A Finer-Grain Analysis of the Leakage (Non) Resilience of OCB

OCB3 is one of the winners of the CAESAR competition and is among the most popular authenticated encryption schemes. In this paper, we put forward a fine-grain study of its security against side-channel attacks. We start from trivial key recoveries in settings where the mode can be attacked with standard Differential Power Analysis (DPA) against some block cipher calls in its execution (namely, initialization, processing of associated data or last incomplete block and decryption). These attacks imply that at least these parts must be strongly protected thanks to countermeasures like masking. We next show that if these block cipher calls of the mode are protected, practical attacks on the remaining block cipher calls remain possible. A first option is to mount a DPA with unknown inputs. A more efficient option is to mount a DPA that exploits horizontal relations between consecutive input whitening values. It allows trading a significantly reduced data complexity for a higher key guessing complexity and turns out to be the best attack vector in practical experiments performed against an implementation of OCB3 in an ARM Cortex-M0. Eventually, we consider an implementation where all the block cipher calls are protected. We first show that exploiting the leakage of the whitening values requires mounting a Simple Power Analysis (SPA) against linear operations. We then show that despite being more challenging than when applied to non-linear operations, such an SPA remains feasible against 8-bit implementations, leaving its generalization to larger implementations as an interesting open problem. We last describe how recovering the whitening values can lead to strong attacks against the confidentiality and integrity of OCB3. Thanks to this comprehensive analysis, we draw concrete requirements for side-channel resistant implementations of OCB3.

Monitoring of Casting Quality Using Principal Component Analysis Based Self-Organizing Map

The monitoring of casting quality is very important to ensure the safe operation of casting processes. In this paper, in order to improve the accurate detection of casting defects, a combined method based on Principal Component Analysis (PCA) and Self-Organizing Map (SOM) is presented. The proposed method reduces the dimensionality of the original data by the projection of the data onto a smaller subspace through PCA. It uses Hotelling’s T2 and Q statistics as essential features for characterizing the process functionality. The SOM is used to improve the separation between casting defects. It computes the metric distances based similarity, using the T2 and Q (T2Q) statistics as input. A comparative study between conventional SOM, SOM with reduced data and SOM with selected features is examined. The proposed method is used to identify the running conditions of the low pressure lost foam casting process. The monitoring results indicate that the SOM based on T2Q as feature vectors remains important comparatively to conventional SOM and SOM based on reduced data.

Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks

Herein, an artificial neural network (ANN)-based approach for the efficient automated modeling and simulation of isotropic hyperelastic solids is presented. Starting from a large data set comprising deformations and corresponding stresses, a simple, physically based reduction of the problem’s dimensionality is performed in a data processing step. More specifically, three deformation type invariants serve as the input instead of the deformation tensor itself. In the same way, three corresponding stress coefficients replace the stress tensor in the output layer. These initially unknown values are calculated from a linear least square optimization problem for each data tuple. Using the reduced data set, an ANN-based constitutive model is trained by using standard machine learning methods. Furthermore, in order to ensure thermodynamic consistency, the previously trained network is modified by constructing a pseudo-potential within an integration step and a subsequent derivation which leads to a further ANN-based model. In the second part of this work, the proposed method is exemplarily used for the description of a highly nonlinear Ogden type material. Thereby, the necessary data set is collected from virtual experiments of discs with holes in pure plane stress modes, where influences of different loading types and specimen geometries on the resulting data sets are investigated. Afterwards, the collected data are used for the ANN training within the reduced data space, whereby an excellent approximation quality could be achieved with only one hidden layer comprising a low number of neurons. Finally, the application of the trained constitutive ANN for the simulation of two three-dimensional samples is shown. Thereby, a rather high accuracy could be achieved, although the occurring stresses are fully three-dimensional whereas the training data are taken from pure two-dimensional plane stress states.

Flexible Fitting of PROTAC Concentration–Response Curves with Changepoint Gaussian Processes

A proteolysis-targeting chimera (PROTAC) is a new technology that marks proteins for degradation in a highly specific manner. During screening, PROTAC compounds are tested in concentration–response (CR) assays to determine their potency, and parameters such as the half-maximal degradation concentration (DC50) are estimated from the fitted CR curves. These parameters are used to rank compounds, with lower DC50 values indicating greater potency. However, PROTAC data often exhibit biphasic and polyphasic relationships, making standard sigmoidal CR models inappropriate. A common solution includes manual omitting of points (the so-called masking step), allowing standard models to be used on the reduced data sets. Due to its manual and subjective nature, masking becomes a costly and nonreproducible procedure. We therefore used a Bayesian changepoint Gaussian processes model that can flexibly fit both nonsigmoidal and sigmoidal CR curves without user input. Parameters such as the DC50, maximum effect Dmax, and point of departure (PoD) are estimated from the fitted curves. We then rank compounds based on one or more parameters and propagate the parameter uncertainty into the rankings, enabling us to confidently state if one compound is better than another. Hence, we used a flexible and automated procedure for PROTAC screening experiments. By minimizing subjective decisions, our approach reduces time and cost and ensures reproducibility of the compound-ranking procedure. The code and data are provided on GitHub ( https://github.com/elizavetasemenova/gp_concentration_response ).

Automated stopping criterion for spectral measurements with active learning

The automated stopping of a spectral measurement with active learning is proposed. The optimal stopping of the measurement is realised with a stopping criterion based on the upper bound of the posterior average of the generalisation error of the Gaussian process regression. It is revealed that the automated stopping criterion of the spectral measurement gives an approximated X-ray absorption spectrum with sufficient accuracy and reduced data size. The proposed method is not only a proof-of-concept of the optimal stopping problem in active learning but also the key to enhancing the efficiency of spectral measurements for high-throughput experiments in the era of materials informatics.

Classification of Ataxic Gait

Gait disorders accompany a number of neurological and musculoskeletal disorders that significantly reduce the quality of life. Motion sensors enable high-quality modelling of gait stereotypes. However, they produce large volumes of data, the evaluation of which is a challenge. In this publication, we compare different data reduction methods and classification of reduced data for use in clinical practice. The best accuracy achieved between a group of healthy individuals and patients with ataxic gait extracted from the records of 43 participants (23 ataxic, 20 healthy), forming 418 segments of straight gait pattern, is 98% by random forest classifier preprocessed by t-distributed stochastic neighbour embedding.

Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs

Hyperspectral images (HSIs) contain large amounts of spectral and spatial information, and this provides the possibility for ground object classification. However, when using the traditional method, achieving a satisfactory classification result is difficult because of the insufficient labeling of samples in the training set. In addition, parameter adjustment during HSI classification is time-consuming. This paper proposes a novel fusion method based on the maximum noise fraction (MNF) and adaptive random multigraphs for HSI classification. Considering the overall spectrum of the object and the correlation of adjacent bands, the MNF was utilized to reduce the spectral dimension. Next, a multiscale local binary pattern (LBP) analysis was performed on the MNF dimension-reduced data to extract the spatial features of different scales. The obtained multiscale spatial features were then stacked with the MNF dimension-reduced spectral features to form multiscale spectral-spatial features (SSFs), which were sent into the RMG for HSI classification. Optimal performance was obtained by fusion. For all three real datasets, our method achieved competitive results with only 10 training samples. More importantly, the classification parameters corresponding to different hyperspectral data can be automatically optimized using our method.

The Role of Fresh Graduates as Learning Volunteers in the Covid-19 Pandemic

This study aim to knowing the effectiveness of efforts to empower the latest undergraduate graduates to help volunteer for learning during the Covid-19 pandemic. This research is qualitative research with a descriptive method that seeks to describe the role that the undergraduate can become a volunteer for learning. Data analysis techniques are in the form of collecting data from respondents. Data is reduced, data is presented, and then analyzed. The results obtained were found that fresh graduates were graduates who had just graduated from campuses around the Flores area in general and IKIP Muhammadiyah Maumere in particular and their roles in many ways teachers, students and being assistants. Parents in accompanying their children to study at and from home. From the implementation of this volunteer, it was found that there were changes in connection with the assisted learning process from home, which was enforced, school children felt helped in terms of progress in subject matter and were not left behind in understanding the material provided by teachers from school, parents and students also got new knowledge about the use of IT.

ON CREATIVITY THROUGH MATHEMATIZATION IN SOLVING NON-ROUTINE PROBLEMS

This study aimed to describe and compare the students’ fluency, flexibility, and originality in solving non-routine problems in the Palembang context. They were depicted from the student’s fluency, flexibility, and originality of solving the horizontal and vertical mathematization forms. This qualitative study employed. The subjects of this study were 30 students of grade nine of junior high schools in Palembang. The instruments used were tests and interviews. The tests were employed to investigate the written horizontal and vertical mathematizations forms. Meanwhile, the interviews were to explore the students’ ideas with inadequately detailed answers. Then, the test and interview data were reduced and grouped based on the indicators of creativity. The reduced data were presented in a descriptive form for conclusions. The results of the data analysis showed that the high-ability students were the most fluent and flexible in solving the problems. Still, the provided solutions were less original and tended to use formal mathematics in the forms of formulas, symbols, and operations. Meanwhile, the moderate-ability students tended to start to solve problems by simplifying them, then presenting them in visual images. The answer sheets of the moderate-ability students revealed their fluency in understanding the problems and solutions, flexibility, and originality of thinking. This study obtained different results from the low-ability students who tended to have difficulties understanding the problems and made many errors in solving them. Such a condition showed their inability to write the known data and relate the data to other facts they had already learned. As a result, their answers did not represent fluency, flexibility, and originality.