Reviewerly/exHarmony huggingface dataset:
Automobile Insurance Fraud Detection Using Data Mining: A Systematic Literature Review

Insurance is a pivotal element in modern society, but insurers face a persistent challenge from fraudulent behaviour performed by policyholders. This behaviour could be detrimental to both insurance companies and their honest customers, but the intricate nature of insurance fraud severely complicates its efficient, automated detection. This study surveys fifty recent publications on automobile insurance fraud detection, published between January 2019 and March 2023, and presents both the most commonly used data sets and methods for resampling and detection, as well as interesting, novel approaches. The study adopts the highly-cited Systematic Literature Review (SLR) methodology for software engineering research proposed by Kitchenham and Charters and collected studies from four online databases. The findings indicate limited public availability of automobile insurance fraud data sets. In terms of detection methods, the prevailing approach involves supervised machine learning methods that utilise structured, intrinsic features of claims or policies and that lack consideration of an example-dependent cost of misclassification. However, alternative techniques are also explored, including the use of graph-based methods, unstructured textual data, and cost-sensitive classifiers. The most common resampling approach was found to be oversampling. This SLR has identified commonly used methods in recent automobile insurance fraud detection research, and interesting directions for future research. It adds value over a related review by also including studies published from 2021 onward, and by detailing the used methodology. Limitations of this SLR include its restriction to a small number of considered publication years and limited validation of choices made during the process.
Lung cancer diagnosis based on weighted convolutional neural network using gene data expression

Abstract Lung cancer is thought to be a genetic disease with a variety of unknown origins. Globocan2020 report tells in 2020 new cancer cases identified was 19.3 million and nearly 10.0 million died owed to cancer. GLOBOCAN envisages that the cancer cases will raised to 28.4 million in 2040. This charge is superior to the combined rates of the former generally prevalent malignancies, like breast, colorectal, and prostate cancers. For attribute selection in previous work, the information gain model was applied. Then, for lung cancer prediction, multilayer perceptron, random subspace, and sequential minimal optimization (SMO) are used. However, the total number of parameters in a multilayer perceptron can become extremely large. This is inefficient because of the duplication in such high dimensions, and SMO can become ineffective due to its calculating method and maintaining a single threshold value for prediction. To avoid these difficulties, our research presented a novel technique including Z-score normalization, levy flight cuckoo search optimization, and a weighted convolutional neural network for predicting lung cancer. This result findings show that the proposed technique is effective in precision, recall, and accuracy for the Kent Ridge Bio-Medical Dataset Repository.
The Evaluation of Course Teaching Effect Based on Improved RBF Neural Network

As basic education is increasingly digitized, the need for better teaching and learning quality also rises. Teaching reform is crucial to achieve this, and incorporating the Levenberg-Marquardt (L-M) into the Radial Basis Function (RBF) can help establish a fair online teaching evaluation system. The experimental results showed that the convergence ability of the model was significantly improved compared with the traditional RBF neural network. The overall mean square error of the improved model was 100. The actual value prediction accuracy of the improved model is higher than that of the Backpropagation (BP). When the actual value was at its peak, the accuracy reached 98%, the overall fluctuation range of absolute error was low, the highest absolute error value reached 0.78, and the average absolute error was below 0.5. With targeted improvements, teachers and students could better understand and change their own learning situations, as reflected in empirical evaluations.
Assessment of catastrophic forgetting in continual credit card fraud detection

The volume of e-commerce continues to increase year after year. Buying goods on the internet is easy and practical, and took a huge boost during the lockdowns of the Covid crisis. However, this is also an open window for fraudsters and the corresponding financial loss costs billions of dollars. In this paper, we study e-commerce credit card fraud detection, in collaboration with our industrial partner, Worldline. Transactional companies are more and more dependent on machine learning models such as deep learning anomaly detection models, as part of real-world fraud detection systems (FDS). We focus on continual learning to find the best model with respect to two objectives: to maximize the accuracy and to minimize the catastrophic forgetting phenomenon. For the latter, we proposed an evaluation procedure to quantify the forgetting in data streams with delayed feedback: the plasticity/stability visualization matrix. We also investigated six strategies and 13 methods on a real-size case study including five months of e-commerce credit card transactions. Finally, we discuss how the trade-off between plasticity and stability is set, in practice, in the case of FDS.
A novel Elman neural network based on Gaussian kernel and Improved SOA and Its Applications

To address challenges encountered in traditional Elman neural networks (ENNs), such as low convergence accuracy, difficulties in hyperparameter selection, and issues with gradient disappearance, a new ENN based on the Gaussian kernel and an improved seagull optimization algorithm (SOA) named GSENN is proposed. First, the principle of the ENN is introduced, and the effects of the network structure, parameters, and gradient descent algorithm on its output are analyzed. Building upon the analysis results, an input layer for the ENN using the Gaussian kernel is designed. The outstanding local feature extraction capability of the Gaussian kernel is used to enhance convergence accuracy, and the predicted trends are more in line with real data. In addition, we propose an SOA incorporating a nonlinear factor to optimize the weights and thresholds of the ENN. This approach aims to resolve the challenges in hyperparameter selection and problems of gradient vanishing in the ENN. Seven commonly used neural network structures and the latest improved techniques were chosen for comparative experiments across different datasets. The experimental results show that GSENN achieves a mean relative error rate of 4.286%. The MSE results indicate a 45.600% improvement over the original ENN and a 37.142% improvement over BP neural networks. The experimental results demonstrate that GSENN exhibits efficient forecasting capability and offers reliable and effective forecasting for the relevant departments.
Robust image steganography against JPEG compression based on DCT residual modulation

Robust steganography is the technology of hiding secret message in cover image so that the message can be recovered after additional image processing across social network. Traditional robust steganography schemes mainly relied on error correction code and embedding domain selection, which could not effectively address the additional errors introduced during JPEG compression. In this paper, we analyze the lossy operations of JPEG compression and present the DCT Residual Modulation (DRM) algorithm to specifically address spatial pixel overflow, which is a predominant factor leading to alterations in quantized DCT coefficients following recompression. To enhance robustness, unit-based shuffling is applied to the encoded message, and the DRM algorithm is employed for processing the cover image and the stego image. To enhance resistance against steganalysis, the embedding domain is shifted to lower-frequency regions and the redundancy of error correction coding is reduced. Experimental results show that the proposed scheme outperforms existing schemes in terms of robustness and maintains a high level of anti-steganalysis capabilities.
RASNet: Renal Automatic Segmentation Using an Improved U-Net with Multi-scale Perception and Attention Unit

For the treatment of renal disease, the application of radioactive equipment has become one of the important methods. Accurate segmentation of renal contour plays an important role in clinical diagnosis. However, manual renal contour drawing is not only inefficient but also prone to inaccurate outlining results due to different manual proficiency and fatigue caused by long-term work. There is little research on automatic renal segmentation with renal dynamic imaging. To address this issue, an improved model based on a deep neural network called Renal Automatic Segmentation Network (RASNet) is proposed, to aid in the automatic segmentation of renal contours. Besides, a multi-scale spatial perception module and a decoding module with attention connection are introduced to enrich the semantic information and further improve the accuracy of network segmentation. Extensive experiments were conducted on a renal dynamic medical image database established in this paper. Analysis results show the superiority of the proposed RASNet to several existing segmentation frameworks.
A heuristic method for discovering multi-class classification rules from multi-source data in cloud–edge system

The integration of diverse devices has made the establishment of multi-class classification models for multi-source data a primary concern for data mining in cloud–edge system. Developing rule-based classifiers is essential because they present results that express the reasons for classification. However, existing rule learning methods are not compatible with multi-source tabular data containing mixed features, imbalanced labels and missing values, making it difficult to build cross-data source and cross-device data mining models. We developed a heuristic multi-class rule learning method that can handle complex tabular datasets without relying too much on cumbersome preprocessing techniques. We abstract the training process of the classifier into a multi-objective optimization problem and design a novel hybrid evolutionary algorithm to obtain Pareto-optimal solutions as rule-based classifier. Compared with the existing explainable classification methods, this method has obvious advantages in the classification performance of tabular data.
MVDet: Encrypted malware traffic detection via multi-view analysis

Detecting encrypted malware traffic promptly to halt the further propagation of an attack is critical. Currently, machine learning becomes a key technique for extracting encrypted malware traffic patterns. However, due to the dynamic nature of network environments and the frequent updates of malware, current methods face the challenges of detecting unknown malware traffic in open-world environment. To address the issue, we introduce MVDet, a novel method that employs machine learning to mine the behavioral features of malware traffic based on multi-view analysis. Unlike traditional methods, MVDet innovatively characterizes the behavioral features of malware traffic at 4-tuple flows from four views: statistical view, DNS view, TLS view, and business view, which is a more stable feature representation capable of handling complex network environments and malware updates. Additionally, we achieve a short-time behavioral features construction, significantly reducing the time cost for feature extraction and malware detection. As a result, we can detect malware behavior at an early stage promptly. Our evaluation demonstrates that MVDet can detect a wide variety of known malware traffic and exhibits efficient and robust detection in both open-world and unknown malware scenarios. MVDet outperforms state-of-the-art methods in closed-world known malware detection, open-world known malware detection, and open-world unknown malware detection.
d2kg: An integrated ontology for knowledge graph-based representation of government decisions and acts

To implement Open Governance a crucial element is the efficient use of the big amounts of open data produced in the public domain. Public administration is a rich source of data and potentially new knowledge. It is a data intensive sector producing vast amounts of information encoded in government decisions and acts, published nowadays on the World Wide Web. The knowledge shared on the Web is mostly made available via semi-structured documents written in natural language. To exploit this knowledge, technologies such as Natural Language Processing, Information Extraction, Data mining and the Semantic Web could be used, embedding into documents explicit semantics based on formal knowledge representations such as ontologies. Knowledge representation can be made possible by the deployment of Knowledge Graphs, collections of interlinked representations of entities, events or concepts, based on underlying ontologies. This can assist data analysts to achieve a higher level of situational awareness, facilitating automated reasoning towards different objectives, such as for knowledge management, data maintenance, transparency and cybersecurity. This paper presents a new ontology d2kg [d(iavgeia) 2(to) k(nowledge) g(raph)] integrating in a unique way standard EU ontologies, core and controlled vocabularies to enable exploitation of publicly available data from government decisions and acts published on the Greek platform Diavgeia with the aim to facilitate data sharing, re-usability and interoperability. It demonstrates a characteristic example of a Knowledge Graph based representation of government decisions and acts, highlighting its added value to respond to real practical use cases for the promotion of transparency, accountability and public awareness. The developed d2kg ontology in owl is accessible at: http://w3id.org/d2kg, as well as documented at: http://w3id.org/d2kg/documentation.
A Survey on Implementing Augmented Reality in Learning Data Structures

Data structures are fundamental building blocks of computer science, but their abstract nature can pose challenges for students. This research explores the potential of Augmented Reality (AR) to enhance the learning of data structures. The paper describes the development and analysis of an AR-based learning system for specific data structures. The system utilizes data visualization and interactive manipulation through 3D models and animations to explain how AR features aid in learning. The results were compelling, demonstrating significant improvement in both understanding and performance among students utilizing the AR tool. In general, this study indicates that AR can function as a valuable tool for studying data structures. This study not only highlights the effectiveness of AR in demonstrating data structures but also paves the way for future advancements in computer science education, ultimately leading to a more easily accessible and engaging learning environment for all.
Dura-Europos Stories: Developing interactive storytelling applications using knowledge graphs for cultural heritage exploration

We introduce Dura-Europos Stories, a multimedia application for viewing artifacts and places related to the Dura-Europos archaeological excavation. We describe the process of mapping data to the Wikidata data model as well as the process of contributing data to Wikidata. We provide an overview of the functionality of an interactive application for viewing images of the artifacts in the context of their metadata. We contextualize this project as an example of using knowledge graphs in research projects in order to leverage technologies of the Semantic Web in such a way that data related to the project can be easily combined with other data on the web. Presenting artifacts in this story-based application allows users to explore these objects visually, and provides pathways for further exploration of related information.
Exponential Capacity of Dense Associative Memories

Recent generalizations of the Hopfield model of associative memories are able to store a number P of random patterns that grows exponentially with the number N of neurons, P=exp(αN). Besides the huge storage capacity, another interesting feature of these networks is their connection to the attention mechanism which is part of the Transformer architecture widely applied in deep learning. In this work, we study a generic family of pattern ensembles using a statistical mechanics analysis which gives exact asymptotic thresholds for the retrieval of a typical pattern, α1, and lower bounds for the maximum of the load α for which all patterns can be retrieved, αc, as well as sizes of attraction basins. We discuss in detail the cases of Gaussian and spherical patterns, and show that they display rich and qualitatively different phase diagrams.Received 26 July 2023Revised 29 November 2023Accepted 11 January 2024DOI:https://doi.org/10.1103/PhysRevLett.132.077301© 2024 American Physical SocietyPhysics Subject Headings (PhySH)Physical SystemsDisordered systemsSpin glassesTechniquesDeep learningMachine learningReplica theoryStatistical Physics & ThermodynamicsInterdisciplinary Physics
Simulating photon counting from dynamic quantum emitters by exploiting zero-photon measurements

Many applications of quantum optics demand delicate quantum properties of light carefully tailored to accomplish a specific task. To this end, numerical simulations of quantum light sources are vital for designing, characterizing, and optimizing quantum photonic technology. Here, I show that exploiting information hidden in zero-photon measurement outcomes provides an exponential speedup for time-integrated photon-counting simulations, realizing eight orders of magnitude reduction in the time to compute six-photon detection probabilities while achieving 10 orders of magnitude higher precision compared to the state of the art. This enables simulations of large photonic experiments with an unprecedented level of physical detail. It can accelerate the design of sources to generate photonic resource states for quantum sensing and measurement-based quantum computing while capturing realistic imperfections. It also establishes a general theoretical framework to study dynamic interactions between stationary qubits mediated by measurements of flying qubits, which can be used to model distributed quantum computing and quantum communication.
Geometrical Structure of Bifurcations during Spatial Decision-Making

In the study, authors reveal new geometric principles of bifurcations during spatiotemporal decision-making and demonstrate that a non-Euclidean neural representation of space simplifies decision-making in multi-target environments.
Using quantum annealing to design lattice proteins

Quantum annealing has shown promise for finding solutions to difficult optimization problems, including protein folding. Recently, we used the D-Wave Advantage quantum annealer to explore the folding problem in a coarse-grained lattice model, the HP model, in which amino acids are classified into two broad groups: hydrophobic (H) and polar (P). Using a set of 22 HP sequences with up to 64 amino acids, we demonstrated the fast and consistent identification of the correct HP model ground states using the D-Wave hybrid quantum-classical solver. An equally relevant biophysical challenge, called the protein design problem, is the inverse of the above, where the task is to predict protein sequences that fold to a given structure. Here, we approach the design problem by a two-step procedure, implemented and executed on a D-Wave machine. In the first step, we perform a pure sequence-space search by varying the type of amino acid at each sequence position, and seek sequences which minimize the HP-model energy of the target structure. After mapping this task onto an Ising spin glass representation, we employ a hybrid quantum-classical solver to deliver energy-optimal sequences for structures with 30-64 amino acids, with a 100% success rate. In the second step, we filter the optimized sequences from the first step according to their ability to fold to the intended structure. In addition, we try solving the sequence optimization problem using only the QPU, which confines us to sizes $\le$20, due to exponentially decreasing success rates. To shed light on the pure QPU results, we investigate the effects of control errors caused by an imperfect implementation of the intended Hamiltonian on the QPU, by numerically analyzing the Schr\"odinger equation. We find that the simulated success rates in the presence of control noise semi-quantitatively reproduce the modest pure QPU results for larger chains.
Bayesian estimation of the Kullback-Leibler divergence for categorical systems using mixtures of Dirichlet priors

In many applications in biology, engineering, and economics, identifying similarities and differences between distributions of data from complex processes requires comparing finite categorical samples of discrete counts. Statistical divergences quantify the difference between two distributions. However, their estimation is very difficult and empirical methods often fail, especially when the samples are small. We develop a Bayesian estimator of the Kullback-Leibler divergence between two probability distributions that makes use of a mixture of Dirichlet priors on the distributions being compared. We study the properties of the estimator on two examples: probabilities drawn from Dirichlet distributions and random strings of letters drawn from Markov chains. We extend the approach to the squared Hellinger divergence. Both estimators outperform other estimation techniques, with better results for data with a large number of categories and for higher values of divergences.
Multi-source adaptive thresholding adaboost with application to virtual metrology

To maintain high-quality semiconductor wafer production processes, it is necessary to build high-quality virtual metrology (VM) model. Based on the result of the VM, the engineer can monitor and control the specific process. In the manufacturing process, various sources are obtained from sensor equipment. It is important to consider that these source data exhibit varying characteristics during the model construction. However, when all data sources are simply aggregated into a single model, the performance of the overall model may degrade, especially if any of the individual sources contain outliers or noise. To address this issue, we develop a data-fusion model designed to incorporate diverse data sources into a unified multi-source model. In particular, we improve an Adaboost regression algorithm to make it suitable for multi-source data in the field of VM. The algorithm combines the residuals of the models derived from each individual data source and all sources to adaptively adjust the thresholding value, which, in turn, determines whether the predicted values are accurate or not for each weak learner. Extensive practical validations on real-world processing data from semiconductor manufacturers have demonstrated that the proposed method outperforms single learning algorithms and is more robust than all benchmarks.