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Hyperparameter tuning plays a crucial role in optimizing the performance of predictive learners. Cross-validation (CV) is a widely adopted technique for estimating the error of different hyperparameter settings. Repeated cross-validation (RCV) is commonly employed to reduce the variability of CV errors. This study investigates the efficacy of blocking cross-validation partitions and algorithm initialization seeds during hyperparameter tuning. The proposed approach, termed Controlled Cross-Validation (CCV), reduces variability in error estimates, enabling fairer and more reliable comparisons of predictive model performance. We provide both theoretical and empirical evidence to demonstrate that this blocking approach lowers the variance of the estimates compared to RCV. Our experiments indicate that the algorithm's internal random behavior often does not significantly affect CV error variability. We present extensive examples using real-world datasets to compare the effectiveness and efficiency of blocking the CV partitions when tuning the hyperparameters of different supervised predictive learning algorithms. [ABSTRACT FROM AUTHOR]

Researchers have created cryptography algorithms that encrypt data using a public or private key to secure it from intruders. It is insufficient to protect the data by using such a key. No research article has identified an algorithm capable of protecting both the data and the associated key, nor has any mechanism been developed to determine whether access to the data is permissible or impermissible based on the authentication of the key. This paper presents a WEDEx-Kerberotic Framework for data protection, in which a user-defined key is firstly converted to a cipher key using the "Secure Words on Joining Key (SWJK)" algorithm. Subsequently, a WEDEx-Kerberotic encryption mechanism is created to protect the data by encrypting it with the cipher key. The first reason for making the WEDEx-Kerberotic Framework is to convert the user-defined key into a key that has nothing to do with the original key, and the length of the cipher key is much shorter than the original key. The second reason is that each ciphertext and key value are interlinked. When an intruder utilizes the snatching mechanism to obtain data, the attacker obtains data or a key unrelated to the original data. No matter how efficient the algorithm is, an attacker cannot access the data when these methods and algorithms are used to protect it. Finally, the proposed algorithm is compared to the previous approaches to determine the uniqueness of the algorithm and assess its superiority to the previous algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we present Forest GUMP (for Generalized, Unifying Merge Process) a tool for verification and precise explanation of Random forests. Besides pre/post-condition-based verification and equivalence checking, Forest GUMP also supports three concepts of explanation, the well-known model explanation and outcome explanation, as well as class characterization, i.e., the precise characterization of all samples that are equally classified. Key technology to achieve these results is algebraic aggregation, i.e., the transformation of a Random Forest into a semantically equivalent, concise white-box representation in terms of Algebraic Decision Diagrams (ADDs). The paper sketches the method and demonstrates the use of Forest GUMP along illustrative examples. This way readers should acquire an intuition about the tool, and the way how it should be used to increase the understanding not only of the considered dataset, but also of the character of Random Forests and the ADD technology, here enriched to comprise infeasible path elimination. As Forest GUMP is publicly available all experiments can be reproduced, modified, and complemented using any dataset that is available in the ARFF format. [ABSTRACT FROM AUTHOR]

Generating streams of true random numbers is a critical component of many electronic and information systems. The design of fully integrated, area and power efficient true random number generators (TRNGs) is a challenge. We propose a fully integrated, lightweight implementation that uses the random telegraph noise (RTN) of standard MOSFET as entropy source. It is not analog-intensive, and without traditional post-processing algorithms, the generated random bit sequence passes the National Institute of Standards and Technology (NIST) tests. [ABSTRACT FROM AUTHOR]

Statistical learning includes methods that extract knowledge from complex data. Statistical learning methods beyond generalized linear models, such as shrinkage methods or kernel smoothing methods, are being increasingly implemented in public health research and epidemiology because they can perform better in instances with complex or high-dimensional data-settings in which traditional statistical methods fail. These novel methods, however, often include random sampling which may induce variability in results. Best practices in data science can help to ensure robustness. As a case study, we included four statistical learning models that have been applied previously to analyze the relationship between environmental mixtures and health outcomes. We ran each model across 100 initializing values for random number generation, or 'seeds', and assessed variability in resulting estimation and inference. All methods exhibited some seed-dependent variability in results. The degree of variability differed across methods and exposure of interest. Any statistical learning method reliant on a random seed will exhibit some degree of seed sensitivity. We recommend that researchers repeat their analysis with various seeds as a sensitivity analysis when implementing these methods to enhance interpretability and robustness of results. [ABSTRACT FROM AUTHOR]

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Random numbers play a vital role in cryptography, where they are used to generate keys, nonce, one-time pads, and initialization vectors for symmetric encryption. The quality of random number generator (RNG) has significant implications on vulnerability and performance of these algorithms. A pseudo-RNG uses a deterministic algorithm to produce numbers with a distribution very similar to uniform. True RNGs (TRNGs), on the other hand, use some natural phenomenon/process to generate random bits. They are nondeterministic, because the next number to be generated cannot be determined in advance. In this paper, a novel on-chip noise source, random telegraph noise (RTN), is exploited for simple and reliable TRNG. RTN, a microscopic process of stochastic trapping/detrapping of charges, is usually considered as a noise and mitigated in design. Through physical modeling and silicon measurement, we demonstrate that RTN is appropriate for TRNG, especially in highly scaled MOSFETs. Due to the slow speed of RTN, we purpose the system for on-chip seed generation for random number. Our contributions are: 1) physical model calibration of RTN with comprehensive 65- and 180-nm transistor measurements; 2) the scaling trend of RTN, validated with silicon data down to 28 nm; 3) design principles to achieve 50% signal probability by using intrinsic RTN physical properties, without traditional postprocessing algorithms, the generated sequence passes the National Institute of Standards and Technology (NIST) tests; and 4) solutions to manage realistic issues in practice, including multilevel RTN signal, robustness to voltage and temperature fluctuations and the operation speed. [ABSTRACT FROM PUBLISHER]