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AbstractThe project aimed to revolutionize home security by developing innovative, user-friendly, and highly secure smart security devices requiring minimal installation and robust data protection. Through meticulous research and critical design phases, the project successfully achieved its objectives, surpassing initial goals and setting new standards for home security.Objectives & Research Findings:The project aimed to address the critical needs of homeowners, tenants/renters, and property managers by focusing on damage-free installation, easy setup, remote monitoring, and encrypted data transmission. Analysis of burglary statistics emphasized the vulnerability of inadequate door-locking mechanisms, while exploration of sensor technologies identified viable options for detecting door status. Patent analysis provided insights into prior art and innovation trends, guiding design decisions and strategies for intellectual property protection.Critical Design Phase & Achievements:Detailed engineering analysis, testing protocols, risk assessment, and compliance considerations were conducted. Hardware components were carefully selected and tested for optimal performance, and housing units were designed for durability and practical installation. Non-invasive sensors facilitated risk-free installation, while battery-powered sensors ensured portability and longevity. Invasive sensor options were developed for permanent home installation, and user-friendly mobile/web applications provided real-time property security status with secure and encrypted data transmission.Future Recommendations & Conclusion:Future recommendations include extending battery life, enhancing data protection measures, implementing a notification system within the application, and ensuring compatibility with other smart home systems. The project demonstrates how innovative security solutions can evolve alongside technology to meet user needs effectively, setting new benchmarks for convenience, flexibility, and data protection in home security systems. Lessons learned will guide future improvements, ensuring continued innovation and user satisfaction.

Youth screen media activity is a growing concern, though few studies include objective usage data. Through the longitudinal, U.S.-based Adolescent Brain Cognitive Development (ABCD) Study, youth (mage = 14; n = 1415) self-reported their typical smartphone use and passively recorded three weeks of smartphone use via the ABCD-specific Effortless Assessment Research System (EARS) application. Here we describe and validate passively-sensed smartphone keyboard and app use measures, provide code to harmonize measures across operating systems, and describe trends in adolescent smartphone use. Keyboard and app-use measures were reliable and positively correlated with one another (r = 0.33) and with self-reported use (rs = 0.21-0.35). Participants recorded a mean of 5 h of daily smartphone use, which is two more hours than they self-reported. Further, females logged more smartphone use than males. Smartphone use was recorded at all hours, peaking on average from 8 to 10 PM and lowest from 3 to 5 AM. Social media and texting apps comprised nearly half of all use. Data are openly available to approved investigators ( https://nda.nih.gov/abcd/ ). Information herein can inform use of the ABCD dataset to longitudinally study health and neurodevelopmental correlates of adolescent smartphone use.

The study focused mainly with the development of Android-based Learning Materials in Genetics based from the Curriculum Guide of Senior High School given by the Department of Education conducted at Morong National High School - Senior High School School Year 2021-2022. The topics are included in the Curriculum Guide of General Biology 2 based on the K to 12 Basic Education Curriculum for Senior High School - Science, Technology, Engineering and Mathematics (STEM) Track. The study included the evaluation of the developed Android-Based Learning Materials in Genetics through a modified evaluation tool for quantitative analysis of data. The study employed the Developmental and Descriptive methods of Research specifically, in describing the development of the mobile application and assessing the quality of the developed learning material through the utilization of a Questionnaire-Checklists for the two groups of respondents which are the twenty (20) Teachers of Science and twenty (20) Information and Communication Technology (ICT) Experts as they were capable to objectively evaluate the developed learning material in terms of instructional content, alignment to the curriculum, depth of knowledge, learner engagement interactivity, graphics and multimedia, lay-outs, operation, and performance. Based on the intensified treatment of the data gathered, the Android-Based Learning Materials in Genetics was designed and constructed aligned to the learning competencies of the current curriculum. The evaluation of the developed mobile application in terms of instructional content, alignment to the curriculum, depth of knowledge, learner engagement interactivity, graphics and multimedia, lay-outs, operation, and performance had a 100% Highly Agree as interpretation on the developed material. In the light of the summary of the findings, it is concluded that the developed Android-Based Learning Materials in Genetics was generally efficient, simple to learn, easy to navigate, appealing and engaging. It was also pedagogically constructive as the content and the tools used in the application were useful from the perspective of both the content experts and the ICT experts. Thus, accomplishing the primary goal of this research study by providing effective instruction through mobile learning. Based from the results of evaluation and conclusion on the developed Android-Based Learning Materials in Genetics, the following are hereby recommended: the developed Android-Based Learning Materials in Genetics can be used to facilitate teaching-learning process. The developed Android-Based Learning Materials in Genetics maybe subjected to revision and modifications in the future depending on the needed competency for a particular topic and the needs and abilities and sustainability of learning styles of the future learners. It may be subjected for improvement to be utilized in iOS or apple devices. The developed Android-Based Learning Materials in Genetics may be modified for an automatic update of the content. Encourage other teachers in science specifically Biology teachers to use the developed Android-Based Learning Materials in Genetics. Additional studies maybe conducted with regards to more content-based courses to be placed in other areas that could supplement the findings of this study. [ABSTRACT FROM AUTHOR]

Over 430 million people worldwide have disabling hearing loss, and 2.2 billion suffer from vision impairment. Assisting these populations with enhanced indoor navigation tools is critical for improving their independence and quality of life. This research advances existing indoor spatial cognition systems for individuals with hearing or visual impairments by developing two Java-based Android applications on the Android 10 operating system. The first application gathers the WiFi BSSIDs data via the Java Android class WifiManager to design a WiFi map inside the multistage building. In the second application, the WiFi map is employed to localize individuals with impairments and provide audio feedback for the visually impaired; for individuals with disabling hearing loss, the feedback is provided in a form of 3D GLB colored virtual object with textual information via the Google ARCore augmented reality library. Experimental results show a 100 % positioning accuracy in indoor localization at the multistage academic building at the University of Central Asia (Naryn campus, Kyrgyzstan). [ABSTRACT FROM AUTHOR]

In behavioral sciences, there is growing concern about the inflation of false-positive rates due to the amount of under-powered studies that have been shared in the past years. While problematic, having the possibility to recruit (lots of) participants (for a lot of time) is realistically not achievable for many research facilities. Factors that hinder the reaching of optimal sample sizes are, to name but a few, research costs, participants' availability and commitment, and logistics. We challenge these issues by introducing PsySuite, an Android app designed to foster a remote approach to multimodal behavioral testing. To validate PsySuite, we first evaluated its ability to generate stimuli appropriate to rigorous psychophysical testing, measuring both the app's accuracy (i.e., stimuli's onset, offset, and multimodal simultaneity) and precision (i.e., the stability of a given pattern across trials), using two different smartphone models. We then evaluated PsySuite's ability to replicate perceptual performances obtained using a classic psychophysical paradigm, comparing sample data collected with the app against those measured via a PC-based setup. Our results showed that PsySuite could accurately reproduce stimuli with a minimum duration of 7 ms, 17 ms, and 30 ms for the auditory, visual, and tactile modalities, respectively, and that perceptual performances obtained with PsySuite were consistent with the perceptual behavior observed using the classical setup. Combined with the high accessibility inherently supported by PsySuite, here we ought to share the app to further boost psychophysical research, aiming at setting it to a cheap, user-friendly, and portable level. [ABSTRACT FROM AUTHOR]

Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and ultrasound scans being widely used to assist radiologists and medical experts in reaching concrete diagnosis. Given the recent massive uplift in the storage and processing capabilities of computers, and the publicly available big data, Artificial Intelligence (AI) has also started contributing to improving diagnostic radiology. Edge computing devices and handheld gadgets can serve as useful tools to process medical data in remote areas with limited network and computational resources. In this research, the capabilities of multiple platforms are evaluated for the real-time deployment of diagnostic tools. MRI classification and segmentation applications developed in previous studies are used for testing the performance using different hardware and software configurations. Cost–benefit analysis is carried out using a workstation with a NVIDIA Graphics Processing Unit (GPU), Jetson Xavier NX, Raspberry Pi 4B, and Android phone, using MATLAB, Python, and Android Studio. The mean computational times for the classification app on the PC, Jetson Xavier NX, and Raspberry Pi are 1.2074, 3.7627, and 3.4747 s, respectively. On the low-cost Android phone, this time is observed to be 0.1068 s using the Dynamic Range Quantized TFLite version of the baseline model, with slight degradation in accuracy. For the segmentation app, the times are 1.8241, 5.2641, 6.2162, and 3.2023 s, respectively, when using JPEG inputs. The Jetson Xavier NX and Android phone stand out as the best platforms due to their compact size, fast inference times, and affordability. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a progressive neurological disorder that is very common among older adults. In truth, taking care of Alzheimer's patients can be a very tedious job that requires a lot of time and effort. On the other hand, smartphone applications (apps) have become an essential part of all of our daily life fields. Almost every human activity can be related to an app from checking the weather forecasts to attending a business meeting online. As a natural result of this, many useful apps were developed to serve and help Alzheimer's patients and families around the world. Sadly, the apps launched in the Arabic area were very poor in their content and limited in their features. In this work, we present Alzheimer's care system (ACS) which is a smartphone Android app that aims to serve Alzheimer's early stages patients, their caregivers, and their doctors. ACS has an Arabic interface along with the English one to serve the patients in the Arabic talking countries who may struggle using similar apps in other languages. The app provides three account types and includes many useful features like contacting the caregivers and doctors through messages, keeping track of daily life tasks, reminding the patient of medication doses and times, and many more. Moreover, ACS has a Chatbot that provides general knowledge regarding the disease. Our app has been practically tested for one month by a group of users who were asked to fill out a 12 questions survey at the end of the test period. The survey results were very positive and encouraging in general. As future work, we plan to add other languages, develop an iOS version, and add new features to the app. [ABSTRACT FROM AUTHOR]

Smartphones are intricately connected to the modern society. The two widely used mobile phone operating systems, iOS and Android, profoundly affect the lives of millions of people. Android presently holds a market share of close to 71% among these two. As a result, if personal information is not securely protected, it is at tremendous risk. On the other hand, mobile malware has seen a year-on-year increase of more than 42% globally in 2022 mid-year. Any group of human professionals would have a very tough time detecting and removing all of this malware. For this reason, deep learning in particular has been used recently to overcome this problem. Deep learning models, however, were primarily created for picture analysis. Despite the fact that these models have shown promising findings in the field of vision, it has been challenging to fully comprehend what the characteristics recovered by deep learning models are in the area of malware. Furthermore, the actual potential of deep learning for malware analysis has not yet been fully realized due to the translation invariance trait of well-known models based on CNN. In this paper, we present ViTDroid, a novel model based on vision transformers for the deep learning-based analysis of opcode sequences of Android malware samples from large real-world datasets. We have been able to achieve a false positive rate of 0.0019 as compared to the previous best of 0.0021. However, this incremental improvement is not the major contribution of our work. Our model aims to make explainable predictions, i.e., it not only performs the classification of malware with high accuracy, but it also provides insights into the reasons for this classification. The model is able to pinpoint the malicious behavior-causing instructions in the malware samples. This means that our model can actually aid in the field of malware analysis itself by providing insights to human experts, thus leading to further improvements in this field. [ABSTRACT FROM AUTHOR]

The expanding development of android applications is partially due to the communication model, named inter‐component communication (ICC) model. PendingIntent (PI) is a powerful feature that is used for ICC. Many android developers use PI in their apps, but if it is used insecurely, it can pose risks and result in different types of attacks like denial of service, privilege escalation, and data leakage. Hence, it is crucial to detect vulnerabilities related to PI before android apps are released on Android app stores. In this paper, a new PI‐related vulnerability is introduced, which is detected by the proposed method in addition to the vulnerabilities pointed out in other methods. In addition, the proposed method that is based on static analysis takes less time than other methods to detect the vulnerabilities. For evaluation, we compare the proposed method with PIAnalyzer tool. Results on 51 application benchmarks show that the proposed method detects the new PI‐related vulnerability that is not detected by PIAnalyzer. Also, the proposed method detects vulnerabilities 27% faster than PIAnalyzer. [ABSTRACT FROM AUTHOR]

The eyes play a special role in human communications. Previous psychological studies have reported reflexive attention orienting in response to another individual's eyes during live interactions. Although robots are expected to collaborate with humans in various social situations, it remains unclear whether robot eyes have the potential to trigger attention orienting similarly to human eyes, specifically based on mental attribution. We investigated this issue in a series of experiments using a live gaze-cueing paradigm with an android. In Experiment 1, the non-predictive cue was the eyes and head of an android placed in front of human participants. Light-emitting diodes in the periphery served as target signals. The reaction times (RTs) required to localize the valid cued targets were faster than those for invalid cued targets for both types of cues. In Experiment 2, the gaze direction of the android eyes changed before the peripheral target lights appeared with or without barriers that made the targets non-visible, such that the android did not attend to them. The RTs were faster for validly cued targets only when there were no barriers. In Experiment 3, the targets were changed from lights to sounds, which the android could attend to even in the presence of barriers. The RTs to the target sounds were faster with valid cues, irrespective of the presence of barriers. These results suggest that android eyes may automatically induce attention orienting in humans based on mental state attribution. [ABSTRACT FROM AUTHOR]

Two Factor Authentication (2FA) using One Time Password (OTP) codes via SMS messages is widely used. In order to improve user experience, Google has proposed APIs that allow the automatic verification of the SMS messages without the intervention of the users themselves. They reduce the risks of user error, but they also have vulnerabilities. One of these APIs is the SMS Retriever API for Android devices. This article presents a method to study the vulnerabilities of these OTP exchange APIs in a given sector. The most popular API in the sector is selected, and different scenarios of interaction between mobile apps and SMS OTP servers are posed to determine which implementations are vulnerable. The proposed methodology, applied here to the banking sector, is nevertheless simple enough to be applied to any other sector, or to other SMS OTP APIs. One of its advantages is that it proposes a method for detecting bad implementations on the server side, based on analyses of the apps, which boosts reusability and replicability, while offering a guide to developers to prevent errors that cause vulnerabilities. Our study focuses on Spain's banking sector, in which the SMS Retriever API is the most popular. The results suggest that there are vulnerable implementations which would allow cybercriminals to steal the users SMS OTP codes. This suggests that a revision of the equilibrium between ease of use and security would apply in order to maintain the high level of security which has traditionally characterized this sector. [ABSTRACT FROM AUTHOR]

This paper studies personalised smart apps, from a data protection and security point of view. More precisely, having as a reference model the provisions stemming from the General Data Protection Regulation, we investigate whether such apps, whose philosophy is based on the provision of personalised services, adopt appropriate data protection techniques, focusing especially on aspects from the data protection by design and by default principles, as well as on their security features. Our analysis over ten popular such Android apps illustrates the existence of several privacy concerns, including the facts that several data processes are by default enabled without requesting users' consent, as well as that several data processes are not well justified or sufficiently transparent to the users. Moreover, interestingly enough, the apps studied are not free of known security weaknesses. [ABSTRACT FROM AUTHOR]

This study introduces an innovative approach to tackle evolving Android malware threats using explainable artificial intelligence (XAI) methods combined with deep learning techniques. The framework enhances detection accuracy and provides interpretable insights into the model's decision-making process. The research utilizes the CICInvesAndMal2019 dataset for training with Deep Neural Network (DNN) techniques. It incorporates Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) XAI techniques to refine the model's features and understand its predictions. This framework uses permissions and intents as static features from Android apps. The proposed framework reduces the execution time, reducing model loss to an impressively lower value of 0.26, and exhibits a commendable accuracy of 97.86%. [ABSTRACT FROM AUTHOR]

In recent years, Global Navigation Satellite Systems (GNSSs) have become integral to our daily lives because of their precise positioning and navigation capabilities. Widespread use of smartphones equipped with GNSS receivers results in the generation of a huge amount of positioning data. Therefore, real-time cloud computing has emerged as a promising approach to effectively leverage this wealth of location information. In this study, we developed an Android app that captures raw GNSS data from smartphones, leverages cloud computing resources, calculates the position of the device, and returns the computed solution to the user. Integration of cloud-based processing not only conserves the device resources but also enables real-time position calculation, paving the way for enhanced location-based applications and services. [ABSTRACT FROM AUTHOR]

Arabic Language Education Study Program students must be able to write theses in Arabic. A common obstacle students face is relying on Google Translate to help them translate from Indonesian to Arabic. However, even though it is easy to use, Google Translate still has obstacles when measured using Larson's translation quality indicators. This study aims to improve the quality of Google Translate translation results for Arabic theses using an Android-based term dictionary. This study is a type of classroom action research carried out in 2 cycles, each including planning, action, observation, and reflection procedures. Data collection was carried out through an assessment of the translation results of 22 students selected based on the criteria of being in the process of completing their thesis. The study results showed that of the 12 sub-indicators of translation quality, the aspects of equivalence and suitability of the source and target languages were the lowest quality. Using an Android-based term dictionary significantly reduced error scores and improved translation quality compared to Google Translate as a translation machine. However, the dictionary does not match the efficiency or time savings the translation machine provides. Furthermore, researchers are expected to be able to produce translation machines that can accommodate the intricacies and complexities of the Arabic language in the future. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach to the early detection of Android malware, focusing on a dynamic pre-exploitation phase identification system. Traditional methods often rely on static thresholding to delineate the pre-exploitation phase of malware attacks, which can be insufficient due to the diverse behaviors exhibited by various malware families. This study introduces the Dynamic Pre-exploitation Boundary Definition and Feature Extraction (DPED-FE) system to address these limitations, which utilizes entropy for change detection, thus enabling more accurate and timely identification of potential threats before they reach the exploitation phase. A comprehensive analysis of the system's methodology is provided, including the use of vector space models with Kullback-Leibler divergence for dynamic boundary detection and advanced feature extraction techniques such as Weighted Term Frequency- Inverse Document Frequency (WF-IDF) to enhance its predictive capabilities. The experimental results demonstrate the superior performance of DPED-FE compared to traditional methods, highlighting its effectiveness in real-world scenarios. [ABSTRACT FROM AUTHOR]

To produce appropriate chord harmonies on the guitar, tuning or tuning the strings is required. However, most guitar learners perform tuning manually based on hearing. This will certainly take a long time because, in the tuning process, the user must turn the string knob repeatedly to get a harmonious and precise tone. Although there are currently many guitars tuning applications on Android, in the tuning process, users must turn the string knob manually. This research aims to create a tool called Learn Guitar Chords to perform the tuning process automatically, and the results are fast and accurate according to the frequency of standard guitar string tones using the Fast Fourier Transform (FFT) algorithm. FFT can convert signals from the time domain into the frequency domain, a series of numbers in the time domain f(x) is converted into the frequency domain f(u). Considering the test results using the black box testing method that has been carried out, it can be said that the Fast Fourier Transform-based guitar tuning synchronization design application on Android can obtain the frequency of user input properly. In addition, accuracy testing was also carried out by testing the tuning process by comparing it with 2 applications, namely Absolute Guitar and Guitar Tuner. The results obtained from the application comparison prove that the accuracy of the tuning process in the Learn Guitar Chords application is very good because it can produce the same results as other applications. Although the equal temperament scale is one of the most popular tuning techniques for stringed instruments, other techniques should also be considered because it is used in various musical instruments. [ABSTRACT FROM AUTHOR]

Abstract This study employs Conversation Analysis to create a recursive model that improves the quality of human-robot interaction. Our research goal is to create a dialogue robot that offers pleasant experiences for users, so they are willing to engage in repeated interactions in daily lives. While there has been dramatic progress in the performance of dialogue robots, there has been less attention to the importance of users’ interactional experience compared to the “specs” of the dialogue system. Employing Goffmanian insights and using research in Conversation Analysis (CA), the present study develops a dialogue closing system to exit the interaction. We then experimentally verified that the robot with the dialogue closing system performs better in the user’s perception of the robot (i.e. likeability, politeness, and dialogue satisfaction) than the control group. Further, by analyzing the dialogue between the human and the robot through CA, we propose to build a recursive, reflective model to improve the dialogue model design. A constructive approach urges us to reproduce complicated social phenomena in human-robot interaction so that we can investigate the underlying cognitive mechanisms of humans and create robots that can convey human-like cognition functions and coexist with humans. Taking such a constructive approach, we posit that our recursive model for dialogue systems that uses CA insights and then qualitatively analyzes conversational data can enhance the quality of dialogue systems because the model elucidates which properties of a conversation humans need to experience a conversational robot as human-like. Our study suggests that interactional morality - particularly conversational closings - is one property of human interactions that humans likely require social robots to adhere to.

Augmented Reality (AR) has the potential to transform product marketing by creating immersive and interactive experiences. This study presents the development of an AR-based application to enhance the marketing of outdoor equipment at Sekaben Camp, a camper rental company in Pangkalpinang, Bangka Belitung. The application allows users to visualize and interact with three-dimensional (3D) models of rental gear on their Android smartphones, making the selection process more engaging and informative. Using a prototyping approach—an iterative process of building and refining a preliminary model—the research includes gathering requirements, developing a prototype, coding the system, testing, and final deployment. Key features such as AR scanning, equipment ordering, and a price listing interface were designed to enhance product visualization and user engagement. User testing revealed that 85% of participants found the application intuitive and reported a more realistic understanding of the gear's size and functionality, resulting in a 30% increase in customer satisfaction during the rental process.

Semakin meningkatnya pengguna smartphone serta lengkapnya fitur-fitur yang tersedia di beragam merek smartphone, memungkinkan pengguna dengan mudah dalam bertransaksi hanya dengan sekali sentuhan layar. Mobile commerce adalah model layanan yang sedang berkembang sehingga, memungkinkan orang untuk memesan dan membayar barang dengan mudah menggunakan perangkat seluler. Pemesanan menu di Kantin disebuah institusi saat ini masih banyak menggunakan metode tradisional, yang mengakibatkan beberapa masalah: antrean pembayaran yang lama karena proses pengembalian dana, kasus pelanggan yang melarikan diri tanpa membayar, kekeliruan pesanan antara meja, pesanan yang terlupakan, utang siswa saat memesan, dan tidak adanya menu serta daftar harga di beberapa kantin. Penelitian ini mengembangkan sistem E-Kantin berbasis Android dengan teknologi NFC. Penelitian ini dikategorikan sebagai penelitian kualitatif yang dilakukan dengan metodologi Penelitian dan Pengembangan (R&D). Penggunaan Teknologi NFC sangat sesuai dalam objek riset ini karena setiap tag NFC memiliki identitas unik, sehingga duplikasi menjadi tidak mungkin. Selain itu, NFC beroperasi dalam jarak dekat, meningkatkan keamanan dengan membuat penyadapan menjadi sulit. Aplikasi ini memungkinkan pengguna untuk melakukan pemesanan, melakukan pembayaran untuk barang, dan mengisi saldo melalui metode tunai atau transfer. Secara bersamaan, admin dapat memverifikasi saldo, mengonfirmasi pesanan, menambah atau mengubah menu, dan menerima notifikasi terkait permintaan validasi saldo saat masuk sebagai admin. Aplikasi diujikan dengan metode blackbox. Hasil pengujian dan implementasi menunjukkan semua fungsionalitas sistem beroperasi dengan baik. Pengujian yang dilakukan dengan menempatkan pesanan simultan dari beberapa akun berhasil dieksekusi, dengan semua hasil pesanan tercatat dalam daftar pesanan bersama nomor pesanan masing-masing. Performa waktu rata-rata dalam mengakses setiap fitur adalah 2 Menit.

This study aims to develop a valid and feasible Android-based interactive e-module on the subject of salt hydrolysis to improve High order thinking skills of students. The research method used is Research and Development (R&D) with the ADDIE model. The research instrument is a validation test questionnaire and feasibility test with qualitative and quantitative data analysis techniques. The results of the expert validation test obtained an average value of 81.85% with Good criteria and in the feasibility test obtained an average value of 88.18% with Good criteria. So that the interactive android-based e-module on the material of salt hydrolysis to improve High order thinking skills is feasible and practical to use in learning.

The freshness or spoilage of meat is critical in terms of meat color and quality criteria. Detecting the condition of the meat is important not only for consumers but also for the processing of the meat itself. Meat quality is influenced by various pre-slaughter factors including housing conditions, diet, age, genetic background, environmental temperature, and stress factors. Additionally, spoilage can occur due to the slaughtering process, though post-slaughter spoilage is more frequent and has a stronger correlation with postslaughter factors. The primary indicator of meat quality is the pH value, which can be high or low. Variations in pH values can lead to adverse effects in the final product such as color defects, microbial issues, short shelf life, reduced quality, and consumer complaints. Many of these characteristics are visible components of quality. This study aimed to develop a mobile application using deep learning-based image processing techniques for the rapid detection of freshness. The attributes of the source and the targeted predictions were found satisfactory, indicating that further advancements could be made in developing future versions of the application.

Abstract The eyes play a special role in human communications. Previous psychological studies have reported reflexive attention orienting in response to another individual’s eyes during live interactions. Although robots are expected to collaborate with humans in various social situations, it remains unclear whether robot eyes have the potential to trigger attention orienting similarly to human eyes, specifically based on mental attribution. We investigated this issue in a series of experiments using a live gaze-cueing paradigm with an android. In Experiment 1, the non-predictive cue was the eyes and head of an android placed in front of human participants. Light-emitting diodes in the periphery served as target signals. The reaction times (RTs) required to localize the valid cued targets were faster than those for invalid cued targets for both types of cues. In Experiment 2, the gaze direction of the android eyes changed before the peripheral target lights appeared with or without barriers that made the targets non-visible, such that the android did not attend to them. The RTs were faster for validly cued targets only when there were no barriers. In Experiment 3, the targets were changed from lights to sounds, which the android could attend to even in the presence of barriers. The RTs to the target sounds were faster with valid cues, irrespective of the presence of barriers. These results suggest that android eyes may automatically induce attention orienting in humans based on mental state attribution.

The malicious infestations of Android malware caused huge economic losses to users over the past few years. Machine learning-based malware detection enhances the accuracy and partially mitigates these security threats. However, when the static or dynamic features cannot effectively represent software behavior, the accuracy of the model will be reduced. For this issue, a multi-features hybrid malware detection and category classification method HGDetector is proposed, this approach provides a more comprehensive representation of software behavior. HGDetector first extracts the software static function call graph and constructs the network behavior function call graph, then applies the dynamic network traffic features of the software to build the node interaction graph and edge-node graph; Subsequently, these features were fused and converted into a vector representation employing graph embedding method; Finally, combined with the proposed HGDetector, different classifiers were used to test the accuracy of malware detection and category classification. The experimental results demonstrate that the fusion of hybrid features can enhance malware detection accuracy by approximately 4 % when network traffic features effectively capture APP's behavior. Conversely, in cases where network traffic features alone are insufficient to represent software's network behavior, the application of hybrid features can improve malware detection accuracy by 21 %-26 %.

This research aims to develop Arabic reading skills learning media based on Role Playing Games. This research discusses efforts to increase interest and motivation in reading and understanding Arabic texts among students. This study applies the ADDIE model in the development of Role Playing Games-based learning media in MAN 1 Bogor in the Gaza semester of the 2023/2024 academic year. The Media Role Playing Games developed have been validated by media, language, and material experts. The validation results showed that this medium was very good, with a percentage of 97% of media experts, 86% of linguists, and 98% of material experts. In addition, the response from teachers also showed positive results with a percentage of 95%. User tests of students also gave satisfactory results, with one-on-one tests obtaining 86%, small group tests 94%, and large group tests 89%. Based on the results of validation and user tests, it can be concluded that the learning media of Arabic reading skills based on Role Playing Games is very feasible to use for grade XI MAN students. In addition, student responses show that this Arabic-based Role Playing Games media is interesting, easy to use, and can be accessed and played for free on smartphones.

The purpose of this research is to make an Android-based educational game to introduce endemic animals to the island of Borneo using Construct 2, the results of which can be useful for the community to increase knowledge about endemic animals on the island of Borneo. This educational game has a game of arranging animal name letters and multiple choice questions which are expected to hone the user's memory of endemic animals on the island of Borneo. The method used in this research is the Multimedia Development Life Cycle (MDLC) method are concept, design, material collecting, assembly, testing, and distribution. In this study, Black Box Testing was carried out and the result was that the game system ran well and its functionality could work smoothly. Based on the results of the questionnaire which was filled in by 30 respondents, the Endemic Animals of Kalimantan game was in the interval of strongly agreeing with the questionnaire and application category with a percentage of 85.42%, which means that the Endemic Animals of Kalimantan game is in demand by the public.

Nowadays, smartphones are used for various activities, including checking emails, paying bills, and playing games, which have become essential parts of daily life. Also, IoT devices can be managed and controlled using applications. While applications can provide numerous benefits, they have also led to several security risks, such as theft of data, eavesdropping, compromised data, and denial-of-service attacks. This study examines security breaches, attacks targeting Android system applications, and vulnerabilities present at every layer of the Android architecture. Additionally, the study aims to compare and evaluate various treatment methods to identify their advantages and disadvantages. Furthermore, the study aims to examine Android's architecture for weaknesses that might lead to app vulnerabilities and potential attacks. To achieve the objectives of this study, a comprehensive analysis of security breaches and attacks targeting Android system applications will be conducted. Various treatment methods will be compared and evaluated through rigorous examination. Additionally, Android's architecture will be thoroughly examined to identify potential weaknesses and vulnerabilities. The analysis will focus on identifying the security risks associated with the use of applications on smartphones and IoT devices. The vulnerabilities present at every layer of the Android architecture will also be analyzed. Furthermore, the advantages and disadvantages of various treatment methods will be assessed. The findings of this study will reveal the various security risks, vulnerabilities, and potential weaknesses present in Android system applications and the Android architecture. The advantages and disadvantages of different treatment methods will also be highlighted. This study contributes to the development of more precise and robust security measures for Android, aiming to mitigate security breaches, attacks, and vulnerabilities. By identifying weaknesses and vulnerabilities, this study provides valuable insights for improving the overall security of Android system applications. Doi: 10.28991/HIJ-2024-05-03-020 Full Text: PDF

Abstract In recent times, the number of malware on Android mobile phones has been growing, and a new kind of malware is Android ransomware. This research aims to address the emerging concerns about Android ransomware in the mobile sector. Previous studies highlight that the number of new Android ransomware is increasing annually, which poses a huge threat to the privacy of mobile phone users for sensitive data. Various existing techniques are active to detect ransomware and secure the data in the mobile cloud. However, these approaches lack accuracy and detection performance with insecure storage. To resolve this and enhance the security level, the proposed model is presented. This manuscript provides both recognition algorithms based on the deep learning model and secured storage of detected data in the cloud with a secret key to safeguard sensitive user information using the hybrid cryptographic model. Initially, the input APK files and data are preprocessed to extract features. The collection of optimal features is carried out using the Squirrel search optimization process. After that, the Deep Learning-based model, adaptive deep saliency The AlexNet classifier is presented to detect and classify data as malicious or normal. The detected data, which is not malicious, is stored on a cloud server. For secured storage of data in the cloud, a hybrid cryptographic model such as hybrid homomorphic Elliptic Curve Cryptography and Blowfish is employed, which includes key computation and key generation processes. The cryptographic scheme includes encryption and decryption of data, after which the application response is found to attain a decrypted result upon user request. The performance is carried out for both the Deep Learning-based model and the hybrid cryptography-based security model, and the results obtained are 99.89% accuracy in detecting malware compared with traditional models. The effectiveness of the proposed system over other models such as GNN is 94.76%, CNN is 95.76%, and Random Forest is 96%.

The improvement of data innovation has affected the advancement of human life, moving from ordinary strategies to present day computerized strategies. One application of data innovation in everyday life is online planning and booking. This investigate points to design an Android-based futsal field booking application utilizing the Primary Come To begin with Serve (FCFS) and Multilevel Input Line (MLFQ) calculations.This application is expected to energize clients in making futsal field reservations viably and effectively. The modify strategy utilized in this ask generally is Remarkable Programming (XP). The FCFS calculation was chosen for its straightforwardness in serving requests based on range arrange, though the MLFQ calculation endowments prioritization based on noteworthiness or specific criteria, engaging crucial bookings to be taken care of speedier. The comes approximately of this think approximately appear that the planned application capacities well concurring to client needs and gives ease inside the futsal field booking handle. By combining these two calculations, the application is expected to make a versatile and versatile reservation system, which isn't because it were compelling in directing booking lines but additionally sensible for all clients. The utilization of this application outlines exceptional potential in advancing the quality of futsal field booking organizations and can serve as a appear for making comparable applications in other ranges.

The Android malware has various features and capabilities. Various malware has distinctive characteristics. Ransomware threatens financial loss and system lockdown. This paper proposes a threat-assessing approach using the Grey Wolf Optimizer (GWO) to train and tune the Adaptive Neuro-Fuzzy Inference System (ANFIS) to categorize Android malware accurately. GWO improves efficiency and efficacy in ANFIS training and learning for Android malware feature selection and classification. Our approach categorizes Android malware as a high, moderate, or low hazard. The proposed approach qualitatively assesses risk based on critical features and threats. Our threat-assessing mechanism’s scale categorizes Android malware. The proposed approach resolves the issue of overlapping features in different types of malware. Comparative results with other classifiers show that the ensemble of GWO is effective in the training and learning process of ANFIS and thus achieves 95% F-score, 94% specificity, and 94% accuracy. The ensemble makes fast learning possible and improves classification accuracy.

Abstract Youth screen media activity is a growing concern, though few studies include objective usage data. Through the longitudinal, U.S.-based Adolescent Brain Cognitive Development (ABCD) Study, youth (mage = 14; n = 1415) self-reported their typical smartphone use and passively recorded three weeks of smartphone use via the ABCD-specific Effortless Assessment Research System (EARS) application. Here we describe and validate passively-sensed smartphone keyboard and app use measures, provide code to harmonize measures across operating systems, and describe trends in adolescent smartphone use. Keyboard and app-use measures were reliable and positively correlated with one another (r = 0.33) and with self-reported use (rs = 0.21–0.35). Participants recorded a mean of 5 h of daily smartphone use, which is two more hours than they self-reported. Further, females logged more smartphone use than males. Smartphone use was recorded at all hours, peaking on average from 8 to 10 PM and lowest from 3 to 5 AM. Social media and texting apps comprised nearly half of all use. Data are openly available to approved investigators ( https://nda.nih.gov/abcd/ ). Information herein can inform use of the ABCD dataset to longitudinally study health and neurodevelopmental correlates of adolescent smartphone use.

In recent times, the number of malware on Android mobile phones has been growing, and a new kind of malware is Android ransomware. This research aims to address the emerging concerns about Android ransomware in the mobile sector. Previous studies highlight that the number of new Android ransomware is increasing annually, which poses a huge threat to the privacy of mobile phone users for sensitive data. Various existing techniques are active to detect ransomware and secure the data in the mobile cloud. However, these approaches lack accuracy and detection performance with insecure storage. To resolve this and enhance the security level, the proposed model is presented. This manuscript provides both recognition algorithms based on the deep learning model and secured storage of detected data in the cloud with a secret key to safeguard sensitive user information using the hybrid cryptographic model. Initially, the input APK files and data are preprocessed to extract features. The collection of optimal features is carried out using the Squirrel search optimization process. After that, the Deep Learning-based model, adaptive deep saliency The AlexNet classifier is presented to detect and classify data as malicious or normal. The detected data, which is not malicious, is stored on a cloud server. For secured storage of data in the cloud, a hybrid cryptographic model such as hybrid homomorphic Elliptic Curve Cryptography and Blowfish is employed, which includes key computation and key generation processes. The cryptographic scheme includes encryption and decryption of data, after which the application response is found to attain a decrypted result upon user request. The performance is carried out for both the Deep Learning-based model and the hybrid cryptography-based security model, and the results obtained are 99.89% accuracy in detecting malware compared with traditional models. The effectiveness of the proposed system over other models such as GNN is 94.76%, CNN is 95.76%, and Random Forest is 96%. [ABSTRACT FROM AUTHOR]

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

[Objective] The number of effective tillers per plant is one of the important agronomic traits affecting rice yield. In order to solve the problems of high cost and low accuracy of effective tiller detection caused by dense tillers, mutual occlusion and ineffective tillers in rice, a method for dividing effective tillers and ineffective tillers in rice was proposed. Combined with the deep learning model, a highthroughput and low-cost mobile phone App for effective tiller detection in rice was developed to solve the practical problems of effective tiller investigation in rice under field conditions. [Methods] The investigations of rice tillering showed that the number of effective tillers of rice was often higher than that of ineffective tillers. Based on the difference in growth height between effective and ineffective tillers of rice, a new method for distinguishing effective tillers from ineffective tillers was proposed. A fixed height position of rice plants was selected to divide effective tillers from ineffective tillers, and rice was harvested at this position. After harvesting, cross-sectional images of rice tillering stems were taken using a mobile phone, and the stems were detected and counted by the YOLOv8 model. Only the cross-section of the stem was identified during detection, while the cross-section of the panicle was not identified. The number of effective tillers of rice was determined by the number of detected stems. In order to meet the needs of field work, a mobile phone App for effective tiller detection of rice was developed for real-time detection. GhostNet was used to lighten the YOLOv8 model. Ghost Bottle-Neck was integrated into C2f to replace the original BottleNeck to form C2f-Ghost module, and then the ordinary convolution in the network was replaced by Ghost convolution to reduce the complexity of the model. Based on the lightweight Ghost-YOLOv8 model, a mobile App for effective tiller detection of rice was designed and constructed using the Android Studio development platform and intranet penetration counting. [Results and Discussions] The results of field experiments showed that there were differences in the growth height of effective tillers and ineffective tillers of rice. The range of 52 % to 55 % of the total plant height of rice plants was selected for harvesting, and the number of stems was counted as the number of effective tillers per plant. The range was used as the division standard of effective tillers and ineffective tillers of rice. The accuracy and recall rate of effective tillers counting exceeded 99%, indicating that the standard was accurate and comprehensive in guiding effective tillers counting. Using the GhostNet lightweight YOLOv8 model, the parameter quantity of the lightweight Ghost-YOLOv8 model was reduced by 43%, the FPS was increased by 3.9, the accuracy rate was 0.988, the recall rate was 0.980, and the mAP was 0.994. The model still maintains excellent performance while light weighting. Based on the lightweight Ghost-YOLOv8 model, a mobile phone App for detecting effective tillers of rice was developed. The App was tested on 100 cross-sectional images of rice stems collected under the classification criteria established in this study. Compared with the results of manual counting of effective tillers per plant, the accuracy of the App's prediction results was 99.61%, the recall rate was 98.76%, and the coefficient of determination was 0.985 9, indicating the reliability of the App and the established standards in detecting effective tillers of rice. [Conclusions] Through the lightweight Ghost-YOLOv8 model, the number of stems in the cross-sectional images of stems collected under the standard was detected to obtain the effective tiller number of rice. An Android-side rice effective tillering detection App was developed, which can meet the field investigation of rice effective tillering, help breeders to collect data efficiently, and provide a basis for field prediction of rice yield. Further research could supplement the cross-sectional image dataset of multiple rice stems to enable simultaneous measurement of effective tillers across multiple rice plants and improve work efficiency. Further optimization and enhancement of the App's functionality is necessary to provide more tiller-related traits, such as tiller angle. [ABSTRACT FROM AUTHOR]

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

Smart security devices, such as smart locks, smart cameras, and smart intruder alarms are increasingly popular with users due to the enhanced convenience and new features that they offer. A significant part of this convenience is provided by the device's companion smartphone app. Information on whether secure and ethical development practices have been used in the creation of these applications is unavailable to the end user. As this work shows, this means that users are impacted both by potential third-party attackers that aim to compromise their device, and more subtle threats introduced by developers, who may track their use of their devices and illegally collect data that violate users' privacy. Our results suggest that users of every application tested are susceptible to at least one potential commonly found vulnerability regardless of whether their device is offered by a known brand name or a lesser-known manufacturer. We present an overview of the most common vulnerabilities found in the scanned code and discuss the shortcomings of state-of-the-art automated scanners when looking at less structured programming languages such as C and C++. Finally, we also discuss potential methods for mitigation, and provide recommendations for developers to follow with respect to secure coding practices. [ABSTRACT FROM AUTHOR]

Android malware has become an increasingly important threat to individuals, organizations, and society, posing significant risks to data security, privacy, and infrastructure. As malware evolves in sophistication and complexity, the detection and mitigation of these malicious software instances have become more challenging and time consuming since the required number of features to identify potential malware can be very high. To address this issue, we have developed an effective feature selection methodology for malware detection in Android. The critical concern in the field of malware detection is the complexity of algorithms and the use of features that are used to detect malware. The present paper delivers a methodology for pre-processing datasets to select the most optimal features that will allow detecting malware, while maintaining very high accuracy. The proposed methodology has been tested on two real world datasets and the results indicate that the number of features is significantly reduced from 489 to between 19 and 28 for the first dataset and from 9503 to between 9 and 27 for the second dataset, whilst the accuracy is maintained as if all features were used. [ABSTRACT FROM AUTHOR]

Inter-Component Communication (ICC) plays a crucial role in facilitating information exchange and functionality integration within the complex ecosystem of Android systems. However, the security and safety implications arising from ICC interactions pose significant challenges. This paper is an extended work building upon our previously published research that focuses on the verification of safety properties in the ICC mechanism. We address the previously observed issues of data leakage and privilege escalation by incorporating a sandbox mechanism and permission control. The sandbox mechanism provides an isolated and controlled environment in which ICC components can operate while permission control mechanisms are introduced to enforce fine-grained access controls, ensuring that only authorized entities have access to sensitive resources. We further leverage formal methods, specifically communicating sequential processes (CSP), to verify several properties of the enhanced ICC mechanism. By employing CSP, we aim to systematically model and analyze the flow of information, the behavior of components, and the potential vulnerabilities associated with the enhanced ICC mechanism. The verification results highlight the effectiveness of our approach in enhancing the security and reliability of ICC mechanisms, ultimately contributing to the development of safer and more trustworthy Android Systems. [ABSTRACT FROM AUTHOR]

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The majority of Android smartphone apps are free. When an application is used, advertisements are displayed in order to generate revenue. Adware-related advertising fraud costs billions of dollars each year. Adware is a form of advertising-supported software, that turns into malware when it automatically installs additional malware and adware on an infected device, steals user data, and exposes other vulnerabilities. Better techniques for detecting adware are needed due to the evolution of increasingly sophisticated evasive malware, particularly adware. Even though significant work has been done in the area of malware detection, the adware family has received very little attention. This paper presents a deep learning-based scheme called MadDroid to detect malicious Android adware based on static features. Moreover, this paper delivers a novel dataset that consists of malicious Adware and benign applications and an optimised Convolutional neural network (CNN) for detecting Adware infected by malware based on the permissions of the applications. The results indicate an average classification rate that is higher than previous work for individual adware family classification in terms of well-known evaluation metrics. [ABSTRACT FROM AUTHOR]

This study applies a forensic examination to six distinct Android fitness applications centered around monitoring running activities. The applications are Adidas Running, MapMyWalk, Nike Run Club, Pumatrac, Runkeeper and Strava. Specifically, we perform a post mortem analysis of each application to find and document artifacts such as timelines and Global Positioning System (GPS) coordinates of running workouts that could prove helpful in digital forensic investigations. First, we focused on the Nike Run Club application and used the gained knowledge to analyze the other applications, taking advantage of their similarity. We began by creating a test environment and using each application during a fixed period. This procedure allowed us to gather testing data, and, to ensure access to all data generated by the apps, we used a rooted Android smartphone. For the forensic analysis, we examined the data stored by the smartphone application and documented the forensic artifacts found. To ease forensic data processing, we created several Python modules for the well-known Android Logs Events And Protobuf Parser (ALEAPP) digital forensic framework. These modules process the data sources, creating reports with the primary digital artifacts, which include the workout activities and related GPS data. [ABSTRACT FROM AUTHOR]

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

Background/Objectives: Jump test assessment is commonly used for physical tests, with different type of devices used for its evaluation. The purpose of the present study was to examine the validity and reliability of a freely accessible mobile application (VertVision, version 2.0.5) for measuring jump performance. Methods: With that intent, thirty-eight college age recreationally active subjects underwent test assessment after a specific warm-up, performing countermovement jumps (CMJs) and squat jumps (SJs) on a contact platform while being recorded with a smartphone camera. Jump height was the criterion variable, with the same formula being used for both methods. Data analysis was performed by two experienced observers. Results: The results showed strong correlations with the contact platform (ICC > 0.9) for both jumps. Furthermore, between-observer reliability was also high (ICC > 0.9; CV ≤ 2.19), with lower values for smallest worthwhile change (≤0.23) and typical error of measurement (≤0.14). Estimation error varied when accounting for both observers, with the SJ accounting for bigger differences (4.1–6.03%), when compared to the CMJ (0.73–3.09%). Conclusions: The study suggests that VertVision is a suitable and handy method for evaluating jump performance. However, it presents a slight estimation error when compared to the contact platform. [ABSTRACT FROM AUTHOR]