Privacy and Confidentiality in Process Mining: Threats and Research Challenges

Privacy and confidentiality are very important prerequisites for applying process mining to comply with regulations and keep company secrets. This article provides a foundation for future research on privacy-preserving and confidential process mining techniques. Main threats are identified and related to a motivation application scenario in a hospital context as well as to the current body of work on privacy and confidentiality in process mining. A newly developed conceptual model structures the discussion that existing techniques leave room for improvement. This results in a number of important research challenges that should be addressed by future process mining research.

On the Spectroscopic Analyses of Polytetrafluoroethylene Coated with Nano ZnO and SiO2

On polytetrafluoroethylene (PTFE) polymer nanocomposites coated with basically two metal oxides (MOs), SiO2 and ZnO, as well as a mixture of the two MOs, density functional theory (DFT) computations were performed. The B3LYPL/LAN2DZ model was used to evaluate PTFE polymer nano composites suggested model structures. The physical and electrical properties of PTFE modified on surface with ZnO and SiO2 coated layer by layer change Total dipole moment (TDM) and HOMO/LUMO band gap energy ∆Eto be 13.0082 Debye and 0.6889 eV, respectively. Moreover, TDM and band gap energy (∆E) improved to 10.6053 Debye and 0.2727 eV, respectively, when the nanofiller was increased to 8 atoms. In addition, the results of the Molecular Electrostatic Potential (MESP) and the Quantitative Structure Activity Relationship (QSAR) showed that PTFE coated with ZnO and SiO2 improved electrical characteristics and thermal stability. As PTFE coated with ZnO and SiO2 layer by layer, all stability characteristics, including electrical and thermal stability, were enhanced. The improved PTFE can be used as a corrosion-inhibiting layer for astronaut suits, according to the predicted results.

Evaluation of CMIP6 Climate Models for Climate Change Impact Assessments in Upper Awash Basin, Ethiopia

Identifying GCMs that represent the climate of a specific area is crucial for climate change studies. However, the uncertainties in GCMs caused by computational constraints, such as coarser resolution, physical parameterizations, initializations, and model structures, make it imperative to identify a representative individual or group of GCM for a climate impact study. An advanced envelope-based multi-criteria selection approach was used to identify a subset of the most appropriate future GCMs in the Upper Awash Basin. The skill accounting is based on (1) the range of projected mean changes of climate variables, (2) range of variability in climate extremes and, (3) model run performance to represent historical climate data. Statistical downscaling and bias correction were made for the selected model runs. The downscaled and bias-corrected monthly values for precipitation are expected to increase from 0.42% to 2.82% in mid-century and 0.15% to 3.79% by the end century considering the SSP4.5 scenario. For SSP8.5, it increases from 1.45% to 5.51% and 2.57% to 9.78% in the respective periods. Likewise, under the SSP4.5 forcing scenario, the monthly average air temperature projected to be warmer, which increased from 0.68°C to 1.55°C during mid-century and 0.09°C to 1.92°C end-of-century. Meanwhile, for SSP8.5, the projection indicates an increment of 0.19°C to 1.98°C under mid-century and 2.37°C to 7.00°C end-century. The projected change of future precipitation and temperature in the study basin increases the precipitation intensities, wet days and dry spells due to high-temperature increment.

Structure of the Active Nanocomplex of Antiviral and Anti-Infectious Iodine-Containing Drug FS-1

Chromatographic analysis shows that the ionic nanostructured complex of the FS-1 drug contains nanocomplexes of α-dextrin with a size of ~40–48 Å. Based on good agreement between the UV spectra of the model structures and the experimental spectrum of the FS-1 drug, the structure of the active FS-1 nanocomplex is proposed. The structure of the active centers of the drug in the dextrin ring was calculated using the quantum-chemical approach DFT/B3PW91. The active centers, i.e., a complex of molecular iodine with lithium halide (I), a binuclear complex of magnesium and lithium containing molecular iodine, triiodide (II), and triiodide (III), are located inside the dextrin helix. The polypeptide outside the dextrin helix forms a hydrogen bond with dextrin in Complex I and coordinates the molecular iodine in Complex II. It is revealed that the active centers of the FS-1drug can be segregated from the dextrin helix and form complexes with DNA nucleotide triplets. The active centers of the FS-1 drug are only segregated on specific sections of DNA. The formation of a complex between the DNA nucleotide and the active center of FS-1 is a key stage in the mechanisms of anti-HIV, anti-coronavirus (Complex I) and antibacterial action (Complex II).

Homotopy theory of Moore flows (II)

This paper proves that the q-model structures of Moore flows and of multipointed d-spaces are Quillen equivalent. The main step is the proof that the counit and unit maps of the Quillen adjunction are isomorphisms on the q-cofibrant objects (all objects are q-fibrant). As an application, we provide a new proof of the fact that the categorization functor from multipointed d-spaces to flows has a total left derived functor which induces a category equivalence between the homotopy categories. The new proof sheds light on the internal structure of the categorization functor which is neither a left adjoint nor a right adjoint. It is even possible to write an inverse up to homotopy of this functor using Moore flows.

Model the structure of nano-modified aluminium alloy with the addition of boron carbide

Nanocomposite thin films based on Al-Si alloy with the addition of boron carbide (B4C) particles are widely used in various fields of modern high-tech industry. For their synthesis, the method of laser nanomodification was used, which made it possible to obtain samples with a dendritic structure. The parameters of laser radiation were selected on the basis of preliminary modeling of the temperature field of the system. To describe the ensemble of nanodendrites on the surface, we used modeling of their structure in variable phase field and temperature for the initial stages, as well as the approximation of diffusion-limited aggregation and fractal Brownian motion for subsequent time intervals. The model showed satisfactory adequacy, estimated on the basis of the ratio of fractal dimensions of experimental and model structures. The proposed approach can be useful for predicting the structure of nanomodified aluminum alloys with various additions.

Adaptive Compression for Online Computer Vision: An Edge Reinforcement Learning Approach

With the growth of computer vision-based applications, an explosive amount of images have been uploaded to cloud servers that host such online computer vision algorithms, usually in the form of deep learning models. JPEG has been used as the de facto compression and encapsulation method for images. However, standard JPEG configuration does not always perform well for compressing images that are to be processed by a deep learning model—for example, the standard quality level of JPEG leads to 50% of size overhead (compared with the best quality level selection) on ImageNet under the same inference accuracy in popular computer vision models (e.g., InceptionNet and ResNet). Knowing this, designing a better JPEG configuration for online computer vision-based services is still extremely challenging. First, cloud-based computer vision models are usually a black box to end-users; thus, it is challenging to design JPEG configuration without knowing their model structures. Second, the “optimal” JPEG configuration is not fixed; instead, it is determined by confounding factors, including the characteristics of the input images and the model, the expected accuracy and image size, and so forth. In this article, we propose a reinforcement learning (RL)-based adaptive JPEG configuration framework, AdaCompress. In particular, we design an edge (i.e., user-side) RL agent that learns the optimal compression quality level to achieve an expected inference accuracy and upload image size, only from the online inference results, without knowing details of the model structures. Furthermore, we design an explore-exploit mechanism to let the framework fast switch an agent when it detects a performance degradation, mainly due to the input change (e.g., images captured across daytime and night). Our evaluation experiments using real-world online computer vision-based APIs from Amazon Rekognition, Face++, and Baidu Vision show that our approach outperforms existing baselines by reducing the size of images by one-half to one-third while the overall classification accuracy only decreases slightly. Meanwhile, AdaCompress adaptively re-trains or re-loads the RL agent promptly to maintain the performance.

Estimating the Extreme Values in Gridding Data using Gauss Elimination Method: A New Approach in Potential Field Processing

The position of a maximum point of a function depends on its coefficients and order. The maximum horizontal gradient method is a popular method that greatly contributes to the detection of maximum points and approximation of geological structures edges. By adopting a mathematical logic, Blakely and Simpson established a quadratic function based on the characteristic of three points of a straight line in the fundamental directions. However, for potential field data like gravity and magnetic data, the coefficients of a quadratic function in each direction are not only dependent on the values of three points on a straight line, but also, they depend on the values of the surrounding points. This article proposes an algorithm which can detect maximum points more effectively in order to delineate geological structures boundaries from potential field data. The proposed algorithm uses a 3×3 neighborhood data grid to establish a two-variables function and to determine its coefficients by applying the Gaussian elimination method. After the two-variables function has been established, the algorithm estimates any extreme points and their positions from a set of four single-variable functions which correspond to the horizontal, vertical and the two diagonal directions by the cases x = 0, y = 0, y = -x and y = x of the main function. Finally, the conditions to detect the maximum point from the extreme points are defined. The validity of the algorithm was demonstrated on synthetic datasets generated by two different model structures. A real data application of the method has also been realized by estimating the geological boundaries by gravity data in the Vietnam’s continental shelf. The results obtained from the synthetic applications of the algorithm proved that it can determine more maximum points as compared to Blakely and Simpson method, and as a result, in all the test cases, it has drawn the real boundaries of the model structures more accurately. The application results of the method on real data revealed new boundary delineations in the research area, interpreted to be faults or fractures which lies between deep trench in the East Vietnam Sea.

Educational Beliefs of Prospective Physical Education and Sports Teachers

This study was conducted to reveal the educational beliefs of prospective physical education and sports teachers, who receive education at the higher education level in Turkey, according to dependent and independent variables. Within this scope, the “Educational Belief Scale” was used to collect the data from 359 prospective teachers. The data collected from the sample were first analyzed by SPSS 22.0 package software. In the correlation analysis, it was observed that the “Educational Beliefs Scale” and the progressivism subscale were correlated very strongly and positively, which was the strongest correlation in the analysis (r = 0.918; p < 0.05). In the regression analysis, four different model structures were created, where it was determined that the subscale with the highest prediction power for the “Educational Beliefs Scale” was the progressivism subscale that predicted 84% of the variance of the “Educational Beliefs Scale” (R2 = 0.840). In conclusion, it was discovered that the prospective teachers did not internalize the fundamentalism, which is included in the traditional educational philosophy. Significant responsibilities fall on the shoulders of academics and decision-makers to enable prospective teachers to save the valuable sections of basic information and values of the past and transfer them to new generations while approaching teaching with a perspective that promotes the potential of new generations in building a better civilization upon the achievements of past generations.

BSE49, a diverse, high-quality benchmark dataset of separation energies of chemical bonds

We present an extensive and diverse dataset of bond separation energies associated with the homolytic cleavage of covalently bonded molecules (A-B) into their corresponding radical fragments (A. and B.). Our dataset contains two different classifications of model structures referred to as “Existing” (molecules with associated experimental data) and “Hypothetical” (molecules with no associated experimental data). In total, the dataset consists of 4502 datapoints (1969 datapoints from the Existing and 2533 datapoints from the Hypothetical classes). The dataset covers 49 unique X-Y type single bonds (except H-H, H-F, and H-Cl), where X and Y are H, B, C, N, O, F, Si, P, S, and Cl atoms. All the reference data was calculated at the (RO)CBS-QB3 level of theory. The reference bond separation energies are non-relativistic ground-state energy differences and contain no zero-point energy corrections. This new dataset of bond separation energies (BSE49) is presented as a high-quality reference dataset for assessing and developing computational chemistry methods.