Reviewerly/exHarmony huggingface dataset:
Multi-objective optimization algorithm based on clustering guided binary equilibrium optimizer and NSGA-III to solve high-dimensional feature selection problem

Feature selection (FS) is an indispensable activity in machine learning, whose purpose is to identify relevant predictive values from a high-dimensional feature space to improve performance and reduce model learning time. However, the large increase in feature dimensions poses a great challenge to FS methods. Therefore, a multi-objective optimization algorithm consisting of an Equilibrium Optimizer (EO) and NSGA-III was proposed to solve the FS problem with high-dimensional data. Through S-shaped, V-shaped and U-shaped transfer functions, the conversion from real number coding to binary coding is realized to solve discrete problems, and the influence of these three transfer functions on the effect of FS is compared. In addition, the algorithm optimizes the population in the binary search space by building an external archive, and realizes the selection and optimization of external archive individuals based on the clustering strategy. The KNN classifier was used to realize the classification progress. The simulation experiments are divided into two groups with 18 medium and high dimensional data. The first group analyzes the optimization effect of the proposed framework under three transfer functions. The second group of experiments selects the algorithm that wins in the first group of experiments and compares it with eleven classical multi-objective optimization algorithms. The evaluation criteria includes two optimization objectives of the FS problem and the optimization indices of HV and IGD. The first set of experiments showed that the U-shaped transfer function family performed best in the FS problem, with U3 being the most excellent, followed by V-shaped and S-shaped. Compared to other multi-objective optimization algorithms, the simulation results also confirm the effectiveness of the proposed strategy.
Mind meets machine: Unravelling GPT-4’s cognitive psychology

Cognitive psychology delves on understanding perception, attention, memory, language, problem-solving, decision-making, and reasoning. Large Language Models (LLMs) are emerging as potent tools increasingly capable of performing human-level tasks. The recent development in the form of Generative Pre-trained Transformer 4 (GPT-4) and its demonstrated success in tasks complex to humans exam and complex problems has led to an increased confidence in the LLMs to become perfect instruments of intelligence. Although GPT-4 report has shown performance on some cognitive psychology tasks, a comprehensive assessment of GPT-4, via the existing well-established datasets is required. In this study, we focus on the evaluation of GPT-4's performance on a set of cognitive psychology datasets such as CommonsenseQA, SuperGLUE, MATH and HANS. In doing so, we understand how GPT-4 processes and integrates cognitive psychology with contextual information, providing insight into the underlying cognitive processes that enable its ability to generate the responses. We show that GPT-4 exhibits a high level of accuracy in cognitive psychology tasks relative to the prior state-of-the-art models. Our results strengthen the already available assessments and confidence on GPT-4's cognitive psychology abilities. It has significant potential to revolutionize the field of Artificial Intelligence (AI), by enabling machines to bridge the gap between human and machine reasoning.
A lightweight distillation CNN-transformer architecture for remote sensing image super-resolution

Remote sensing images exhibit rich texture features and strong autocorrelation. Although the super-resolution (SR) method of remote sensing images based on convolutional neural networks (CNN) can capture rich local information, the limited perceptual field prevents it from establishing long-distance dependence on global information, leading to the low accuracy of remote sensing image reconstruction. Furthermore, it is difficult for existing SR methods to be deployed in mobile devices due to their large network parameters and high computational demand. In this study, we propose a lightweight distillation CNN-Transformer SR architecture, named DCTA, for remote sensing SR, addressing the aforementioned issues. Specifically, the proposed DCTA first extracts the coarse features through the coarse feature extraction layer and then learns the deep features of remote sensing at different scales by fusing the feature distillation extraction module of CNN and Transformer. In addition, we introduce the feature fusion module at the end of the feature distillation extraction module to control the information propagation, aiming to select the informative components for better feature fusion. The extracted low-resolution (LR) feature maps are reorganized through the up-sampling module to obtain high-resolution (HR) feature maps with high accuracy to generate high-quality HR remote sensing images. The experiments comparing different methods demonstrate that the proposed approach performs well on multiple datasets, including NWPU-RESISC45, Draper, and UC Merced. This is achieved by balancing reconstruction performance and network complexity, resulting in both competitive subjective and objective results.
Attention-based bidirectional-long short-term memory for abnormal human activity detection

Abstract Abnormal human behavior must be monitored and controlled in today’s technology-driven era, since it may cause damage to society in the form of assault or web-based violence, such as direct harm to a person or the propagation of hate crimes through the internet. Several authors have attempted to address this issue, but no one has yet come up with a solution that is both practical and workable. Recently, deep learning models have become popular as a means of handling massive amounts of data but their potential to categorize the aberrant human activity remains unexplored. Using a convolutional neural network (CNN), a bidirectional long short-term memory (Bi-LSTM), and an attention mechanism to pay attention to the unique spatiotemporal characteristics of raw video streams, a deep-learning approach has been implemented in the proposed framework to detect anomalous human activity. After analyzing the video, our suggested architecture can reliably assign an abnormal human behavior to its designated category. Analytic findings comparing the suggested architecture to state-of-the-art algorithms reveal an accuracy of 98.9%, 96.04%, and 61.04% using the UCF11, UCF50, and subUCF crime datasets, respectively.
Outlier detection: How to Select <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si208.svg" display="inline" id="d1e2443"><mml:mi>k</mml:mi></mml:math> for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si208.svg" display="inline" id="d1e2448"><mml:mi>k</mml:mi></mml:math>-nearest-neighbors-based outlier detectors

Unsupervised k-nearest-neighbor-based outlier detectors play a vital role in data science research. However, the detectors' performance relies on the choice of the parameter k. However, autonomous selection of the optimal k is poorly documented in literature as it is very challenging. Conventional methods prove ineffective and lack universality as they fail to account for both application and detector factors simultaneously. This article proposes neighborhood consistency, a new concept which tackles the existing issues of selecting the optimal k by considering both application and detector factors. This concept was used to develop a method termed k finder based on neighborhood consistency (KFC). KFC does not rely on any extra parameter and has linear time complexity. Simulations show that KFC outperformed baselines and had a good generality to different datasets and detectors. The implementation of the proposed methods can be found on www.OutlierNet.com for reproducibility.
PRISMA hyperspectral data for lithological mapping in the Egyptian Eastern Desert: Evaluating the support vector machine, random forest, and XG boost machine learning algorithms

In essence, targeting mineralization necessitates exact structural delineation and thorough lithological mapping. The latter is still a challenge for geologists and its lack hinders meticulous exploration for various mineralizations. Here we show for the first time over a case study from Arabian Nubian Shield (ANS), the application of hyperspectral PRISMA (PRecursore IperSpettrale della Missione Applicativa) data for objective lithological mapping using the well-known Random Forest (RF), XGboost (XGB), and Support Vector Machine (SVM) algorithms. Our results manifested the worthiness of PRISMA data in further lithological mapping, especially with SVM with a resultant accuracy depending mainly on the input data combination. Upon field verification, the current research reveals the usefulness of PRISMA and its preceding four principal components in delivering a detailed lithological map for the study area. Additionally, the eligibility of RF, XGB, and SVM was confirmed in delivering acceptable results. SVM exceeds XGB and RF in their overall accuracy (95 %, 92 %, and 90 % for SVM, XGB, and RF respectively). Our research strongly recommends blending the vantages of Machine Learning Algorithms' (MLAs) objectivity and the wealth of PRISMA spectral coverage for further precise lithological mapping before applicable mineral exploration programs in similar terrains.
Hypersphere guided embedding for masked face recognition

Hypersphere Guided Embedding for Masked Face Recognition has been proposed to address the problem encountered in the Masked Face Recognition task, which arises due to non-biological information from occlusions. While some existing algorithms prefer to digesting the existence of masks by probing and covering, others aim to integrate face recognition and masked face recognition tasks into a unified solution domain. In this paper, we propose a framework to enable existing methods to accommodate multiple data distributions by orthogonal subspaces. Specifically, We introduce constraints on multiple hypersphere manifolds via Multi-Center Loss and employ a Spatial Split Strategy to ensure the orthogonality of base vectors associated with different hypersphere manifolds, corresponding to distinct distribution. Our method is extensively evaluated on publicly available datasets on face recognition, mask face recognition and occlusion, demonstrating promising performance. Our code is available on an anonymous website: https://github.com/CaptainKai/HE_MFR.
The “SMARTER” framework and real application cases of blockchain

Today, blockchain is a very critical technology for many industrial operations. In this study, we aim to study the applications of blockchain in the industry. We reveal the latest blockchain-supported solutions developed by companies, identify the key challenges and discuss the long-term benefits of blockchain adoption. To be specific, this study adopts a multi-methodological approach by systematically reviewing the literature, and conducting exploratory case studies and a semi-structured interview. Based on the findings, we establish a "SMARTER" framework to illustrate the "SMART" challenges that companies may face and discuss the "ER" long-term benefits that can be generated to the business enterprises. We further propose some important future research areas, such as the impacts of blockchain technology to support corporate social responsibility, the co-opetition effect when the companies form partnership in the blockchain development, the feasibility of integration between blockchain technology and other disruptive technologies, and the values of blockchain adoption in the industry.
A graph neural network-based data cleaning method to prevent intelligent fault diagnosis from data contamination

The success of deep learning (DL) based-mechanical fault diagnosis hinges on the high quality of training data. However, it is difficult to acquire high-quality mechanical monitoring data due to data contamination: 1) Monitoring device irregularities, such as sensor malfunction and signal transmission disruption, bring anomalies into the training data; 2) human labour-based data annotation inevitably produces incorrectly labeled data. These two types of data contamination degrade the performance of DL models. To address the aforementioned issue, this paper proposes a graph neural network-based data-cleaning method. In the first stage, a group anomaly detector is designed to identify the presence of anomalous data. This detector incorporates affinity graphs for depicting data groups and subsequently calculates the group anomaly score to determine the abnormal group. In the second stage, a graph clustering model is developed to relabel the mislabeled data. This model takes advantage of the graph neural network's proficiency in handling affinity graphs to prepare clean labels for subsequent network training. Experimental results, conducted on a pump and an industrial robot joint reducer, show the proposed method's ability to effectively detect anomalous data and rectify incorrect labeling, surpassing the performance of baseline methods in mechanical fault diagnosis.
Contrastive self-representation learning for data clustering

This paper is concerned with self-representation subspace learning. It is one of the most representative subspace techniques, which has attracted considerable attention for clustering due to its good performance. Among these methods, low-rank representation (LRR) has achieved impressive results for subspace clustering. However, it only considers the similarity between the data itself, while neglecting the differences with other samples. Besides, it cannot well deal with noise and portray cluster-to-cluster relationships well. To solve these problems, we propose a Contrastive Self-representation model for Clustering (CSC). CSC simultaneously takes into account the similarity/dissimilarity between positive/negative pairs when learning the self-representation coefficient matrix of data while the form of the loss function can reduce the effect of noise on the results. Moreover, We use the ℓ1,2-norm regularizer on the coefficient matrix to achieve its sparsity to better characterize the cluster structure. Thus, the learned self-representation coefficient matrix well encodes both the discriminative information and cluster structure. Extensive experiments on seven benchmark databases indicate the superiority of our proposed method.
Engineering Supply Chain Transportation Indexes through Big Data Analytics and Deep Learning

Deep learning has experienced an increased demand for its capabilities to categorize and optimize operations and provide higher-accuracy information. For this purpose, the implication of deep learning procedures has been described as a vital tool for the optimization of supply chain firms’ transportation operations, among others. Concerning the indexes of transportation operations of supply chain firms, it has been found that the contribution of big data analytics could be crucial to their optimization. Due to big data analytics’ variety and availability, supply chain firms should investigate their impact on their key transportation indexes in their effort to comprehend the variation of the referred indexes. The authors proceeded with the gathering of the required big data analytics from the most established supply chain firms’ websites, based on their (ROPA), revenue growth, and inventory turn values, and performed correlation and linear regression analyses to extract valuable insights for the next stages of the research. Then, these insights, in the form of statistical coefficients, were inserted into the development of a Hybrid Model (Agent-Based and System Dynamics modeling), with the application of the feedforward neural network (FNN) method for the estimation of specific agents’ behavioral analytical metrics, to produce accurate simulations of the selected key performance transportation indexes of supply chain firms. An increase in the number of website visitors to supply chain firms leads to a 60% enhancement of their key transportation performance indexes, mostly related to transportation expenditure. Moreover, it has been found that increased supply chain firms’ website visibility tends to decrease all of the selected transportation performance indexes (TPIs) by an average amount of 87.7%. The implications of the research outcomes highlight the role of increased website visibility and search engine ranking as a cost-efficient means for reducing specific transportation costs (Freight Expenditure, Inferred Rates, and Truckload Line Haul), thus achieving enhanced operational efficiency and transportation capacity.
Bridge the gap between supervised and unsupervised learning for fine-grained classification

Unsupervised learning technology has caught up with or even surpassed supervised learning technology in general object classification (GOC) and person re-identification (re-ID). However, it has been discovered that the unsupervised learning of fine-grained visual classification (FGVC) is more difficult than GOC and person re-ID. To bridge the gap between unsupervised and supervised learning for FGVC, we investigate the essential factors (including feature extraction, clustering, and contrastive learning) for the performance gap between supervised and unsupervised FGVC. Furthermore, we propose a simple, effective, and practical method, termed as unsupervised fine-grained clustering learning (UFCL), to alleviate this gap. Three key issues are concerned and improved: First, we introduce a robust and powerful backbone, ResNet50-IBN, which has the ability of domain adaptation when we transfer ImageNet pre-trained models to FGVC tasks. Next, we propose to introduce HDBSCAN rather than DBSCAN for clustering, which can generate better clusters for adjacent categories with fewer hyper-parameters. Finally, we propose a weighted feature agent and its update mechanism to perform contrastive learning by employing pseudo labels with unavoidable noise, which can enhance the optimization process of learning the network's parameters. The effectiveness of our UFCL was confirmed on the CUB-200-2011, Oxford-Flowers, Oxford-Pets, Stanford-Dogs, Stanford-Cars, and FGVC-Aircraft datasets. Under the unsupervised FGVC setting, we achieved state-of-the-art results and examined the primary factors and crucial parameters to offer practical guidance.
Deep internally connected transformer hashing for image retrieval

Transformer based on self-attention mechanism has made remarkable achievements in natural language processing, which inspired the application research of Transformer in computer vision. The current deep hashing algorithms extract image features through the convolutional neural network (CNN). CNN concentrates on local information, and features lack global dependency information, which has an impact on image retrieval accuracy. To remedy the above defects, this paper proposes deep internally connected Transformer hashing for image retrieval (DICTH). DICTH has designed an improved Transformer block: internally connected Transformer block (ICT). ICT performs an embedded transformation on the feature maps, splices the generated Keys and Queries, to explore the rich context information between Query–Key pairs, and then dynamically encodes through multi-layer convolution to learn the context multi-head self-attention matrix. By combining ICT and ResNet18 to achieve self-attention injection, a long-distance dependency is established in the feature space to make up for the shortcomings of pure CNN in the feature extraction process and guide the algorithm to learn more accurate hash codes. At the same time, in the face of complex label information in big data sets, this paper uses an improved cross-entropy loss function: T-cross-entropy loss, to promote network learning of hash codes with more ability to distinguish between classes. In this paper, a lot of experiments have been conducted on CIFAR10, NUS-WIDE and MS-COCO datasets to verify the performance of DICTH.
Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project

Integrating intricate environmental data within a unified analytical framework for extensive conservation and monitoring initiatives encounters several challenges. These challenges encompass defining a conceptual model outlining cause-and-effect relationships, addressing dissimilarities in data source quantity and information content, grappling with missing or noisy data, fine-tuning model optimization, achieving accurate predictions, and tackling the issue of imbalanced observations across factors. In the context of the Santos project, dedicated to comprehending the spatio-temporal dynamics of benthic, pelagic, and physical systems for the facilitation of conservation and monitoring programs, the application of machine learning’s random forest (RF) technique for modeling univariate data offers notable advantages. This approach adeptly handles non-linearity, covariation, and interactive effects among predictors. For modeling multivariate data sets, a hybrid strategy combining a self-organizing map (SOM) and RF is harnessed to effectively tackle the challenges. Addressing missing values, the bagging imputation technique demonstrated superior performance compared to other methods. Both machine learning techniques discussed herein exhibit resilience against the impact of noisy data, yet the identification of noisy data remains feasible based on model outputs. In scenarios of imbalanced data sets, we investigate the correlation between the RF model’s overall statistics and those of individual classes. The joint interpretation of these statistics aids in comprehending model limitations and facilitates discussions on the environmental mechanisms shaping observed patterns. We propose two analytical workflows that not only enable the exploration and enhancement of model accuracy but also facilitate the investigation of potential cause-and-effect relationships inherent in the data. Furthermore, these workflows lay the foundation for implementing long-term learning algorithms, a pivotal increment for monitoring initiatives. Notably, these workflows, alongside the discussed analytical challenges, can be seamlessly implemented within iMESc, an open-source application.
LMSD-Net: A Lightweight and High-Performance Ship Detection Network for Optical Remote Sensing Images

Ship detection technology has achieved significant progress recently. However, for practical applications, lightweight ship detection still remains a very challenging problem since small ships have small relative scales in wide images and are easily missed in the background. To promote the research and application of small-ship detection, we propose a new remote sensing image dataset (VRS-SD v2) and provide a fog simulation method that reflects the actual background in remote sensing ship detection. The experiment results show that the proposed fog simulation is beneficial in improving the robustness of the model for extreme weather. Further, we propose a lightweight detector (LMSD-Net) for ship detection. Ablation experiments indicate the improved ELA-C3 module can efficiently extract features and improve the detection accuracy, and the proposed WGC-PANet can reduce the model parameters and computation complexity to ensure a lightweight nature. In addition, we add a Contextual Transformer (CoT) block to improve the localization accuracy and propose an improved localization loss specialized for tiny-ship prediction. Finally, the overall performance experiments demonstrate that LMSD-Net is competitive in lightweight ship detection among the SOTA models. The overall performance achieves 81.3% in AP@50 and could meet the lightweight and real-time detection requirements.