Computational Problems Research Articles

A Bayesian approach for exploring person × environment interaction within the environmental sensitivity meta-framework.

For investigating the individual-environment interplay and individual differences in response to environmental exposures as captured by models of environmental sensitivity including Diathesis-stress, Differential Susceptibility, and Vantage Sensitivity, over the last few years, a series of statistical guidelines have been proposed. However, available solutions suffer of computational problems especially relevant when sample size is not sufficiently large, a common condition in observational and clinical studies. In the current contribution, we propose a Bayesian solution for estimating interaction parameters via Monte Carlo Markov Chains (MCMC), adapting Widaman etal. (Psychological Methods, 17, 2012, 615) Nonlinear Least Squares (NLS) approach. Findings from an applied exemplification and a simulation study showed that with relatively big samples both MCMC and NLS estimates converged on the same results. Conversely, MCMC clearly outperformed NLS, resolving estimation problems and providing more accurate estimates, particularly with small samples and greater residual variance. As the body of research exploring the interplay between individual and environmental variables grows, enabling predictions regarding the form of interaction and the extent of effects, the Bayesian approach could emerge as a feasible and readily applicable solution to numerous computational challenges inherent in existing frequentist methods. This approach holds promise for enhancing the trustworthiness of research outcomes, thereby impacting clinical and applied understanding.

Feedback stabilization of probabilistic finite state machines based on deep Q-network

BackgroundAs an important mathematical model, the finite state machine (FSM) has been used in many fields, such as manufacturing system, health care, and so on. This paper analyzes the current development status of FSMs. It is pointed out that the traditional methods are often inconvenient for analysis and design, or encounter high computational complexity problems when studying FSMs.MethodThe deep Q-network (DQN) technique, which is a model-free optimization method, is introduced to solve the stabilization problem of probabilistic finite state machines (PFSMs). In order to better understand the technique, some preliminaries, including Markov decision process, ϵ-greedy strategy, DQN, and so on, are recalled.ResultsFirst, a necessary and sufficient stabilizability condition for PFSMs is derived. Next, the feedback stabilization problem of PFSMs is transformed into an optimization problem. Finally, by using the stabilizability condition and deep Q-network, an algorithm for solving the optimization problem (equivalently, computing a state feedback stabilizer) is provided.DiscussionCompared with the traditional Q learning, DQN avoids the limited capacity problem. So our method can deal with high-dimensional complex systems efficiently. The effectiveness of our method is further demonstrated through an illustrative example.

Non‐Euclidean Sliced Optimal Transport Sampling

AbstractIn machine learning and computer graphics, a fundamental task is the approximation of a probability density function through a well‐dispersed collection of samples. Providing a formal metric for measuring the distance between probability measures on general spaces, Optimal Transport (OT) emerges as a pivotal theoretical framework within this context. However, the associated computational burden is prohibitive in most real‐world scenarios. Leveraging the simple structure of OT in 1D, Sliced Optimal Transport (SOT) has appeared as an efficient alternative to generate samples in Euclidean spaces. This paper pushes the boundaries of SOT utilization in computational geometry problems by extending its application to sample densities residing on more diverse mathematical domains, including the spherical space 𝕊d, the hyperbolic plane ℍd, and the real projective plane ℙd. Moreover, it ensures the quality of these samples by achieving a blue noise characteristic, regardless of the dimensionality involved. The robustness of our approach is highlighted through its application to various geometry processing tasks, such as the intrinsic blue noise sampling of meshes, as well as the sampling of directions and rotations. These applications collectively underscore the efficacy of our methodology.

Malware Executables Detection Using XGBOOST

Abstract: Malicious software, called malware, is a big problem for computers. It can steal information, cause financial losses, and more. We came up with a new way to find malware using a smart computer program called XGBoost. XGBoost is good at solving problems with computers. We used it to tell if a computer program is malware or not. We looked at different things about the program, like how it's made and what it does. In our study, we explain how our system works. We talk about how we got the data ready, what things we looked at in the programs, and how we taught the XGBoost program to find malware. Our tests show that our system is good at finding malware. It's accurate and doesn't make many mistakes. This means it can help protect computers from malware. In short, our research helps keep computers safe from bad programs like malware. We use a smart program called XGBoost and look at many things in the program to tell if it's harmful or not. It works well and can be used in real life to stop malware.

Understanding the Characteristics of Students’ Behavioral Processes in Solving Computational Thinking Problems Based on the Behavioral Sequences

Previous research has not adequately explored students’ behavioral processes when addressing computational thinking (CT) problems of varying difficulty, limiting insights into students’ detailed CT development characteristics. This study seeks to fill this gap by employing gamified CT items across multiple difficulty levels to calculate comprehensive behavioral sequence quality indicators. And then, through latent profile analysis, we identified four distinct latent classes of behavioral process. We then examined the in-game performance differences among these classes, uncovering each class’s unique attributes. Class 1 students consistently demonstrated high-quality, efficient behavioral sequences regardless of item difficulty. In contrast, class 2 students applied significant cognitive effort and trial-and-error strategies, achieving acceptable scores despite low behavioral sequence quality. Class 3 students excelled in simpler items but faltered with more complex ones. Class 4 students displayed low motivation for challenging items, often guessing answers quickly. Additionally, we investigated the predictive value of students’ performance in gamified items and their behavioral process classes for their external CT test scores. The study finally elaborated on the theoretical implications for researchers and the practical suggestions for teachers in CT cultivation.

STEM: Its Potential in Developing Students' Computational Thinking

Computational thinking skills are a person’s ability to solve problems with several passed processes. The process is recognizing patterns, decomposing patterns, compiling algorithms, making models, and doing abstractions. This ability is essential for students to master understanding aspects of computational problems. It also helps students evaluate the suitability of computational techniques to solve the problem. In addition, this computational thinking ability can also help students understand the limitations and strengths of computing techniques, apply or adapt computing tools/techniques to new uses, recognize opportunities to use computing in new ways, and apply computational strategies. The STEM approach, is learning that integrates science, technology, engineering, and mathematics to develop creativity in solving problems. The transformation of the educational process in the current era that requires a high level of technology makes STEM one of the keys to progress and innovation in developing human competence. Based on a study of more than 30 articles and research results related to the effect of STEM on student learning outcomes, we can conclude that learning using STEM has the potential to develop students’ computational thinking skills. This article discusses how, theoretically, we can acquire computational thinking skills through education with a STEM approach. Keywords: STEM, developing students’, computational thingking

A Systematic Review of Multi-Objective Evolutionary Algorithms Optimization Frameworks

The study of evolutionary algorithms (EAs) has witnessed an impressive increase during the last decades. The need to explore this area is determined by the growing request for design and the optimization of more and more engineering problems in society, such as highway construction processes, food and agri-technologies processes, resource allocation problems, logistics and transportation systems, microarchitectures, suspension systems optimal design, etc. All of these matters refer to specific highly computational problems with a huge design space, hence the obvious need for evolutionary algorithms and frameworks, or platforms that allow for the implementing and testing of such algorithms and methods. This paper aims to comparatively analyze the existing software platforms and state-of-the-art multi-objective optimization algorithms and make a review of what features exist and what features might be included next as further developments in such tools, from a researcher’s perspective. Additionally, it is essential for a framework to be easily extendable with new types of problems and optimization algorithms, metrics and quality indicators, genetic operators or specific solution representations and results analysis and comparison features. After presenting the most relevant existing features in these types of platforms, we suggest some future steps and the developments we have been working on.

SEHRNet: A lightweight, high‐resolution network for aircraft keypoint detection

AbstractCurrent research on apron conflict detection is often limited to the interaction between the aircraft as a whole and other objects, making it difficult to accomplish targeted identification of vulnerable and high‐cost aircraft components. However, the implementation of detailed aircraft identification is of great significance to enhance the safety of airport surface operations. Based on the excellent performance of High‐Resolution Network (HRNet) in keypoint detection, a lightweight end‐to‐end keypoint detection network, namely Squeeze and Excitation High‐Resolution Network (SEHRNet), is proposed in this paper to solve the problems of HRNet's slower computation and more redundancy. First, the errors arising from coordinate transformations in the coding and decoding process are solved by an improved feature map coding and decoding process. Second, replace the BasicBlock in HRNet with the Depthwise separable convolutions based on the Squeeze‐and‐Excitation Networks, which drastically cuts the computational cost of the network. Third, the improved Bottleneck module is used to further enhance the capability of feature extraction. Experimental results prove that, based on the aircraft keypoint detection dataset, the SEHRNet proposed in this paper shows stronger applicability compared to the current mainstream networks. Compared with the original HRNet, the improved network has higher accuracy, faster speed, and lighter model for aircraft keypoint detection.

A distributed data processing scheme based on Hadoop for synchrotron radiation experiments.

With the development of synchrotron radiation sources and high-frame-rate detectors, the amount of experimental data collected at synchrotron radiation beamlines has increased exponentially. As a result, data processing for synchrotron radiation experiments has entered the era of big data. It is becoming increasingly important for beamlines to have the capability to process large-scale data in parallel to keep up with the rapid growth of data. Currently, there is no set of data processing solutions based on the big data technology framework for beamlines. Apache Hadoop is a widely used distributed system architecture for solving the problem of massive data storage and computation. This paper presents a set of distributed data processing schemes for beamlines with experimental data using Hadoop. The Hadoop Distributed File System is utilized as the distributed file storage system, and Hadoop YARN serves as the resource scheduler for the distributed computing cluster. A distributed data processing pipeline that can carry out massively parallel computation is designed and developed using Hadoop Spark. The entire data processing platform adopts a distributed microservice architecture, which makes the system easy to expand, reduces module coupling and improves reliability.

Mitigating topological freezing using out-of-equilibrium simulations

Motivated by the recently-established connection between Jarzynski’s equality and the theoretical framework of Stochastic Normalizing Flows, we investigate a protocol relying on out-of-equilibrium lattice Monte Carlo simulations to mitigate the infamous computational problem of topological freezing. We test our proposal on 2d CPN−1 models and compare our results with those obtained adopting the Parallel Tempering on Boundary Conditions proposed by M. Hasenbusch, obtaining comparable performances. Our work thus sets the stage for future applications combining our Monte Carlo setup with machine learning techniques.

Особенности преподавания аналитической химии студентам фармацевтического профиля

The article reveals some features of teaching analytical chemistry as one of the main subjects for training future pharmacists. It is shown that the course of analytical chemistry forms the basic practical skills necessary in subsequent professional activity. The difficulties of teaching analytical chemistry associated with time constraints and a large volume of material that is submitted for consideration at the lecture are noted. This requires a very clear structure and sequence from the teacher, as well as multimedia accompaniment, which contributes to a better perception of the material presented and its assimilation. It is necessary that each practical lesson be provided with methodological recommendations, materials for preparing for a practical lesson, tests for knowledge control, situational tasks, as well as methodological recommendations for independent work of students. Special attention is paid to the importance of solving computational problems, which is an integral part of the practical lesson. This allows you to better understand the basic methods and techniques of chemical analysis, as well as acquire comprehensive chemical knowledge necessary for the successful study of professional disciplines. Assimilation of knowledge in analytical chemistry is a necessary step in preparing students for the conscious perception of specialized disciplines – pharmaceutical chemistry, technology, pharmacology, pharmacognosy, toxicological chemistry.

Two-phase optimization of binary sequences with low peak sidelobe level value

The search for binary sequences with low peak sidelobe level value represents a formidable computational problem. To locate better sequences for this problem, we designed a stochastic algorithm that uses two fitness functions. In these fitness functions, the value of the autocorrelation function has a different impact on the final fitness value. It is defined with the value of the exponent over the autocorrelation function values. Each function is used in the corresponding optimization phase, and the optimization process switches between these two phases until the stopping condition is satisfied. The proposed algorithm was implemented using the compute unified device architecture and therefore allowed us to exploit the computational power of graphics processing units. This algorithm was tested on sequences with lengths L=2m−1, for 14≤m≤20. From the obtained results it is evident that the two-phase optimization improved the efficiency of the algorithm significantly, the solver speed is increased significantly by using graphics processing units, new-best known solutions were achieved, and the achieved peak sidelobe level values were significantly less than L.

FRAUD DETECTION IN UPI TRANSACTIONS USING ML

Significant obstacles to financial security have arisen as a result of the quick uptake of Unified Payments Interface (UPI) for online transactions and a commensurate rise in fraudulent activity. This paper suggests a novel fraud detection method that makes use of cutting-edge machine learning (ML) algorithms to address this urgent issue. It focuses on integrating a Hidden Markov Model (HMM) into the UPI transaction process. In order to enable the system to identify departures from these learnt behaviors as possibly fraudulent, the HMM is trained to predict the typical transaction patterns for particular cardholders. The suggested system uses a variety of contemporary approaches, such as Kmeans Clustering, Auto Encoder, Local Outlier Factor, and artificial neural networks, to improve algorithmic diversity and flexibility to changing fraud patterns. In addition to addressing issues like test data creation for training and validation, the system emphasizes a heuristic approach to solving high-complexity computational problems, guaranteeing efficacy in a variety of settings. This study, which is positioned as a proactive and adaptable solution, emphasizes how crucial it is to stop UPI fraud and provides a thorough foundation for reliable fraud detection in the ever-changing world of online transactions.

Some New Identities Related to Dedekind Sums Modulo a Prime

The main purpose of this article is to use some identities of the classical Gauss sums, the properties of character sums, and Dedekind sums (modulo an odd prime) to study the computational problem of one-kind mean values related to Dedekind sums and give some interesting identities for them.

The structural power of reconfigurable circuits in the amoebot model

AbstractThe amoebot model (Derakhshandeh et al. in: SPAA ACM, pp 220–222. https://doi.org/10.1145/2612669.2612712, 2014) has been proposed as a model for programmable matter consisting of tiny, robotic elements called amoebots. We consider the reconfigurable circuit extension (Feldmann et al. in J Comput Biol 29(4):317–343. https://doi.org/10.1089/cmb.2021.0363, 2022) of the geometric amoebot model that allows the amoebot structure to interconnect amoebots by so-called circuits. A circuit permits the instantaneous transmission of signals between the connected amoebots. In this paper, we examine the structural power of the reconfigurable circuits. We start with fundamental problems like the stripe computation problem where, given any connected amoebot structure S, an amoebot u in S, and some axis X, all amoebots belonging to axis X through u have to be identified. Second, we consider the global maximum problem, which identifies an amoebot at the highest possible position with respect to some direction in some given amoebot (sub)structure. A solution to this problem can be used to solve the skeleton problem, where a cycle of amoebots has to be found in the given amoebot structure which contains all boundary amoebots. A canonical solution to that problem can be used to come up with a canonical path, which provides a unique characterization of the shape of the given amoebot structure. Constructing canonical paths for different directions allows the amoebots to set up a spanning tree and to check symmetry properties of the given amoebot structure. The problems are important for a number of applications like rapid shape transformation, energy dissemination, and structural monitoring. Interestingly, the reconfigurable circuit extension allows polylogarithmic-time solutions to all of these problems.

Optimal Feature Selection from High-dimensional Fusion of Blood Smear Images for Leukemia Diagnosis

The goal of this study is to improve blood smear image-based blood cancer prediction through medical diagnostic advancements. Blood cancers, particularly leukemia, are challenging to diagnose because of the complexity of biological data and the dimensionality of medical images. There are interpretability and computational problems with each currently in use. We suggest the Random Forest-Recurrent Feature Elimination (RF-RFE) model to increase the precision and dependability of blood cancer diagnosis. This model integrates machine learning and image processing, optimizes feature selection and refinement from high-dimensional data, and applies the XGBoost algorithm to guarantee diagnosis accuracy. Recent model analysis reveals that RF-RFE performs better than them on a wide range of metrics. The RF-RFE offered a sensible, well-rounded strategy. More research on medical diagnostics is made possible by its adaptability in multi-class classification and effectiveness in handling high-dimensional feature values. The optimized feature set and computational efficiency of the model, which may enhance leukemia detection and diagnostics, are highlighted in this study.

On n-Dimensional Maxwell and Dirac Equations in Curved Space-Time and Its Applications in SO(P,Q) Group Theoretic Image Processing

Maxwell equations in p time and q spatial dimensions are formulated. Properties of the Green’s function for the associated (p,q)-wave operator are derived based on contour integration. The notions of electric and magnetic fields in (p,q)-dimensions is explored by analogy with four dimensional physics and the problem of far field computation of the radiation field generated by charges and currents in \((1,n-1)\) space-time is analyzed. SO(p,q) invariance properties of the Maxwell equations are deduced and used to formulate SO(p,q)-group theoretic image processing problems in (p,q)-dimensions. Dirac’s equation in (p,q)-dimensional space-time is derived using the Clifford algebra of the Dirac gamma matrices. SO(p,q)-invariance of the Dirac equation based on the spinor representation of \(SO(p,q)\) is mentioned. The Maxwell’s equations in (p,q)-dimensional curved space-time is analyzed using perturbation theory in the context of maximally symmetric spaces which play a fundamental role in \((p,q)\)-dimensional cosmological models for homogeneous and isotropic spaces. The Einstein-Maxwell equations for (p,q)-dimensional gravity and electromagnetism is studied and used to derive the equations of motion of point charges carrying mass moving under mutual gravitational and electromagnetic interactions in general relativity. Finally, Dirac’s equation in (p,q)-dimensional curved space-time interacting with the (p,q)-dimensional electromagnetic field is looked at. \(U(1)\)-Gauge, local SO(p,q) Lorentz and diffeomorphism invariance of this equation is analyzed. Local \(SO(p,q)\) invariance of the curved space-time Dirac equation is deduced based on transformation properties of the Dirac matrices under the spinor representation. The appendix presents some applications of group representation theoretic statistical image processing on a manifold to the situation when the image field is described by the six component electromagnetic field tensor on which the Lorentz group acts.

Biomarker discovery with quantum neural networks: a case-study in CTLA4-activation pathways

BackgroundBiomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery from genetic data.MethodWe propose a Quantum Neural Networks architecture to discover genetic biomarkers for input activation pathways. The Maximum Relevance-Minimum Redundancy criteria score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware.ResultsWe demonstrate the proof of concept on four activation pathways associated with CTLA4, including (1) CTLA4-activation stand-alone, (2) CTLA4-CD8A-CD8B co-activation, (3) CTLA4-CD2 co-activation, and (4) CTLA4-CD2-CD48-CD53-CD58-CD84 co-activation.ConclusionThe model indicates new genetic biomarkers associated with the mutational activation of CLTA4-associated pathways, including 20 genes: CLIC4, CPE, ETS2, FAM107A, GPR116, HYOU1, LCN2, MACF1, MT1G, NAPA, NDUFS5, PAK1, PFN1, PGAP3, PPM1G, PSMD8, RNF213, SLC25A3, UBA1, and WLS. We open source the implementation at: https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks.

Applications of Finite Non-Abelian Simple Groups to Cryptography in the Quantum Era

AbstractThe theory of finite simple groups is a (rather unexplored) area likely to provide interesting computational problems and modeling tools useful in a cryptographic context. In this note, we review some applications of finite non-abelian simple groups to cryptography and discuss different scenarios in which this theory is clearly central, providing the relevant definitions to make the material accessible to both cryptographers and group theorists, in the hope of stimulating further interaction between these two (non-disjoint) communities. In particular, we look at constructions based on various group-theoretic factorization problems, review group theoretical hash functions, and discuss fully homomorphic encryption using simple groups. The Hidden Subgroup Problem is also briefly discussed in this context.

Evolutionary Multitasking for Costly Task Offloading in Mobile-Edge Computing Networks

The offloading of computation-intensive tasks to an edge server near resource-constrained mobile devices can provide improved application performance and user experience. However, with the rapid growth of mobile devices connected to the edge server, it is challenging to directly obtain an optimal task offloading scheme due to increasing computational cost and problem scale. In this study, we model the costly task offloading problem (CTOP) in mobile edge computing networks to achieve efficient joint optimization of energy consumption and processing latency for mobile devices. Inspired by the success of evolutionary multitasking in solving complex optimization problems by leveraging the experience of simple optimization problems, we develop a novel multitasking framework whose effectiveness is demonstrated in solving the CTOP. In this framework, auxiliary tasks are created to optimize the local processing overhead and the edge processing overhead of task offloading. On this basis, we propose an effective multitask evolutionary algorithm that includes segmented knowledge transfer and auxiliary task update. Specifically, source and extended decision variables are considered as different knowledge to be utilized, while the auxiliary tasks are allowed to be updated dynamically. Related knowledge that is learned from cheap and simple auxiliary tasks promotes the evolutionary search for CTOP. Experimental results verify the effectiveness of knowledge transfer. Compared to existing multitasking and single-tasking algorithms, the proposed algorithm shows competitive performance in CTOP instances and achieves better comprehensive performance in terms of energy consumption and processing latency.