Reviewerly/exHarmony huggingface dataset:
Exploring the user guidance for more accurate building segmentation from high-resolution remote sensing images

In recent years, the computer vision domain has witnessed a surge of interest in interactive object segmentation, an area of study that seeks to expedite the annotation process for pixel-wise segmentation tasks through user guidance. Despite this growing focus, existing methods mainly focus on a single type of pre-annotation and neglect the quality of boundary prediction, which significantly influences subsequent manual adjustments to segmentation boundaries. To address these limitations, we introduce a novel end-to-end network to facilitate more precise building segmentation using diverse types of user guidance. In our proposed method, a centroid map is generated to provide foreground prior information crucial to the subsequent segmentation procedure, and the boundary correction module automatically refines the segmentation mask from existing segmentation networks. Extensive experiments on two popular building extraction datasets demonstrate that our method outperforms all existing approaches given various user guidance (bounding boxes, inside-outside points, or extreme points), achieving the IoU scores of over 95% on SpaceNet-Vegas dataset and over 93% on Inria-building dataset. The remarkable performance of our method further demonstrates its immense potential to alleviate the labor-intensive annotation process associated with remote sensing datasets. The code of our proposed method is available at https://github.com/StephenDHYang/UGBS-pytorch.
GCDN-Net: Garbage classifier deep neural network for recyclable urban waste management

The escalating waste volume due to urbanization and population growth has underscored the need for advanced waste sorting and recycling methods to ensure sustainable waste management. Deep learning models, adept at image recognition tasks, offer potential solutions for waste sorting applications. These models, trained on extensive waste image datasets, possess the ability to discern unique features of diverse waste types. Automating waste sorting hinges on robust deep learning models capable of accurately categorizing a wide range of waste types. In this study, a multi-stage machine learning approach is proposed to classify different waste categories using the "Garbage In, Garbage Out" (GIGO) dataset of 25,000 images. The novel Garbage Classifier Deep Neural Network (GCDN-Net) is introduced as a comprehensive solution, adept in both single-label and multi-label classification tasks. Single-label classification distinguishes between garbage and non-garbage images, while multi-label classification identifies distinct garbage categories within single or multiple images. The performance of GCDN-Net is rigorously evaluated and compared against state-of-the-art waste classification methods. Results demonstrate GCDN-Net's excellence, achieving 95.77% accuracy, 95.78% precision, 95.77% recall, 95.77% F1-score, and 95.54% specificity when classifying waste images, outperforming existing models in single-label classification. In multi-label classification, GCDN-Net attains an overall Mean Average Precision (mAP) of 0.69 and an F1-score of 75.01%. The reliability of network performance is affirmed through saliency map-based visualization generated by Score-CAM (class activation mapping). In conclusion, deep learning-based models exhibit efficacy in categorizing diverse waste types, paving the way for automated waste sorting and recycling systems that can mitigate costs and processing times.
Differentially Private Enhanced Permissioned Blockchain for Private Data Sharing in Industrial IoT

The integration of permissioned blockchain such as Hyperledger fabric (HF) and Industrial internet of Things (IIoT) has opened new opportunities for interdependent supply chain partners to improve their performance through data sharing and coordination. The multichannel mechanism, private data collection and querying mechanism of HF enable private data sharing, transparency, traceability, and verification across the supply chain. However, the existing querying mechanism of HF needs further improvement for statistical data sharing because the query is evaluated on the original data recorded on the ledger. As a result, it gives rise to privacy issues such as leaking of business secrets, tracking of resources and assets, and disclosing of personal information. Therefore, we solve this problem by proposing a differentially private enhanced permissioned blockchain for private data sharing in the context of supply chain in IIoT which is known as (EDH-IIoT). First, we integrate differential privacy into the chaincode (smart contract) of HF which evaluates the query and adds a calibrated noise into it. Second, we propose an algorithm to efficiently utilize ϵ through reuse of the privacy budget for the repeated queries. Third, we also propose an algorithm to track the privacy budget (ϵ) and avoid the degrade of privacy preservation in case of multiple queries on the same portion of the ledger's data. Furthermore, the reuse and tracking of ϵ enables the data owner to ensure that ϵ does not exceed the threshold which is the maximum privacy budget (ϵt). Finally, we model two privacy attacks namely linking attack and composition attack to evaluate and compare privacy preservation, and the efficiency of reuse of ϵ with the default chaincode of HF and traditional differential privacy model, respectively. The results confirm that EDH-IIoT obtains an accuracy of 97% in the shared data for ϵ = 1, and a reduction of 35.96% in spending of ϵ.
Hybrid ML-MDKL feature subset selection and classification technique accompanied with rat swarm optimizer to classify the multidimensional breast cancer mammogram image

Breast cancer is a pathological condition characterized by the abnormal proliferation of cells inside the breast tissue. The biggest challenge at the moment is determining the most optimal subset from large datasets. Numerous methodologies have been presented for the purpose of selecting the most suitable subset from datasets with high dimensions. However, the outcomes have proven to be insufficient in effectively addressing extensive quantities of multidimensional datasets. This publication presents a proposal for an efficient approach of selecting numerous feature subsets and performing classification on Multidimensional Datasets (MDD). In this study, the Moment Invariant Wavelet Feature Extraction (MI-WFE) approach is employed for the purpose of feature extraction. The New Adaptive Hybrid Levy Flight based Cuckoo Search Optimization Algorithm is employed to identify the most pertinent subset of features. This study presents a novel approach, referred to as H-RS-LVCSO, which aims to enhance the local search capability and optimization speed of the algorithm. This is achieved through the hybridization of the Rat Swarm Optimizer with the levy flight based cuckoo search optimization technique. In this study, it is suggested a hybridized approach for classifying breast cancer, utilizing a Multilayer Multiple Deep Kernel Learning (ML-MDKL) Classifier in conjunction with the standard Support Vector Machine (SVM) Classifier. The simulation of the suggested method is conducted using the MATLAB software. The approach under consideration is evaluated in comparison to two pre-existing methods. The suggested method demonstrates an accuracy improvement of 10.58% compared to existing methods. Furthermore, it outperforms these methods by 17.5% in terms of accuracy. The proposed method exhibits a precision improvement of 6.52% and 14.23% compared to the existing methods.
Knowledge-guided pre-training and fine-tuning: Video representation learning for action recognition

Video-based action recognition is an important task in the computer vision community, aiming to extract rich spatial–temporal information to recognize human actions from videos. Many approaches adopt self-supervised learning in large-scale unlabeled datasets and exploit transfer learning in the downstream action recognition task. Though much progress has been made for action recognition with video representation learning, two main issues remain for most existing methods. Firstly, the pre-training with self-supervised pretext tasks usually learns neutral and not much informative representations for the downstream action recognition task. Secondly, the valuable learned knowledge from large-scaled pre-training datasets will be gradually forgotten in the fine-tuning stage. To address such issues, in this paper, we propose a novel video representation learning method with knowledge-guided pre-training and fine-tuning for action recognition, which incorporates external human parsing knowledge for generating informative representation in the pre-training, and preserves the pre-trained knowledge in the fine-tuning stage to avoid catastrophic forgetting via self-distillation. Our model, with contributions from the external human parsing knowledge, video-level contrastive learning, and knowledge preserving self-distillation, achieves state-of-the-art performance on two popular benchmarks, i.e., UCF101 and HMDB51, verifying the effectiveness of the proposed method.
Facial attribute editing method combined with parallel GAN for attribute separation

Facial attribute editing encounters problems of incorrect changes to face regions and artifacts in generated images. We propose a facial attribute editing method combined with parallel GAN for attribute separation. First, the method integrates the U2-net encoder and Trans-GAN decoder as a model encoder to extract and generate facial spatial information effectively. Second, RGB images and semantic mask images are used to train a parallel generator and discriminator respectively. Semantic consistency loss is introduced to ensure that the two branches have consistent semantic output and achieve the effect of convergence in the same direction parallel generator and discriminator. The proposed model, trained on the CelebAMask-HQ original dataset and verified by the CelebA dataset, adopts the separation of the face mask image and the background mask image, to improve the correct rate of face attribute editing. Compared with existing facial attribute editing methods, the proposed method is capable of balancing attribute editing ability and details preservation ability. It can accurately edit the target attribute area and greatly improve the quality of facial images.
Relevance Feedback with Brain Signals

The Relevance Feedback (RF) process relies on accurate and real-time relevance estimation of feedback documents to improve retrieval performance. Since collecting explicit relevance annotations imposes an extra burden on the user, extensive studies have explored using pseudo-relevance signals and implicit feedback signals as substitutes. However, such signals are indirect indicators of relevance and suffer from complex search scenarios where user interactions are absent or biased. Recently, the advances in portable and high-precision brain-computer interface (BCI) devices have shown the possibility to monitor user’s brain activities during search process. Brain signals can directly reflect user’s psychological responses to search results and thus it can act as additional and unbiased RF signals. To explore the effectiveness of brain signals in the context of RF, we propose a novel RF framework that combines BCI-based RF with pseudo-relevance signals and implicit signals to improve the performance of document re-ranking. The experimental results on the user study dataset show that incorporating brain signals leads to significant performance improvement in our RF framework. Besides, we observe that brain signals perform particularly well in several hard search scenarios, especially when implicit signals as feedback are missing or noisy. This reveals when and how to exploit brain signals in the context of RF.
Automated modeling of nonlinear systems using fuzzy modular neural network

Modular neural networks (MNNs) have garnered substantial attention in the field of nonlinear system modeling. However, even though MNNs require fewer hyperparameters due to their hierarchical structure compared to traditional NNs, determining the optimal module arrangement remains challenging. To address these issues, a novel approach named fuzzy modular neural networks (FMNN) is introduced. This method employs conditional fuzzy clustering and incremental radial basis function (RBF) neural networks to automatically construct sub-modules within the MNN framework. The resultant sub-modules are chosen utilizing a distance-based fuzzy integrative strategy, effectively diminishing the necessity for manual intervention. To showcase the superiority of the FMNN approach, a series of experiments are carried out employing three benchmark examples. These experiments encompass a comparison of modeling accuracy against other extensively employed neural network models. The experimental findings illustrate that FMNN surpasses alternative neural network models in terms of model precision.
BNDCNet: Bilateral nonlocal decoupled convergence network for semantic segmentation

The perceptual scope of deep convolutional neural networks is inherently confined to a local scale due to the inherent limitations of convolution operations. This confinement subsequently hampers a comprehensive understanding of intricate scenes. In response, an innovative approach called Bilateral Non-Local Decoupled Convergence Network (BNDCNet) is proposed to facilitate contextual interaction of global information. The proposed module decouples the information from the input feature map and uses a bilateral non-local architecture for system processing. This strategy facilitates inter-pixel interaction and aggregates global information. An important aspect of our approach involves the computation of adaptive convolutional channel weights for the feature map. This innovation greatly improves the efficacy and performance of the model. The proposed method achieved high performance on the competitive scene-parsing datasets CamVid, Cityscapes, and KITTI, and thus demonstrated effectiveness and generality. Code has been released: https://github.com/Mantee0810/BNDC.
Improved DWT and IDWT architectures for image compression

In the recent era, a rapid development in the field of image processing has been observed. One of the important applications in image processing is compression. Several wavelet transform based image compression techniques have already been introduced. In this paper, Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) based improved image compression and decompression techniques have been proposed by incorporating a scaling factor. The DWT and IDWT algorithms are implemented using folded architecture. To reduce the usages of hardware resources, a multiplier is recursively used. Image compression and decompression schemes based on proposed DWT and IDWT architectures are tested using four different image databases. The proposed technique provides better results in terms of bits per pixel, compression ratio, mean square error, peak-signal-to-noise ratio, normalized correlation coefficient and structural similarity index. FPGA based synthesis has been performed using Xilinx Vivado Synthesis tool in terms of slice LUTs, slice registers, clock frequency, delay and power. The synthesis results show that proposed DWT and IDWT architectures are amenable for image compression and decompression applications.
Distance metric-based learning for long-tail object detection

Despite the recent success of general object detection, almost all models perform unsatisfactorily on long-tail datasets. The main cause of performance degradation is the imbalance in the number of positive samples between categories. The traditional approaches can lead to distortion of the classification feature space, which in turn can seriously affect the classification ability of the network. To address the above issues, we propose a novel distance metric-based learning approach for long-tail object detection (LTDL) in this paper. Specifically, we directly use the feature space as the optimization target, thus allowing clearer decision boundaries between classes. In order to optimize the decision boundary, we adjust the intra-class and inter-class distances by Margin Module (MAM). Meanwhile, to further exploit the information provided by the dataset, we introduce supervised information of labels for distance weighting using the Semantic Module (SEM). In addition, to protect the learning of tail samples and optimize the classifier, we propose a Distance-based Equilibrium Loss (DEL). Extensive experiments conducted on the LVIS benchmark have demonstrated the strength of our proposed approach. The experimental results show that our method improves the baseline by 2.9% AP. And our best model can outperform almost all other representative methods.
On the linearity of the periods of subtraction games

A subtraction game is an impartial combinatorial game involving a finite set S of positive integers. The nim-sequence GS associated with this game is ultimately periodic. In this paper, we study the nim-sequence GS∪{c} where S is fixed and c varies. We conjecture that there is a multiple q of the period of GS, such that for sufficiently large c, the pre-period and period of GS∪{c} are linear in c if c modulo q is fixed. We prove it in several cases. We also give new examples with period 2 inspired by this conjecture.
Evolutionary feature selection based on hybrid bald eagle search and particle swarm optimization

Feature selection is a complicated multi-objective optimization problem with aims at reaching to the best subset of features while remaining a high accuracy in the field of machine learning, which is considered to be a difficult task. In this paper, we design a fitness function to jointly optimize the classification accuracy and the selected features in the linear weighting manner. Then, we propose two hybrid meta-heuristic methods which are the hybrid basic bald eagle search-particle swarm optimization (HBBP) and hybrid chaos-based bald eagle search-particle swarm optimization (HCBP) that alleviate the drawbacks of bald eagle search (BES) by utilizing the advantages of particle swarm optimization (PSO) to efficiently optimize the designed fitness function. Specifically, HBBP is proposed to overcome the disadvantages of the originals (i.e., BES and PSO) and HCBP is proposed to further improve the performance of HBBP. Moreover, a binary optimization is utilized to effectively transfer the solution space from continuous to binary. To evaluate the effectiveness, 17 well-known data sets from the UCI repository are employed as well as a set of well-established algorithms from the literature are adopted to jointly confirm the effectiveness of the proposed methods in terms of fitness value, classification accuracy, computational time and selected features. The results support the superiority of the proposed hybrid methods against the basic optimizers and the comparative algorithms on the most tested data sets.
Compositional data analysis (CoDA) and geochemical signatures of the terminal complex aquifer in an arid zone (northeastern Algeria)

The Biskra region, one of the arid regions in North-East Algeria, has known remarkable agricultural development, necessitating additional mobilization of groundwater resources, which are almost the only source of water in the region. In the long term, this heavy mobilization may threaten the quantity and quality of groundwater. This study was carried out to gain a better understanding of groundwater quality in the Terminal Complex (TC) aquifer of the Biskra region, to determine the hydrogeochemical processes controlling water mineralization, and further to investigate the suitability of the groundwater for consumption. For this purpose, multivariate statistical analysis, based on compositional data analysis (CoDA), including robust PCA, cluster analysis (HCA), ilr-ion diagram, and correlation analysis as well as graphical approaches, was carried out on the hydrochemical results of forty-four (44) groundwater samples analysed by standard methods. The results showed that most groundwater parameters exceeded WHO-recommended safe limits for drinking water. Robust CoDA revealed three distinct processes controlling groundwater hydrochemistry: the first “carbonate link” [NO3−, HCO3−, pH] at the northern boundary, which was progressively replaced by the second link [Ca2+, Mg2+, SO42−], and the third “mineralization link” [Na+, Cl−, K+, EC, TDS], located on the Outaya, Biskra, Sidi Okba, and El-Houche axes. In addition, around 93% of samples were within the geochemical zone [SO42− +Cl− >CO32-- HCO3−], while only 7% fell within the [CO32− - HCO3−> SO42− + Cl−] zone, with the following chemical facies dominating: Ca–SO4 (52.27%), Ca–Mg–SO4 (22.72%), Na–Cl (20.45%), and Ca–HCO3 (4.56%). The modified water quality index (WQI) revealed five quality classes: excellent (9.10%), good (11.36%), poor (65.90%), very poor (11.36%), and unsuitable quality (2.28%), with an apparent deterioration in water quality from north to south, following the same direction of groundwater flow. Finally, the use of CoDA with isometric log-ratio plotting, for the first time in the study area, has enabled a more thorough and reliable assessment of the processes controlling groundwater quality, which is valuable for rationalizing pumping and can help decision-makers to plan current and future water resource management practices.
Detecting multiple moving objects for unusual crowd activities detection in video surveillance system

Unusual crowd activity detection is a challenging problem in surveillance video applications because feature extraction is difficult process in crowded scenes. The main objective of this research work is to detect unusual crowd activities and to detect unusual splits of moving objects. Various methods have been employed to address these challenges. However, there is still a lack of appropriate handling of this problem due to frames having occlusion, noise, and congestion. This paper proposes a novel clustering approach to detect unusual crowd activities. The proposed method consists of five phases including foreground extraction, foreground enhancement, foreground estimation, clustering crowds, and the Unusual Crowd Activities (UCA) model. The UCA model can find unusual crowd activities and unusual splits of moving objects using the Laplacian Matrix formulation. Two public datasets viz. PETS 2009 and UMN dataset are used for evaluating the proposed methodology. To estimate the effectiveness of the proposed work, several unusual event detection methods are compared with the proposed work results. The experimental results revealed that the proposed method gives better results than the existing methods.