Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through ...deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region's image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.
The digital age has seen the rise of service systems involving highly distributed, heterogeneous, and resource-integrating actors whose relationships are governed by shared institutional logics, ...standards, and digital technology. The cocreation of service within these service systems takes place in the context of a paradoxical tension between the logic of generative and democratic innovations and the logic of infrastructural control. Boundary resources play a critical role in managing the tension as a firm that owns the infrastructure can secure its control over the service system while independent firms can participate in the service system. In this study, we explore the evolution of boundary resources. Drawing on Pickering’s (1993) and Barrett et al.’s (2012) conceptualizations of tuning, the paper seeks to forward our understanding of how heterogeneous actors engage in the tuning of boundary resources within Apple’s iOS service system. We conduct an embedded case study of Apple’s iOS service system with an in-depth analysis of 4,664 blog articles concerned with 30 boundary resources covering 6 distinct themes. Our analysis reveals that boundary resources of service systems enabled by digital technology are shaped and reshaped through distributed tuning, which involves cascading actions of accommodations and rejections of a network of heterogeneous actors and artifacts. Our study also shows the dualistic role of power in the distributed tuning process.
This study proposes an extended state observer-based sliding mode control (ESO-SMC) strategy for trajectory tracking of a four mecanum wheeled mobile platform (FMWMP) with unknown disturbances and ...model uncertainties (UDMU) considered. Especially, the extended state observer (ESO) is designed to estimate not only the UDMU but also the unmeasured velocities of FMWMP. Based on the designed ESO, a sliding mode control (SMC) scheme is utilised to ensure the tracking performance as expected. By using Lyapunov synthesis, it is shown that all the signals of the whole system can be guaranteed to be uniformly ultimately bounded. To verify the effectiveness of the proposed control strategy, simulations and experiments are carried out with two different kinds of reference trajectories. Furthermore, a comparative work is done to show that the ESO-SMC controller has better control performance than traditional proportional–integral–derivative controller.
Currently, Android malware detection is mostly performed on server side against the increasing number of malware. Powerful computing resource provides more exhaustive protection for app markets than ...maintaining detection by a single user. However, apart from the applications (apps) provided by the official market (i.e., Google Play Store), apps from unofficial markets and third-party resources are always causing serious security threats to end-users. Meanwhile, it is a time-consuming task if the app is downloaded first and then uploaded to the server side for detection, because the network transmission has a lot of overhead. In addition, the uploading process also suffers from the security threats of attackers. Consequently, a last line of defense on mobile devices is necessary and much-needed. In this paper, we propose an effective Android malware detection system, MobiTive, leveraging customized deep neural networks to provide a real-time and responsive detection environment on mobile devices. MobiTive is a pre-installed solution rather than an app scanning and monitoring engine using after installation, which is more practical and secure. Although a deep learning-based approach can be maintained on server side efficiently for malware detection, original deep learning models cannot be directly deployed and executed on mobile devices due to various performance limitations, such as computation power, memory size, and energy. Therefore, we evaluate and investigate the following key points: (1) the performance of different feature extraction methods based on source code or binary code; (2) the performance of different feature type selections for deep learning on mobile devices; (3) the detection accuracy of different deep neural networks on mobile devices; (4) the real-time detection performance and accuracy on different mobile devices; (5) the potential based on the evolution trend of mobile devices' specifications; and finally we further propose a practical solution (MobiTive) to detect Android malware on mobile devices.
As digital platform ecosystems grow in prominence, their interconnectedness and complexity also grow, making operational failure likely. How failures in such systems affect user perceptions of ...separate ecosystem components, however, is not well understood. This research investigates attribution of responsibility and discontinuance recommend
ations for ecosystem components after failures of ambiguous origin. Building on attribution theory, platform ecosystems literature, and research on digital borders, we conducted two scenario-based experiments investigating negative consequences of failure for ecosystem components. We also explored contingent effects from design elements (border strength) and contextual factors (disruption severity). Results demonstrated that when failures occur, negative consequences diffuse to all ecosystem components, with apps receiving the strongest discontinuance recommendations. Greater disruption severity increased discontinuance recommendations for the app. Furthermore, border strength between ecosystem components shifted negative consequences for failure toward the platform (e.g., operating system OS and device). Perceptions of locus and controllability were the primary mechanisms driving attributions of responsibility for failure. However, contrary to attribution theory, lack of failure stability increased blame for the app instead of reducing it. Despite higher coordination costs, our results indicate the importance of better-integrated ecosystems that experience fewer faults and that app developers bear the greatest burden in delivering this experience. Furthermore, attribution for failure can be shaped by clearly delineated borders. Thus, design decisions affecting border strength should be actively managed by ecosystem participants, and app developers may be incentivized to elevate border strength.
With the development of the air pollution control, the low-cost sensors are widely used in air quality monitoring, while the data quality of these sensors is always the most concern for users. In ...this study, data from nine air monitoring stations with standard PM instruments were used as reference and compared with the data of mobile and fixed PM sensors in Jinan, the capital city of Shandong Province, China. Data quality of PM sensors was checked by the cross-comparison among standard method, fixed and mobile sensors. And the impacts of relative humidity and size distribution (PM2.5/PM10) on the performance of PM sensors were evaluated as well. To optimize the calibration method for both fixed and mobile PM sensors, a two-step model was designed, in which the RH and PM2.5/PM10 ratio were both used as input parameters. We firstly calibrated the sensors with five independent models, and then all the calibrated data were linearly fitted by the LR-final model. In comparison with standard instruments, the LR-final model increased the R2 values of the PM2.5 and PM10 measured by fixed sensors from 0.89 and 0.79 to 0.98 and 0.97, respectively. The R2 values of PM2.5 and PM10 measured by the mobile sensors both increased to 0.99 from 0.79 and 0.62. Overall, the two-step calibration model appeared to be a promising approach to solve the poor performance of low-cost sensors.
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•Comparison of standard instruments, fixed sensors, and mobile sensors.•The effects of RH and particle size distribution on the PM sensor were studied.•A two-step calibration model was established, including linear and nonlinear models.•The two-step model can effectively solve the poor performance of low-cost sensors.
Adaptive authentication enables smartphones and enterprise apps to decide when and how to authenticate users based on contextual and behavioral factors. In practice, a system may employ multiple ...policies to adapt its authentication mechanisms and access controls to various scenarios. However, existing approaches suffer from contradictory or insecure adaptations, which may enable attackers to bypass the authentication system. Besides, most existing approaches are inflexible and do not provide desirable access controls. We design and build a multi-stage risk-aware adaptive authentication and access control framework (MRAAC), which provides the following novel contributions: Multi-stage: MRAAC organizes adaptation policies in multiple stages to handle different risk types and progressively adapts authentication mechanisms based on context, resource sensitivity, and user authenticity. Appropriate access control: MRAAC provides libraries to enable sensitive apps to manage the availability of their in-app resources based on MRAAC’s risk awareness. Extensible: While existing proposals are tailored to cater to a single use case, MRAAC supports a variety of use cases with custom risk models. We exemplify these advantages of MRAAC by deploying it for three use cases: an enhanced version of Android Smart Lock, guest-aware continuous authentication, and corporate app for BYOD. We conduct experiments to quantify the CPU, memory, latency, and battery performance of MRAAC. Our evaluation shows that MRAAC enables various stakeholders (device manufacturers, enterprise and secure app developers) to provide complex adaptive authentication workflows on COTS Android with low processing and battery overhead.
Offshore mobile platforms are important facilities for the development of deep-water offshore oil and gas resources. In life-cycle safety assessment of offshore mobile platforms, there remains a lack ...of effective structural corrosion models to guide design evaluation. From a large amount of corrosion data provided by the China Classification Society regarding the operation of offshore mobile platforms, relevant data were statistically analysed, and available corrosion data were selected for analysis and comparison. By analysing the experimental data of offshore mobile platforms, it was verified that the corrosion rate of local components of offshore mobile platforms follows the Weibull distribution, which is consistent with Paik's verification that the corrosion rate of local components of marine mobile platforms follows Weibull distribution. Therefore, the corrosion evolution model of local components of offshore mobile platforms was established by referring to the ship corrosion rate model.
•The realization of commercial large-scale application of agricultural mobile platform is inseparable from its autonomous mobile capability.•The autonomous mobility is affected by the mobile ...structure and the navigation system.•The application of in facility agriculture is relatively mature, and the application in field is a challenging.•It is more mature for commercial application in agricultural harvesting tasks.•Nonstructural effects caused by plant shading and changes in topography and water flow have greater significance.
With the gradual disappearance of the demographic dividend and the labor shortage brought by the aging population, it is necessary and urgent to achieve a high degree of automation in agricultural scenarios. And with the development of science and technology, it has made certain progress. The autonomous mobility of platform plays a very important role in this progress. It expands the point-to-point agricultural activities of a single plant into the entire agriculture scene. The autonomous mobility of platforms is driven by the considerable multiple scenarios with different requirements and the need for an adaptable and reliable system. It contains mobile structure and motion control, which complement each other. This paper gives a detailed introduction and summarizes the state-of-the-art agricultural mobile platforms, sensor-based autonomous positioning and navigation methods, as well as their practical applications in different specific agricultural scenarios, especially harvesting task. It accounts for the absolute amount of labor in the overall agricultural task. Unlike, traditional industrial and urban, the agricultural scenarios have more unstructured complex environment. High autonomous mobility of the agricultural mobile platform, which requires the installation of more complex and comprehensive sensors sensitively perceive scene information and more intelligent positioning and navigation strategy, enable the mobile platform to securely, flexible perform agricultural tasks. Therefore, special attention is paid to the navigation strategy and mobile cruise strategy based on various sensor, to improve the autonomy of agricultural mobile platforms. It's potential challenges and future are prospected based on the development trend of agricultural mobile platforms.
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