Search

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.

Kopi merupakan salah satu jenis bahan baku minuman yang penting diseluruh dunia. Indonesia merupakan salah satu penghasil dan pengekspor kopi di dunia dan saat ini jumlah konsumsi kopi semakin meningkat.Untuk menjaga rasa kopi makan perlu menjaga kualitas biji kopi, pemerintah menetapkan Standar Nasional Indonesi (SNI) 01-2907-2008 tentang biji kopi. Proses penentuan kualitas mutu biji dilakukan oleh prosesor yang dimana akan melakukan langkah-langkah yang ada pada SNI secara manual. Oleh karena itu, untuk mempercepat proses penentuan mutu serta memberikan pengetahuan terhadap masyarakat biasa penulis membuat sebuah aplikasi deteksi mutu biji kopi berbasis mobile untuk menentukan grade biji kopi berbasis android. Dalam aplikasi ini akan dimuat sebuah model machine learning dengan basis deteksi objek. Untuk implementasi machine learning menggunakan tensorflow dan Convolutional Neural Network (CNN) sebagai arsitektur. Dari hasil data yang di training didapatkan rata-rata akurasi sebesar 84% yang menunjukkan bahwa aplikasi rata-rata mendeteksi dengan bener dan pengujian Selain itu pengujian jugaa dilakukan kepada beberapa smartphone dan jarak kedekatan mendeteksi. Dari hasil pengujian didapatkan bahwa jarak paling baik untuk melakukan deteksi adalah 15 cm untuk mendapatkan data yang sesuai dan terdeteksi dan smartphone yang paling tinggi untuk nilainya adalah Smartphone A30 dengan resolusi 16 MP.

Semua warga negara Indonesia berhak memperoleh pendidikan yang setara, termasuk anak-anak dengan kebutuhan khusus. Sekolah Luar Biasa (SLB) di Indonesia memberikan layanan pendidikan bagi siswa berkebutuhan khusus, salah satunya tunagrahita, yang memiliki tingkat IQ bervariasi dan memengaruhi kemampuan belajar, terutama dalam membaca. Metode pembelajaran membaca konvensional masih sering digunakan di SLB, membutuhkan kesabaran tinggi dan bergantung pada kemampuan guru. Untuk mengatasi kendala ini, dikembangkan aplikasi media pembelajaran membaca berbasis Android sebagai alternatif. Aplikasi ini memuat materi membaca, latihan soal, audio, gambar, manajemen siswa, dan catatan riwayat belajar. Proses pengembangan meliputi analisis kebutuhan pengguna, perancangan, implementasi, dan pengujian. Bahasa pemrograman Kotlin digunakan untuk pengembangan aplikasi, sementara Firebase digunakan sebagai basis data. Pengujian menunjukkan aplikasi ini telah memenuhi standar kualitas melalui beta testing, responsive testing, instrumentation testing, dan user acceptance testing. Aplikasi ini diharapkan menjadi alat bantu efektif bagi guru dalam meningkatkan keterampilan membaca siswa tunagrahita.

This article examines the process of building user interfaces (UI) using two leading mobile application development tools: Android and Flutter. By developing common login interfaces, the UI construction between these platforms was compared and contrasted. The study highlights key differences in UI elements, design paradigms, and performance metrics, offering a comprehensive analysis. A detailed experimental plan to assess and compare UI development in Flutter and Android was designed, followed by performance evaluations based on metrics such as application size, runtime speed, and memory usage. The findings aim to help beginners quickly grasp and master these tools. Furthermore, the implications for developers, particularly regarding code complexity, reusability, and the overall development experience were discussed. This research contributes to the ongoing discourse on mobile app development best practices, helping guide tool selection for specific project requirements.

Tekanan darah merupakan tekanan yang ditimbulkan pada pembuluh nadi atau arteri ketika darah dipompa dari jantung ke seluruh tubuh. Nilai dari tekanan darah dapat digunakan untuk dijadikan sebagai acuan dalam menetukan kondisi kesehatan tubuh. Untuk melakukan pengecekan terhadap nilai tekanan darah dapat dilakukan dengan menggunakan metode invasive dan non-invasive. Pengecekan tekanan darah perlu dilakukan secara langsung oleh tenaga medis dan pasien. Dengan perkembangan teknologi yang semakin pesat pengecekan ini dapat dilakukan oleh tenaga medis tanpa perlu bertemu langsung dengan pasien.Untuk melakukan monitoring ini dapat memanfaatkan smartphone untuk menampilkan informasi monitoring terhadap tekanan darah dari orang lain tanpa perlu melakukan pengecekan langsung menggunakan alat terhadap orang lain. Pada penelitian ini digunakan metode non-invasive dengan memanfaatkan pembacaan sinyal osilometrik dari perubahan tekanan pada cuff yang terbaca oleh sensor tekanan MPX5500DP untuk melakukan pembacaan tekanan darah yang kemudian ditampilkan pada perangkat android. Dengan menggunakan pendekatan menggunakan sinyal osilometrik ini akan diproses pada mikrokontroler Arduino UNO R3 untuk mencari nilai Mean Arterial Pressure, systole dan diastole menggunakan metode Maximum Amplitude Algorithm (MAA). Pada alat yang dikembangkan nilai hasil pemprosesan menggunakan metode MAA akan dikirimkan menggunakan modul bluetooth HC-05 untuk ditampilkan pada perangkat android. Pada pengujian sistem, didapatkan tingkat akurasi pembacaan nilai systole sebesar 78.23% dan tingkat akurasi pembacaan diastole sebesar 88.39% yang dibandingkan dengan pembacaan menggunakan tensimeter manual. Abstract Blood pressure is the pressure that exerted on the veins or arteries when blood is pumped from the heart to the rest of the body. The value of blood pressure can be used as a reference in determining the health condition of the body. To check the blood pressure values can be done using invasive and non-invasive methods. Checking blood pressure needs to be done directly by medical personnel and patients. With the rapid development of technology, this medical check can be done by medical personnel without the need to meet directly with patients. To do this medical monitoring, a smartphone can be used to display monitoring information on blood pressure from others without the need to check directly using the tool against others. In this study writer used a non-invasive method by utilizing oscillometric value readings of pressure changes in the cuff are read by the MPX5500DP pressure sensor to perform blood pressure readings are then displayed on the android device. By using this oscillometric value approach will be processed on the Arduino UNO R3 microcontroller to find the value of Mean Arterial Pressure, systole and diastole using the Maximum Amplitude Algorithm(MAA). In the developed tool, the result value of the processing using the MAA method will be sent using the bluetooth module HC-05 to be displayed on the android device. In testing the system, obtained the reading accuracy rate of systole value of 78.23% and diastole reading accuracy rate of 88.39% compared with readings using a manual tensimeter.

Smartphone telah menjadi bagian tak terpisahkan dari kehidupan manusia. Namun, penggunaan berlebihan dari smartphone dapat membawa dampak negatif dan dapat merugikan penggunanya seperti penurunan fungsi kognitif dan adiksi yang menyebabkan pengguna dapat gagal fokus. Penelitian ini bertujuan untuk mengurangi kurangnya fokus pada remaja baik pelajar SMP, SMA, dan mahasiswa dengan mengimplementasikan sistem fokus timer pomodoro dengan tracking penggunaan smartphone sesuai analisis kebutuhan yang dilakukan terlebih dahulu. Analisa kebutuhan yang dilakukan menitikberatkan pada pembangunan perangkat lunak dengan menentukan kebutuhan pengguna lewat penggalian kebutuhan menggunakan kuisoner yang dilakukan oleh 50 orang dan wawancara terhadap 8 orang. Penelitian ini mengadaptasi metode waterfall dengan tahapan studi kepustakaan, analisis kebutuhan, perancangan, implementasi, pengujian dan analisa, serta kesimpulan dan saran. Pada setiap tahapannya, diintegrasikan konsep gamifikasi sebagai konsep untuk memicu pengguna agar terus menggunakan aplikasi. Digunakan pengembangan cross-platform untuk mempermudah peneliti dalam melakukan pengembangan pada Android. Sistem diuji menggunakan pengujian blackbox menggunakan uji validasi dengan hasil keberhasilan 100%, pengujian usability menggunakan kuisoner SUPR-Qm dengan skor 84,87, dan pengujian compatibility dengan kompatibilitas aplikasi bisa dijalankan pada Android dengan API level 21 sampai API level 29. Abstract Smartphones have become an inseparable part of human life. However, excessive use of smartphones can have negative impacts and can harm users such as decreased cognitive function and addiction which causes users to fail to focus. This study aims to reduce the lack of focus on adolescents, both junior high school, high school, and university students by implementing a pomodoro focus timer system by tracking smartphone usage according to the needs analysis that was carried out beforehand. The software development analysis involved identifying user needs through a questionnaire and interviews with 50 and 8 individuals, respectively.. This research adapts the waterfall method with the stage of literature study, analysis needs, design, implementation, testing and analysis, as well as conclusions and suggestions. At each stage, the concept of gamification is integrated as a concept to trigger users to continue using the application. Cross-platform development is used to make it easier for researchers to develop on Android. The system was tested using blackbox testing using a validation test with 100% success results, usability testing using the SUPR-Qm questionnaire with a score of 84,87, and compatibility testing with application compatibility running on Android with API level 21 to API level 29.

Tujuan dalam penelitian ini ialah untuk mengetahui validitas praktikalitas, dan efektifitas media pembelajaran bahasa inggris berbasis Android menggunakan Adobe Flash. Jenis penelitian ini adalah penelitian dan pengembangan (Reseach and Development) model penelitian ADDIE terdiri dari 5 tahap pengembangan yaitu Analysis, Design, Development, Implementation, and Evaluation. Subjek dalam penelitian ini adalah siswa kelas V SDN 106809 Kolam yang berjumlah 26 siswa. Teknik pengumpulan data dalam penelitian ini menggunakan observasi, angket, dan soal tes. Hasil dalam penelitian pengembangan media pembelajaran ini menunjukkan hasil validasi oleh validator ahli materi memperoleh skor 46 dengan persentase 92% kategori “sangat layak” , hasil validasi oleh ahli media memperoleh skor 85 dengan persentase 94,4% kategori “sangat layak”, serta hasil uji praktikalitas memperoleh skor 56 dengan persentase 93,3% kategori “sangat praktis”. Berdasarkan uji keefektifan media pembelajaran bahasa inggris berbasis android yang dikembangkan, nilai siswa mengalami peningkatan dari sebelum menggunakan media pembelajaran, siswa mendapat nilai dengan rata-rata 48,53% meningkat menjadi 91,35%, nilai rata-rata meningkat sebesar 42,82%. Dari hasil peneitian dan data yang diperoleh menunjukkan bahwa pengembangan media pembelajaran bahasa inggris berbasis android ini sudah sangat layak digunakan dalam proses pembelajaran di sekolah.

Philip K. Dick’s science fiction classic Do Androids Dream of Electrics Sheep? and its adaptation movie Blade Runner by Ridley Scott feature a devastated earth where bounty hunter Rick Deckard stalks authentic human replicants, designed for a short term highly flexible labor power. A group of these androids infiltrate to the earth of the productive apparatus which manufactured them, trying to persuade their maker to re-program their genetic makeup. Biopolitics as conceived by Roberto Esposito is apt at exploiting the reserves of sense present in Dick’s critical scenario, and managing the mixing of languages of politics and biology, which originally were kept apart in the dystopic dimension of the novel and in the political philosophy tradition. Discarding the frontal approach to the categories of politics, Esposito urges to interrogate the categories of politics obliquely, thus entering the hidden layers of their meaning, fostering an innovative coexistence of opposites.

The advancement of information technology aimed to enhance efficiency in information management, including rapid and timely processing as well as accuracy of information. Resto Sampali (Sp) Kitchen frequently encountered challenges in manually recapitulating data due to the necessity of recording sales and purchases individually each day, stored in logbooks. This situation led to inefficient transaction management, resulting in a lack of information regarding product sales, customers, peak hours, and other crucial data essential for strategic decision-making. Furthermore, incomplete transaction records caused uncertainty in tracking income and expenditure flows, thereby increasing the risk of human error and fraud. Manual recording methods also jeopardized data integrity and validity. This research aimed to address these issues by constructing an Order and Payment Management Information System integrated within an Android-based Cashier Service, utilizing a qualitative approach and the waterfall system development method. Results from system testing via Blackbox Testing indicated that the system operated in accordance with the anticipated design, enhancing accuracy in transaction management, revenue, and payments, while delivering a more satisfying experience for customers and café owners.

Malware remains a big threat to cyber security, calling for machine learning-based malware detection. Malware variations exhibit common behavioral patterns indicative of their source and intended use to enhance the existing framework’s usefulness. Here we present a novel model, i.e., African Vulture Optimization-based Decision Tree (AVO-DT) to increase the overall optimization.

This study addresses the pervasive concern surrounding battery performance degradation in electronic devices. While some attribute this decline to device aging, a significant portion of the population lacks awareness of the precise factors contributing to diminished battery efficiency. Consequently, the research investigates the factors related to battery performance, aiming to identify the determinants of reduced efficiency. Decision trees are used to meticulously analyze the intricate relationships between variables and discern the factors that respondents perceive as causative of diminished battery performance. This algorithm is chosen since, in predicting high-capacity lithium-ion battery performance, the decision tree outperforms other algorithms in machine learning in accuracy. The study elucidates diverse user preferences, with 55.38% favoring Android and 44.62% expressing a preference for iOS, indicating disparate perceptions of battery health: 61.54% consider their batteries as "Good," while 38.46% acknowledge a decline. The decision tree analysis of 195 participants underscores the pronounced impact of prolonged usage on battery health, revealing that 95% maintain good battery performance. In contrast, 27.69% of Android users face reduced battery performance, emphasizing the need for targeted user education and Android manufacturers to prioritize device longevity. The ultimate objective is to give readers a comprehensive understanding of the dynamics of battery performance in the context of device aging and its contributing factors and give some input to manufacturers and service providers.

Abstract A growing dependence on real-time positioning apps for navigation, safety, and location-based services necessitates a deep understanding of latency challenges within cloud-based Global Navigation Satellite System (GNSS) solutions. This study analyses a GNSS real-time positioning app on smartphones that utilizes cloud computing for positioning data delivery. The study investigates and quantifies diverse latency contributors throughout the system architecture, including GNSS signal acquisition, data transmission, cloud processing, and result dissemination. Controlled experiments and real-world scenarios are employed to assess the influence of network conditions, device capabilities, and cloud server load on overall positioning latency. Findings highlight system bottlenecks and their relative contributions to latency. Additionally, practical recommendations are presented for developers and cloud service providers to mitigate these challenges and guarantee an optimal user experience for real-time positioning applications. This study not only elucidates the complex interplay of factors affecting GNSS app latency, but also paves the way for future advancements in cloud-based positioning solutions, ensuring the accuracy and timeliness critical for safety–critical and emerging applications.

To support the management of large-scale area potential such as rural spatial planning, Kaligentong Village uses area mapping as an effort in village development planning. The mapping is displayed in paper media which has limited access so that this is inefficient and hinders the Kaligentong Village government in managing the assets and potential contained in it. Therefore, an information system is needed that can monitor the assets and potential of Kaligentong Village in the form of web-based digital maps and android. This study aims to create a dynamic digital mapping of villages for Kaligentong Village so that it is more efficient in finding and managing information related to geographical conditions and potential of Kaligentong Village. The method used is waterfall which consists of the stages of analysis, planning, modeling, construction, and testing. The system is built using javascript leaflets, native PHP, QGIS, and MySQL databases. Based on data from black box testing, the village digital map application runs according to the functional system. The satisfaction rate of application and web users is 94.10%. The average response time per activity generated by the application was 1.44s at 96 Mbps bandwidth and 0.89s at 144 Mbps bandwidth for websites.

Coffee is the most consumed type of drink in the world. Each type of coffee has different physical characteristics from leaves, fruits to seeds. Now technology is needed in the world of agriculture in making decisions. To determine the type of coffee with fission characteristics, there are still many people who do not understand in distinguishing the physical characteristics of coffee plants. In this case, an application was developed using the RAD method by utilizing the flutter framework and the Convolutional Neural Network model that has been trained. The pre-train model used is NasNet Mobile with a dataset of 900 photos and 100 epochs with early-stopping utilization and heti at epoch 55 with an accuracy of 90.67%. In this study, implementing existing models into Android applications using the Flutter framework. With the implementation process carried out by the application can help the detection process using an android device. The implementation results get good test results with a score of 0.97. This application can help the process of identifying the type of coffee and minimize errors in identifying directly.

Este artigo explora a limitada crítica cultural sobre séries de ficção científica no Brasil. A cultura contemporânea de séries, caracterizada por narrativas complexas e modos dinâmicos de consumo, requer uma nova abordagem adaptada às plataformas digitais. Embora tal aspecto tenha avançado, a disseminação da crítica sobre séries ainda é insatisfatória. Assim, o objetivo desta investigação é demonstrar que a complexidade narrativa da ficção científica torna a produção de críticas um processo intrincado, adotando-se como estudo de caso a série Westworld. Metodologicamente, foi realizada uma revisão bibliográfica, levantamento de críticas em plataformas e análise fílmica da série. O artigo propõe duas outras explicações para a escassez de críticas além da complexidade das narrativas de ficção científica: o atendimento mercadológico das expectativas da audiência e o foco da crítica tradicional em gêneros mais próximos da realidade, como o melodrama e o humor.

Lidah merupakan bagian dari organ muscular yang memiliki peran penting dalam sistem oral (mulut) dan maksilofasial (rahang, leher, dan wajah). Dengan melakukan latihan gerakan lidah bisa membantu terapi pasien dengan penyakit tertentu, seperti sleep apnea. Pada penelitian ini akan dirancang dan dikembangkan aplikasi LTL berbasis Android dan Web, dengan menggunakan alat LTL yang telah dirancang oleh Novie, dkk. Aplikasi berbasis Android di rancang untuk dapat membantu memonitor hasil keluaran sensor FSR dari alat LTL dari sisi pengguna dan aplikasi berbasis Web dirancang untuk memantau kemajuan dari proses LTL yang telah dilakukan dari sisi tenaga medis. Jenis gerakan latihan lidah yang digunakan pada penelitian ini adalah Tongue Slide-1, Tongue Slide-2, dan Tongue Press. Pada aplikasi Android akan ditunjukkan data identitas pasien, data hasil pembacaan tekanan lidah, dan data repetisi gerakan. Desain aplikasi Android LTL yang telah berhasil direalisasikan, menyajikan fitur-fitur untuk membatnu kegiatan pematauan hasil pembacaan latihan tekanan lidah. Hasil pengujian subjektif tampilan dan kemudahan aplikasi android LTL dan web LTL, mendapatkan penilaian yang baik, karena semua penilaian nilainya berada pada indeks 86.3%-92,5%. Sedangkan dari hasil skenario percobaan untuk Pengujian Sistem LTL, bahwa rata-rata tekanan lidah dari seluruh responden yang dilakukan dalam dua kali percobaan untuk gerakan tongue press, gerakan tongue slide-1 dan gerakan tongue slide-2, diatas batas ambang yang ditetapkan yaitu diatas 500 gf. Penelitian lanjutan untuk skenario percobaan dan penggunaan aplikasi ini bisa diterapkan untuk pasien sebenarnya.

In order to study the financial management mode and innovation of electric power enterprises under the current situation, a research method of electric power financial management innovation in the era of Internet of Things is proposed. According to the network topology of the system and the analysis of the Android system, the client of the financial management system, the main functional modules of the system and the data warehouse are designed. The client of the financial management system will divide the client into an information interaction unit and a data calculation unit according to the actual needs of the client; the main functional modules of the system are divided into account management unit, salary management unit, project management unit, system management unit, and fixed asset management unit. Unit, revenue and expenditure management unit and report management unit, the sub-units of each unit are divided into brief analysis, and the outpatient registration fee management module and inpatient fee management module of the revenue and expenditure management unit are introduced in detail; in the hospital financial data warehouse, in the application layer An interactive platform is built between it and the data warehouse, and the module is integrated into the existing data warehouse to realize the processing, storage and operation of massive data. The proposed system has a large operating safety factor, high user satisfaction, and an average system flexibility factor of 0.94. The power financial management information platform based on the Internet of Things has a very good performance in the application. Therefore, the method in this paper can enhance the work efficiency of electric power enterprises and enhance the core competitiveness of electric power enterprises.

The Android system has good compatibility, freedom, rich hardware resources and a good development environment. At the same time, it can also work with Google services to obtain external support. Android is an open source operating system platform, which is similar to the iOS platform. In contrast, developers have more room for development, making the Android platform more people use. This article aims to develop a mobile medical system based on the Android operating system that integrates search engines, maps, and hospital HIS systems. The users are mainly patients, doctors and system administrators. Patients can register online and use mobile healthcare to pay online. They can also upload various physical indicators to record their daily health status. The establishment of health files is convenient for doctors to make health assessments and provide patients with medical consultation and medical health protection. Patients can also realize hospital navigation, intra-hospital navigation, and view various hospital inspection reports through the APP. The design of mobile medical service system is to design user behavior in the system based on the concept of service design. Therefore, in the process of system design, it is necessary to comprehensively consider service design principles and related factors, and propose behavior design principles that conform to service design theory. This study uses a mutual continuation care platform to implement seamless care for patients. Patients and medical staff can realize the continuation of the relationship and the continuity of information, and the patients can receive comprehensive guidance in a timely and effective manner. It can provide a reference basis for improving the continuity management and information management of patients in China, and provide reference value for the standardization and standardization of patients' continuation of nursing care standards and procedures.

Background Problems: The development of technology and sports science plays a crucial role in the world of sports. Research Objectives: The objective of this research is to create a volleyball smash drill training tool based on voice reaction in the form of an Android application as an innovation and to determine the level of impact produced by the tool. This volleyball smash training tool is equipped with a voice-reacting Android component that can be adjusted according to the user’s needs. Methods: The research employs both qualitative and quantitative research methods using the Research and Development (R&D) approach, encompassing stages such as needs analysis, development planning, expert validation, small-group testing, large-group testing, revisions, and final product development. To assess the effectiveness of the tool, experiments are conducted using the tool during training sessions. Data collection is carried out through observations, interviews, and questionnaires. Both quantitative and qualitative analyses are utilised for data analysis. Findings and Results: The research findings indicate that the Android-based voice command-reactive Acuspike volleyball product is deemed appropriate and can significantly contribute to athlete development, improving game quality, and overall volleyball sports development. However, the limitation of the research lies in the subject being studied at one location. Therefore, further research is recommended for broader generalisation over the long term. Conclusion: In conclusion, volleyball smash skills can be enhanced using the Acuspike volleyball smash tool, emphasising the importance of considering the use of Acuspike tools in both practicing and learning volleyball smash techniques.

The development of technology today has a great influence on human life. This influence also includes the use of technology in education, such as the utilization of learning media connected to information technology. This research was conducted to validate the use of learning media in the form of Android applications with the help of iSpring Suite in learning Pythagorean Theorem material. The research method used is RnD by applying the ADDIE model. The research subjects consisted of 3 experts, one practitioner, and 5 grade VIII students. Data collection involved qualitative and quantitative data. The results of the analysis showed that the experts gave an average assessment of 92.09% with the category "Very Valid." Trials conducted on practitioners and small groups also showed a "Very Valid" assessment with an average of 90%. Overall, it can be concluded that the use of Android-based learning media with iSpring Suite is appropriate and effective in achieving learning objectives. With this learning media that has been proven valid and effective, it can be an interesting alternative to improve the quality of mathematics learning in the classroom. Integrating technology in learning can also provide a more interactive and interesting learning experience for students. Thus, the results of this study make a positive contribution to answering the challenges of education in the digital era, enriching learning methods, and increasing the attractiveness of learning materials.

Abstract: Android-based learning media with the Ispring suite is a solution for today's educators to develop media that is relevant to the pace of development. This research developed android-based mathematics learning media with Ispring Suite online and angle material. The development method uses a 4D model with the subject of research of grade 7C students of SMPN 1 Lawang, Malang Regency, East Java. The instruments used are checklist sheets and questionnaires. The results showed the validity of learning media by 85% with a percentage of media attractiveness of 89%. Based on these two things, Android-based mathematics learning media with the Ispring suite is worth using. Abstrak: Media pembelajaran berbasis android dengan Ispring suite menjadi solusi bagi para pendidik masa kini untuk mengembangkan media yang relevan dengan laju perkembangan zaman. Penelitian ini mengembangkan media pembelajaran matematika berbasis android dengan Ispring suite pada materi garis dan sudut. Metode pengembangan menggunakan model 4D dengan subjek penelitian siswa kelas 7C SMPN 1 Lawang Kabupaten Malang Jawa Timur. Instrumen yang digunakan lembar cek list dan angket. Hasil penelitian menunjukkan validitas media pembelajaran sebesar 85% dengan persentase kemenarikan media sebesar 89%. Berdasarkan kedua hal tersebut, media pembelajaran matematika berbasis android dengan Ispring suite layak digunakan.

Abstract Water is the most basic need for humans and a source of livelihood for humans. Lack of human awareness to maintain water quality, causing water to become polluted, by both industrial and household waste, impacts on human health and material loss. Thus, it is important to create technology that can monitor water pollution automatically and quickly. This research aims to create a system which utilizes the Internet of Things (IoT) technology that can facilitate quality of water by measuring parameters such as pH and turbidity. The methodology of the system progresses by the usage of a controller which is ATmega328P-AU, pH sensor to measure acidity, turbidity sensor to measure turbidity level, LPWAN LoRa works like a communication of data transmission as well as cloud service, namely Antares, to store data that are sent via Android. Based on the outcomes, the proposed system has achieved a reliable accuracy with percentage error of 99.73% in pH sensor and 99.41% in the turbidity sensor. Also, 2.6 s is the average required time to deliver the results to the cloud service.

Abstract Background Being overweight or obese has become a serious public health concern, and accurate assessment of body composition is particularly important. More precise indicators of body fat composition include visceral adipose tissue (VAT) mass and total body fat percentage (TBF%). Study objectives included examining the relationships between abdominal fat mass, measured by quantitative computed tomography (QCT), and the whole-body and regional fat masses, measured by dual energy X-ray absorptiometry (DXA), as well as to derive equations for the prediction of TBF% using data obtained from multiple QCT slices. Methods Whole-body and regional fat percentage were quantified using DXA in Chinese males (n = 68) and females (n = 71) between the ages of 24 and 88. All the participants also underwent abdominal QCT measurement, and their VAT mass and visceral fat volume (VFV) were assessed using QCT and DXA, respectively. Results DXA-derived TBF% closely correlated with QCT abdominal fat percentage (r = 0.89–0.93 in men and 0.76–0.88 in women). Stepwise regression showed that single-slice QCT data were the best predictors of DXA-derived TBF%, DXA android fat percentage and DXA gynoid fat percentage. Cross-validation analysis showed that TBF% and android fat percentage could be accurately predicted using QCT data in both sexes. There were close correlations between QCT-derived and DXA-derived VFV (r = 0.97 in men and 0.93 in women). Conclusion Clinicians can assess the TBF% and android and gynoid fat percentages of Chinese women and men by analysing existing abdominal CT-derived data using the QCT technique.

Breast cancer (BC) continues to be the primary cause of high mortality rates among women globally. Early and automated detection of this disease plays a significant role in clinical standards for better diagnosis and improved survival rates. Ultrasonography is a widely used non-invasive imaging test to diagnose BC. The traditional ultrasound-based automated diagnostic systems for detecting BC rely on cloud-based processing, which has high latency, requires constant internet connectivity, and raises the privacy of patient’s ultrasound image data. This paper proposes a lightweight deep convolutional neural network (LWDCNN) implemented on edge devices (field programmable gate array (FPGA) and android devices) for real-time detection of BC using breast ultrasound (BUS) images. FPGA is used due to its parallel processing capability, low latency, and low power for the real-time processing of BUS images. Similarly, the Android device provides portability and a user-friendly system to process BUS images for automated detection of BC. The proposed LWDCNN is trained on the Google Cloud CPU-based framework to obtain the optimized model with weight and bias parameters. The compression methods (pruning and fixed-point precision-based representation of weight values) are applied to the LWDCNN model, and the inference of this model is performed on Android and PYNQ-Z2 FPGA-based edge devices to detect malignant tumor classes using BUS images. The proposed approach is evaluated using BUS images from Kaggle and breast ultrasound imaging databases. The experimental results reveal that the LWDCNN achieved average accuracy values of 94.15% on Google Cloud CPU and 93.16% on FPGA-based edge devices for detecting malignant tumors in the inference phase using hold-out validation. Similarly, the post-training quantization of LWDCNN with an integer 8-bit (INT8) case implemented on an Android device has yielded an accuracy value of 93.76% for detecting malignant tumors using BUS images. The presented deep-learning approach has demonstrated higher classification accuracy than different transfer-learning techniques and existing methods using the BUS images from the same database.

The need to detect and classify malware on Android devices has become crucial due to the widespread use of these devices daily. This paper presents a method for detecting and classifying malware that uses static and dynamic features. Specifically, we extract permissions and intent filters from static analysis files and APK API data from dynamic analysis files. Then, we use Simhash to encode the selected parts of the analysis files to create feature vectors. These vectors are then used to train different Machine Learning algorithms for detecting and classifying malware. We conducted several experiments to evaluate the effectiveness of our approach on a dataset of 38,355 labeled Android apps and tested various hash functions and tokenizing methods in the Simhash algorithm. Our results showed that using Random Forest as a classifier, SHA-512 the hash function and 2-WORD tokenization led to high accuracy with reduced time and memory requirements for training, validating, and testing. This indicates a highly accurate model for detecting and classifying malware into 24 families.

Android’s open-source nature has contributed to the platform’s rapid growth and its widespread adoption. However, this widespread adoption of the Android operating system (OS) has also attracted the attention of malicious actors who develop malware targeting these devices. Android malware threatens users’ privacy, data security, and overall device performance. Machine learning (ML) plays a significant role in malware analysis and detection because it can process huge amounts of data, identify complex patterns, and adjust to changing threats. The purpose of this paper is to provide a comprehensive review of the existing research on ML-based techniques used to detect and analyze Android malware. In this paper, the security weaknesses in Android OS are explored and the reasons why these weaknesses do not exist in the iPhone operating system (iOS) are discussed. Further, the authors examine the existing studies that have been proposed by researchers and outlines their strengths and limitations. The findings reveal that the existing researches utilize different ML models, features, and detection techniques, including static, dynamic, and hybrid approaches. Moreover, directions for future research and potential areas that require more attention and improvement in this field are highlighted.

Adversarial attacks pose significant threats to Android malware detection by undermining the effectiveness of machine learning-based systems. The rapid increase in Android apps complicates the management of malicious software that can compromise user defense solutions. Many current Android defense techniques rely on deep learning methods. Malicious users exploit GAN-based attacks to achieve adversarial attack transferability and deceive target models by crafting adversarial examples based on known models. We propose a new model based on a Cycle Generative Adversarial Network (CycleGAN) to detect GAN-based attacks. This model incorporates a gradient penalty to enhance the detection rate of the target model. Our investigation focuses on a gray box scenario, where the attacker has partial information about the model. The results show that our model outperforms existing classifiers in detecting adversarial attacks.

As the leading mobile operating system, Android powers critical infrastructure and personal devices across sectors such as finance and healthcare and a wide range of user scenarios. However, its open-source nature presents considerable security challenges. Exploiting vulnerabilities in native and custom permissions, malicious API calls, intents, signatures, and manifests, threat actors can gain access to sensitive data and device control. The continuously evolving landscape of Android malware necessitates robust and generalized detection methods. While traditional machine learning (ML) models have been employed to address this issue, they have limitations. Focusing on single datasets can impede both generalization and effective detection. Further, the dynamic nature of malware renders many traditional methods inadequate for providing comprehensive and real-time protection. This paper addresses this critical need by proposing DeepImageDroid, an advanced and efficient deep learning (DL) framework for Android malware detection. DeepImageDroid harnesses the combined power of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), utilizing three diverse Android malware datasets. This hybrid approach significantly improves detection accuracy and model generalization compared to existing solutions. By employing the weighted average ensemble method, DeepImageDroid achieved a remarkable 96% accuracy.

Website clicks that redirect Android-phone users to malicious websites with fake virus alerts or phishing attacks are increasing exponentially. Although a uniform resource locator (URL) blocklist is considered a suitable countermeasure to such attacks, it is difficult to efficiently identify malicious websites. To the best of our knowledge, no research has focused on detecting attacks that redirect Android-phone users to malicious websites. Therefore, we propose a redirect-detection method that focuses on the URL bar-switching interval of Android-based Google Chrome browser. The proposed method, which can be easily installed as an Android application, uses the Android accessibility service to detect unintended redirects to malicious websites without collecting information about these websites in advance. This paper details the design, implementation, and evaluation results of the proposed application on an actual Android device. We determined the threshold values for the number of times the URL bar switches and the elapsed time to determine redirects to malicious websites for the proposed method. Based on the results, we investigated the causes of false-positive detection of redirects to benign websites and offer solutions on handling them. We also present the threshold values that can minimize the false positive and negative rates, as well as the detection accuracy of the proposed method based on these threshold values. Additionally, we present the evaluations results based on the access logs of actual users participating in the WarpDrive project experiment, which indicate that the proposed method minimizes false positives and successfully detects most redirects to malicious websites.

Push notifications are widely used in Android apps to show users timely and potentially sensitive information outside the apps’ regular user interface. Google’s default service for sending push notifications, Firebase Cloud Messaging (FCM), provides only transport layer security and does not offer app developers message protection schemes to prevent access or detect modifications by the push notification service provider or other intermediate systems. We present and discuss an in-depth mixed-methods study of push notification message security and privacy in Android apps. We statically analyze a representative set of 100,000 up-to-date and popular Android apps from Google Play to get an overview of push notification usage in the wild. In an in-depth follow-up analysis of 60 apps, we gain detailed insights into the leaked content and what some developers do to protect the messages. We find that (a) about half of the analyzed apps use push notifications, (b) about half of the in-depth analyzed messaging apps do not protect their push notifications, allowing access to sensitive data that jeopardizes users’ security and privacy and (c) the means of protection lack a standardized approach, manifesting in various developer-defined encryption schemes, custom protocols, or out-of-band communication methods. Our research highlights gaps in developer-centric security regarding appropriate technologies and supporting measures that researchers and platform providers should address.

Abstract Fine-grained function-level encrypted traffic classification is an essential approach to maintaining network security. Machine learning and deep learning have become mainstream methods to analyze traffic, and labeled dataset construction is the basis. Android occupies a huge share of the mobile operating system market. Instant Messaging (IM) applications are important tools for people communication. But such applications have complex functions which frequently switched, so it is difficult to obtain function-level labels. The existing function-level public datasets in Android are rare and noisy, leading to research stagnation. Most labeled samples are collected with WLAN devices, which cannot exclude the operating system background traffic. At the same time, other datasets need to obtain root permission or use scripts to simulate user behavior. These collecting methods either destroy the security of the mobile device or ignore the real operation features of users with coarse-grained. Previous work (Chen et al. in Appl Sci 12(22):11731, 2022) proposed a one-stop automated encrypted traffic labeled sample collection, construction, and correlation system, A3C, running at the application-level in Android. This paper analyzes the display characteristics of IM and proposes a function-level low-overhead labeled encrypted traffic datasets construction method for Android, F3L. The supplementary method to A3C monitors UI controls and layouts of the Android system in the foreground. It selects the feature fields of attributes of them for different in-app functions to build an in-app function label matching library for target applications and in-app functions. The deviation of timestamp between function invocation and label identification completion is calibrated to cut traffic samples and map them to corresponding labels. Experiments show that the method can match the correct label within 3 s after the user operation.

Mexico exhibits one of the highest rates of identity theft in relation to bank accounts, with 56% of cardholders encountering fraudulent processes. Consequently, financial institutions have elevated security measures in their authentication protocols and implemented more robust procedures. To address this issue, this study advocates a multifactor authentication approach wherein an algorithm incorporates the user’s location as a transparent authentication factor. Traditional multifactor authentication techniques typically necessitate intricate combinations of factors, including passwords, biometrics, external hardware, or time-based one-time passwords, to enhance security levels, albeit inadvertently introducing cumbersome steps. This research proposes a simple authentication implementation that retains the benefits of complex multifactor procedures -such as precision, accuracy, and security-, with the additional feature of utilizing an imperceptible location factor. The experimental phase encompasses the development of two functional prototypes: a software implementation on the Android environment -tested in 79 distinct locations-, and an implementation on Arduino hardware -tested in four locations-. These experiments were conducted in real-world scenarios, spanning a 21-day period, and involving data collection from different participants. The most significant result of this research is intricately linked to the runtime of each authentication process, where the average time elapsed from the user input stage to the completion of the validation section is 1.76 seconds. This time optimization is primarily attributed to the integration of native Android libraries. The findings demonstrate that it is possible to attain 100% accuracy for all secure locations with a 12-meter radius in 1.76 seconds in the case of the Android app. As for the Arduino-based security box, a 100% accuracy rate in all opening attempts is possible by employing a radius of 30 meters. The research endeavors to emphasize the versatility and applicability of the proposed solution for integration into diverse real-world scenarios where the preservation of data integrity holds utmost importance.

The increasing demand for Android applications in line with technological evolution and the development of new features often leads to frequent updates and releases of applications. However, in this update cycle, developers sometimes make hasty changes that result in poor design choices, leading to the emergence of bad smell code. The impact of smells involves a decrease in quality, performance, and understanding, and impedes the software maintenance process. Some previously developed smell detection tools rely on JavaParser to parse source code into an Abstract Syntax Tree representation, meaning the extracted information is limited to Java programming language source code. Meanwhile, Google recommends Kotlin as the language for developing Android applications, and over 60% of professional Android developers use Kotlin. This research is using Program Structure Interface representation and applies a software metric-based approach as a method of smell detection and implements it into a Kotlin Android bad smell code detection tool. This approach was chosen because the characteristics of each smell can be represented with software metrics. The tool’s evaluation results on five Kotlin Android projects showed an F-measure of 100% for Brain Class detection, 93.77% for God Class, and 85.71% for Brain Method. Comparing the tool’s detection results with the iPlasma tool on the Quran project showed that the developed tool detected more smell occurrences. Usability testing results indicated that seven participants strongly agreed with the easy-to-understand detection results, easy-to-find presented functionality, clearly presented information, and the tool helped to perform metric calculation and smell detection more quickly and efficiently. The average rating given for each question was between 4.29 – 4.71.

Dynamic analysis is a technique that is used to fully understand the internals of a system at runtime. On Android, dynamic security analysis involves real-time assessment and active adaptation of an app’s behaviour, and is used for various tasks, including network monitoring, system-call tracing, and taint analysis. The research on dynamic analysis has made significant progress in the past years. However, to the best of our knowledge, there is a lack in secondary studies that analyse the novel ideas and common limitations of current security research. The main aim of this work is to understand dynamic security analysis research on Android to present the current state of knowledge, highlight research gaps, and provide insights into the existing body of work in a structured and systematic manner. We conduct a systematic literature review (SLR) on dynamic security analysis for Android. The systematic review establishes a taxonomy, defines a classification scheme, and explores the impact of advanced Android app testing tools on security solutions in software engineering and security research. The study’s key findings centre on tool usage, research objectives, constraints, and trends. Instrumentation and network monitoring tools play a crucial role, with research goals focused on app security, privacy, malware detection, and software testing automation. Identified limitations include code coverage constraints, security-related analysis obstacles, app selection adequacy, and non-deterministic behaviour. Our study results deepen the understanding of dynamic analysis in Android security research by an in-depth review of 43 publications. The study highlights recurring limitations with automated testing tools and concerns about detecting or obstructing dynamic analysis.

With the widespread use of mobile devices, Android has become the most popular operating system, and new applications being uploaded to the Android market every day. However, due to the ease of modifying and repackaging Android binaries, Android applications can easily be modified and imitated by other developers and released in third-party Android markets. Therefore, determining the original developers of Android applications is a challenging problem known as authorship attribution. This study explores the distinctive features of Android applications to identify their authors. Software developers generally leave a footprint that reflects their writing styles in their applications. Therefore, this footprint, which can be extracted from either the source code or the binary code, can help identify the authors of software applications. Since obtaining the source code of applications in the wild can be impractical, especially when dealing with malware, researchers prefer to focus on the binaries of applications. Therefore, this study proposes an approach that identifies Android developers by deriving a wide range of features from different parts of Android applications, such as smali files, libraries, manifest files, and metadata information. Moreover, other features such as configuration, dex code, resource-based, and string-related features are inherited from other studies in Android authorship attribution and fused with the proposed feature set. The proposed approach was evaluated on benign and malware datasets and compared with those of other studies. The results show that the proposed features increase the accuracy by showing 82.5% and 95.6% in the market and malware datasets, respectively. The results demonstrate the positive impact of the proposed features on Android authorship attribution.

Communicating via email or various chat applications on smartphones is part of most people’s daily lives. But in written form, human communication loses a lot of valuable information, such as the facial expressions and emotions of the person you are communicating with. Thanks to techniques from the field of image processing, it is now possible to capture these non-verbal phenomena, and supplement written input with their non-verbal characteristics. In this paper, we explore the possibilities of emotion recognition from front camera images in mobile and embedded devices. A total of 63 classification and 28 regression models based on twelve different neural network architectures optimized for low performance mobile devices were trained and evaluated for success rate and latency. The training and evaluation of each neural network model is performed within the Keras API of the TensorFlow library and then converted to the TensorFlow Lite standard to reduce memory and computational requirements. Great care is taken to ensure that the entire process, from face detection to emotion classification, can operate in real time. To demonstrate and compare the performance of the evaluated models, a freely available optimized application running on Android mobile devices is created and published on Google Play, the source code of which is also available.

In an era marked by ubiquitous reliance on mobile applications for nearly every need, the opacity of apps’ behavior poses significant threats to their users’ privacy. Although major data protection regulations require apps to disclose their data practices transparently, previous studies have pointed out difficulties in doing so. To further delve into this issue, this article describes an automated method to capture data-sharing practices in Android apps and assess their proper disclosure according to the EU General Data Protection Regulation. We applied the method to 9,000 random Android apps, unveiling an uncomfortable reality: over 80% of Android applications that transfer personal data off device potentially fail to meet GDPR transparency requirements. We further investigate the role of third-party libraries, shedding light on the source of this problem and pointing towards measures to address it.