Context-driven progressive enhancement of mobile web applications: a multicriteria decision-making approach

Personal computing has become all about mobile and embedded devices. As a result, the adoption rate of smartphones is rapidly increasing and this trend has set a need for mobile applications to be available at anytime, anywhere and on any device. Despite the obvious advantages of such immersive mobile applications, software developers are increasingly facing the challenges related to device fragmentation. Current application development solutions are insufficiently prepared for handling the enormous variety of software platforms and hardware characteristics covering the mobile eco-system. As a result, maintaining a viable balance between development costs and market coverage has turned out to be a challenging issue when developing mobile applications. This article proposes a context-aware software platform for the development and delivery of self-adaptive mobile applications over the Web. An adaptive application composition approach is introduced, capable of autonomously bypassing context-related fragmentation issues. This goal is achieved by incorporating and validating the concept of fine-grained progressive application enhancements based on a multicriteria decision-making strategy

An approach for uncertainty aggregation using generalised conjunction/disjunction aggregators

Decision Support Systems are often used in the area of system evaluation. The quality of the output of such a system is only as good as the quality of the data that is used as input. Uncertainty on data, if not taken into account, can lead to evaluation results that are not representative. In this paper, we propose a technique to extend Generalised Con- junction/Disjunction aggregators to deal with un- certainty in Decision Support Systems. We first de- fine the logic properties of uncertainty aggregation through reasoning on strict aggregators and after- wards extend this logic to partial aggregators

A Novel Latin Square Image Cipher

In this paper, we introduce a symmetric-key Latin square image cipher (LSIC) for grayscale and color images. Our contributions to the image encryption community include 1) we develop new Latin square image encryption primitives including Latin Square Whitening, Latin Square S-box and Latin Square P-box ; 2) we provide a new way of integrating probabilistic encryption in image encryption by embedding random noise in the least significant image bit-plane; and 3) we construct LSIC with these Latin square image encryption primitives all on one keyed Latin square in a new loom-like substitution-permutation network. Consequently, the proposed LSIC achieve many desired properties of a secure cipher including a large key space, high key sensitivities, uniformly distributed ciphertext, excellent confusion and diffusion properties, semantically secure, and robustness against channel noise. Theoretical analysis show that the LSIC has good resistance to many attack models including brute-force attacks, ciphertext-only attacks, known-plaintext attacks and chosen-plaintext attacks. Experimental analysis under extensive simulation results using the complete USC-SIPI Miscellaneous image dataset demonstrate that LSIC outperforms or reach state of the art suggested by many peer algorithms. All these analysis and results demonstrate that the LSIC is very suitable for digital image encryption. Finally, we open source the LSIC MATLAB code under webpage https://sites.google.com/site/tuftsyuewu/source-code.Comment: 26 pages, 17 figures, and 7 table

Bipolarity and multipolarity in aggregation structures

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Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms

AbstractLandslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we evaluated the landslide susceptibility, and its spatial differencing in the whole Qinghai-Tibetan Plateau region using five state-of-the-art learning algorithms; deep neural network (DNN), logistic regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM), differing from previous studies only in local areas of QTP. The 671 landslide events were considered, and thirteen landslide conditioning factors (LCFs) were derived for database generation, including annual rainfall, distance to drainage $${(\mathrm{Ds}}_{\mathrm{d}})$$ ( Ds d ) , distance to faults $${(\mathrm{Ds}}_{\mathrm{f}})$$ ( Ds f ) , drainage density ($${D}_{d})$$ D d ) , elevation (Elev), fault density $$({F}_{d})$$ ( F d ) , lithology, normalized difference vegetation index (NDVI), plan curvature $${(\mathrm{Pl}}_{\mathrm{c}})$$ ( Pl c ) , profile curvature $${(\mathrm{Pr}}_{\mathrm{c}})$$ ( Pr c ) , slope $${(S}^{^\circ })$$ ( S ∘ ) , stream power index (SPI), and topographic wetness index (TWI). The multi-collinearity analysis and mean decrease Gini (MDG) were used to assess the suitability and predictability of these factors. Consequently, five landslide susceptibility prediction (LSP) maps were generated and validated using accuracy, area under the receiver operatic characteristic curve, sensitivity, and specificity. The MDG results demonstrated that the rainfall, elevation, and lithology were the most significant landslide conditioning factors ruling the occurrence of landslides in Qinghai-Tibetan Plateau. The LSP maps depicted that the north-northwestern and south-southeastern regions ( 45% of total area). Moreover, among the five models with a high goodness-of-fit, RF model was highlighted as the superior one, by which higher accuracy of landslide susceptibility assessment and better prone areas management in QTP can be achieved compared to previous results. Graphical Abstrac

Augmenting Land Cover/Land Use Classification by Incorporating Information from Land Surface Phenology: An Application to Quantify Recent Cropland Expansion in South Dakota

Understanding rapid land change in the U.S. NGP region is not only critical for management and conservation of prairie habitats and ecosystem services, but also for projecting production of crops and biofuels and the impacts of land conversion on water quality and rural transportation infrastructure. Hence, it raises the need for an LCLU dataset with good spatiotemporal coverage as well as consistent accuracy through time to enable change analysis. This dissertation aims (1) to develop a novel classification method, which utilizes time series images from comparable sensors, from the perspective of land surface phenology, and (2) to apply the land cover/land use dataset generated from the phenometrically-based classification approach to quantify crop expansion in South Dakota. A novel classification approach from the perspective of land surface phenology (LSP) uses rich time series datasets. First, surface reflectance products at 30 m spatial resolution from Landsat Collection-1, its newer structure—Landsat Analysis Ready Data, and the Harmonized Landsat Sentinel-2 (HLS) data are used to construct vegetation index time series, including the Enhanced Vegetation Index (EVI), and the 2-band EVI (EVI2), and various spectral variables (spectral band and normalized ratio composites). MODIS Level-3 Land Surface Temperature & Emissivity 8-day composite products at 1 km spatial resolution from both the Aqua and Terra satellites are used to compute accumulated growing degree-days (AGDD) time series. The EVI/EVI2 and AGDD time series are then fitted by two different land surface phenology models: the Convex Quadratic model and the Hybrid Piecewise Logistic Model. Suites of phenometrics are derived from the two LSP models and spectral variables and input to Random Forest Classifiers (RFC) to map land cover of sample areas in South Dakota. The results indicate that classifications using only phenometrics can accurately map major crops in the study area but show limited accuracy for non-vegetated land covers. RFC models using the combined spectralphenological variables can achieve higher accuracies than those using either spectral variables or phenometrics alone, especially for the barren/developed class. Among all sampling designs, the “same distribution” models—proportional distribution of the sample is like proportional distribution of the population—tends to yield best land cover prediction. A “same distribution” random sample dataset covering approximately 0.25% or more of the study area appears to achieve an accurate land cover map. To characterize crop expansion in South Dakota, a trajectory-based analysis, which considers the entire land cover dataset generated from the LSP-based classifications, is proposed to improve change detection. An estimated cropland expansion of 5,447 km2 (equivalent to 14% of the existing cropland area) occurred between 2007 and 2015, which matches more closely the reports from the National Agriculture Statistics Service—NASS (5,921 km2) and the National Resources Inventory—NRI (5,034 km2) than an estimation from a bi-temporal change approach (8,018 km2). Cropland gains were mostly concentrated in 10 counties in northern and central South Dakota. An evaluation of land suitability for crops using the Soil Survey Geographic Database—SSURGO indicates a scarcity in high-quality arable land available for cropland expansion