Fall Support Assistant for Elderly and Aged People

Cardiovascular disease (CVD) patients need continuous monitoring of bio-signals and regular hospital visit to improve health conditions. A portable health monitoring device can provide continuous monitoring of chronic diseases. In this paper, a low-cost health monitoring system is introduced to observe Electrocardiogram (ECG), body temperature and heart-beat. This research illustrates the use of android smartphone and Android applications to process and visualize the ECG signal, heart rate, and temperature. An android application is developed to monitor, store and share this biomedical signal data with an expert to get the fastest treatment. The system can also store the previous data to observe patient's history easily whenever necessary. The design strategy, experimental data with android apps, smartphone synchronization, real-time monitoring have been presented in this paper. The prototype can be utilized to control heart diseases for the people in the underdeveloped area.

Many studies have evaluated and compared existing open-sources semantic web platforms for ontologies development. However, none of these studies has included the dot NET-based semantic web platforms in the empirical investigations. This study conducts a comparative analysis of open-source and dot NET-based semantic web platforms for ontologies development. Two popular dot NET-based semantic web platforms, namely, SemWeb.NET and dotNetRDF are analyzed and compared against open-source environments including Jena API, Protege and RDF4J also known as Sesame SDK. Various metrics are used to compare the two categories of platforms such as the storage mode, query support, consistency checking, interoperability with other tools, and many more. A number of five ontologies of different sizes are used in the experiments. The experimental results showed that the open-source platforms provides more facilities for creating, storing and processing ontologies compared to the dot NET-based tools. Furthermore, the experiments revealed that Protege and RDF4J open-source and dotNetRDF platforms provide both graphical user interface (GUI) and command line interface for ontologies processing, whereas, Jena open-source and SemWeb.NET are command line platforms. Moreover, the results showed that the open-source platforms are capable of processing multiple ontologies' files formats including RDF and OWL formats, whereas, the dot NET-based tools only process RDF ontologies. Finally, the experiments enabled to learn that the dot NET-based platforms have limited memories size as they failed to load and query larges ontologies compared to open-source environments.

Android Apps are frequently updated, every couple of weeks, to keep up with changing user, hardware and business demands. Correctness of App updates is checked through extensive testing. Recent research has proposed tools for automated GUI event generation in Android Apps. These techniques, however, are not efficient at checking App updates as the generated GUI events do not prioritise updates, and instead explore other App behaviours. We address this need in this paper with CAT (Change-focused Android GUI Testing). For App updates, at the source code or GUI level, CAT performs change impact analysis to identify GUI elements affected by the update. CAT then generates GUI event sequences to interact with the affected GUI elements. Our empirical evaluations using 21 publicly available open source and 2 commercial Android Apps demonstrate that CAT is able to automatically identify GUI elements affected by App updates, generate and execute GUI event sequences focusing on change-affected GUI elements. Comparison with two popular GUI event generation tools, DroidBot and DroidMate, revealed that CAT was more effective at interacting with the change-affected GUI elements. Finally, CAT was able to detect previously unknown change-related bugs in two open source Apps. Developers of the commercial Apps found CAT was more effective than their in-house GUI testing tool in interacting with changed elements and faster at detecting seeded bugs.

This paper presents an operational comparison of two open source-based control platforms for a practical, low cost do-it-yourself underwater remotely operated vehicle (ROV) developed by Underwater Technology Research Group (UTeRG), Universiti Teknikal Malaysia Melaka. Research institutes and hobbyist have shown a growing interest in the development of observation class remotely operated vehicle using open-source platform. Two low-cost open-source solutions for such ROV are known as OpenROV, an open-source platform that is introduced by OpenROV company, and the Ardusub, an open-source ROV kit promoted by Blue Robotics Company. Both open-source hardware and software platforms are being used worldwide for the development of small range of electrical powered ROV system’s architecture, with support from the literature in the internet and the extensive experience acquired with the development of robotic exploration systems. The comparison of both open-source platform is discussed in detail, to provide guidance on how to build our own do-it-yourself ROV, with the lowest possible cost.KeywordsOpenROVUnderwater dronesOpen-source

Computation offloading is a promising way to improve the performance as well as reducing the battery power consumption of a smartphone application by executing some parts of the application on a remote server. Supporting such capability is not easy for smartphone application developers due to (1) correctness: some code, e.g., that for GPS, gravity, and other sensors, can run only on the smartphone so that developers have to identify which parts of the application cannot be offloaded; (2) effectiveness: the reduced execution time must be greater than the network delay caused by computation offloading so that developers need to calculate which parts are worth offloading; (3) adaptability: smartphone applications often face changes of user requirements and runtime environments so that developers need to implement the adaptation on offloading. More importantly, considering the large number of today's smartphone applications, solutions applicable for legacy applications will be much more valuable. In this paper, we present a tool, named DPartner, that automatically refactors Android applications to be the ones with computation offloading capability. For a given Android application, DPartner first analyzes its bytecode for discovering the parts worth offloading, then rewrites the bytecode to implement a special program structure supporting on-demand offloading, and finally generates two artifacts to be deployed onto an Android phone and the server, respectively. We evaluated DPartner on three real-world Android applications, demonstrating the reduction of execution time by 46%-97% and battery power consumption by 27%-83%.

This chapter examines the use of Open Source Software (OSS) technologies that can be used to improve the learning of Science, Technology, Engineering, and Mathematics (STEM). Explored are the various methods that can be utilized to improve the percentage of STEM majors in the American educational system with resources such as: Open Source as Alternative (OSALT), virtualization, cloud computing, Linux distributions, open source programming, and open source hardware platforms. Increasing the amount of students that pursue STEM majors is important because the projected job growth in the STEM field compared to non-STEM jobs is 33%. OSALT provides cost-effective alternatives to commercial products such as Microsoft Office Suite and Adobe Photoshop. Second, creating Virtual Machines (VMs) is another avenue to teach complex concepts in computer science, engineering, and Information Technology (IT). Third, cloud computing is an inexpensive way for clients to access information from multiple locations and devices. Fourth, universities can use the Operating System (OS) Linux and its various distributions as replacements for commercial operating systems like Windows in order to reduce IT costs. Lastly, open source programming languages like Python and their associated Integrated Development Environments (IDEs) provide comprehensive facilities for software engineers for application development or testing.

Responsiveness is one of the most important properties of Android applications to both developers and users. Recent survey on automated improvement of non-functional properties of Android applications shows there is a gap in the application of search-based techniques to improve responsiveness. Therefore, we explore the use of genetic improvement (GI) to achieve this task. We extend Gin, an open source GI framework, to work with Android applications. Next, we apply GI to four open source Android applications, measuring frame rate as proxy for responsiveness. We find that while there are improvements to be found in UI-implementing code (up to 43%), often applications’ test suites are not strong enough to safely perform GI, leading to generation of many invalid patches. We also apply GI to areas of code which have highest test-suite coverage, but find no patches leading to consistent frame rate reductions. This shows that although GI could be successful in improvement of Android apps’ responsiveness, any such test-based technique is currently hindered by availability of test suites covering UI elements.

There is no question that preventing child mortality should be a priority for all populations. There are moral and philosophical reasons to do so (the ‘fair innings’ argument). The replacement of populations depends on sufficient children surviving to reproductive age. It is also critical for indicators of overall population development: reductions in infant and child mortality exert substantially greater impact on levels of life expectancy than similar proportionate declines in death rates at older ages. The long-term decline in child mortality in the industrialized countries that has accompanied economic and social development has depended in part on the implementation of technologies and programmes that target specific diseases, or groups of diseases that often co-occur in the sick child. A reliable information system to monitor progress with programme implementation, and to highlight areas where greater, or new investment is needed, is a key support for public policy to improve child survival. But how reliably do we know the causes of child death in different countries, particularly in poorer populations? Arguably, the most appropriate mechanism for understanding change in the leading causes of death is a nationally representative vital registration system which routinely captures all deaths and assigns an underlying cause, certified by a medical practitioner. Such systems are well established in all industrialized countries, and increasingly in many developing populations as well, but for over half the countries of the world, particularly in Africa and Asia, vital registration of the fact of death, let alone the cause, is woefully inadequate for public policy. 1 But does the absence of a functioning vital registration system imply that little is usefully known about the leading causes of child death in populations? Certainly not, as the article by Morris and colleagues in this issue demonstrates. 2 Vital registration, even on a sample basis as is done in China, 3 provided it is implemented with diligence and a reasonable understanding of its benefits, is an invaluable source of data on causes of death at all ages. Unfortunately, the achievement of sufficient quality of vital registration appears to be largely dependent on statistical infrastructure investments for the routine monitoring of vital events; investments which are generally of lowest priority for Ministries of Health, or even more removed, for Ministries of the Interior charged with maintaining vital registration. What are the most appropriate strategies to meet the needs of countries for monitoring trends in causes of child mortality, and to guide programme implementation and priority setting? A useful first step is to understand, with reasonable certainty, the overall level of child mortality. Knowing how many children die is of itself useful for policy, but, more importantly, this figure will constrain the individual cause-specific estimates within the bounds of additivity. With the substantial investment in measuring child mortality levels through censuses and survey programmes over the past few decades, levels of child mortality are reasonably reliably known for all but a handful of countries, primarily in Africa. 4 The Demographic and Health Surveys programme alone has provided good quality data on levels of child mortality in over 40 countries over the past 5 years. Information on the age distribution of these child deaths can be used to guide the estimation of causes of death, as for example has been done to separate out the main causes of neonatal deaths. 5 Enough is known about the relationship between broad causes of death and overall levels of child mortality to be reasonably confident about leading causes. This was initially modelled by Preston, 6 with subsequent adaptation of his work to child mortality. 7 But such models are unlikely to capture the important and real variations in causes of child death across populations that occur due to such factors as climate, wars, efficiency and coverage of health services, the differential effects of education, and the emergence of new hazards such as human immunodeficiency virus (HIV)/AIDS. This is best assessed on the basis of a systematic review of community studies and other local epidemiological research according to strict evaluation criteria. There are several examples in the literature, ranging from disease-specific reviews, e.g. for malaria, 8 to systematic assessments of leading causes of child mortality at earlier

Android applications (APPS) are in widespread use and have enriched our life. To ensure the quality and security of the apps, many approaches have been proposed in recent years for detecting bugs and defects in the apps, of which program analysis is a major one. This paper mainly makes an investigation of existing works on the analysis of Android apps. We summarize the purposes and proposed techniques of existing approaches, and make a taxonomy of these works, based on which we point out the trends and challenges of research in this field. From our survey, we sum up four main findings: (1) program analysis in Android security field has gained particular attention in the past years, the fields of functionality and performance should also gain proper attention; the infrastructure that supports detection of various defects should be enriched to meet the industry’s need; (2) many kinds of defects result from developers’ misunderstanding or misuse of the characteristics and mechanisms in Android system, thus the works that can systematically collect and formalize Android recommendations are in demand; (3) various program analysis approaches with techniques in other fields are applied in analyzing Android apps; however, they can be improved with more precise techniques to be more applicable; (4) The fragmentation and evolution of Android system blocks the usability of existing tools, which should be taken into consideration when developing new approaches.

Smartphone applications have millions of users. Their energy efficiency is very important. However, we investigated 174 Android applications and found 33 of them suffering serious energy inefficiency problems. Many of these problems are due to ineffective use of sensors and their data. In this paper, we propose a novel approach to systematically diagnose energy inefficiency problems in Android applications. We derive an application execution model from Android specifications, and leverage it to realistically simulate an application's runtime behavior. Our approach can automatically analyze an application's sensory data utilization at different states, and report actionable information to help developers locate energy inefficiency problems and identify their root causes. We built a tool called GreenDroid on top of Java PathFinder and evaluated it using six popularly downloaded Android applications. GreenDroid analyzed these applications in a few minutes, and successfully located real energy inefficiency problems in them.

Android apps have been traditionally built using Java since the inception of Android. However, Google announced Kotlin as an official supported language for the Android platform in May 2017. Since then, the popularity of Kotlin for Android projects has steadily increased, to the point that Google announced in 2019 that "Android development will be Kotlin-first" with nearly 60% of the top 1,000 Android apps containing Kotlin code. Yet, the transition from Java to Kotlin seems gradual and most applications still partially use Java. Outside open-source apps, little is known about the real proportion of code written in Kotlin inside apps. This paper supports a better understanding of the adoption of Kotlin in the Android ecosystem. We propose an approach to identify the language, Java or Kotlin, in which a class bytecode of an Android Package Kit (APK) originate from. We applied our model on more than 200k closed-source APKs from app stores and found that (i) most of the apps classes are still written in Java, indicating a mitigated adoption of Kotlin in less popular apps, (ii) the penetration of Kotlin is steadily increasing since 2017. We believe our insights are valuable to assess the adoption of Kotlin at large.

The presence of malicious software (malware), for example, in Android applications (apps), has harmful or irreparable consequences to the user and/or the device. Despite the protections app stores provide to avoid malware, it keeps growing in sophistication and diffusion. In this paper, we explore the use of machine learning (ML) techniques to detect malware in Android apps. The focus is on the study of different data pre-processing, dimensionality reduction, and classification techniques, assessing the generalization ability of the learned models using public domain datasets and specifically developed apps. We find that the classifiers that achieve better performance for this task are support vector machines (SVM) and random forests (RF). We emphasize the use of feature selection (FS) techniques to reduce the data dimensionality and to identify the most relevant features in Android malware classification, leading to explainability on this task. Our approach can identify the most relevant features to classify an app as malware. Namely, we conclude that permissions play a prominent role in Android malware detection. The proposed approach reduces the data dimensionality while achieving high accuracy in identifying malware in Android apps.

An important field of research within mechatronics includes the development of robotic platforms based on open source platforms, primarily focused on the operating system ROS (Robot Operating System). The main disadvantage regarding commercial robotic manipulators is based on the lack of flexibility in design, because constantly hardware is protected by copyright or partially released. There is a need to develop a platform that facilitates robotics teaching and learning focused on open hardware and software with industrial applications. For the development of the project, the mechatronic systems design methodology proposed in the VDI 2206 standard was used. As a result, an anthropomorphic Open Software and Open Hardware Robot (OSH-OSS) with six degrees of freedom were obtained with a precision of 2.03 [mm] and repeatability of \pm 4.89 [mm], with a payload of 0.15 [kg] and a payload ratio of 5.21%. The proposal to replicate the manipulator within the subject of industrial robotics at the undergraduate level proved to improve the technical performance of the participants, facilitating the learning and the assimilation of complex concepts to acquire theoretically.

As the number of android applications (apps) available in the market are increasing rapidly, various types of security attacks using the android apps are also increasing with the same pace. The ransomware attack is one of these kind of security attacks in which the attackers locks the user's phone, encrypts user's data or blocks the user's access to their own data and threatens the user to pay a ransom to gain the access back. This cyber-threat is terrorizing the world from many years as it performs mimicry attacks i. e. combination of encryption & locking attacks. Android devices are more prone to these ransomware attacks compared to Windows and IOS devices. RansomwareElite is an android application which detects the presence of ransomware in the apps installed on an android device by checking the presence of any threatening text in app code or by verifying the permissions requested by the app from the user. In this paper, we focused on improving the performance of RansomwareElite app by extending its features. Now, the RansomwareElite app also searches the presence of any threatening image or file containing threatening text by analyzing the Android Package Kit(APK) file of android app. Moreover, it also detects some specific methods and classes in the code of the APK which could be used for locking the device and checks some specific permissions requested, for uninstalled apps now. Further, it maintains a database on the online server for the records of all the suspicious and ransomware apps detected by RansomwareElite. We have tested RansomwareElite with 9 Test Apps which are manually created based on the features of ransomware family and on 48 android devices. After analyzing the test results, we have found that the performance of RansomwareElite is improved after incorporating the new features and RansomwareElite app detects the presence of ransomware in installed as well as uninstalled apps present on an android's device in an efficient manner.

With the increasing popularity of the Android platform, we have seen the rapid growth of malicious Android applications recently. Considering that the heavy use of applications on mobile phones such as games, emails, and social network services has become a crucial part of our daily life, we have become more vulnerable to malicious applications running on mobile devices. To alleviate this hostile environment of Android mobile applications, we propose a malware detection approach that (1) extracts both built-in permissions and custom permissions requested by Android apps from their Manifest.xml and (2) applies the permissions and a Random Forest classifier to Android applications for classifying them into benign and malicious. The Random Forest classifier learns a model using the permissions to classify the input dataset of 45,311 Android applications. In the learned model, an optimal subset of permissions has been identified and then using the subset of permissions we could achieve 94.23% accuracy in detecting malware.