvik bytecode. Nevertheless, Android allows developers to use C or C++ code in their programs compiled into various binary architectures. Moreover, Java and the native code components (C or C++) can collaborate using the Java Native Interface. Recent research shows that native codes are frequently used in both benign and malicious Android applications. Most of the present Android static analysis tools avert considering native codes in their analysis and applied trivial models for their data-flow analysis. As we know, only the open-source JN-SAF tool has tried to solve this issue statically. However, there are still challenges like libC functions and multi-threading in native codes that we want to address in this work. We presented SANT as an extension of JN-SAF for supporting Static Analysis of Native Threads. We considered modeling libC functions in our data-flow analysis to have a more precise analysis when dealing with security vetting of native codes. We also used control flow and data dependence graphs in SANT to handle multiple concurrent threads and find implicit data-flow between them. Our experiments show that the conducted improvements outperform JN-SAF in real-world benchmark applications.

Among the topics in the field of software engineering, energy efficiency is an influential factor in software development and maintenance, especially for battery-limited devices. Although software refactoring can improve software quality, recent studies suggest that applying some refactoring operators may lead to conflicts with energy consumption and execution time of Android applications. In this paper, we analyze the impact of code refactoring for eight Android/Java bad code smells and anti-patterns. To conduct the studies and obtain the results, we use a testbed of five real and one synthetic Android applications. In the first step, we measure energy consumption, execution time and quality design of application before and after refactoring. The results show that in some cases, refactoring leads to a decrease in energy consumption and execution time, and in others, it increases energy consumption and application execution time. We then propose a novel refactoring recommendation approach based on evolutionary multi-objective optimization that accounts for energy consumption, execution time and refactoring effort for Android/Java anti-patterns.  For this purpose, we use Nondominated Sorting Genetic Algorithm-II (NSGA-II) with three objectives: 1) energy consumption, 2) execution time, and 3) refactoring effort. The obtained results show that this approach can generate refactoring recommendations with a median precision of 65% and 76% for improving energy and execution time, respectively, while the median of removed antipatterns in testbed applications is 42%.

In traditional speech processing, feature extraction and classification were conducted as separate steps. The advent of deep neural networks has enabled methods that simultaneously model the relationship between acoustic and phonetic characteristics of speech while classifying it directly from the raw waveform. The first convolutional layer in these networks acts as a filter bank. To enhance interpretability and reduce the number of parameters, researchers have explored the use of parametric filters, with the SincNet architecture being a notable advancement. In SincNet's initial convolutional layer, rectangular bandpass filters are learned instead of fully trainable filters. This approach allows for modeling with fewer parameters, thereby improving the network's convergence speed and accuracy. Analyzing the learned filter bank also provides valuable insights into the model's performance. The reduction in parameters, along with increased accuracy and interpretability, has led to the adoption of various parametric filters and deep architectures across diverse speech processing applications. This paper introduces different types of parametric filters and discusses their integration into various deep architectures. Additionally, it examines the specific applications in speech processing where these filters have proven effective.

There are hundreds of ventilator modes on different ventilators. Despite different names, many are similar in functions and options. Educational institutions only teach a limited collection of ventilator modes of a few numbers of models; therefore, graduates may have substantial difficulties encountering new ventilator models with unfamiliar mode names on them. In this article, an Android application for finding similar modes on different ventilators is presented. The aim is to help an intensive care practitioner to easily find a familiar mode on a new ventilator.

With the increase in the Internet's penetration rate in life and the use of this technology in all aspects, the use of mobile phones has increased as well. This, in addition to creating many benefits, has expanded and accelerated the release of some malicious programs called malware. In this study, it is attempted to use a multilayer neural network and learning machine diagnosis of zero daytime malware on smartphones. For this purpose, the standard database has been labeled with more than 15,000 samples of malware and goodware. In the pre -processing phase, the data is first performed using normalization and alignment of the data and by analyzing the main components of the feature of the selection of the feature and selected from 1183 features 215 features that have higher variances, followed by the model. A suggestion is introduced from the multilayer neural network class and the optimization algorithm based on the training and learning that apply it to the databases and compare its classification results with vector algorithms, genetic algorithm, nearest neighbor. And ... it can be seen that the neural network training increases accuracy and accuracy. The results of the use of multilayer neural network based on education and learning indicate 99% accuracy and 98% accuracy.

Aims: Breast cancer is among the most common cancer types. This study aimed to develop an Android-based detection application for assessing breast cancer risk. Materials & Methods: This quasi-experimental study utilized a research and development approach, employing a pre- and post-test design with one group. The development and field-testing phase took place between July and August 2023, involving 59 women of childbearing age purposively selected within the operational vicinity of Puskesmas Simpang IV Sipin, Jambi City, Indonesia. The application successfully underwent testing, including evaluation by media and material validators. Subsequently, a comprehension test was conducted with three respondents individually, ten individuals in a small group, and 59 participants in a large group. In the field test, data are presented descriptively, including frequencies. The Wilcoxon test was utilized to determine a causal relationship between the product’s usage and the observed impact. Findings: The development of the breast cancer risk assessment application involved several key stages, including the identification stage (comprising problem analysis, context, and literature), the application model design stage, and the material and media validation stage. The media validation process was conducted twice, with the findings yielding a score of 67, averaging 3.35 (meeting valid criteria), while material validation received an average score of 3.0 (also meeting valid criteria). A Wilcoxon test conducted on the knowledge variable revealed a significant increase, with the mean value before the intervention at 8.44 and post-intervention rising to 12.29. Conclusion: Women of childbearing age readily accept Android-based breast cancer risk detection applications, and their usage has a positive impact on increasing their knowledge.