Privacy Rating of Mobile Applications Based on Crowdsourcing and Machine Learning

With the advent of the 5G network era, the convenience of mobile smartphones has become increasingly prominent, the use of mobile applications has become wider and wider, and the number of mobile applications. However, the privacy of mobile applications and the security of users' privacy information are worrying. This article aims to study the ratings of data and machine learning on the privacy security of mobile applications, and uses the experiments in this article to conduct data collection, data analysis, and summary research. This paper experimentally establishes a machine learning model to realize the prediction of privacy scores of Android applications. The establishment of this model is based on the intent of using sensitive permissions in the application and related metadata. It is to create a regression function that can implement the mapping of applications to score . Experimental data shows that the feature vector prediction model can uniquely be used to represent the actual usage and scheme of a system's specific permissions for the application.

Privacy Rating of Mobile Applications Based on Crowdsourcing and Machine Learning

With the advent of the 5G network era, the convenience of mobile smartphones has become increasingly prominent, the use of mobile applications has become wider and wider, and the number of mobile applications. However, the privacy of mobile applications and the security of users' privacy information are worrying. This article aims to study the ratings of data and machine learning on the privacy security of mobile applications, and uses the experiments in this article to conduct data collection, data analysis, and summary research. This paper experimentally establishes a machine learning model to realize the prediction of privacy scores of Android applications. The establishment of this model is based on the intent of using sensitive permissions in the application and related metadata. It is to create a regression function that can implement the mapping of applications to score . Experimental data shows that the feature vector prediction model can uniquely be used to represent the actual usage and scheme of a system's specific permissions for the application.

Livelihood functions of fisher households under a cold-water production environment of Kashmir, Jammu and Kashmir, India

This study comprehensively investigated the livelihood security scenario of fisher households (FHs) employing the CARE framework with little modifications, in Kashmir, India. Primary data for this study was collected from selected FHs, and a regression function was fitted to quantify the determinants of livelihood security. The findings revealed that fishing has been their dominant livelihood option. The landholding owned by the households was meagre enough to carry out farming or domesticate animals on commercial lines. Poor capital endowments place them at less livelihood security level; however, the respondents with diversified income have a relatively higher index value for livelihood. The regression estimates indicated that barring social and natural capital, all forms of capital have a significant role to play in securing their livelihood. Poor livelihood security, coupled with less income flow, has made their survival vulnerable to various distresses and health disorders, including the prevalence of Infant & Maternal Mortality. Their dietary intake was undesirably less than their dietary recommendations. The COVID-19 pandemic was perceived as a shock to their livelihood security. Further, public investment, which is pertinent for the growth of the fisheries sector, has shown a discouraging trend. The study concluded with a few policy suggestions for securing the livelihood of the fisher community.

On the estimation of the Bernoulli regression function using Bernstein polynomials for group observations

The estimate for the Bernoulli regression function is constructed using the Bernstein polynomial for group observations. The question of its consistency and asymptotic normality is studied. A testing hypothesis is constructed on the form of the Bernoulli regression function. The consistency of the constructed tests is investigated.

Economic analysis of mushroom enterprise in Chitwan district, Nepal

The majority of the population (66%) in-country “Nepal” are engaged in agriculture. However, domestic production finds it difficult to meet the annual demand of the people. Hence, people are moving from subsistence agriculture to embrace mushroom farming. This study focuses on economic analysis and analysis of the present status of mushroom farming and enterprise in this country. The study was conducted in the land area of Kalika Municipality and Bharatpur Metropolitan City. 30 mushroom farmers with two huts and at least three years of experience were selected from the study area. The primary data were collected through face-to-face interviews with the farmers, focus group discussion (FGD) and key informant interviews (KII). The secondary data was collected through various published articles and documents. The data analysis was done using basic statistics and a regression function. The benefit-cost ratio is 2.54 and a high gross margin is NRs.490,876.65 per kattha per year. The return to scale (RTS) is 0.80. Five marketing channels are present among which wholesalers and local collectors contributed the highest percentage of the share. However, the dominance of the intermediaries, timely unavailability of inputs, price fluctuation, disease and pest infestation were the major constraints. Disease and pest control, formation of the producer organization, improvised cultivation practices, timely and affordable availability of quality can be the major solution measures. Whereas, suitable climatic conditions, high productivity and growing market demand are the strengths of mushroom production in this study area. Mushroom farming is found to be a profitable business concerning competitive and comparative markets.

Satellite data validation: a parametrization of the natural variability of atmospheric mixing ratios

High-resolution model data are used to estimate typical variabilities of mixing ratios of trace species as a function of spatial and temporal distance. These estimates can be used to explain that part of the differences between observations made with different observing systems that are due to less than perfect collocation of the measurements. The variability values are described by a two-parameter regression function. A reparametrization of the variabilities values as function of latitudinal graidents is proposed, and season-independence of linear approximation of such function is demonstrated.

Penalized wavelet estimation and robust denoising for irregular spaced data

Nonparametric univariate regression via wavelets is usually implemented under the assumptions of dyadic sample size, equally spaced fixed sample points, and i.i.d. normal errors. In this work, we propose, study and compare some wavelet based nonparametric estimation methods designed to recover a one-dimensional regression function for data that not necessary possess the above requirements. These methods use appropriate regularizations by penalizing the decomposition of the unknown regression function on a wavelet basis of functions evaluated on the sampling design. Exploiting the sparsity of wavelet decompositions for signals belonging to homogeneous Besov spaces, we use some efficient proximal gradient descent algorithms, available in recent literature, for computing the estimates with fast computation times. Our wavelet based procedures, in both the standard and the robust regression case have favorable theoretical properties, thanks in large part to the separability nature of the (non convex) regularization they are based on. We establish asymptotic global optimal rates of convergence under weak conditions. It is known that such rates are, in general, unattainable by smoothing splines or other linear nonparametric smoothers. Lastly, we present several experiments to examine the empirical performance of our procedures and their comparisons with other proposals available in the literature. An interesting regression analysis of some real data applications using these procedures unambiguously demonstrate their effectiveness.

Seismic Fragility Analysis of the Reinforced Concrete Continuous Bridge Piers Based on Machine Learning and Symbolic Regression Fusion Algorithms

In the fragility analysis, researchers mostly chose and constructed seismic intensity measures (IMs) according to past experience and personal preference, resulting in large dispersion between the sample of engineering demand parameter (EDP) and the regression function with IM as the independent variable. This problem needs to be solved urgently. Firstly, the existing 46 types of ground motion intensity measures were taken as a candidate set, and the composite intensity measures (IMs) based on machine learning methods were selected and constructed. Secondly, the modified Park–Ang damage index was taken as EDP, and the symbolic regression method was used to fit the functional relationship between the composite intensity measures (CIMs) and EDP. Finally, the probabilistic seismic demand analysis (PSDA) and seismic fragility analysis were performed by the cloud-stripe method. Taking the pier of a three-span continuous reinforced concrete hollow slab bridge as an example, a nonlinear finite element model was established for vulnerability analysis. And the composite IM was compared with the linear composite IM constructed by Kiani, Lu Dagang, and Liu Tingting. The functions of them were compared. The analysis results indicated that the standard deviation of the composite IM fragility curve proposed in this paper is 60% to 70% smaller than the other composite indicators which verified the efficiency, practicality, proficiency, and sufficiency of the proposed machine learning and symbolic regression fusion algorithms in constructing composite IMs.

Regression-Based Beam Training for UAV mmWave Communications

For the unmanned aerial vehicle (UAV) Millimeter-Wave (mmWave) communication systems, an efficient and accurate beam training method is urgently required to overcome the severe path loss. By taking into account the mmWave propagation environment,a three-dimensional (3D) intelligent beam training strategy by leveraging the polynomial regression model and optimized beam patterns is proposed in this paper. We treat the mmWave beam selection as a polynomial regression problem. The regression function is obtained by a machine learning (ML) method based on the dataset and a special beam pattern is achieved to obtain the dataset consisting of measured powers and estimated angles. Furthermore, a noise suppression method involving the use of denoising autoencoder (DAE) is developed to improve the robustness of the proposed regression model.Numerical simulation results demonstrate that our proposed beam training strategy is capable of getting the same precision as the exhaustive search methods with a shorter time.

Forecasting of Tourism Processes in Uzbekistan in Correlation Regression Models

The article provides detailed information on the process of developing effective plans for the development of the tourism industry and choosing the optimal one based on them, forecasting the future development of the industry. It also considers the processes of using special computational and arithmetic methods that allow predicting the events and happenings in the tourism industry, to determine the regression function as a result of the interaction and interaction of indicators representing the type of activity. As a result of targeted research, using correlation-regression models, a forecast of the development trend of the tourism industry based on socio-economic factors affecting the tourism process was developed.