Reviewerly/exHarmony huggingface dataset:
Hybrid approach of type-2 fuzzy inference system and PSO in asthma disease

This research work presents a hybrid approach combining a type-2 fuzzy inference system with particle swarm optimization (PSO) to develop a type-2 fuzzy optimized inference system, specifically tailored for asthma patient data. Addressing the inherent uncertainty in medical diagnostics, this model enhances traditional type-1 fuzzy logic by incorporating ambiguity into linguistic variables and utilizing type-2 fuzzy if-then rules. The system is trained to minimize diagnostic error in asthma disease identification. Applied to a dataset comprising eight medical entities from asthma patients, the model demonstrates substantial accuracy improvements. Numerical computations validate the system, showing a decrease in error rate from 1.445 to 0.03, indicating a significant enhancement in diagnostic precision. These results underscore the potential of our model in medical diagnostic problems, providing a novel and effective tool for tackling the complexities of asthma diagnosis.
GA-RCNN:Graph self-attention feature extraction for 3D object detection

In recent years, 3D object detection based on LiDAR point clouds is a key component of autonomous driving. In pursuit of enhancing the accuracy of 3D point cloud feature extraction and point cloud detection, this paper introduces a novel 3D object detection model, termed as Graph Self-Attention-RCNN (GA-RCNN). This model is designed to integrate voxel information and point location information, enhancing the quality of 3D object proposals while maintaining contextual accuracy. The first stage rectifies the previous approach that relied on local features for preselected boxes, overlooking crucial global contextual information. An improved method is suggested in this work, utilizing BEV to capture long-range dependencies via a cross-attention mechanism. The second stage addresses the overreliance on local neighborhood point feature extraction. The Graph Self-Attention Pooling method is proposed, characterized by its dynamic computation of contribution weights for inputs. This enhances the model’s flexibility and generalization performance. Extensive evaluations on KITTI and Waymo datasets demonstrate GA-RCNN’s superior accuracy compared to other methods, affirming its efficacy in 3D object detection.
LIKFCM: Linear interpolation-based kernelized fuzzy C-means clustering imputation method for handling incomplete data

Addressing missing values is a persistent challenge in the field of data mining. The presence of incomplete data can significantly compromise the overall data quality. Consequently, it is crucial to handle incomplete data efficiently. This paper presents a novel approach for imputing missing values that incorporates Kernelized Fuzzy C-Means (KFCM) clustering and proposes a method termed LIKFCM, which combines its benefits with Linear Interpolation (LI). The proposed LIKFCM’s performance is assessed through a comparison against nine state-of-the-art imputation techniques (mean, median, LI, EMI, KNNI, KMI, FKMI, LIFCM, and LIPFCM) across ten widely used real-world datasets from the UCI repository with six combinations of missing ratios to assess the efficacy of the proposed imputation method. From the experimental results, it is evident that our proposed method outperforms the existing imputation methods with significant improvements in terms of RMSE &amp; MAE for these datasets. Additionally, experiments examining the effect of missing values validate the robustness of the proposed approach by handling different missing ratios. The performance validation of the proposed approach against other state-of-the-art imputation methods has been conducted utilizing a Kendall’s W statistical test, involving a comparison of their mean ranks across different missing ratios. The outcomes indicate that LIKFCM has outperformed other imputation methods, attaining the highest rank in terms of different evaluation criteria.
Hierarchical alignment network for domain adaptive object detection in aerial images

Domain Adaptive Object Detection (DAOD) alleviates the reliance on labeled data by transferring knowledge learned from labeled source domain to unlabeled target domain. Recent DAOD methods is modeled mainly based on ground-level images. Compared to ground-level images, aerial images suffer from scale variation and viewpoint diversity. This means that domain adaptive object detection in aerial images is a more challenging task. In this work, we address the domain shift in aerial images from two levels: 1) image-level shifts, such as weather, lighting, viewpoint, etc., 2) instance-level shifts, such as object appearance, scale, etc. Specifically, multiple domain-confusion classifiers are designed to learn image-level common knowledge of the source and target domains. Different levels of domain classifiers are further assigned adaptive weights to coordinate the transferability and discriminability of the adaptive detectors. Meanwhile, instance-level alignment is realized by forcing the intrinsic relationship between classes in both domains to be consistent. In addition, we perform instance-level alignment in different semantic-level feature layers to improve the scale awareness of the adaptation model. Extensive experimental results on VisDrone, UAVDT, DIOR and DOTA datasets demonstrate that our method achieves optimal detection performance in four domain adaption scenarios compared to other state-of-the-art methods, e.g., in Daytime → Night (VisDrone), the mAP50 is 23.5 %; in VisDrone → UAVDT, DIOR → UAVDT and DOTA → VisDrone, the AP50 of the car is 63.1 %, 46.6 % and 44.8 % respectively. Code will be available online (https://github.com/MaYou1997/HANet).
A learning and evolution-based intelligence algorithm for multi-objective heterogeneous cloud scheduling optimization

The multi-objective directed acyclic graph scheduling problem (MDAGSP) is prevalent in cloud scheduling systems, involving the selection, assignment, and execution of multiple tasks/jobs with complex coupling interdependencies. High-quality solutions can yield substantial economic benefits. However, prevailing methods face challenges in obtaining a set of superior solutions for MDAGSP, due to the multifaceted nature of its variables, objectives, constraints, and heterogeneity. Firstly, this paper formulates a three-objective MDAGSP that includes makespan, energy costs and revenue, to model cloud scheduling systems. Subsequently, we propose a composite algorithm consisting of a selection phase and an assignment phase to automatically generate an efficient scheduling policy for this model. During the selection phase, a graph convolutional neural network learns high-level features to extract complex dependencies between tasks. During the assignment phase, an adaptive evolutionary algorithm assigns tasks to the appropriate executors. Finally, a series of experiments are conducted to validate the model's accuracy and assess the algorithm's efficiency. Compared to heuristic approaches, the algorithm achieves at least a 20.1% makespan reduction and 3.17% revenue increase. Remarkably, the algorithm obtains at least an 8.86% reduction in energy costs over the eleven baselines. In conclusion, the proposed algorithm provides decision-makers with a global view of scheduling plan.
Efficient shallow learning mechanism as an alternative to deep learning

Deep learning architectures comprising tens or even hundreds of convolutional and fully-connected hidden layers differ greatly from the shallow architecture of the brain. Here, we demonstrate that by increasing the relative number of filters per layer of a generalized shallow architecture, the error rates decay as a power law to zero. Additionally, a quantitative method to measure the performance of a single filter, shows that each filter identifies small clusters of possible output labels, with additional noise selected as labels outside the clusters. This average noise per filter also decays for a given generalized architecture as a power law with an increasing number of filters per layer, forming the underlying mechanism of efficient shallow learning. The results are supported by the training of the generalized LeNet-3, VGG-5, and VGG-16 on CIFAR-100 and suggest an increase in the noise power law exponent for deeper architectures. The presented underlying shallow learning mechanism calls for its further quantitative examination using various databases and shallow architectures.
A corpus-based Japanese translation model using fuzzy semantic optimal control technique

Textural translations from diverse foreign languages require reference corpora for providing error-free readability. This applies to any foreign language example English or any other language translation to Japanese. Therefore Japanese corpus repository verifies the consistency of the translated texts, words, and sentences for its readability. This article introduces a Semantic Translation Model (STM) using Fuzzy Control (FC) for supporting the aforementioned fact. The proposed model analyzes the translated word semantics based on its sentence occurrence and meaning. These two factors are analyzed using two-level fuzzy control; the first level identifies the word placement/occurrence-based readability and the second level identifies the meaning retention. If any inconsistency is observed in the first level, the second is not carried out preventing readability errors. The fuzzy control process relies on near-to-same Japanese corpus inputs for improving readability. If the case fails then a new word replacement or displacement of the semantics is enforced. Therefore the fuzzy control levels are expanded based on different word occurrences, preventing time complexity.
DiffTAD: Denoising diffusion probabilistic models for vehicle trajectory anomaly detection

Vehicle trajectory anomaly detection plays an essential role in the fields of traffic video surveillance, autonomous driving navigation, and taxi fraud detection. Deep generative models have been shown to be promising solutions for anomaly detection, avoiding the costs involved in manual labeling. However, existing popular generative models such as Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs) are often plagued by training instability, mode collapse, and poor sample quality. To resolve the dilemma, we present DiffTAD, a novel vehicle trajectory anomaly detection framework based on the emerging diffusion models. DiffTAD formalizes anomaly detection as a noisy-to-normal process that progressively adds noise to the vehicle trajectory until the path is corrupted to pure Gaussian noise. The core idea of our framework is to devise deep neural networks to learn the reverse of the diffusion process and to detect anomalies by comparing the difference between a query trajectory and its reconstruction. DiffTAD is a parameterized Markov chain trained with variational inference and allows the mean square error to optimize the reweighted variational lower bound. In addition, DiffTAD integrates decoupled Transformer-based temporal and spatial encoders to model the temporal dependencies and spatial interactions among vehicles in the diffusion models. Experiments on the real-world trajectory dataset TRAFFIC demonstrate that our DiffTAD achieves significant improvements over existing state-of-the-art methods, with the maximum enhancements reaching 25.87% and 35.59% in terms of AUC and F1. While on the synthetic datasets CROSS, SynTra, and MAAD, the maximum improvements in AUC/F1 are 27.47%/38.56%, 25.38%/31.42%, and 58.22%/50.04%, respectively.
RGMU: A High-flexibility and Low-cost Reconfigurable Galois Field Multiplication Unit Design Approach for CGRCA

Finite field multiplication is a non-linear transformation operator that appears in the majority of symmetric cryptographic algorithms. Numerous specified finite field multiplication units have been proposed as a fundamental module in the coarse-grained reconfigurable cipher logic array to support more cryptographic algorithms; however, it will introduce low flexibility and high overhead, resulting in reduced performance of the coarse-grained reconfigurable cipher logic array. In this article, a high-flexibility and low-cost reconfigurable Galois field multiplication unit (RGMU) is proposed to balance the tradeoffs between the function, delay, and area. All the finite field multiplication operations, including maximum distance separable matrix multiplication, parallel update of Fibonacci linear feedback shift register, parallel update of Galois linear feedback shift register, and composite field multiplication, are analyzed and two basic operation components are abstracted. Further, a reconfigurable finite field multiplication computational model is established to demonstrate the efficacy of reconfigurable units and guide the design of RGMU with high performance. Finally, the overall architecture of RGMU and two multiplication circuits are introduced. Experimental results show that the RGMU can not only reduce the hardware overhead and power consumption but also has the unique advantage of satisfying all the finite field multiplication operations in symmetric cryptography algorithms.
Modeling of Aquila Optimizer with Hybrid ResNet-DenseNet enabled Breast Cancer Classification on Histopathological Images

Breast cancer (BC) is the most common cancer amongst women that threatens the health of women, initial diagnosis of BC becomes essential. Though there were several means to diagnose BC, the standard way is pathological analysis. Precise diagnosis of BC necessitates experienced histopathologists and needs more effort and time for completing this task. Recently, machine learning (ML) was successfully implemented in text classification, image recognition, and object recognition. With the emergence of computer aided diagnoses (CAD) technology, ML was effectively implemented for BC diagnosis. Histopathological image classification depends on deep learning (DL), particularly convolution neural network (CNN), which frequently needs a large amount of labelled training models, whereas the labelled data was hard to obtain. This study develops an Aquila Optimizer(AO) with Hybrid ResNet-DenseNet Enabled Breast Cancer Classification on Histopathological Images (AOHRD-BC2HI). The proposed AOHRD-BC2HI technique inspects the histopathological images for the diagnosis of breast cancer. To accomplish this, the presented AOHRD-BC2HI technique uses hybridization of Resnet with Densenet (HRD) model for feature extraction. Moreover, the HRD method can be enforced for feature extracting procedure in which the DenseNet (feature value memory by concatenation) and ResNet (refinement of feature value by addition) were interpreted. For BC detection and classification, the DSAE model is utilized. The AO algorithm is exploited to improve the detection performance of DSAE model. The experimental validation of the presented AOHRD-BC2HI approach is tested using benchmark dataset and the results are investigated under distinct measures.Also the proposed model achieved the accuracy of 96%. The comparative result reports the improved performance of the presented AOHRD-BC2HI technique over other recent methods.
A domain generalized person re-identification algorithm based on meta-bond domain alignment☆

Domain Generalization (DG) model is an important tool to improve the robustness of person re-identification algorithm, but the domain gap makes it difficult to transfer knowledge cross-domain effectively. To solve the above problems, this paper proposes a generalization model based on a Meta-Bond Domain Alignment (M−BDA) model. To learn a generalizable model, a meta-learning strategy is introduced to simulate the training–testing process of the domain generalization. Then a bond-domain module is constructed in the training to align the source domain, which can reduce the domain gap between the source domain and the target domain, and facilitate the knowledge transfer. Finally, the bond-domain loss is counted on the feature space of the bond-domain to prevent the generated bond-domain from overfitting to the source domain. The experimental results show that the proposed algorithm achieves better performance in DukeMTMC-ReID, Market1501 and MSMT17 alternating as the source and the target domain tasks. In the generalization experiments of Market-1501 → DukeMTMC-ReID, mAP and Rank-1 increased by 7.7 % and 3.6 %, respectively, and in the generalization experiments of DukeMTMC-ReID → Market-1501, mAP and Rank-1 are increased by 9.3 % and 3.5 %, respectively, which are significantly better than the newer representative algorithms.
A physics-oriented shadowgraph motion estimation algorithm for turbulent flow structures

Shadowgraph imaging is a simple, cost-effective, and non-invasive technique for visualizing strong gradients in transparent medium. This paper developed a shadowgraph motion estimation algorithm to resolve dense motion fields for turbulent flow structures. The algorithm inherits the framework of the optical flow method, which resolves the two-dimensional velocity vectors from two basic constraints using optimization techniques. In the current motion estimation scheme, a physics-oriented constraint is derived from the optical equation of shadowgraph imaging and the fluid continuity equation, while the other constraint adopts a second-order div-curl equation. The improved constraints are specifically suitable for shadowgraph imaging, which can resolve more accurate estimations. An energy function is constructed with the two constraints, and the velocity components are solved by minimizing the energy function. Several state-of-the-art optimization techniques are adopted, including multi-resolution pyramids, weighted median filter and graduated non-convex algorithms. The algorithm is applied to two test conditions covering a wide velocity range, including an acoustic excited premixed flame and a sonic jet. The comparison to Horn–Schunck optical flow indicates that the current algorithm is able to resolve more details of the turbulent flow field, and the velocity shows better continuity. The proposed algorithm has great potential for motion estimation based on shadowgraph images in high speed turbulent flow and combustion research.
Entropy Computation for Oscillator-based Physical Random Number Generators

Abstract In this paper, we provide a complete set of algorithms aimed at the design and security evaluation of oscillator-based True Random Number Generators (TRNG). While depending on some TRNG design assumptions, the proposed algorithms use as inputs the statistical parameters of the underlying random physical process such as the clock jitter originating from the thermal noise and give a lower bound of the entropy rate of the generated bit stream as output. We describe the general structure of a TRNG composed of multiple free-running oscillators and samplers, the outputs of which are post-processed by an entropy conditioner. Depending on the specification of the entropy conditioner, which can usually be any Boolean function, we describe several algorithmic optimizations. We then explain how to compute and efficiently manage the entropy rate at the output of such a post-processing block and at the output of the generator as a whole.
Subspace learning machine (SLM): Methodology and performance evaluation

Inspired by the feedforward multilayer perceptron (FF-MLP), decision tree (DT) and extreme learning machine (ELM), a new classification model, called the subspace learning machine (SLM), is proposed in this work. SLM first identifies a discriminant subspace, S0, by examining the discriminant power of each input feature. Then, it uses probabilistic projections of features in S0 to yield 1D subspaces and finds the optimal partition for each of them. This is equivalent to partitioning S0 with hyperplanes. A criterion is developed to choose the best q partitions that yield 2q partitioned subspaces among them. We assign S0 to the root node of a decision tree and the intersections of 2q subspaces to its child nodes of depth one. The partitioning process is recursively applied at each child node to build an SLM tree. When the samples at a child node are sufficiently pure, the partitioning process stops and each leaf node makes a prediction. The idea can be generalized to regression, leading to the subspace learning regressor (SLR). Furthermore, ensembles of SLM/SLR trees can yield a stronger predictor. Extensive experiments are conducted for performance benchmarking among SLM/SLR trees, ensembles and classical classifiers/regressors.
Integrating subsystem embedding subalgebras and coupled cluster Green’s function: a theoretical foundation for quantum embedding in excitation manifold

Abstract In this study, we introduce a novel approach to coupled-cluster Green’s function (CCGF) embedding by seamlessly integrating conventional CCGF theory with the state-of-the-art sub-system embedding sub-algebras coupled cluster (SES-CC) formalism. This integration focuses primarily on delineating the characteristics of the sub-system and the corresponding segments of the Green’s function, defined explicitly by active orbitals. Crucially, our work involves the adaptation of the SES-CC paradigm, addressing the left eigenvalue problem through a distinct form of Hamiltonian similarity transformation. This advancement not only facilitates a comprehensive representation of the interaction between the embedded sub-system and its surrounding environment but also paves the way for the quantum mechanical description of multiple embedded domains, particularly by employing the emergent quantum flow algorithms. Our theoretical underpinnings further set the stage for a generalization to multiple embedded sub-systems. This expansion holds significant promise for the exploration and application of non-equilibrium quantum systems, enhancing the understanding of system–environment interactions. In doing so, the research underscores the potential of SES-CC embedding within the realm of quantum computations and multi-scale simulations, promising a good balance between accuracy and computational efficiency.
Multi-Task Learning with Sequential Dependence Toward Industrial Applications: A Systematic Formulation

Multi-task learning (MTL) is widely used in the online recommendation and financial services for multi-step conversion estimation, but current works often overlook the sequential dependence among tasks. In particular, sequential dependence multi-task learning (SDMTL) faces challenges in dealing with complex task correlations and extracting valuable information in real-world scenarios, leading to negative transfer and a deterioration in the performance. Herein, a systematic learning paradigm of the SDMTL problem is established for the first time, which applies to more general multi-step conversion scenarios with longer conversion paths or various task dependence relationships. Meanwhile, an SDMTL architecture, named Task-Aware Feature Extraction (TAFE), is designed to enable the dynamic task representation learning from a sample-wise view. TAFE selectively reconstructs the implicit shared information corresponding to each sample case and performs the explicit task-specific extraction under dependence constraints, which can avoid the negative transfer, resulting in more effective information sharing and joint representation learning. Extensive experiment results demonstrate the effectiveness and applicability of the proposed theoretical and implementation frameworks. Furthermore, the online evaluations at MYbank showed that TAFE had an average increase of 9.22% and 3.76% in various scenarios on the post-view click-through &amp; conversion rate (CTCVR) estimation task. Currently, TAFE is deployed in an online platform to provide various traffic services.