Reviewerly/exHarmony huggingface dataset:
Fractal encoding uses and variations in contemporary biometrics applications

Fractal encoding, as part of Computer Vision processes, has gained major success as the base of a lossy compression algorithm. Yet, the principle of self-similarity, which under-pins fractal encoding, has sparked attention in the Biometrics research field throughout the years. In contrast to the era of Deep Learning techniques, fractal encoding and its integration with Machine Learning produced intriguing and competitive outcomes in real-world use applications. The goal of this article is to gather biometrics applications that have used the fractal encoding approach to tackle authentication, recognition, or classification problems in these sectors in recent years. We investigate how the authors adopted fractal encoding to be a main feature in a biometric system in a reasonable encoding computational time. The approaches for classifying and regressing fractal encoding to get the desired output are described, and the more promising results are emphasized. After a review of the methodologies previously researched in this field, we outline a direction for future research on fractal encoding applied to biometrics. The findings achieved by the approaches discussed in this study show that there is still a large and potential scope for research in this field.
Artificial Neural Network Based Diabetes Prediction Model and Reducing Impact of Class Imbalance on Its Performance

Diabetes is characterised by low insulin concentrations in the blood. Signs of high blood sugar include an increased need to urinate, increased thirst, and hunger. It can cause several issues and even death if left untreated. Different algorithms present a variety of diverse decision-support systems to medical professionals. Diabetes, a common and well-known chronic disease, inhibits the body's ability to process blood glucose and has severe health repercussions. Diabetes may allow for a long and healthy life if it is properly treated. Early illness detection is essential. Our investigation focuses on the accuracy with which a machine learning (ML) algorithm can detect diabetes on the Indian diabetes dataset (PIDD). We are well-versed in the categorization and prediction techniques offered by machine learning. Moreover, diabetes has been a source of concern in the field of study due to the availability of incomplete and inconsistent data. Despite the fact that the algorithm's efficacy is affected by a number of variables, artificial intelligence is playing an increasingly important role in disease prediction in the medical business today. Artificial Neural Network (ANN) is chosen for constructing the model to predict diabetes since it shows the most promise for Non-linear data among the many available Machine Learning approaches. Given a set of inputs, the goal is to construct a decision support system for making as accurate a forecast or diagnosis of diabetes as possible. Optimal precision is attained by modifying the settings. We established a strategy for predicting diabetes classes in the face of inconsistent results, data gaps, and data class imbalance in this work. Use of an adaptive synthetic sampling technique , the detrimental impacts of inequality between classes on the accuracy of the proposed Estimation methodology were reduced. Afterwards, this method utilised a specific feature selection methodology and use of multilayer perceptron classifiers to improve generalisation when producing predictions and evaluations. Using a neural network model, the accuracy of this experiment increased by 84% relative to the baseline.
Who Validates the Validators?

Given a fip:Metadata-schema and a validator for it, such as a sh:Validator or a JSON Schema, how do you determine that the validator is…valid? That it speaks the desired fip:Knowledge-representation-language, that it knows all the terms in a desired fip:Structured-vocabulary and checks their usage against a desired fip:Semantic-model? In other words, that it adheres to a doap:Specification? I do not know. However, I suspect that it is more important to check output rather than...
Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data

The sudden resignation of core employees often brings losses to companies in various aspects. Traditional employee turnover theory cannot analyze the unbalanced data of employees comprehensively, which leads the company to make wrong decisions. In the face the classification of unbalanced data, the traditional Support Vector Machine (SVM) suffers from insufficient decision plane offset and unbalanced support vector distribution, for which the Synthetic Minority Oversampling Technique (SMOTE) is introduced to improve the balance of generated data. Further, the Fuzzy C-mean (FCM) clustering is improved and combined with the SMOTE (IFCM-SMOTE-SVM) to new synthesized samples with higher accuracy, solving the drawback that the separation data synthesized by SMOTE is too random and easy to generate noisy data. The kernel function is combined with IFCM-SMOTE-SVM and transformed to a high-dimensional space for clustering sampling and classification, and the kernel space-based classification algorithm (KS-IFCM-SMOTE-SVM) is proposed, which improves the effectiveness of the generated data on SVM classification results. Finally, the generalization ability of KS-IFCM-SMOTE-SVM for different types of enterprise data is experimentally demonstrated, and it is verified that the proposed algorithm has stable and accurate performance. This study introduces the SMOTE and FCM clustering, and improves the SVM by combining the data transformation in the kernel space to achieve accurate classification of unbalanced data of employees, which helps enterprises to predict whether employees have the tendency to leave in advance.
Unsupervised Light Field Depth Estimation via Multi-view Feature Matching with Occlusion Prediction

Depth estimation from light field (LF) images is a fundamental step for numerous applications. Recently, learning-based methods have achieved higher accuracy and efficiency than the traditional methods. However, it is costly to obtain sufficient depth labels for supervised training. In this paper, we propose an unsupervised framework to estimate depth from LF images. First, we design a disparity estimation network (DispNet) with a coarse-to-fine structure to predict disparity maps from different view combinations. It explicitly performs multi-view feature matching to learn the correspondences effectively. As occlusions may cause the violation of photo-consistency, we introduce an occlusion prediction network (OccNet) to predict the occlusion maps, which are used as the element-wise weights of photometric loss to solve the occlusion issue and assist the disparity learning. With the disparity maps estimated by multiple input combinations, we then propose a disparity fusion strategy based on the estimated errors with effective occlusion handling to obtain the final disparity map with higher accuracy. Experimental results demonstrate that our method achieves superior performance on both the dense and sparse LF images, and also shows better robustness and generalization on the real-world LF images compared to the other methods.
Robust Model Design by Comparative Evaluation of Clustering Algorithms

The K-means algorithm, widely used in cluster analysis, is a centroid-based clustering method known for its high efficiency and scalability. However, in realistic situations, the operating environment is susceptible to contamination issues caused by outliers and distribution departures, which may lead to clustering results from K-means that are distorted or rendered invalid. In this paper, we introduce three other alternative algorithms, including K-weighted-medians, K-weighted-L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -medians, and K-weighted-HLs, to address these issues under the consideration of data with weights. The impact of contamination is investigated by examining the estimation effects on optimal cluster centroids. We explore the robustness of the clustering algorithms from the perspective of the breakdown point, and then conduct experiments on simulated and real datasets to evaluate their performance using new numerical metrics. The results demonstrate the effectiveness of the proposed K-weighted-HLs algorithm, surpassing other algorithms in scenarios involving both contamination issues.
Dual Attention Network for Unsupervised Domain Adaptive Person Re-Identification

Clustering-based unsupervised domain adaptive person re-identification methods reduce much of the annotation cost.However, many pseudo-labels with inaccurate labels are produced when finetuning the target domain as a result of the shortcomings of the less than ideal network model and less than excellent clustering.In order to remove the mislabeled pseudo-labels and improve the purity of the network to obtain the correctly labeled pseudo-labels, this paper proposes a dual attention network framework.Specifically, in order to make the network learn to focus on the focal object, this paper introduces the CBAM attention mechanism, which can focus well on the information on the image channel and space, and then identify the correct and incorrect pseudo-labels to improve the overall performance of the network model.In addition, in order to allow the network to extract richer semantic information, this paper introduces Nonlocal blocks, which can directly capture the remote dependencies between image features by computing the interaction between any two locations.Accordingly, we conducte extensive experiments on four common unsupervised domain adaptive person re-identification tasks, namely, DukeMTMC→Market-1501, Market-1501→DukeMTMC, DukeMTMC→MSMT17, and Market-1501→MSMT17, in which the mAP/R1 and baseline compared to 3.6%/1.4%,1.8%/2.5%,4.7%/5.2%,and 3.4%/3.0%,respectively.
An Enhanced Recommendation Model Based on Review Text Graph and Interaction Graph

User’s review text data and rating data, as two major information sources of the recommender system, reflect user’s preferences and item characteristics from two different perspectives. Many existing methods rely on one or the other data to make recommendations, ignoring the potential collaborative effects between the two. In terms of processing review data, the existing neural combination models mainly capture the local and continuous dependencies between adjacent words in the review text but have limited ability to capture the global and discontinuous dependencies. Therefore, we propose an enhanced recommendation model based on both the review text graph and the interaction graph. On the one hand, the model represents the review text of each user and item as a graph and uses the graph structure to capture the long-term, global, and discontinuous dependencies between words in the review text. A graph attention network based on connection relationships is used to aggregate the adjacency information of each node while taking word order relationships into account. On the other hand, the model builds an interaction graph based on user-item ratings for feature mining. The results of the two parts are combined to complete the prediction. We conduct experiments on three datasets and the results show that the proposed method can improve the recommendation performance.
Application of Split Residual Multilevel Attention Network in Speaker Recognition

Current speaker recognition systems are mainly for the combined application of network architectures and attention mechanisms, however, lightweight networks are not able to extract frame-level features of speaker speech well, and deeper and wider networks also face the problems of slower inference and excessive number of parameters. To this end, we proposes Split-ResNet, a network structure for split residuals, which can obtain a combination of multiple receptive field at a finer-grained level, thus obtaining a variety of feature representations with different scale combinations and producing more informative and comprehensive multi-scale features. In addition we propose a dual time-frequency attention (DTFA) that enhances key features and suppresses unimportant features by focusing on features in the time and frequency domains and learning weights from the time and frequency channels, respectively. We finally tested the speaker recognition system using a combination of Split-ResNet and DTFA against other speaker recognition systems on the Voxceleb1-O test set. The test results show that the speaker recognition system proposed in this paper is 0.98%, 0.39%, 0.69% and 0.47% lower in EER compared with SpeechNAS, RawNet2, Y-vector and CNN+Transformer, respectively, proving that DTFA+Split-ResNet is a speaker recognition system with good speaker audio feature extraction capability and discriminative capability.
QUATRE-PM: QUasi-Affine TRansformation Evolution With Perturbation Mechanism

Differential Evolution(DE) is a widely used technique to tackle complex optimization problems owing to its easy-implementation and excellent performance, nevertheless, the inborn weakness of the crossover operation has not been solved even in the recent state-of-the-art DE algorithms. There are two commonly used crossover schemes in DE, the exponential crossover and binomial crossover. The exponential crossover is actually a combination of 1-point and 2-point crossover schemes originated with GA, and it has positional bias because of the dependence on parameter separation. The binomial crossover tackles the positional bias by separating each dimension separately and treating them independently, however, bias still exists from a higher dimensional view, we name it selection bias, and that is the reason why the QUATRE algorithm was proposed. The evolution matrix is the primary component of the QUATRE algorithm which solves the selection bias of DE, however, the previous QUATRE variants still suffer the adaptation of the evolution matrix and can not be able to escape some local optima in complex optimization. Therefore, this paper proposes a new QUATRE with better adaptations of evolution matrix and control parameter, moreover, a perturbation mechanism is firstly proposed for the enhancement of population diversity. The main contributions of our algorithm can be summarized as follows. First, a new generation of evolution matrix is proposed, which can obtain better adaptation to the landscape of the objectives and help the algorithm jump out some local optima. Second, novel adaptations of control parameters are also proposed by incorporating historical memory mechanism and population reduction. Third, a new perturbation mechanism is proposed to enhance the population diversity. In order to validate the proposed algorithm, intensive experiments are conducted under 88 benchmark functions from the universal CEC2013, CEC2014, and CEC2017 test suites in comparison with several excellent DE variants and QUATRE variants, and the results support our superiority.
Causal Intervention for Few-Shot Hypothesis Adaptation

A domain adaptation scenario called few-shot hypothesis adaptation (FHA) only permits access to the well-trained source model and few labeled target data. Previous works may be misled by the discriminative yet undesirable domain-specific information since they learn the spurious correlation between inputs and predictions. In this work, our goal is to achieve the robust adaptation from the causality perspective. Specifically, we abstract FHA into a Structural Causal Model (SCM) and notice that the domain-specific variables are confounders, which lead to the spurious correlation. To eliminate the negative effects, we propose a novel Causal Interventional Few-shot hypothesis Adaption (CIFA) that consists of sampling, mixing and classification modules to perform backdoor adjustment. We also calculate the optimal solution for better convergence. Experiments on the benchmarks demonstrate the effectiveness of our algorithm.
Dealing With Sparse Rewards Using Graph Neural Networks

Deep reinforcement learning in partially observable environments is a difficult task in itself and can be further complicated by a sparse reward signal. Most tasks involving navigation in three-dimensional environments provide the agent with minimal information. Typically, the agent receives a visual observation input from the environment and is rewarded once at the end of the episode. A good reward function could substantially improve the convergence of reinforcement learning algorithms for such tasks. The classic approach to increasing the density of the reward signal is to augment it with supplementary rewards. This technique is called reward shaping. In this study, we propose two modifications of one of the recent reward shaping methods based on graph convolutional networks: the first involving advanced aggregation functions, and the second utilizing the attention mechanism. We empirically validate the effectiveness of our solutions for the task of navigation in a 3D environment with sparse rewards. For the solution featuring the attention mechanism, we can also show that the learned attention is concentrated on edges corresponding to important transitions in the 3D environment.
Neural Network Light Weighting Approach Using Multi-Metric Evaluation of Convolution Kernels

The prevalence of convolutional neural networks is hindered by their intricate structure and voluminous parameters, which consume significant processing resources during both training and inference. This study proposes a novel approach that involves treating convolutional kernels as tensors, utilizing suitable priori metrics to gauge their effectiveness, and employing a clustering algorithm to eliminate redundant convolutional kernels for network pruning. To measure the convolutional kernels, we use appropriate priori metrics and density-Based spatial clustering of applications with noise (DBSCAN) to cluster the convolutional kernels so that the cluster centroids are the reserved convolution kernels. The experimental results show that the method can effectively perform network lightweight while maintaining high accuracy. At the same time, the method can improve the efficiency of convolutional kernel utilization, thus reducing the computational resource consumption of the model. Empirical analyses conducted on datasets indicate that, in certain instances, the proposed pruning method outperforms established state-of-the-art methods.
Spatial Information-Guided Adaptive Context-Aware Network for Efficient RGB-D Semantic Segmentation

Efficient RGB-D semantic segmentation has received considerable attention in mobile robots, which plays a vital role in analyzing and recognizing environmental information. According to previous studies, depth information can provide corresponding geometric relationships for objects and scenes, but actual depth data usually exist as noise. To avoid unfavorable effects on segmentation accuracy and computation, it is necessary to design an efficient framework to leverage cross-modal correlations and complementary cues. In this article, we propose an efficient lightweight encoder–decoder network that reduces the computational parameters and guarantees the robustness of the algorithm. Working with channel and spatial fusion attention modules, our network effectively captures multi-level RGB-D features. A globally guided local affinity context module is proposed to obtain sufficient high-level context information. The decoder uses a lightweight residual unit (LRU) that combines short- and long-distance information with a few redundant computations. Experimental results on the NYUv2, SUN RGB-D, and Cityscapes datasets show that our method achieves a better tradeoff among segmentation accuracy, inference time, and parameters than the state-of-the-art (SOTA) methods.
Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification

There are several statistical descriptors for feature extraction from texture images. Local binary pattern is one of the most popular descriptor for revealing the underlying structure of a texture. Recently several variants of local binary descriptor have been proposed. The completed local binary pattern is an efficient version that can provide discriminant features and consequently providing a high classification rate. It finely characterizes a texture by fusing three histograms of features. Fusing histograms is applied by jointing the histograms and it increases the feature number significantly; therefore, in this paper a weighted constraint feature selection approach is proposed to select a very small number of features without any degradation in classification accuracy. It significantly enhances the classification rate by using a very low number of informative features. The proposed feature selection approach is a filter-based feature selection. It employed a weighted constraint score for each feature. After ranking the features, a threshold estimation method is proposed to select the most discriminant features. For a better comparison, a wide range of different datasets is used as benchmark to assess the compared methods. Implementations on Outex, UIUC, CUReT, MeasTex, Brodatz, Virus, Coral Reef, and ORL face datasets indicate that the proposed method can provide high classification accuracy without any learning step just by selecting a few features of descriptor.
A Novel Adaptive Bandit-Based Selection Hyper-Heuristic for Multiobjective Optimization

Despite the success of meta-heuristics for many computationally hard optimization problems, the design of meta-heuristics is dependent on the problem domain and needs to be tailored by experts. In contrast, selection hyper-heuristics are emerging cross-domain search methodologies that perform the search over the space of a set of low-level heuristics. In this study, we introduce a novel selection hyper-heuristic to learn, select, and apply low-level heuristics to solve multiobjective optimization problems. The proposed selection hyper-heuristic is an iterative process that includes three main components: 1) the reward balance strategy; 2) the bandit-based learning strategy; and 3) the multistage selection strategy. The performance and behavior of the proposed selection hyper-heuristic are investigated on a range of benchmarks as well as several real-world problems. The empirical results demonstrate the effectiveness and the cross-domain ability of the proposed selection hyper-heuristic by comparing it with several state-of-the-art algorithms.