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Android is the most targeted mobile operating system for malware attacks. Most modern anti-malware solutions largely incorporate deep learning or machine learning techniques to detect malwares. In this paper, we conduct a comprehensive analysis on 10 deep learning and 5 machine learning classifiers in their abilities to identify Android malware applications. We used 1-gram dataset, 2-gram dataset and image dataset generated from the system call co-occurrence matrix for our experiments. Among the machine learning classifiers, XGBoost with 2-gram dataset showed the highest F1-score of 0.98. Also, the deep learning classifiers such as extreme learning machine with the system call images demonstrated the best F1-score of 0.952. We experimented using Gabor filters to investigate classifier performance on textures extracted from system call images. We observed an F1-score of 0.953 using the extreme learning machine with the Gabor images. We generated the Gabor image dataset by combining the images generated by passing system call images through 25 different Gabor configurations. In addition, to enhance the performance of the baseline classifiers, we considered the combination of autoencoders with machine learning classifiers. We observed that the amalgam of autoencoder with Random Forest displayed the best F1-score of 0.98. To evaluate the effectiveness of the aforesaid classifiers with diverse features on adversarial examples, we simulated a black-box based attack using a Generative Adversarial Network. The True Positive Rate of XGBoost on the 1-gram dataset, Random Forest on the 2-gram dataset and the Extreme Learning Machine on the system call image dataset significantly dropped to 0 from 0.98, 0.001 from 0.99 and 0 from 0.984 after the attack. Our experiments exposed a crucial vulnerability in classifiers used in modern anti-malware systems. A similar event in a real-world system could potentially render grave catastrophes. To defend against such probable attacks, we should continue further research and develop adequate security mechanisms. [ABSTRACT FROM AUTHOR]

Collaborative robots combined with mobile platforms can move freely and have a wide work area. Technological developments in AI and vision have also led to increased research on recognition of objects and surroundings using mobile manipulators. However, even though such trends have resulted in growing demand for research on mobile manipulators, their high prices make them difficult to purchase. We solved this problem using 3D printing. A mobile manipulator was manufactured by replacing more expensive reducers with 3D printed planetary gear and cycloid gear reducers. As well, a mecanum in-wheel motor equipped with a 3D printed cycloid reducer was developed to increase the usability of the interior space of the mobile platform. The study employed a task-space control algorithm, which controls the mobile platform and the manipulator simultaneously to ensure effective control of the mobile manipulator. Task-space motion control performance was verified through Gazebo simulation and experiment. Additionally, the repeatability of the task-space position control was measured through experiments and its performance was compared with a commercial manipulator. Finally, we have developed a mobile manipulator that can perform similar levels of work as a commercial robot at one tenth the price of a general commercial mobile manipulator. [ABSTRACT FROM AUTHOR]

The underwater environment is filled with various sounds, with its soundscape composed of biological, geographical, and anthropological sounds. Our work focused on developing a novel method to observe and classify these sounds, enriching our understanding of the underwater ecosystem. We constructed a biologging system allowing near-real-time observation of underwater soundscapes. Utilizing deep-learning-based edge processing, this system classifies the sources of sounds, and upon the tagged animal surfacing, it transmits positional data, results of sound source classification, and sensor readings such as depth and temperature. To test the system, we attached the logger to sea turtles (Chelonia mydas) and collected data through a cellular network. The data provided information on the location-specific sounds detected by the sea turtles, suggesting the possibility to infer the distribution of specific species of organisms over time. The data showed that not only biological sounds but also geographical and anthropological sounds can be classified, highlighting the potential for conducting multi-point and long-term observations to monitor the distribution patterns of various sound sources. This system, which can be considered an autonomous mobile platform for oceanographic observations, including soundscapes, has significant potential to enhance our understanding of acoustic diversity. [ABSTRACT FROM AUTHOR]

Mobile manipulator robots that combine mobile platforms and robotic arms have been attracting considerable attention in recent years. In this paper, the motion control problem of the mobile manipulator system is considered with the following proposed control strategy. Firstly, a decoupled dynamic model is created to increase operation safety and to reduce complexity such that the independent controllers can be designed for the mobile system and the manipulator system. Subsequently, an effective reference trajectory generator is proposed to guide the mobile manipulator systems to the proper position for the manipulation being able to grip the target. Thereafter, the fuzzy controllers are designed for the mobile system to eliminate the tracking error and for the manipulator system to accomplish the gripping mission. The stability of the mobile manipulator control system can be guaranteed by the Lyapunov theory. Finally, the numerical simulations are given to demonstrate the effectiveness of the proposed approach. From the simulation results, it can be seen that this paper as well as other compared methods have good control response in case of small controller gain, in which the convergence time are about 4–5 s. However, by increasing the controller gain to improve the control response, the other methods will make the system unstable or the controller output will produce a large amount of chattering. The proposed controller in this paper can not only decrease the convergence time, from 5 to 3 s, but also provide a smooth control response. [ABSTRACT FROM AUTHOR]

Recently, deep learning methods have been widely applied to single image super-resolution reconstruction tasks and achieved great improvement in both quantitative and qualitative evaluations. However, most of the existing convolutional neural network-based methods commonly reduce the number of layers or channels to obtain lightweight model. These strategies may reduce the representation ability of informative features and degrade the network performance. To address this issue, we propose a sparse lightweight attention network (SLAN), a novel SISR algorithm to keep informative features between layers. Specially, a sparse attention feature fusion module with lightweight attention and sparse extracting modules is developed to expand feature extracting receptive field and enhance the informative feature extracting ability. To take advantage of the multi-level features and keep fewer multi-adds, cross fusion is used and demonstrated usefully. Extensive experimental results on public datasets demonstrate the superior performance of our proposed SLAN. The average PSNR(dB)/SSIM values of SLAN are about 0.04/0.0004, 0.06/0.0009, and 0.09/0.0018 larger than the competitors under scaling factors of × 2, × 3 and × 4, respectively. Our SLAN benefits from its model size and low computation cost and may be deployed on mobile platforms. [ABSTRACT FROM AUTHOR]

This study proposes a variable-stiffness mechanism for non-pneumatic tires such that can actively adapt to various environments. Non-pneumatic tire is a compliant wheel structure that offers superior robustness and adaptability compared to pneumatic tires. However, the tire designed for certain terrain exhibits relatively high rolling resistance and inadequate suspension. To address these problems, a stiffness-adjustable wheel (SAW) that can modify the force applied to the contact surface is introduced in this study. In addition, the shape of SAW is optimized to maintain a desirable range of stiffness under different conditions. The optimization is conducted with experimental method, because nonlinear response of material and interference between components make it difficult to predict the characteristic of the wheel at large deformation. The SAW has potential for application in various mobile platforms to provide adequate stiffness for a variety of terrains and driving conditions. [ABSTRACT FROM AUTHOR]

Purpose: This study examined and compared the diagnostic accuracy and correlation levels of the acoustic parameters of the audio recordings obtained from smartphones on two operating systems and from dynamic and condenser types of external microphones. Method: The study included 87 adults: 57 with voice disorder and 30 with a healthy voice. Each participant was asked to perform a sustained vowel phonation (/a/). The recordings were taken simultaneously using five microphones AKG-P220, Shure-SM58, Samson Go Mic, Apple iPhone 6, and Samsung Galaxy J7 Pro microphones in an acoustically insulated cabinet. Acoustic examinations were performed using Praat version 6.2.09. The data were examined using Pearson correlation and receiver-operating characteristic (ROC) analyses. Results: The parameters with the highest area under curve (AUC) values among all microphone recordings in the time-domain analyses were the frequency perturbation parameters. Additionally, considering the correlation coefficients obtained by synchronizing the microphones with each other and the AUC values together, the parameter with the highest correlation coefficient and diagnostic accuracy values was the jitter-local parameter. Conclusion: Period-to-period perturbation parameters obtained from audio recordings made with smartphones show similar levels of diagnostic accuracy to external microphones used in clinical conditions. [ABSTRACT FROM AUTHOR]

With the rapid development and wide popularity of Internet technology, mobile application services have become an indispensable part of people's daily life. As a typical mobile application service, mobile office can provide an anytime, anywhere platform, enabling users to use fragmented time and fully improve office efficiency. However, it differs from the traditional one in that the user's identity cannot be directly identified in real space. Therefore, the need to ensure the authenticity of the user's identity is not negligible. Authentication technology is one of the effective technologies to verify the identity of users. However, there are few authentication schemes for the mobile office. Therefore, this paper proposes a trusted authentication scheme based on a super SIM card for the mobile office. Based on the Generic Bootstrapping Architecture (GBA) authentication technology of the super SIM card, the authentication between the terminal device and the operator is realized. The authentication credentials are generated to provide trusted authentication for mobile office applications. We also have a comprehensive security analysis and proof that the scheme satisfies two-way authentication, anti-replay attack, anti-forgery attack, anti-man-in-the-middle attack and non-repudiation. [ABSTRACT FROM AUTHOR]

We design a two-stage federated learning incentive mechanism by including both a "combinatorial auction" stage and a "bargaining" stage, referred as Combinatorial Auction and Bargaining (CAB). We demonstrate that the CAB-based federated learning incentive mechanism not only maximizes the overall utility of both the federated learning platform and mobile users, but also generates greater profits for the platform than other mechanisms. Further, we apply both theoretical and numerical analysis to prove the effectiveness of the proposed mechanism and show that it is individually rational and incentive compatible, and outperforms other baseline mechanisms. [ABSTRACT FROM AUTHOR]

A robust and high-resolution deconvolution algorithm based on coordinate correction is developed for a compact conformal array on a three-dimensional (3D) moving platform such as underwater glider. First, the coordinate-correcting conventional beamforming is derived to directly estimate the azimuth of the long-range targets in the geodesic coordinate system for a 3D moving platform array. Then, we improve the extended Richardson–Lucy deconvolution (Ex-RL-dCv) beamforming algorithm utilizing coordinate correction to simplify the bearing estimation model from two-dimensional (2D) to one-dimensional (1D). The improved algorithm corrects the deconvolution point spread function (PSF) dictionary mismatch caused by the platform's 3D motion, and has lower sidelobes compared with the Ex-RL-dCv algorithm without coordinate correction. Finally, simulations and results from field-trial data processing are presented. The results demonstrate that the improved Ex-RL-dCv beamforming based on coordinate correction can significantly suppress the high sidelobes caused by deconvolution model mismatch, and successfully realize the robust and high-resolution detection for targets using the compact conformal array on a 3D mobile platform. [ABSTRACT FROM AUTHOR]

Large-scale human social network structure is typically inferred from digital trace samples of online social media platforms or mobile communication data. Instead, here we investigate the social network structure of a complete population, where people are connected by high-quality links sourced from administrative registers of family, household, work, school, and next-door neighbors. We examine this multilayer social opportunity structure through three common concepts in network analysis: degree, closure, and distance. Findings present how particular network layers contribute to presumably universal scale-free and small-world properties of networks. Furthermore, we suggest a novel measure of excess closure and apply this in a life-course perspective to show how the social opportunity structure of individuals varies along age, socio-economic status, and education level. [ABSTRACT FROM AUTHOR]

The wireless network is composed of multiple micronetwork nodes that are difficult to replace. These network nodes are usually located in areas with poor environmental conditions, so the energy consumption and security of nodes have become the main limitations and challenges. Therefore, this paper discusses the network topology, data transmission and node trust mechanism, and proposes a cloud computing technology algorithm for intelligent regional wireless network. According to the data of arrival delay difference in intelligent regional wireless network environment, the approximate location of mobile system is set as different positioning results. The dynamic position of the mobile system is obtained through multiple calculations. With the support of cloud computing technology, this paper also studies the color rendering system of 3D animation. 3D animation can not only express emotions and feelings, but also color render through the facial expressions of characters in the animation, so as to enhance the entertainment and interest visible to the audience. However, due to the great demand for computing resources and color rendering resources, at present, the main research and implementation are carried out on the desktop platform. There are also solutions to use high-end infrared devices for 3D animation color rendering on the mobile system. However, because this method has high cost in use and does not have universality on the mobile device, this paper proposes a rendering system suitable for the general mobile platform. This paper studies the 3D animation color rendering system based on wireless network cloud computing technology, hoping to realize the universality of the system with the support of technology. [ABSTRACT FROM AUTHOR]

Large-scale human social network structure is typically inferred from digital trace samples of online social media platforms or mobile communication data. Instead, here we investigate the social network structure of a complete population, where people are connected by high-quality links sourced from administrative registers of family, household, work, school, and next-door neighbors. We examine this multilayer social opportunity structure through three common concepts in network analysis: degree, closure, and distance. Findings present how particular network layers contribute to presumably universal scale-free and small-world properties of networks. Furthermore, we suggest a novel measure of excess closure and apply this in a life-course perspective to show how the social opportunity structure of individuals varies along age, socio-economic status, and education level. [ABSTRACT FROM AUTHOR]

Large-scale human social network structure is typically inferred from digital trace samples of online social media platforms or mobile communication data. Instead, here we investigate the social network structure of a complete population, where people are connected by high-quality links sourced from administrative registers of family, household, work, school, and next-door neighbors. We examine this multilayer social opportunity structure through three common concepts in network analysis: degree, closure, and distance. Findings present how particular network layers contribute to presumably universal scale-free and small-world properties of networks. Furthermore, we suggest a novel measure of excess closure and apply this in a life-course perspective to show how the social opportunity structure of individuals varies along age, socio-economic status, and education level. [ABSTRACT FROM AUTHOR]

Joint representation learning has been an attractive way to solve image-text retrieval problem due to its efficiency on both time and storage. On the one hand, the most classical methods model the joint semantic subspace with respect to only semantic relationship at the level of holistic images and sentences, which fails to explore the fine-grained semantic relationship. On the other hand, visual-linguistic pretrain-finetune scheme has achieved an impressive performance in many downstream image-text tasks, but more storage cost and computation burden still notably limit their use in some real applications which have strict requirement for low storage cost or rapid response to image acquired in real-time, such as retrieving textual description of some just taken photos on a storage-limited mobile platform. To mitigate the problem above, we proposed a lightweight cross-modal retrieval for learning the joint representation. Instead of only modeling whole joint semantic space, the proposed model captures semantic relationship in multiple subspaces. Specially, we treat the retrieval problem as not only a ranking process but also a decision process, and propose an entropy-based constraint to preserve as much hierarchy-aware information as possible across various semantic subspaces. Experiments show that the proposed method can achieve competitive performance when compared with the state-of-the-art joint representation learning methods on two publicly available datasets. [ABSTRACT FROM AUTHOR]

Visual search has become more popular in recent years, allowing users to search by an image they are taking using their mobile device or uploading from their photo library. One domain in which visual search is especially valuable is electronic commerce, where users seek for items to purchase. Despite the increasing popularity of visual search in e-commerce, no comprehensive study has inspected its characteristics compared to traditional search using a text query. In this work, we present an in-depth comprehensive study of visual e-commerce search. We perform query log analysis of one of the largest e-commerce platforms' mobile search application. We compare visual and textual search by a variety of characteristics, with special focus on the retrieved results and user interaction with them. We also examine image query characteristics, refinement by attributes, and segmentation by user types. Additionally, we examine, for the first time, a wide variety of visual pre- and post-retrieval query performance predictors, several of which showing strong results. Our study points out a variety of differences between visual and textual e-commerce search. We discuss the implications of these differences for the design of future e-commerce search systems. [ABSTRACT FROM AUTHOR]

Mobile applications have gained popularity in healthcare in recent years. These applications are an increasingly important pillar of public health care, as they open up new possibilities for data collection and can lead to new insights into various diseases and disorders thanks to modern data analysis approaches. In this context, Ecological Momentary Assessment (EMA) is a commonly used research method that aims to assess phenomena with a focus on ecological validity and to help both the user and the researcher observe these phenomena over time. One phenomenon that benefits from this capability is the chronic condition tinnitus. TrackYourTinnitus (TYT) is an EMA-based mobile crowdsensing platform designed to provide more insight into tinnitus by repeatedly assessing various dimensions of tinnitus, including perception (i.e., perceived presence). Because the presence of tinnitus is the dimension that is of great importance to chronic tinnitus patients and changes over time in many tinnitus patients, we seek to predict the presence of tinnitus based on the not directly related dimensions of mood, stress level, arousal, and concentration level that are captured in TYT. In this work, we analyzed a dataset of 45,935 responses to a harmonized EMA questionnaire using different machine learning techniques. In addition, we considered five different subgroups after consultation with clinicians to further validate our results. Finally, we were able to predict the presence of tinnitus with an accuracy of up to 78% and an AUC of up to 85.7%. [ABSTRACT FROM AUTHOR]

Purpose: Staff management is the most cited ART/IVF laboratory inspection deficiency. Small ART/IVF clinics may be challenged to perform these activities by low staff volume; similarly, large ART/IVF networks may be challenged by high staff volume and large datasets. Here, we sought to investigate the performance of an automated, digital platform solution to manage this necessary task. Methods: The ART Compass (ARTC) digital staff management platform was used to assess the clinical decision-making of ART laboratory staff. The survey modules presented standardized instructions to technologists and measured inter- and intra-technologist variability for subjective "clinical decision-making" type questions. Internal and external comparisons were achieved by providing technologists two answers: (1) a comparison to their own lab director and (2) to the most popular response collectively provided by all lab director level accounts. The platform is hosted on HIPAA compliant Amazon web servers, accessible via web browser and mobile applications for iOS (Apple) and Android mobile devices. Results: Here, we investigated the performance of a digital staff management platform for single embryologist IVF practices and for three IVF lab networks (sites A, B, C) from 2020 to 2022. Embryology dish preparation survey results show variance among respondents in the following: PPE use, media volume, timing of oil overlay, and timing of moving prepared dishes to incubators. Surveying the perceived Gardner score and terms in use for early blastocysts reveals a lack of standardization of terminology and fair to poor agreement. We observed moderate inter-technologist agreement for ICM and TE grade (0.47 and 0.52, respectively). Lastly, the clinical decision of choice to freeze or discard an embryo revealed that agreement to freeze was highest for the top-quality embryos, and that some embryos can be highly contested, evenly split between choice to freeze or discard. Conclusions: We conclude that a digital platform is a novel and effective tool to automate, routinely monitor, and assure quality for staff-related parameters in ART and IVF laboratories. Use of a digital platform can increase regulatory compliance and provide actionable insight for quality assurance in both single embryologist practices and for large networks. Furthermore, clinical decision-making can be augmented with artificial intelligence integration. [ABSTRACT FROM AUTHOR]

Smartphones are one of the most affordable and familiar devices available to people that are used for many scientific applications. Thus, implementing a dedicated mobile app for earthquake early warning (EEW) systems is one of the best options to save money and time to achieve dissemination of earthquake warnings to the public at large. This paper describes the development of a Warning Dissemination System comprising field sirens and mobile apps and presents details of developing a smartphone app for Android and iOS operating systems. The app is designed for the EEW system currently operational in the Northwest Himalayan State of Uttarakhand in India. The app named "Uttarakhand Bhookamp Alert" is freely available on the Google Play Store and Apple App Store and has already been downloaded more than 10,000 times. This app uses Firebase Cloud Messaging (FCM) service for sending earthquake early warnings to the users. The user's basic information and FCM token are saved at the warning server in a database during the user registration process. The information will be shared with the disaster response authorities after a damaging earthquake to rescue affected users requiring help. This app also provides online earthquake safety recommendations and guidance, including how to prepare yourself, find a safe hiding place, and do's and don'ts during an earthquake. [ABSTRACT FROM AUTHOR]

The learning customization and monitoring are considered key aspects of the teaching-learning processes. Some works have proposed mobile learning systems that provide teachers and students learning monitoring and personalization services. One of the main requirements of these kinds of systems in terms of software quality is usability; however, few works have addressed the usability issues using laboratory studies with users in real domains. In this work, we present a usability evaluation of the learning monitoring and personalization services of a mobile learning platform based on a laboratory study in which nine teachers and ten students participated. In our usability evaluation, the aspects evaluated were effectiveness, efficiency, and level of user satisfaction as proposed by the ISO/IEC 25000 family of standards. The results show that the teachers presented effectiveness, efficiency, and satisfaction considered satisfactory, while the students presented effectiveness and satisfaction classified as satisfactory and acceptable efficiency. The usability evaluation described in this work can serve as a reference for developers seeking to improve learning monitoring and personalization services development. [ABSTRACT FROM AUTHOR]

Most of the traditional athletic performance dynamic change prediction platforms use gray correlation and neural networks to establish prediction methods, and the prediction results are inaccurate and time-consuming. Therefore, the construction of a mobile prediction platform for dynamic changes in athletic performance based on screening factors is proposed to improve the accuracy of its prediction results and shorten the prediction time. On the basis of hardware, the main body of students is determined, the athletic performance data of students is collected by the physical education management system of colleges and universities, and the missing data is filled by the criterion function of K-average algorithm. The KNN algorithm is used to make decisions to obtain the most representative predictors of athletic performance data, and the characteristics of students' athletic performance are extracted. The effective factors are selected according to the tenfold cross-validation fold MSE minimum principle of SVM, and the mandatory screening based on SVM is used to assign weights to each reserved factor. According to the weighted result of the obtained factors, the influencing factors of athletic performance are analyzed, and the weighting process is completed for the factors, and the athletic performance prediction is realized by using support vector machine. In the platform established in this paper, the students' 1000-m running results are predicted. The simulation results show that, compared with the traditional platform, the constructed platform has higher prediction accuracy and takes less time. [ABSTRACT FROM AUTHOR]

To solve the problem of low recognition accuracy and long recognition time in conventional methods for digital moving image feature recognition, a method for digital image feature recognition on mobile platforms based on machine learning is proposed. The support vector machine method is used to cluster the dataset, and then the weight reconstruction method is used to reduce the dimension of digital moving image data. The reduced dimension of the digital moving image is embedded in the coordinates to obtain the reduced dimension of the image data information and complete the feature recognition of the digital moving image. The simulation results show that the recognition accuracy of this method in recognizing features of moving digital images is basically stable at more than 95% and the running time is 0.42s. The recognition effect of the proposed method is good, the recognition efficiency is high, and it has some practical application value. [ABSTRACT FROM AUTHOR]

Innovation in wireless technologies enables business value creation through product and business process innovation. However, business model innovation is the most potent type of digital transformation. The platform business model has attracted significant attention in business research and practice, but mobile platform business models are not well understood. This article proposes a dynamic model that accounts for the dynamic complexity of the mobile platform business model and associated platform ecosystems, focusing on multi-platform firms. Our model maps the key strategic feedback loops that constitute the core structure of the mobile business model. The article provides insights for entrepreneurs who seek to build and optimize mobile platforms. Entrepreneurs should seek to understand and leverage the AI feedback loops that affect their business model. [ABSTRACT FROM AUTHOR]

The mobile redundant manipulator (MRM) shows a great advantage in its in situ high-precision assembly field of large complex components (LCCs), due to its high mobility, flexibility, and dexterity. However, the redundancy degree of freedom of the MRM makes its motion planning difficult. Furthermore, the inconsistent stiffness of different configurations of the MRM may cause poor manufacturing quality. Therefore, it is necessary to optimize the process pose with better stiffness performance during the motion planning. This paper proposes an integrated motion planning method to optimize the kinematics and stiffness performance of the MRM for LCCs' assembly and process. In addition to the multiple constraints such as joint angle, joint velocity, and singular posture avoidance, the time-varying normal direction of LCCs and the stiffness performance of MRM's configuration are considered in the optimization algorithm, by calculating the Roll-Pitch-Yaw angles (RPY angles) at each moment and defining the stiffness index. The simulation results illustrated that the proposed method can effectively plan the desired trajectories for the mobile platform and the manipulator separately. Compared with the non-stiffness constraint planning method, the proposed method enjoys superiority on stiffness performance. [ABSTRACT FROM AUTHOR]

Background: Reliable assessments of clinical skills are important for undergraduate medical education, trustworthy handoffs to graduate medical programs, and safe, effective patient care. Entrustable professional activities (EPAs) for entering residency have been developed; research is needed to assess reliability of such assessments in authentic clinical workspaces. Design: A student-driven mobile assessment platform was developed and used for clinical supervisors to record ad hoc entrustment decisions using the modified Ottawa scale on 5 core EPAs in an 8-week internal medicine (IM) clerkship. After a 12-month period, generalizability (G) theory analysis was performed to estimate the reliability of entrustment scores and determine the proportion of variance attributable to the student and the other facets, including particular EPA, evaluator type (attending versus resident), or case complexity. Decision (D) theory analysis determined the expected reliability based on the number of hypothetical observations. A g-coefficient of 0.7 was used as a generally agreed upon minimum reliability threshold. Key Results: A total of 1368 ratings over the 5 EPAs were completed on 94 students. Variance attributed to person (true variance) was high for all EPAs; EPA-5 had the lowest person variance (9.8% across cases and four blocks). Across cases, reliability ranged from 0.02 to 0.60. Applying this to the Decision study, the estimated number of observations needed to reach a reliability index of 0.7 ranged between 9 and 11 for all EPAs except EPA5 which was sensitive to case complexity. Conclusions: Work place-based clinical skills in IM clerkship students were assessed and logged using a convenient mobile platform. Our analysis suggests that 9–11 observations are needed for these EPA workplace-based assessments (WBAs) to achieve a reliability index of 0.7. Note writing was very sensitive to case complexity. Further reliability analyses of core EPAs are needed before US medical schools consider wider adoption into summative entrustment processes and GME handoffs. [ABSTRACT FROM AUTHOR]

The Android application framework has a pervasive presence. In early 2021, Android has over 70 % share of the operating system mobile market (according to GlobalStats). Components are the main building blocks of Android Applications. These blocks communicate via a rich Inter-Component Communication (ICC) model rather than the traditional inter-process communication model. Intents, Intent-filters, and their Intents resolution (matching) algorithm are main elements of the ICC. However, the Intent resolution algorithm is not robust enough and has flaws that can lead to security breaches. In this paper, we present DLAIR, as an enrichment of the Intent resolution algorithm to overcome its security issues. To this end, we start by presenting a formal model to express and validate the ICC semantics. This includes defining key properties guaranteeing consistent and realistic semantic states. We then demonstrate how the semantics can be used to formally validate ICC aspects and to express and check ICC system updates. We verified our proposed model and all its lemmas and theorems in the Coq Proof Assistant, a machine-assisted verification tool. We extend our semantics to develop DLAIR which is assisted by a heuristic, and lightweight tool, LekInt. This tool identifies suspicious execution paths responsible for intent based sensitive user-information leakage. On a dataset of 2000 real-world apps, we evaluated LekInt against Flowdroid, a state-of-the-art information leakage analysis tool. Experiments show that LekInt is more effective and efficient than Flowdroid which has a higher false-negative rate and lower false-positive rate than LekInt. Considering the dynamic context in which LekInt is designed to work, the advantage of efficiency overcomes the disadvantage of higher false-negative. [ABSTRACT FROM AUTHOR]

In the last decades, mobile-based apps have been increasingly used in several application fields for many purposes involving a high number of human activities. Unfortunately, in addition to this, the number of cyber-attacks related to mobile platforms is increasing day-by-day. However, although advances in Artificial Intelligence science have allowed addressing many aspects of the problem, malware classification tasks are still challenging. For this reason, the following paper aims to propose new special features, called permission maps (Perm-Maps), which combine information related to the Android permissions and their corresponding severity levels. Such features have proven to be very effective in classifying different malware families through the usage of a convolutional neural network. Also, the advantages introduced by the Perm-Maps have been enhanced by a training process based on a federated logic. Experimental results show that the proposed approach achieves up to a 3% improvement in average accuracy with respect to J48 trees and Naive Bayes classifier, and up to 16% compared to multi-layer perceptron classifier. Furthermore, the combined use of Perm-Maps and federated logic allows dealing with unbalanced training datasets with low computational efforts. [ABSTRACT FROM AUTHOR]

This paper presents an energy-efficient design and evaluation of a novel portable device for the automatic correction of presbyopia in human eyes driven by the use of opto-electronic lenses and based on the dynamic pupil response of the subject. Due to the wearable nature of the proposed Dynamic Auto–Accommodation Glasses, in addition to the real-time requirement, an energy-efficient implementation is critical for the success of the device. In this work, the binocular pupil tracking of a subject, followed by the calculation of the eyes' vergence, and the control of a pair of opto-electronic lenses are implemented and evaluated on several hardware platforms, including two mobile GPU/SoCs, a high-end FPGA, a low-cost FPGA, and a desktop GPU (as a reference point). The pupil tracking algorithm has been parallelized, applying different platform-specific optimizations for each case, to design a fast yet energy-efficient wearable. The hardware platforms have been evaluated to determine which one is the most appropriate for the presbyopia correction task. The experimental results show that the most energy-efficient platform is a mobile GPU (Samsung Exynos 8890) capable of processing frames at 0.016 Joules/frame, still allowing real-time processing (24 frames/sec). [ABSTRACT FROM AUTHOR]

This study analyzed the frequency and intensity of acute stress among health professionals caring for COVID-19 patients in four Latin American Spanish-speaking countries during the outbreak. A cross-sectional study involved a non-probability sample of healthcare professionals in four Latin American countries. Participants from each country were invited using a platform and mobile application designed for this study. Hospital and primary care workers from different services caring for COVID-19 patients were included. The EASE Scale (SARS-CoV-2 Emotional Overload Scale, in Spanish named Escala Auto-aplicada de Sobrecarga Emocional) was a previously validated measure of acute stress. EASE scores were described overall by age, sex, work area, and experience of being ill with COVID-19. Using the Mann–Whitney U test, the EASE scores were compared according to the most critical moments of the pandemic. Univariate and multivariate analysis was performed to investigate associations between these factors and the outcome 'acute stress'. Finally, the Kruskal–Wallis was used to compare EASE scores and the experience of being ill. A total of 1372 professionals responded to all the items in the EASE scale: 375 (27.3%) Argentines, 365 (26.6%) Colombians, 345 (25.1%) Chileans, 209 (15.2%) Ecuadorians, and 78 (5.7%) from other countries. 27% of providers suffered middle-higher acute stress due to the outbreak. Worse results were observed in moments of peak incidence of cases (14.3 ± 5.3 vs. 6.9 ± 1.7, p < 0.05). Higher scores were found in professionals in COVID-19 critical care (13 ± 1.2) than those in non-COVID-19 areas (10.7 ± 1.9) (p = 0.03). Distress was higher among professionals who were COVID-19 patients (11.7 ± 1) or had doubts about their potential infection (12 ± 1.2) compared to those not infected (9.5 ± 0.7) (p = 0.001). Around one-third of the professionals experienced acute stress, increasing in intensity as the incidence of COVID-19 increased and as they became infected or in doubt whether they were infected. EASE scale could be a valuable asset for monitoring acute stress levels among health professionals in Latin America. ClinicalTrials: NCT04486404. [ABSTRACT FROM AUTHOR]

Automatic detection and recognition of pavement distresses is the key to timely repair of pavement. Repairing the pavement distresses in time can prevent the destruction of road structure and the occurrence of traffic accidents. However, some other factors, such as a single object category, shading and occlusion, make detection of pavement distresses very challenging. In order to solve these problems, we use the improved YOLOv5 model to detect various pavement distresses. We optimize the YOLOv5 model and introduce attention mechanism to enhance the robustness of the model. The improved model is more suitable for deployment in embedded devices. The optimized model is transplanted to the self-built intelligent mobile platform. Experimental results show that the improved network model proposed in this paper can effectively identify pavement distresses on the self-built intelligent mobile platform and datasets. The precision, recall and mAP are 95.5%, 94.3% and 95%. Compared with YOLOv5s and YOLOv4 models, the mAP of the improved YOLOv5s model is increased by 4.3% and 25.8%. This method can provide technical reference for pavement distresses detection robot. [ABSTRACT FROM AUTHOR]

Accurately measuring the figure of optical elements is important for evaluating the effect of wavefront aberrations in atom interferometry gravimeters. In this work, a setup used to measure the figure of optical elements in vacuum has been built, which is composed of figure measurement device, supporting and mobile platform and vacuum chamber. The root-mean-square wavefront error of this device is less than λ / 100 . The figure of optical elements under different pressures has been measured by this setup. The figure root-mean-square value of a quarter waveplate measured under different pressures varies between 32 nm and 37 nm, and the figure distribution of optical element does not change significantly under different pressures. The results indicate that the figure of optical element measured under atmospheric environment could still be applicable after being installed inside vacuum environment in this situation, which may provide new approach for the evaluation of wavefront aberrations effect in atom interferometry gravimeter. [ABSTRACT FROM AUTHOR]

The time period before, during and after pregnancy represents a unique opportunity for interventions to cultivate sustained healthy lifestyle behaviors to improve the metabolic health of mothers and their offspring. However, the success of a lifestyle intervention is dependent on uptake and continued compliance. To identify enablers and barriers towards engagement with a lifestyle intervention, thematic analysis of 15 in-depth interviews with overweight or obese women in the preconception, pregnancy or postpartum periods was undertaken, using the integrated-Promoting Action on Research Implementation in Health Services framework as a guide to systematically chart factors influencing adoption of a novel lifestyle intervention. Barrier factors include time constraints, poor baseline knowledge, family culture, food accessibility, and lack of relevant data sources. Enabling factors were motivation to be healthy for themselves and their offspring, family and social support, a holistic delivery platform providing desired information delivered at appropriate times, regular feedback, goal setting, and nudges. From the findings of this study, we propose components of an idealized lifestyle intervention including (i) taking a holistic life-course approach to education, (ii) using mobile health platforms to reduce barriers, provide personalized feedback and promote goal-setting, and (iii) health nudges to cultivate sustained lifestyle habits. [ABSTRACT FROM AUTHOR]

The Android operating system is the most popular mobile operating system resulting in a great number of applications being developed for the platform. This makes them vulnerable to security threats such as social engineering, shoulder surfing and Malware. Therefore, Android devices require a secure authentication scheme in order to control access to the device. This paper briefly discusses the mobile security threats, the authentication protocols and Android Security. Then the paper presents an analysis of some of the authentication schemes that are used in mobile devices and some of the threats and technical issues faced. Authentication schemes discussed include password/pin, pattern based authentication, fingerprint recognition, facial recognition, vocal recognition and iris based authentication. In discussing the various authentication methods, it was observed that while biometric based authentication schemes offered the greatest level of security, there was always a trade-off between computational complexity and ease of use/implementation/cost that ensured that more traditional authentication schemes, while not as secure as biometric schemes, are still widely used in mobile devices. [ABSTRACT FROM AUTHOR]

Undertaking an experience-sampling study via smartphones is complex. Scheduling and sending mobile notifications often requires the use of proprietary software that imposes limits on participants' operating systems (whether iOS or Android) or the types of questions that can be asked via the application. We have developed an open-source platform—Samply—which overcomes these limitations. Researchers can access the entire interface via a browser, manage studies, schedule and send notifications linking to online surveys or experiments created in any Internet-based service or software, and monitor participants' responses—all without the coding skills usually needed to program a native mobile application. Participants can download the Samply Research mobile application for free from Google Play or the App Store, join a specific study, receive notifications and web links to surveys or experiments, and track their involvement. The mobile application leverages the power of the React Native JavaScript library, which allows it to be rendered in the native code of Android and iOS mobile operating systems. We describe Samply, provide a step-by-step example of conducting an experience-sampling study, and present the results of two validation studies. Study 1 demonstrates how we improved the website's usability for researchers. Study 2 validates the mobile application's data recording ability by analyzing a survey's participation rate. The application's possible limitations and how mobile device settings might affect its reliability are discussed. [ABSTRACT FROM AUTHOR]

The q-rung orthopair fuzzy sets dynamically change the range of indication of decision knowledge by adjusting a parameter q from decision makers, where q ≥ 1 , and outperform the conventional intuitionistic fuzzy sets and Pythagorean fuzzy sets. Linguistic q-rung orthopair fuzzy sets (Lq-ROFSs), a qualitative type of q-rung orthopair fuzzy sets, are characterized by a degree of linguistic membership and a degree of linguistic non-membership to reflect the qualitative preferred and non-preferred judgments of decision makers. Einstein operator is a powerful alternative to the algebraic operators and has flexible nature with its operational laws and fuzzy graphs perform well when expressing correlations between attributes via edges between vertices in fuzzy information systems, which makes it possible for addressing correlational multi-attribute decision-making (MADM) problems. Inspired by the idea of Lq-ROFS and taking the advantage of the flexible nature of Einstein operator, in this paper, we aim to introduce a new class of fuzzy graphs, namely, linguistic q-rung orthopair fuzzy graphs (Lq-ROFGs) and further explore efficient approaches to complicated MAGDM situations. Following the above motivation, we propose the new concepts, including product-connectivity energy, generalized product-connectivity energy, Laplacian energy and signless Laplacian energy and discuss several of its desirable properties in the background of Lq-ROFGs based on Einstein operator. Moreover, product-connectivity energy, generalized product-connectivity energy, Laplacian energy and signless Laplacian energy of linguistic q-rung orthopair fuzzy digraphs (Lq-ROFDGs) are presented. In addition, we present a graph-based MAGDM approach with linguistic q-rung orthopair fuzzy information based on Einstein operator. Finally, an illustrative example related to the selection of mobile payment platform is given to show the validity of the proposed decision-making method. For the sake of the novelty of the proposed approach, comparison analysis is conducted and superiorities in contrast with other methodologies are illustrated. [ABSTRACT FROM AUTHOR]

The number of mobile applications downloaded recently from Google stores increase steadily. It is believed that mobile applications market revenues will grow fast and receive more attention from software developers. Even though, hardware technologies and operating systems have made a big contribution to improve the battery capacity, power consumption is one of the most important and crucial non-functional properties of mobile applications. Many approaches have been dedicated for analyzing and optimizing the power consumption at different levels. This paper proposes a method for analyzing energy leaks of Android application at the design phase. First, it introduces a formalization of an event-based power consumption model. After that, the paper presents a method to translate this model to Event-B notations. Based on the target model, it is able to check if the application leads to energy leaks or violates the power consumption constraints based on formal proofs. Finally, the motivating examples are shown for the illustration purpose. [ABSTRACT FROM AUTHOR]

Mobile microlearning platforms have increased over the years. Literature shows that platforms use specific instructions or media, such as videos or multiformat materials (e.g., text, audio, quizzes, hands-on exercises). However, few studies investigate whether or how specific design principles used on these platforms contribute to learning efficacy. A mobile microlearning course for journalism education was developed using the design principles and instructional flow reported in literature. The goal of this formative research was to study the mobile microcourse's learning efficacy, defined as effectiveness, efficiency, and appeal. Learners' knowledge before and after the mobile microcourse was analyzed using semistructured questionnaires as well as pretests and posttests to measure differences. The results indicate that learners of this mobile microcourse had an increase in knowledge, more certainty in decisions about practical applications, and an increase in confidence in performing skills. However, automated feedback, timed gamified exercises, and interactive real-world content indicate room for improvement to enhance effective learning. [ABSTRACT FROM AUTHOR]

Purpose: Therapeutic drug monitoring guided by the area under the concentration-time curve (AUC-guided TDM) is recommended for vancomycin. However, validated efficient software remains elusive to popularize AUC-guided TDM in Japan. The aim of this study was to validate a newly developed web application, PAT, for AUC estimation. Methods: PAT was developed on the R ver. 3.6.2 platform for use with mobile phones and personal computers. AUC estimated by PAT (AUCPAT) was evaluated against the reference AUC (AUCREF) calculated with the log-linear trapezoidal rule using eight measured concentrations, or against AUC (AUCBM-P) calculated using an evaluated available software with clinical data. Results: Investigating the best sampling points with limited sampling, PAT produced the least bias using two concentrations at 1 h and 11 h after the end of infusion (slope 1.18, intercept −15.57, median AUCPAT/AUCREF 0.93 [range 0.81–1.24]), where only one estimation (6%) was out of the predetermined acceptable range of 0.8–1.2. Employment of only a trough concentration was more biased (AUCPAT/AUCREF range 0.73–1.30 for 11 h, AUCPAT/AUCREF range 0.62–1.40 for 23 h). In comparison with the evaluated software, AUCPAT was not biased against the AUCBM-P (slope 1.04, intercept −15.80, median AUCPAT/AUCBM-P 1.00 [range 0.86–1.10]). Conclusions: The new application using two concentrations was appropriately validated and might be efficient in popularizing the AUC-guided TDM of vancomycin. [ABSTRACT FROM AUTHOR]

Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and Internet of Thing (IoT). Recent work in the literature has suggested using the K-nearest neighboring algorithm to defend against such attacks. However, such an approach can suffer from low to miss-classification rate accuracy. In this paper, we design an architecture to tackle the Android malware detection problem in IoT systems. We develop an attack mechanism based on silhouette clustering method, modified for mobile Android platforms. We proposed two convolutional neural network-type deep learning algorithms against this Silhouette Clustering-based Label Flipping Attack. We show the effectiveness of these two defense algorithms—label-based semi-supervised defense and clustering-based semi-supervised defense—in correcting labels being attacked. We evaluate the performance of the proposed algorithms by varying the various machine learning parameters on three Android datasets: Drebin, Contagio, and Genome and three types of features: API, intent, and permission. Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19% accuracy when compared with the state-of-the-art method in the literature. [ABSTRACT FROM AUTHOR]

With the continuous advancement of science and technology, the social network based on the Internet has gradually penetrated into people's daily lives. The image data shared on social media using platforms such as mobile phones has exploded, and hundreds of millions of dollars are generated every day with picture information. In the past, the traditional ways of expressing textual information that people are familiar with have begun to be gradually replaced by image information that is not subject to regional culture such as language and script. In modern warfare, with the continuous development and equipment of highly sophisticated weapons and equipment, the amount of information that the entire combat system needs to process will also increase. In particular, the air defense system needs to quickly and accurately identify the aircraft targets that are coming. It mainly uses computer to extract the feature information of the acquired image and converts the content in the image into a feature expression that can be processed by the computer. After the appropriate classification algorithm, the image is the target object is classified by category. In this paper, we propose a complex image recognition algorithm based on immune random forest model. The experimental results show that the proposed algorithm has high recognition efficiency and higher robustness. [ABSTRACT FROM AUTHOR]

Introduction: Mobile apps that utilize the framework of entrustable professional activities (EPAs) to capture and deliver feedback are being implemented. If EPA apps are to be successfully incorporated into programmatic assessment, a better understanding of how they are experienced by the end-users will be necessary. The authors conducted a qualitative study using the Consolidated Framework for Implementation Research (CFIR) to identify enablers and barriers to engagement with an EPA app. Methods: Structured interviews of faculty and residents were conducted with an interview guide based on the CFIR. Transcripts were independently coded by two study authors using directed content analysis. Differences were resolved via consensus. The study team then organized codes into themes relevant to the domains of the CFIR. Results: Eight faculty and 10 residents chose to participate in the study. Both faculty and residents found the app easy to use and effective in facilitating feedback immediately after the observed patient encounter. Faculty appreciated how the EPA app forced brief, distilled feedback. Both faculty and residents expressed positive attitudes and perceived the app as aligned with the department's philosophy. Barriers to engagement included faculty not understanding the EPA framework and scale, competing clinical demands, residents preferring more detailed feedback and both faculty and residents noting that the app's feedback should be complemented by a tool that generates more systematic, nuanced, and comprehensive feedback. Residents rarely if ever returned to the feedback after initial receipt. Discussion: This study identified key enablers and barriers to engagement with the EPA app. The findings provide guidance for future research and implementation efforts focused on the use of mobile platforms to capture direct observation feedback. [ABSTRACT FROM AUTHOR]

Driver drowsiness increases crash risk, leading to substantial road trauma each year. Drowsiness detection methods have received considerable attention, but few studies have investigated the implementation of a detection approach on a mobile phone. Phone applications reduce the need for specialised hardware and hence, enable a cost-effective roll-out of the technology across the driving population. While it has been shown that three-dimensional (3D) operations are more suitable for spatiotemporal feature learning, current methods for drowsiness detection commonly use frame-based, multi-step approaches. However, computationally expensive techniques that achieve superior results on action recognition benchmarks (e.g. 3D convolutions, optical flow extraction) create bottlenecks for real-time, safety-critical applications on mobile devices. Here, we show how depthwise separable 3D convolutions, combined with an early fusion of spatial and temporal information, can achieve a balance between high prediction accuracy and real-time inference requirements. In particular, increased accuracy is achieved when assessment requires motion information, for example, when sunglasses conceal the eyes. Further, a custom TensorFlow-based smartphone application shows the true impact of various approaches on inference times and demonstrates the effectiveness of real-time monitoring based on out-of-sample data to alert a drowsy driver. Our model is pre-trained on ImageNet and Kinetics and fine-tuned on a publicly available Driver Drowsiness Detection dataset. Fine-tuning on large naturalistic driving datasets could further improve accuracy to obtain robust in-vehicle performance. Overall, our research is a step towards practical deep learning applications, potentially preventing micro-sleeps and reducing road trauma. [ABSTRACT FROM AUTHOR]

Advances in both telecommunications and Information technology have improved the way users do business online. Android, an open-source mobile operating system, is becoming an attractive target for cyber criminals to exploit due to its predefined permission model. Without classification, the mobile operating system permits installation of mobile applications of all kinds, including Trojans, thus making its trustworthiness into question. In this paper, we present a security system called collaborative policy-based security scheme (CSS) that permits users to customize the access permissions of Android applications during runtime. The proposed CSS security scheme validates the trustworthiness of each application before being installed. The experimental results show that the proposed CSS successfully detects all malicious applications with a run-time overhead of 2.7%. [ABSTRACT FROM AUTHOR]

With the era of big data coming, whether in computer operating system or mobile operating system, the shortcomings of traditional way of file management become increasingly obvious. For example, data access and data retrieval across devices are very inconvenient. At the same time, storage space in disks and cloud storage services is underutilized. To address these issues, we propose Rio, a personal storage system in multi-device and cloud storage. Cloud storage services, treated as one storage device in Rio, together with shared devices form a storage pool. In Rio, users can store, retrieve and access data through RESTful URI, no matter in which device or cloud services data are stored. Rio also sets up redundant storage in idle space to improve disk storage utilization. In the experiment, we compared Rio with U disk, FTP, Dropbox and other common data sharing methods. Then we tested Rio's data access performance and system overhead. The results show that Rio increases the accessibility of data greatly and only costs small system overhead. [ABSTRACT FROM AUTHOR]

The paper considers route optimization problems for unmanned aerial vehicles (UAV), which act as a team when inspecting or supporting a given set of objects in the presence of alternative and dynamic depots (starting and/or landing sites) and resource constraints. Problem definition and mathematical models are proposed. Such problems, in particular, include UAV flight planning problems, which use mobile platforms as a depot. The optimization criteria are both the total length of the routes and the number of UAVs involved. Algorithms for solving formulated combinatorial optimization problems based on ant colony optimization, tabu search, and exhaustive search have been developed and implemented. The results of the computational experiment are presented. [ABSTRACT FROM AUTHOR]

Intrusion Detection System (IDS) is crucial to protect smartphones from imminent security breaches and ensure user privacy. Android is the most popular mobile Operating System (OS), holding above 85% market share. The traffic generated by smartphones is expected to exceed the one generated by personal computers by 2021. Consequently, this prevalent mobile OS will stay one of the most attractive targets for potential attacks on fifth generation mobile networks (5G). Although Android malware detection has received considerable attention, offered solutions mostly rely on performing resource intensive analysis on a server, assuming a continuous connection between the device and the server, or on employing supervised Machine Learning (ML) algorithms for profiling the malware's behaviour, which essentially require a training dataset consisting of thousands of examples from both benign and malicious profiles. However, in practice, collecting malicious examples is tedious since it entails infecting the device and collecting thousands of samples in order to characterise the malware's behaviour and the labelling has to be done manually. In this paper, we propose a novel Host-based IDS (HIDS) incorporating statistical and semi-supervised ML algorithms. The advantage of our proposed IDS is two folds. First, it is wholly autonomous and runs on the mobile device, without needing any connection to a server. Second, it requires only benign examples for tuning, with potentially a few malicious ones. The evaluation results show that the proposed IDS achieves a very promising accuracy of above 0.9983, reaching up to 1. [ABSTRACT FROM AUTHOR]

High penetration of Android applications along with their malicious variants requires efficient and effective malware detection methods to build mobile platform security. API call sequence derived from API call graph structure can be used to model application behavior accurately. Behaviors are extracted by following the API call graph, its branching, and order of calls. But identification of similarities in graphs and graph matching algorithms for classification is slow, complicated to be adopted to a new domain, and their results may be inaccurate. In this study, the authors use the API call graph as a graph representation of all possible execution paths that a malware can track during its runtime. The embedding of API call graphs transformed into a low dimension numeric vector feature set is introduced to the deep neural network. Then, similarity detection for each binary function is trained and tested effectively. This study is also focused on maximizing the performance of the network by evaluating different embedding algorithms and tuning various network configuration parameters to assure the best combination of the hyper-parameters and to reach at the highest statistical metric value. Experimental results show that the presented malware classification is reached at 98.86% level in accuracy, 98.65% in F-measure, 98.47% in recall and 98.84% in precision, respectively. [ABSTRACT FROM AUTHOR]

Mobile Cloudlet Computing paradigm (MCC) allows execution of resource-intensive mobile applications using computation cloud resources by exploiting computational offloading method for resource-constrained mobile devices. Whereas, computational offloading needs the mobile application to be partitioned during the execution in the MCC so that total execution cost is minimized. In the MCC, at the run-time network contexts (i.e., network bandwidth, signal strength, latency, etc.) are intermittently changed, and transient failures (due to temporary network connection failure, services busy, database disk out of storage) often occur for a short period of time. Therefore, transient failure aware partitioning of the mobile application at run-time is a challenging task. Since, existing MCC offers computational monolithic services by exploiting heavyweight virtual machines, which incurs with long VM startup time and high overhead, and these cannot meet the requirements of fine-grained microservices applications (e.g., E-healthcare, E-business, 3D-Game, and Augmented Reality). To cope up with prior issues, we propose microservices based mobile cloud platform by exploiting containerization which replaces heavyweight virtual machines, and we propose the application partitioning task assignment (APTA) algorithm which determines application partitioning at run-time and adopts the fault aware (FA) policy to execute microservices applications robustly without interruption in the MCC. Simulation results validate that the proposed microservices mobile cloud platform not only shrinks the setup time of run-time platform but also reduce the energy consumption of nodes and improve the application response time by exploiting APTA and FA to the existing VM based MCC and application partitioning strategies. [ABSTRACT FROM AUTHOR]

The aim of the study is to investigate the factors such as balance and audiology parameters and bone mineral density (BMD), which were thought to be associated with the increased risk of fall in osteoporotic patients. Ninety-nine female patients between the ages of 40 and 75 were included in the study. Noise exposure, tinnitus, and vertigo were investigated. BMD measurement, Berg Balance Scale (BBS), Timed Up and Go (TUG), and stabilometry tests were applied to the patients. Patients were divided into three groups according to BMD measurements. Patients with a T score of − 1 and below were considered normal (control) (group 1), those with a T score of − 1 to − 2.5 were considered osteopenic (group 2), and those with a T score of − 2.5 and above were considered osteoporotic (OP) (group 3). BBS was 42.06 ± 5.00, 47.74 ± 5.18, and 51.65 ± 3.64 in groups1, 2, and 3, respectively. The difference between the groups was statistically significant (p = 0.001). OP patients had higher oscillation values in all measurements compared with the control group. However, the difference was statistically significant especially on mobile platforms (p < 0.05). Mean tones of pure tone thresholds at 500–8000 Hz for ears were significantly different in patients with OP compared with controls (p < 0.05 for 500–2000 Hz, p < 0.01 for 4000–8000 Hz). This study demonstrated that BMD reduction was correlated with balance parameters and audiological measurements. Therefore, it can be concluded that OP may affect the risk of fracture occurrence not only by decreasing BMD but also by increasing the risk of falling. Key points: • In the present study, the effects of balance and audiology parameter measurements on osteoporosis (OP) were investigated. In addition, the associations between vertigo, tinnitus, history of falling, body made index, vitamin D, and calcium values and osteoporosis were evaluated. • We determined that bone mineral density (BMD) reduction was related to balance and audiological parameters. It can be speculated that OP may affect the risk of fracture occurrence not only by decreasing BMD but also by increasing the risk of falling. We think that early diagnosis, appropriate treatment, and rehabilitation of hearing loss and OP may decrease the incidence of falling and improve the quality of life of the affected individuals. [ABSTRACT FROM AUTHOR]

An automated visual positioning system is proposed for precision placement of a workpiece on the fixture. The system includes a binocular eye-in-hand system on the end effector of the mobile manipulator and a ConvNet for detecting the relative position of the workpiece based on the holistic views observed by the CMOS cameras. We train the ConvNets with training images that are automatically generated from basis images taken at the target position and annotated with the 2D coordinates of the offset locations. The ConvNet's superior place recognition capability renders a high success rate of coordinate detection subject to high illumination and viewpoint variations. Experimental evidence of workpiece placement confirms that the low-resolution (640 × 480 pixels) camera can obtain a translational precision of ±0.2 mm; the binocular system can control the rotational error within ± 0.1°. Within the 20 × 20-mm2 spatial tolerance of the mobile platform, the proposed system achieves a success rate of 100% in 200 workpiece placement tasks. The entire workpiece placement task can be completed in 60 s; the average elapsed time of precision positioning and placement is less than 20 s, with a total of 4 visual positioning steps. [ABSTRACT FROM AUTHOR]