Machine learning for access control policy verification

Advancements in the implementation of quantum hardware have enabled the acquisition of data that are intractable for emulation with classical computers. The integration of classical machine learning (ML) algorithms with these data holds potential for unveiling obscure patterns. Although this hybrid approach extends the class of efficiently solvable problems compared to using only classical computers, this approach has been only realized for solving restricted problems because of the prevalence of noise in current quantum computers. Here, we extend the applicability of the hybrid approach to problems of interest in many-body physics, such as predicting the properties of the ground state of a given Hamiltonian and classifying quantum phases. By performing experiments with various error-reducing procedures on superconducting quantum hardware with 127 qubits, we managed to acquire refined data from the quantum computer. This enabled us to demonstrate the successful implementation of theoretically suggested classical ML algorithms for systems with up to 44 qubits. Our results verify the scalability and effectiveness of the classical ML algorithms for processing quantum experimental data.

Background & Problem Statement - Software testing is a critical phase in the software development lifecycle (SDLC), ensuring that applications function correctly, meet user requirements, and maintain high- quality standards. Traditional software testing approaches, including manual testing and rule-based automation, often face challenges in scalability, efficiency, and adaptability to dynamic software environments. Traditional testing methods are overwhelmed by complex software systems which slows down defect detection and extends both testing costs and release schedules. Machine Learning (ML) has emerged as a transformative solution, introducing predictive and adaptive capabilities that optimize test case selection, automate defect detection, and enhance overall software quality assurance (QA). This study explores the integration of ML in software testing, addressing the challenges of traditional QA methodologies and demonstrating how AI-driven frameworks improve testing efficiency. Methodology - To investigate the impact of ML in software testing, this research adopts a systematic approach by analyzing ML-driven test automation techniques, including predictive testing, adaptive test execution, and automated test case generation. Research reviews how Google Microsoft Facebook IBM and Deep Code put ML-based quality assurance frameworks into operation. The study leverages supervised learning, reinforcement learning, deep learning, and NLP-based techniques to demonstrate how ML models predict software defects, dynamically adapt test cases, and optimize testing resources. The research tests how ML-based testing models operate within CI/CD pipelines to improve ongoing testing and deployment flow. Analysis & Results - The analysis of ML-driven software testing reveals that predictive analytics improves early defect detection rates. It helps developers spend 37% less time debugging their work. Adaptive testing models, including self-healing test scripts, minimize maintenance costs by 50% and enhance test reliability in agile environments. The integration of NLP-based test case generation increases test coverage. NLP technology enables automatic connection between requirements and test cases at 89% success rate. Additionally, reinforcement learning techniques improve test case selection, reducing redundant test executions by 43%. Our research shows different ML methods work well to lessen incorrect error alerts. ML integration for QA surely increasing defect prediction accuracy and optimizing test execution time. Findings & Contributions - This research contributes to the field of AI-driven software testing by providing a comprehensive framework for ML-based QA methodologies. Our study shows that machine learning helps find more software problems better adapts test cases and lowers testing expenses to solve present software development needs. The study also identifies critical challenges, including data availability, model interpretability, and computational overhead, suggesting future research directions in Explainable AI (XAI), hybrid AI-ML testing models, and AI-driven security testing. As the industry moves toward AI-first software testing, this research paves the way for fully autonomous QA frameworks, enabling intelligent, scalable, and cost- effective software validation techniques. Keywords - Machine Learning, Software Testing, Quality Assurance, Predictive Testing, Adaptive Testing, Test Automation, Defect Prediction, Self- Healing Test Scripts, AI-Driven QA, Reinforcement

Despite the promise for performance and accuracy improvements of quantum inspired (QI) algorithms over classical machine learning (ML) algorithms, such gains have not been realized in practice. The quantum inspired algorithms can theoretically achieve significant speed up based on sampling assumptions and have thus far failed to outperform the existing classical ML models in practical applications. The speedup of quantum machine learning (QML) algorithms assume the access to data in quantum random access memory (QRAM) which is a strong assumption with current quantum architectures. QI algorithms assume sample and query (SQ) access to input vector and norms of matrices using a dynamic data structure. We explore the components of these models and the assumptions in this paper by surveying the recent works in QML and QI Machine learning (QIML) algorithms. We limit our study to QML and QIML models on achieving a speed up over classical ML techniques rather than individual proofs of these algorithms. This study highlights the assumptions being made that are currently not practical for QML and QIML algorithms in achieving performance advantage over classical ML algorithms.

Machine learning today becomes more and more effective instrument to solve many particular problems, where there are difficulties to apply well known and described math model. In other words - it is a great tool to describe nonlinear phenomena. We tried to use this technique to improve existing process of stratigraphy and lithology interpretation and reduce costs on site by applying computer leaded predictions on the basis of existing on-field collected data. Article describes usage of machine learning algorithms for several geology data stratigraphy and lithology boundaries classification based on geophysics logging data for deposits in Kazakhstan. Correct marking of stratigraphy and lithology from geophysics logging data is complex non-linear task. To solve this task we applied several algorithms of machine learning: random forest, logistic regression, gradient boosting (scikit-learn library), k - nearest neighbour (KNN) and XGBoost.

Software testing is one of the most important Software Engineering processes, being the primary activity to check the conformance between the software requirements and its actual behavior. The automation of software testing activities is essential to certify productivity and effectiveness in such activities. Test automation leads testing activities to be conducted under systematic and accurate criteria, raising the chance of testers to reveal faults or inconsistencies. Test oracles are elementary members in software testing automation, being the mechanism responsible for indicating the correctness of software outputs. In testing environments, test oracles can be effectively implemented based on several sources of information about the Software Under Testing (SUT): software specifications, assertions, formal methods (Finite State Machines (FSM), formal specifications, etc, machine-learning methods, and metamorphic relations. Regardless of the implementation strategy, test oracles are vulnerable to false positive/negative verdicts, configuring what the literature describes as "the oracle problem". Therefore, test oracles are a non-trivial and challenging object of studies of the software engineering research area. SUT's outputs in unusual formats make it harder the oracle problem. Audio, images, three-dimensional objects, virtual reality environments, complex statistical compositions, etc, are examples of non-trivial output formats. In the software testing context, SUTs with unusual outputs can be called "complex-output systems". In this doctorate dissertation, we propose and evaluate a novel test oracle approach for complex-output systems called feature-based test oracles. The purpose of feature-based test oracles is the appropriation of a processing image technique called Content-Based Image Retrieval (CBIR) to collect information from features extracted from the SUT's outputs to compose test oracles. Given a query image, CBIR combines feature extraction and similarity functions to alleviate the problem of searching for digital images in large databases. In previous research, we have integrated CBIR concepts in a testing framework to support the automation of testing activities in processing image systems and systems with Graphical User Interfaces (GUI). In this doctorate dissertation, we extended that framework and its concepts to general complex-output systems, addressing the feature-based test oracle approach. We use Text-To-Speech (TTS) systems to validate empirically our test oracle technique. Through the results of five empirical analyses, three of them conducted in line with problems of a real-world industry TTS system, show the proposed technique is a valuable instrument to automate testing activities and alleviate practitioners' efforts on testing complex output systems. We conclude the proposed test oracles are effective because they systematically evaluate the SUT's sensorial output rather than produce verdicts based on subjective specifications. As future work, we plan to conduct investigations towards the reduction of false positives/negatives and the association of the test oracles with machine learning techniques and metamorphic relations.

Although the consortium blockchains commonly adopt consensus algorithms with Byzantine fault tolerance (such as practical Byzantine fault tolerance (PBFT)), its consensus efficiency will be degraded by the existence of malicious nodes or behaviors. However, the existing researches mainly focus on the detection of malicious behaviors for public blockchains, but are rare about consortium blockchains. In this paper, an anomaly detection model based on machine learning (ML) classification algorithm is proposed for consortium blockchains that adopt PBFT. Besides, a two-stage process is proposed to reduce the resource consumption for anomaly detection. The data needed for proposed model only has two dimensions and is convenient to obtain. The results of experiment show that ML is very effective in anomaly detection for consortium blockchains. Specifically, the algorithms with the highest accuracy are convolutional neural networks (CNN), k-nearest neighbor (KNN) and support vector machines (SVM) in turn. However, KNN and SVM are more suitable because resource consumption of both algorithms are one third of CNN, and the accuracy rates are above 0.9 which is 0.9% lower than CNN.

Building machine learning (ML) tools, or systems, for use in manufacturing environments is a challenge that extends far beyond the understanding of the ML algorithm. Yet, these challenges, outside of the algorithm, are less discussed in literature. Therefore, the purpose of this work is to practically illustrate several best practices, and challenges, discovered while building an ML system to detect tool wear in metal CNC machining. Namely, one should focus on the data infrastructure first; begin modeling with simple models; be cognizant of data leakage; use open-source software; and leverage advances in computational power. The ML system developed in this work is built upon classical ML algorithms and is applied to a real-world manufacturing CNC dataset. The best-performing random forest model on the CNC dataset achieves a true positive rate (sensitivity) of 90.3% and a true negative rate (specificity) of 98.3%. The results are suitable for deployment in a production environment and demonstrate the practicality of the classical ML algorithms and techniques used. The system is also tested on the publicly available UC Berkeley milling dataset. All the code is available online so others can reproduce and learn from the results.

Classical machine learning (ML) provides a potentially powerful approach to solving challenging quantum many-body problems in physics and chemistry. However, the advantages of ML over traditional methods have not been firmly established. In this work, we prove that classical ML algorithms can efficiently predict ground-state properties of gapped Hamiltonians after learning from other Hamiltonians in the same quantum phase of matter. By contrast, under a widely accepted conjecture, classical algorithms that do not learn from data cannot achieve the same guarantee. We also prove that classical ML algorithms can efficiently classify a wide range of quantum phases. Extensive numerical experiments corroborate our theoretical results in a variety of scenarios, including Rydberg atom systems, two-dimensional random Heisenberg models, symmetry-protected topological phases, and topologically ordered phases.

The increased technological developments in Unmanned Aerial Vehicles (UAVs) combined with artificial intelligence and Machine Learning (ML) approaches have opened the possibility of remote sensing of extensive areas of arid lands. In this paper, a novel approach towards the detection of termite mounds with the use of a UAV, hyperspectral imagery, ML and digital image processing is intended. A new pipeline process is proposed to detect termite mounds automatically and to reduce, consequently, detection times. For the classification stage, several ML classification algorithms’ outcomes were studied, selecting support vector machines as the best approach for their role in image classification of pre-existing termite mounds. Various test conditions were applied to the proposed algorithm, obtaining an overall accuracy of 68%. Images with satisfactory mound detection proved that the method is “resolution-dependent”. These mounds were detected regardless of their rotation and position in the aerial image. However, image distortion reduced the number of detected mounds due to the inclusion of a shape analysis method in the object detection phase, and image resolution is still determinant to obtain accurate results. Hyperspectral imagery demonstrated better capabilities to classify a huge set of materials than implementing traditional segmentation methods on RGB images only.

The process of building new dataset and the existence of such a data followed the urgent need for the existence of datasets that are specialized in educational lectures, so this will need an accurate classification algorithm to classify it, the benefit of classify such dataset is to minimize the workload of classifying each file manually and individually. In the present paper, authors perform experimentations for conducting an empirical deep learning study, especially, convolutional neural network, for three new datasets of educational lectures which are (PDF, Word and PowerPoint datasets), The three new datasets using real data educational resources lectures collected from various document projects of different universities and institutions. The architecture has been applied to the task of the text classification in the domain of the document with documents data-sets have been obtained from a variety of projects on actual document cases. The aim of the present study is to initially test the performance of each dataset (PDF, Word, and PowerPoint dataset) through using four machine learning classification algorithms which are (Bayes Net, Random Forest, Random Committee, and OneR). Second goal is experimenting the efficiency of the approach of the deep learning in the tasks of classification and after that, comparing the efficiencies with the efficiencies of traditional machine learning classification methods. Mainly two classification techniques used to maximize the benefits of the classification process, the first one is to use the deep learning algorithm which shows an accuracy of classifying file between (95 and 96%) for three new dataset files and standard machine learning algorithms (OneR, Random forest, Bayes net, and Random Committee ) these algorithm shows accuracy 91% for PDF Dataset using random forest and random committee algorithms, for Word dataset the accuracy is 46% using random committee, and for the last dataset PowerPoint the accuracy is 77% using random forest, Therefore, we will choose Deep learning algorithm because it gives higher results and accuracy than machine learning algorithms.

Out of all the phases of Software Development Life Cycle (SDLC), the software testing phase consumes the maximum amount of time when done manually. To reduce the expenses and fasten the process of software testing, researchers have proposed many models to automate the software testing to make it efficient, scaled, and less expensive. However, to automate the software testing process, an optimal and efficient set of test cases is required which can test the software for all possible errors and failures. If test cases are generated just randomly, then the test cases may not be optimal and even if the set is large, it can skip some error checking of software. Teaching learning-based optimization (TLBO) algorithm is used in this paper to generate software test case. Approach level and combined fitness function are used to optimize the test cases such that every path including critical path can be covered. Performance of the approach is compared for both fitness functions considering triangle classification problem (TCP). It is observed that TLBO is able to generate better test cases than some existing models.KeywordsSoftware testingTeaching learning-based optimizationTest case generationApproximation levelBranch distance

Datalog is a popular and widely-used declarative logic programming language. Datalog engines apply many cross-rule optimizations; bugs in them can cause incorrect results. To detect such optimization bugs, we propose an automated testing approach called Incremental Rule Evaluation (IRE), which synergistically tackles the test oracle and test case generation problem. The core idea behind the test oracle is to compare the results of an optimized program and a program without cross-rule optimization; any difference indicates a bug in the Datalog engine. Our core insight is that, for an optimized, incrementally-generated Datalog program, we can evaluate all rules individually by constructing a reference program to disable the optimizations that are performed among multiple rules. Incrementally generating test cases not only allows us to apply the test oracle for every new rule generated—we also can ensure that every newly added rule generates a non-empty result with a given probability and eschew recomputing already-known facts. We implemented IRE as a tool named Deopt, and evaluated Deopt on four mature Datalog engines, namely Soufflé, CozoDB, μZ, and DDlog, and discovered a total of 30 bugs. Of these, 13 were logic bugs, while the remaining were crash and error bugs. Deopt can detect all bugs found by queryFuzz, a state-of-the-art approach. Out of the bugs identified by Deopt, queryFuzz might be unable to detect 5. Our incremental test case generation approach is efficient; for example, for test cases containing 60 rules, our incremental approach can produce 1.17× (for DDlog) to 31.02× (for Soufflé) as many valid test cases with non-empty results as the naive random method. We believe that the simplicity and the generality of the approach will lead to its wide adoption in practice.

This article investigates the problem of classifying network devices based on their security parameters using machine learning. Due to the constant growth of threats in cyberspace and the need to ensure a high level of network security, the relevance of using machine learning technologies to identify and classify secure devices is exceptionally high. Therefore, the article considers the specifics of applying machine learning algorithms for classification and regression tasks in network environments. Particular attention is paid to a short review of the algorithms most commonly used for classification tasks. The work describes in detail the process of developing a machine-learning model aimed at classifying network devices according to their security indicators. It considers the selection of appropriate parameters for model training, the process of data preprocessing, and the selection and adjustment of the classification algorithm. The results of model training on actual data are also presented. The process of training and evaluating the accuracy and efficiency of the model is described. The results are analyzed, and the choice of optimal hyperparameters is justified. As a result of the study, an effective machine learning model has been developed that can accurately classify network devices by security level, improving network security when selecting potentially secure devices. The study found that the Random Forest and Decision Tree models showed the highest accuracy in predicting the security state of network devices compared to other models, such as Logistic Regression, k-NN, and Gradient Boosting. The performance of the trained model was tested on a validation dataset. The Decision Tree model correctly predicted the security level of approximately 78% of network devices.

As we know, software testing is an important part of software development lifecycle. More than 50% of the whole system development work and total cost were spend on the software testing. And it’s estimated that almost 60% of the total test time and cost were spent on the design of test cases. In recent years, the automated testing gradually replaces the traditional manual testing to become an important branch of software testing. In automated testing, the most important thing is to design and generate valid test case automatically. In this paper, In order to reduce the workload of testers on the test case generation, improve the test efficiency and makes test experiences be passed on, we design a common description method for the test case based on ontology; propose a semantic similarity measure method to retrieve the usable test cases from test case library based on WordNet; establish the relationship between the test case and test requirement though rules to modify the retrieved test case; generate the final test case sequence. At last, the practical applicability of the approach is evaluated through an experiment. Keywords-software testing; ontology; test case generation; semantic similarity measure

Artificial intelligence (AI) is a tool thought to revolutionize the field of reproductive medicine in the years to come. Specifically, machine learning (ML), which is a subset of AI methods used to detect patterns and make predictions based on large datasets, has been used in ART to predict implantation outcome, embryo transfer strategies or adverse outcomes for IVF treatments (for a review see, Wang et al., 2019). However, ML methods have not yet been widely adopted in medically assisted reproduction (MAR). This slow uptake could be due to a lack of in-depth interdisciplinary communication between ML experts and clinicians/embryologists, as well as some uncertainty on how ML models could be generalized for different populations. The February edition of the ESHRE Journal Club discussed a paper from Yland et al. (2022) where the authors compared different ML algorithms to predict the chance of pregnancy in couples actively trying to conceive (without undergoing MAR). By using data from the Pregnancy Study Online (PRESTO), a web-based prospective cohort study of 4133 couples in North America (Wise et al., 2015), the authors analysed 163 potential variables and used supervised ML classification algorithms to identify the strength of each variable in predicting pregnancy outcomes of couples across the fertility spectrum (infertile: <12 menstrual cycles; subfertile: within 6 menstrual cycles; and fecundability: the average probability of pregnancy per menstrual cycle). The authors were able to predict pregnancy with a discrimination as high as 71.2% and identify the variables that most consistently predicted conception. These variables included lifestyle and reproductive characteristics such as age, BMI, history of infertility, daily use of vitamins or folic acid, intercourse timing, diet quality and reduced stress. ESHRE Journal Club discussion focused on promoting interdisciplinarity among ML and reproductive medicine specialists. It hosted 50 participants on Twitter together with experts Michelle Perugini, Vajira Thambawita and authors Jennifer Yland, Yannis Paschalidis and Lauren Wise. The discussion resulted in 902 tweets and around 1 million impressions over a 24-h period.