Classical Framework Research Articles

A Comparative Study of Deep Learning and Transfer Learning Approaches for Image Forgery Detection

The rapid growth of online activities such as commerce, education, research, and virtual conferences has led to a greater reliance on digital images as primary information sources on social media and other platforms. The extensive use, combined with the ease of modification via image-editing software, highlights the crucial need for effective image forgery detection tools. Traditional detection methods based on handcrafted features have grown less efficient, prompting the introduction of deep learning-based approaches, many of which combine transfer learning with pre-trained models to improve detection efficiency and shorten training time. This research presents a comprehensive evaluation of image forgery detection algorithms, categorizing them as classical, deep learning, and transfer learning frameworks. The study compares deep learning with transfer learning methods, assessing their strengths in feature extraction, classification, and detection accuracy. The findings indicate that, while transfer learning models are particularly effective at feature extraction using pre-trained architectures, deep learning remains superior for classification tasks. This insight intends to help academics construct high-accuracy, efficient models for detecting various forms of forgeries. Combining pre-trained models for feature extraction and deep learning for classification is the best option for real-time digital forensics, increasing detection accuracy and processing speed.

Development of Quantum Safe Polynomial‐Based Cryptography in Classical and Quantum Framework

ABSTRACTThe advent of quantum computers has brought forth a substantial danger to the security of current cryptographic algorithms, necessitating the development of quantum‐resistant algorithms to ensure data protection. The encryption method proposed in this article uses polynomials and is intended to counter key size‐based attacks. This paper brings a novel method of using polynomial interpolations for encryption and decryption, and the proposed algorithm is implemented in both classical and quantum environments to check its feasibility and accuracy. To ensure protection from the outside world, the proposed technique is applied in key generation, encryption, decryption, and confusion‐diffusion modules. This article also tabulated the National Institute of Standard and Technology (NIST) test suit results for randomness acceptability criteria and compares the new algorithm with the Kyber algorithm. While quantum computers are believed to excel in tasks like factorization and optimization, this paper is the first to explore cryptography algorithms in a quantum environment, opening new research possibilities for future cryptographic developments.

Development of a thermoplastic constitutive model for steel based on the generalized Mises yield criterion

Steel is widely used in building structures, and the constitutive model that describes the steel's response to load is an essential component of structural mechanics analysis. This paper aims to investigate the thermoplastic behavior of steel at elevated temperatures. Based on the classical elastoplastic mechanics framework, this study introduces a generalized Mises yield criterion derived from energy theory, which takes into account the temperature effect. In conjunction with relevant assumptions, hardening condition, flow rule, the generalized Hooke's law, and consistency condition, we derive a novel incremental thermoplastic constitutive model for steel. And the new thermoplastic constitutive model's rationality is further verified by utilizing high-temperature steady-state tensile test data. It is encouraging that this new model can accurately describe the mechanical behavior of steel in high-temperature environment such as fire, which is of significant importance for fire safety design of building structures.

Configuring Psalm 29 as a Poem: Cognitive Strategies and the Artful Reading Experience

The classic modern framework for biblical Hebrew poetry is based upon intertwined conceptions of parallelism and meter. This framework provides certain assumptions for how biblical lines work, as well as (often implicit) strategies for how biblical poems should be read as poems. These assumptions and strategies are apparent in modern analyses of Psalm 29, especially in the kinds of proposals consistently made for altering the Masoretic Text to make it more “regular”, even in the absence of textual evidence. My recent book on biblical poetry (Unparalleled Poetry: A Cognitive Approach to the Free-Rhythm Verse of the Hebrew Bible, 2023) has challenged these assumptions and strategies, proposing a new framework for biblical poetry that is theoretically grounded in cognitive research and based upon the textual data of the biblical poems. Preferring the term “conformation” to parallelism, I propose that the versification system of biblical poetry is constrained by Gestalt perceptual processing and that the listening or reading strategies demanded by this versification system require part–whole processing of lines into line groupings, line groupings into figures, and figures into the whole poem, as the poem aurally unfolds. In this article, I demonstrate that these part–whole strategies of reading biblical poems make sense of the textual shapes of Psalm 29 and lead to an artful experience of the poem.

Improved Visual SLAM Algorithm Based on Dynamic Scenes

This work presents a novel RGB-D dynamic simultaneous localization and mapping (SLAM) method that improves accuracy, stability, and efficiency of localization while relying on deep learning in a dynamic environment, in contrast to traditional static scene-based visual SLAM methods. Based on the classic framework of traditional visual SLAM, we propose a method that replaces the traditional feature extraction method with a convolutional neural network approach, aiming to enhance the accuracy of feature extraction and localization, as well as to improve the algorithm’s ability to capture and represent the characteristics of the entire scene. Subsequently, the semantic segmentation thread was utilized in a target detection network combined with geometric methods to identify potential dynamic areas in the image and generate masks for dynamic objects. Finally, the standard deviation of the depth information of potential dynamic points was calculated to identify true dynamic feature points, to guarantee that static feature points were used for position estimation. We performed experiments based on the public datasets to validate the feasibility of the proposed algorithm. The experimental results indicate that the improved SLAM algorithm, which boasts a reduction in absolute trajectory error (ATE) by approximately 97% compared to traditional static visual SLAM and about 20% compared to traditional dynamic visual SLAM, also exhibited a 68% decrease in computation time compared to well-known dynamic visual SLAM, thereby possessing absolute advantages in both positioning accuracy and operational efficiency.

Some Contributions from Embodied Cognition to Psychonarratology.

Psychonarratology is a discipline that combines classic frameworks in narratology and psycholinguistics. The goal of this review article is to highlight the contributions that recent evidence from causality processing studies, conceptual metaphor theory and embodied cognition can make to Psychonarratology, in order to promote a more comprehensive study of narrative. We argue that, in order to increase its descriptive and explanatory power, Psychonarratology would benefit from a more strongly interdisciplinary approach. This approach would integrate grounded theoretical cognition and recent methods from different disciplines. With this aim, we review study evidence that highlights the fundamental link between cognition and causal connections in narratives, as well as proposals from conceptualization theories. These approaches indicate that metaphorical mappings and discourse connections play a crucial role in the establishment of narrative sequences. Moreover, we elaborate on the contributions that recent advances in research on embodied cognition, causality processing and Conceptual Metaphor Theory can make to the theoretical and methodological framework of Psychonarratology, such as promoting the design of more ecologically valid tasks, and the study of narrative production and comprehension by non-neurotypical participants.

Comparative Analysis of Transit-Oriented Development (TOD) Types in the Metropolitan Region Along the Middle Reaches of the Yangtze River

Currently, with the acceleration of urbanization, traditional transportation modes are increasingly causing congestion, pollution, and resource waste, drawing widespread attention to Transit-Oriented Development (TOD). TOD is an urban development concept that advocates the implementation of high-density, mixed-use land utilization around transit stations to encourage the use of public transportation, reduce reliance on private vehicles, and achieve more sustainable urban growth. The ‘node–place’ model is a classic analytical framework in TOD typology, forming the foundation for assessing TOD effectiveness. However, this model requires expansion due to its limited adaptability. This study aims to bridge this gap by proposing an innovative, network-based ‘node–place–convenience (NPC)’ model to enhance insights on the overall assessment of metro networks. Using a combination of CRITIC weighting and K-means++ clustering, this study evaluates TOD in cities with metros in the middle reaches of the Yangtze River. By assessing node, place, and convenience values of metro stations, this study compares how different urban structures, population distributions, and metro network configurations impact travel behavior, economic vitality, and regional sustainability. The results show that TOD degree in Wuhan decreases from urban to suburban areas, presenting ‘center to sub-center’ pattern in Changsha, and presenting ‘ring-radial’ distribution across the city center in Nanchang. The clustering results divide TOD benefits into six groups, with Changsha performing the best, followed by Wuhan, while Nanchang still has room for improvement. The average TOD benefits for Wuhan, Changsha, and Nanchang are 0.28, 0.35, and 0.28.

Quantum machine learning enhanced laser speckle analysis for precise speed prediction

Laser speckle contrast imaging (LSCI) is an optical technique used to assess blood flow perfusion by modeling changes in speckle intensity, but it is generally limited to qualitative analysis due to difficulties in absolute quantification. Three-dimensional convolutional neural networks (3D CNNs) enhance the quantitative performance of LSCI by excelling at extracting spatiotemporal features from speckle data. However, excessive downsampling techniques can lead to significant information loss. To address this, we propose a hybrid quantum–classical 3D CNN framework that leverages variational quantum algorithms (VQAs) to enhance the performance of classical models. The proposed framework employs variational quantum circuits (VQCs) to replace the 3D global pooling layer, enabling the model to utilize the complete 3D information extracted by the convolutional layers for feature integration, thereby enhancing velocity prediction performance. We perform cross-validation on experimental LSCI speckle data and demonstrate the superiority of the hybrid models over their classical counterparts in terms of prediction accuracy and learning stability. Furthermore, we evaluate the models on an unseen test set and observe that the hybrid models outperform the classical models with up to 14.8% improvement in mean squared error (MSE) and up to 26.1% improvement in mean absolute percentage error (MAPE) evaluation metrics. Finally, our qualitative analysis shows that the hybrid models offer substantial improvements over classical models in predicting blood flow at both low and high velocities. These results indicate that the hybrid models possess more powerful learning and generalization capabilities.

Data and contemporary economics: role, paradox and development

PurposeA more accurate comprehension of data elements and the exploration of new laws governing contemporary data in both theoretical and practical domains constitute a significant research topic.Design/methodology/approachBased on the perspective of evolutionary economics, this paper re-examines economic history and existing literature to study the following: changes in the “connotation of production factors” in economics caused by the evolution of production factors; the economic paradoxes formed by data in the context of social production processes and business models, which traditional theoretical frameworks fail to solve; the disruptive innovation of classical theory of value by multiple theories of value determination and the conflicts between the data market monopoly as well as the resulting distribution of value and the real economic society. The research indicates that contemporary advancements in data have catalyzed transformative innovation within the field of economics.FindingsThe research indicates that contemporary advancements in data have catalyzed disruptive innovation in the field of economics.Originality/valueThis paper, grounded in academic research, identifies four novel issues arising from contemporary data that cannot be adequately addressed within the confines of the classical economic theoretical framework.

A brief review of reduced order models using intrusive and non‐intrusive techniques

AbstractReduced Order Models (ROMs) have gained a great attention by the scientific community in the last years thanks to their capabilities of significantly reducing the computational cost of the numerical simulations, which is a crucial objective in applications like real time control and shape optimization. This contribution aims to provide a brief overview about such a topic. We discuss both a classic intrusive framework based on a Galerkin projection technique and hybrid/non‐intrusive approaches, including Physics Informed Neural Networks (PINN), purely Data‐Driven Neural Networks (NN), Radial Basis Functions (RBF), Dynamic Mode Decomposition (DMD) and Gaussian Process Regression (GPR). We also briefly mention geometrical parametrization and dimensionality reduction methods like Active Subspaces (ASs). Then we test the performance of such approaches in terms of efficiency and accuracy against three academic test cases, the lid driven cavity, the flow past a cylinder and the geometrically parametrized Stanford Bunny. Moreover, we also briefly present some preliminary results related to a more complex case involving an industrial application.

Specification Choices in Quantile Regression for Empirical Macroeconomics

ABSTRACTQuantile regression has become widely used in empirical macroeconomics, in particular for estimating and forecasting tail risks. This paper examines various choices in the specification of quantile regressions for macro applications, including how and to what extent to include shrinkage and whether to apply shrinkage in a classical or Bayesian framework. We focus on forecasting accuracy, measured with quantile scores and quantile‐weighted continuous ranked probability scores at a range of quantiles from the left to right tail. Across applications, we find that shrinkage is generally helpful to quantile forecast accuracy, with Bayesian quantile regression dominating frequentist quantile regression.JEL Classification: C53, E17, E37, F47

Learning properties of quantum states without the IID assumption

We develop a framework for learning properties of quantum states beyond the assumption of independent and identically distributed (i.i.d.) input states. We prove that, given any learning problem (under reasonable assumptions), an algorithm designed for i.i.d. input states can be adapted to handle input states of any nature, albeit at the expense of a polynomial increase in training data size (aka sample complexity). Importantly, this polynomial increase in sample complexity can be substantially improved to polylogarithmic if the learning algorithm in question only requires non-adaptive, single-copy measurements. Among other applications, this allows us to generalize the classical shadow framework to the non-i.i.d. setting while only incurring a comparatively small loss in sample efficiency. We leverage permutation invariance and randomized single-copy measurements to derive a new quantum de Finetti theorem that mainly addresses measurement outcome statistics and, in turn, scales much more favorably in Hilbert space dimension.

Population Distribution Forecasting Based on the Fusion of Spatiotemporal Basic and External Features: A Case Study of Lujiazui Financial District

Predicting the distribution of people in the time window approaching a disaster is crucial for post-disaster assistance activities and can be useful for evacuation route selection and shelter planning. However, two major limitations have not yet been addressed: (1) Most spatiotemporal prediction models incorporate spatiotemporal features either directly or indirectly, which results in high information redundancy in the parameters of the prediction model and low computational efficiency. (2) These models usually incorporate certain basic and external features, and they can neither change spatiotemporal addressed features according to spatiotemporal features nor change them in real-time according to spatiotemporal features. The spatiotemporal feature embedding methods for these models are inflexible and difficult to interpret. To overcome these problems, a lightweight population density distribution prediction framework that considers both basic and external spatiotemporal features is proposed. In the study, an autoencoder is used to extract spatiotemporal coded information to form a spatiotemporal attention mechanism, and basic and external spatiotemporal feature attention is fused by a fusion framework with learnable weights. The fused spatiotemporal attention is fused with Resnet as the prediction backbone network to predict the people distribution. Comparison and ablation experimental results show that the computational efficiency and interpretability of the prediction framework are improved by maximizing the scalability of the spatiotemporal features of the model by unleashing the scalability of the spatiotemporal features of the model while enhancing the interpretability of the spatiotemporal information as compared to the classical and popular spatiotemporal prediction frameworks. This study has a multiplier effect and provides a reference solution for predicting population distributions in similar regions around the globe.

INTELLECTUAL SUPPORT OF THE PROCESSES OF SEARCHING AND EXTRACTION OF PRECEDENTS IN CASE-BASED REASONING APPROACH

Context. The situational approach is based on the real-time decision-making methods for solving current problem situation. An effective tool for implementing the concept of a situational approach is an experience-based technique that widely known as сasebased reasoning approach. Reasoning by precedents allows solving new (latest) problems using knowledge and accumulated experience of previously solved problems. Since cases (precedents) describing a scenario for solving a certain problem situation are stored in the case library, their search and retrieval directly determine the system response time. In these conditions, there is a need to find ways of solving an actual scientific and practical problem aimed at optimizing case searching and extracting processes. The object of the paper is the processes of searching and extracting of cases from the case library. Objective. The purpose of the article is to improve the process of cases searching in CBR approach by narrowing down the set of cases permissible for solving the current target situation, and excluding from further analysis such cases that do not correspond to the given set of parameters of the target situation. Method. The research methodology is based on the application of rough set theory methods to improve the decision-making procedure based on reasoning by precedents. The proposed two-stage procedure for narrowing the initial set of cases involves preliminary filtering of precedents whose parameter values belong to the given neighborhoods of the corresponding parameters of the target situation at the first stage, and additional narrowing of the obtained subset of cases by the methods of rough set theory at the second stage. The determination of the R-lower and R-upper approximations of a given target set of cases within the notation of rough set theory allows dividing (segmenting) the original set of cases available for solving the current problem stored in case library into three subgroups (segments). The search for prototype solutions can be performed among a selected subset of cases that can be accurately classified as belonging to a given target set; which with some degree of probability can be attributed to the given target set, or within the framework of the union of these two subsets. The third subset contains cases that definitely do not belong to the given target set and can be excluded from further consideration. Results. The problem of presentation and derivation of knowledge based on precedents has been considered. The procedure for searching for precedents in case library has been improved in order to reduce the system response time required to find the solution closest to the current problem situation by narrowing the initial set of cases. Conclusions. The case-based reasoning approach is received the further development by segmenting cases in terms of their belonging to a given target set of precedents uses methods of the rough set theory, then the search for cases is carried out within a given segment. The proposed approach, in contrast to the classic CBR framework, uses additional knowledge derived from obtained case segment; allows modeling the uncertainty regarding the belonging / non-belonging of a case to a given target set; removing from further consideration cases that do not correspond to a given target set.

A New ENSO Statistical Prediction Model considering Extratropical Effects and Its Application to the Prediction of the 2015/16 El Niño Event

Abstract Understanding the evolution mechanisms and providing accurate predictions for ENSO continue to be a challenge for the research community. In recent years, some studies pointed out that the extratropical Pacific atmosphere and ocean variations may affect the evolution of ENSO, which is different from the classical theoretical framework of ENSO. In this study, by combining tropical atmosphere and ocean predictors with extratropical precursors, an ENSO statistical prediction model was constructed. It was found that including extratropical predictors can significantly enhance the prediction skills of the model. The Victoria mode index (VMI) can significantly improve the prediction skills in the 10–12-month lead model and can effectively help the model cross the spring predictability barrier (SPB). When predicting 6–8 months in advance, the South Pacific Oscillation index (SPOI) can improve the prediction skills, but the degree of the improvement is relatively small. The model was also applied to conduct prediction experiments on the 2015/16 El Niño event. It was found that the model can successfully predict the event nearly 1 year in advance, mainly due to the inclusion of the VMI. Due to the interdecadal variation in the relationship between predictors and ENSO, when constructing the 6-month lead model, using data since 2000 and replacing the 6-month lead VMI with a 10-month lead, one can effectively improve the model’s prediction skills, especially in predicting the intensity of 2015/16 El Niño events, which has shown significant improvement.

Research on the ultra-high acceleration method based on dual-mass shock amplifier

Abstract Spacecraft are exposed to rigorous mechanical conditions throughout the launch, explosive decoupling, and landing stages. Electronic devices intended for space deployment are required to endure shock-induced reliability assessments. This study delves into the composition and operational principles of a high-g shock testing system. Furthermore, the paper derives a collision kinematics model from established classical collision dynamics and constructs analytical frameworks for both a single-degree-of-freedom and a two-degree-of-freedom spring-mass systems. Additionally, it investigates the principles of acceleration amplification. The finite element analysis model was computed, and its simulation outcomes were compared and contrasted with the data from shock experiments. The findings indicate that the finite element model is capable of accurately capturing the peak acceleration for both the drop table and DMSA table, with a discrepancy of no more than 5%. The acceleration of the amplifier is enhanced by approximately 40 times following the momentum exchange during the second collision. Moreover, the shock system, equipped with a dual mass shock amplifier (DMSA), is capable of producing accelerations exceeding 30,000G.

Research ethics implications in the development of personalised therapy for pediatric SIRS patients

Abstract Background Pediatric Systemic Inflammatory Response Syndrome (SIRS) presents diagnostic and therapeutic challenge that can be addressed through personalised therapy. Related ethical challenges are understudied. Our report aims at identification and analysis of research ethics implications within the bigger ERA PerMed funded project of development of tailored immunotherapy for paediatric SIRS patients (TIPS) coordinated by Prof. Catharina Schütz from Technic University Dresden. Methods A sample of 10 experts with different backgrounds was studied through semi-structured interviews. Systematic literature review grounded the interview questionnaire. The transcripts were subjected to thematic analysis by two independent researchers. Results Five classical research ethics issues were identified: respect for autonomy; informed consent; risk-benefits ratio; data protection; social justice. All experts favoured the empowerment of children (N = 10) and obtainment of informed consent rather than assent was seen as a common goal (N = 5). The emergency context of SIRS research impeded the informed consent. Despite favourable risk-benefits ratio, no direct benefits resulted to study participants. Data anonymisation though necessary (N = 10), hinders proper dealing with incidental findings. Social justice issues apparently accompanied research design in personalised medicine (N = 2) but should not extend to access to personalised therapy. Conclusions The classical framework of research ethics is challenged by the new fields of medical research. Traditional concepts, such as informed consent in minors, are subject to rethinking. Current ethical and legal guidelines fall short of proper practical instructions in the specific case of SIRS pediatric patients. Thus, ethical aspects of new technology development should always be studied in parallel to the biometrics within personalised medicine projects so as to the development of guidelines is more evidence-based than precautionary driven. Key messages • Current ethical and legal guidelines fall short of proper practical instructions in the specific case of SIRS pediatric patients. • Ethical aspects of new technology development should always be studied within personalised medicine projects so as to the development of guidelines is more evidence-based than precautionary driven.

Gaze and hand behaviors during haptic abilities testing-An update to multimedia learning theory.

The Cognitive Theory of Multimedia Learning (CTML) suggests humans learn through visual and auditory sensory channels. Haptics represent a third channel within CTML and a missing component for experiential learning. The objective was to measure visual and haptic behaviors during spatial tasks. The haptic abilities test (HAT) quantifies results in several realms, accuracy, time, and strategy. The HAT was completed under three sensory conditions using sight (S), haptics (H), and sight with haptics (SH). Subjects (n = 22, 13 females (F), 20-28 years) completed the MRT (10.6 ± 5.0, mean ± SD) and were classified as high or low spatial abilities scores with respect to mean MRT: high spatial abilities (HSA) (n = 12, 6F, MRT = 13.7 ± 3.0), and low spatial ability (LSA) groups (n = 10, 7F, MRT = 5.6 ± 2.0). Video recordings gaze and hand behaviors were compared between HSA and LSA groups across HAT conditions. The HSA group spent less time fixating on mirrored objects, an erroneous answer option, of HAT compared to the LSA group (11.0 ± 4.7 vs. 17.8 ± 7.3 s, p = 0.020) in S conditions. In haptic conditions, HSA utilized a hand-object interaction strategy characterized as palpation, significantly less than the LSA group (23.2 ± 16.0 vs. 43.1 ± 21.5 percent, p = 0.022). Before this study, it was unclear whether haptic sensory inputs appended to the mental schema models of the CTML. These data suggest that if spatial abilities are challenged, LSA persons both benefit and utilize strategies beyond the classic CTML framework by using their hands as a third input channel. This data suggest haptic behaviors offer a third type of sensory memory resulting in improved cognitive performance.

A novel image classification framework based on variational quantum algorithms

Image classification is a crucial task in machine learning with widespread practical applications. The existing classical framework for image classification typically utilizes a global pooling operation at the end of the network to reduce computational complexity and mitigate overfitting. However, this operation often results in a significant loss of information, which can affect the performance of classification models. To overcome this limitation, we introduce a novel image classification framework that leverages variational quantum algorithms (VQAs) hybrid approaches combining quantum and classical computing paradigms within quantum machine learning. The major advantage of our framework is the elimination of the need for the global pooling operation at the end of the network. In this way, our approach preserves more discriminative features and fine-grained details in the images, which enhances classification performance. Additionally, employing VQAs enables our framework to have fewer parameters than the classical framework, even in the absence of global pooling, which makes it more advantageous in preventing overfitting. We apply our method to different state-of-the-art image classification models and demonstrate the superiority of the proposed quantum architecture over its classical counterpart through a series of state vector simulation experiments on public datasets. Our experiments show that the proposed quantum framework achieves up to a 9.21% increase in accuracy and up to a 15.79% improvement in F1 score, compared to the classical framework. Additionally, we explore the impact of shot noise on our method through shot-based simulation and find that increasing the number of measurements does not always lead to better results. Selecting an appropriate number of measurements can yield optimal results, even surpassing those obtained from state vector simulation.

Multidimensional and Nonlinear Fractional Langevin Equations: Advancing the Theory in of Anomalous Diffusion

This paper presents a comprehensive theoretical analysis of fractional Langevin equations (FLEs) and their applications in modeling anomalous diffusion processes. We extend the classical FLE framework in several significant directions to address the complexities observed in various physical, biological, and social systems. First, we derive a multidimensional extension of the FLE, providing a robust tool for studying anomalous diffusion in higher-dimensional spaces. Second, we incorporate non-Gaussian Lévy α-stable noise into the FLE, enabling the description of systems characterized by heavy-tailed distributions and extreme events. Third, we develop a nonlinear variant of the FLE to model more intricate restoring forces and potential landscapes. Finally, we analyze a generalized FLE with different orders of fractional derivatives for the inertial and friction terms, offering a flexible framework capable of capturing a wider range of anomalous diffusion phenomena. For each extension, we prove the existence and uniqueness of solutions, derive the corresponding probability distributions, and analyze the scaling behavior of the mean squared displacement in the long-time limit. Our results demonstrate how these generalized FLEs can describe various regimes of anomalous diffusion, from subdiffusive to superdiffusive behavior. This work provides a unified theoretical foundation for understanding and modeling complex diffusive processes across diverse disciplines, paving the way for more accurate descriptions of systems exhibiting memory effects, non-Gaussian statistics, and multiscale dynamics.