New data-driven estimation of metal element in rocks using a hyperspectral data and geochemical data

Abstract: The inversion of Cu contents in rocks based on hyperspectral remote sensing is indicative of geochemical exploration as well as mineral resource exploration. Due to the strong redundancy of band information in hyperspectral data, characteristic band selection is the key to improving the accuracy of metal element inversion models in hyperspectral data. In this study, we proposed a framework that innovatively focuses on secondary screening method (SSM) as the core, allowing users to add different data preprocessing methods and machine learning algorithms to the framework based on expert experience, to obtain high-precision metal element inversion models suitable for sampling data. we first preprocessed the measured spectral curves using the first order derivative, second order derivative, continuum removal, multiple scattering correction and variance analysis to highlight band absorption characteristics. In addition, we performed SSM to obtain Cu-related characteristic bands, which were further used as input data to train and compare the performance of Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Random Forest (RF) and Gradient Boosting Regression Tree (GBRT). The results show that both the spectral pretreatment and modeling method could potentially impact the inversion accuracy. The accuracy of the ensemble method applied in fresh faces data (i.e., a CR-SSM-RF with R2 values of 0.77 and CR-SSM-GBRT with R2 values of 0.81) performed better than other models with an accuracy improvement of up to 80%. Compared to the full band model, the accuracy of characteristic bands selection model increased 50%. This also illustrates that the CR-SSM-RF or CR-SSM-GBRT model significantly improves the accuracy of the inversion of Cu contents in rocks compared to the original spectral reflectance. Our study provides a new idea for selecting different spectral preprocessing methods as the input values for the future model and will also offer an executable framework for the inversion of Cu contents in rocks of different lithology on a large scale in the future.


Advances in machine learning optimization for classical and quantum photonics

Abstract: The development and optimization of photonic devices and various other nanostructure electromagnetic devices present a computationally intensive task. Much optimization relies on finite-difference time-domain or finite element analysis simulations, which can become very computationally demanding for finely detailed structures and dramatically reduce the available optimization space. In recent years, various inverse design machine learning (ML) techniques have been successfully applied to realize previously unexplored optimization spaces for photonic and quantum photonic devices. In this review, recent results using conventional optimization methods, such as the adjoint method and particle swarm, are examined along with ML optimization using convolutional neural networks, Bayesian optimizations with deep learning, and reinforcement learning in the context of new applications to photonics and quantum photonics.


Generating high-quality texture via panoramic feature aggregation for large mask inpainting

Abstract: With the development of image generation and processing techniques, image inpainting techniques based on deep learning have achieved impressive results. Especially the emphasis on global context during inpainting enables the network to generate reasonably coarse inpainting results at low resolutions. However, how to better achieve high-quality texture filling at high resolutions is still a challenging problem. To address this problem, most methods design two-stage networks to achieve structure and texture restoration separately. But in the face of large-scale masks, the generated textures still suffer from blurring and artifacts. Therefore, in order to achieve inpainting of images with large-scale masks and generate fine textures, this paper proposes an end-to-end generative adversarial model for large mask inpainting, called Panoramic Feature Aggregation Network (PFAN). First, this paper designs a Euclidean Attention Mechanism (EAM) which exploits encoder features to generate low-resolution structure restoration. Then a Feature Aggregation Synthesis Block (FASB) is proposed in the decoder to achieve high-resolution complex texture filling. With the global receptive fields of these two modules, texture filling results with satisfactory performance even under large-scale masks. Experiments on CelebA-HQ, Paris Street View and FFHQ datasets show that the proposed method has superior performance.


AD-YOLOv5: An object detection approach for key parts of sika deer based on deep learning

Abstract: Accurate and rapid fixation of key parts of sika deer is essential for the mechanized and intelligent collection of both deer blood and velvet antler. To solve the low detection accuracy problem of key parts of deer caused by the diversity of deer farm breeding environment and the rapid movement of deer, this paper proposes an AD-YOLOv5 algorithm for detecting key parts of sika deer based on YOLOv5s. Specifically, a new weighted bidirectional feature pyramid network S-BiFPN is first proposed, which both simplifies the structure and effectively performs skip feature fusion to better capture object information at different scales, so that the adaptive feature fusion capability of the model is optimized. Second, this paper proposes to add a SENet module after the C3 module of the 9th Layer in the backbone part of the network, so that the network can adaptively readjust the channel weights of feature maps to enhance the important features for key parts of sika deer and improve the expression ability of the model for key parts. Furthermore, to make the model more stable in the training process, a novel bounding box regression loss function SIoU is led into, which can better learn the position and size of the bounding box by fusing the orientation information between the ground truth box and the predicted box. The effectiveness of the proposed algorithm is demonstrated through ablation experiments. Experimental results show that the mAP@0.5 of the proposed algorithm reaches 97.30 % for the detection of key parts of sika deer, which is 12.85 %, 9.65 %, 9.18 %, 7.13 %, 3.59 %, 4.6 %, 0.90 % and 0.10 % higher than SSD, EfficientDet, YOLOv3, YOLOv4, Faster R-CNN, YOLOv5s, YOLOv7 and YOLOv8s, respectively. This research provides a new idea for detecting key parts of animals in complex environments and lays a theoretical foundation for the development of intelligent fixation devices for sika deer.


Real-time action localization of manual assembly operations using deep learning and augmented inference state machines

Abstract: The real-time monitoring of assembly operations in manufacturing industries can be used for manufacturing process optimization, which is crucial to manufacturers. It helps to improve productivity by automatically identifying the bottleneck and enhancing product quality by detecting errors and providing feedback to rectify them in real-time. However, developing a robust and reliable assembly monitoring system is not trivial due to the varying length of fine-grained assembly steps and anthropometric variations associated with assembly workers. To tackle the challenge, conventionally, wireless body-worn sensors have been used, leading to operator discomfort, and raising safety concerns. In this work, we propose a novel technique to automatically recognize and localize the assembly steps in real time called the State Machine Integrated Recognition and Localization (SMIRL). SMIRL is comprised of an inference machine and a state machine. They are responsible for detecting and localizing the actions, respectively. SMIRL can measure the duration of individual assembly steps and the entire cycle. Additionally, SMIRL can also detect the mistakes that may occur in an assembly and raise an alert to notify the operators in real time. The mistakes here can be breaking the predefined assembly sequence (Sequence Break) or missing any of the assembly steps (Missed steps). The effectiveness of SMIRL was evaluated against two datasets, with one being from an assembly workstation in industry and the other from a laboratory. The result shows that SMIRL can detect and localize the actions with an Intersection over Union (IoU) score of 87.53%, and identify Sequence Breaks and Missed Steps with F1-Scores of 86.64% and 87.45%, respectively. Through this study, we aim to contribute towards real-time monitoring of human-centric assembly operations to facilitate smart manufacturing.


CIS: Connect It Strong! [Editor's Remarks]

Abstract: The beginning of a year is a good timing to conclude on what memorable moments have happened in the preceding year. Last summer, I was invited to give talks at the 2023 IEEE CIS Summer School and Summit Forum held at the Southern University of Science and Technology (SUSTech) in Shenzhen, China. Several IEEE CIS officers, Editors-in-Chief, and I gathered to introduce CI techniques and their advancements to beginners in the field—mainly undergraduate and master students. Approximately 400 participants engaged in discussions immensely, and the vibe was very vigorous. I was delighted to share my research results on CI for composing music. Attending this summer school was absolutely a pleasant and fulfilling experience in 2023. I do love to see that CI technologies have been getting more attention among students and young scholars, from the level of this event's participation, and look forward to taking part in various activities held by CIS. Speaking of our Society, Professor Yaochu Jin assumes the CIS President for 2024-2025. We first met at a conference dated back in 2004, and participated or worked together in many CIS activities for two decades since. CIS is such a platform that brings people with similar research interests together to exchange academic ideas and create opportunities for collaboration. It is definitely worthwhile to join CIS and devote time to, because the sense of achievement and rewarding to work with people having similar passion is humongous. Perhaps someone you meet at a CIS conference today could become the CIS President in 20 years, you never know! LOL!


Development of advanced hybrid computational model for description of molecular separation in liquid phase via polymeric membranes

Abstract: Separation of molecules using membranes has been computationally studied in this work for understanding the separation process and also assessment of integrating machine learning (ML) techniques to computational fluid dynamics (CFD) techniques. We initially performed CFD computations to simulate mass transfer in a membrane-based molecular separation system, and then exported the mass transfer data for training ML models. Indeed, a hybrid model was developed and employed for membrane process in separating a solute from a solution. We have modeled a data set containing more than 14 thousand data rows, which has two inputs r (m) and z (m) and single response, i.e., C (mol/m3) that is solute' content in the solution. Decision tree-based models are used in this modeling. Simple Decision Tree (DT), Boosted Decision Tree (BDT) that is DT alongside AdaBoost, and Random Forest (RF) are the selected models for this research. The ML techniques were further optimized via a novel HHO algorithm. Final assessments show the R2 scores of 0.9991, 0.9874, and 0.9997 respectively for BDT, DT, and RF models. Based on this result and other analysis, RF is selected as the best method in this study for simulation of membrane process. This model has an RMSE error rate of 2.22 × 103.


Multi-domain gate and interactive dual attention for multi-domain dialogue state tracking

Abstract: Multi-domain dialogue state tracking (MDST) is a crucial component of task-oriented dialogue systems. In the context of multi-turn dialogues between the user and the system, MDST necessitates the continuous keeping track of dialogue states based on the information present in the current dialogue utterance and the dialogue states from the preceding turn. Recent work achieves the successful execution of multi-domain dialogue tasks by adopting an approach that treats each state as an individual label, while regrettably neglecting the potential benefits of incorporating domain-specific information associated with these states. Simultaneous, existing models exhibit a deficiency in effectively modelling the explicit correlations between dialogue contextual semantics and dialogue states. In this paper, we introduce the modules of multi-domain gate and interactive dual attention as novel solutions to address the aforementioned concerns. For the efficient exploitation of domain-specific information within states, we leverage the multi-domain gate as indices to amplify the states pertinent to the current utterance domain while filtering out irrelevant states. Interactive dual attention comprises utterance attention and slot attention, effectively modelling the correlation between dialogue utterances and slots. Additionally, interactive dual attention ensures that each dialogue utterance interacts with the slots once to derive all state updates, thereby ensuring computational efficiency. Specifically, slot attention models the associations between slots by incorporating semantic features to forecast updates in slot values. Meanwhile, utterance attention captures the semantics of dialogue context and integrates it with slot name features to generate dialogue states. All the aforementioned modules are designed based on a slot-independent framework, enabling efficient scalability of slots and circumventing issues related to model input limitations. The experimental results on the multi-domain dialogues dataset MultiWOZ 2.4 demonstrate the superior performance of our model compared to the baselines. Additionally, we conduct a comprehensive analysis of the effectiveness of the multi-domain gate and interactive dual attention modules, elucidating their contribution to the performance of the model through visualization and case studies.


SICNet: Learning selective inter-slice context via Mask-Guided Self-knowledge distillation for NPC segmentation

Abstract: Accurate segmentation of nasopharyngeal carcinoma (NPC) in magnetic resonance (MR) images is crucial for radiotherapy planning. However, vanilla 2D/3D deep convolutional networks fail to gain satisfying NPC segmentation results, since 3D methods suffer from inter-slice discontinuities and 2D methods lack inter-slice context learning. To address this problem, this paper proposes a 2.5D learning Selective Inter-slice Context Network (SICNet) for NPC segmentation. The proposed SICNet comprises a main encoder that extracts target features from the middle slice and an auxiliary encoder that learns inter-slice context from adjacent slices. The features from two encoders are fused by the Inter-slice Context Fusion (ICF) module and then sent to the decoder for predictions. Furthermore, a knowledge distillation module named Mask-Guided Self-knowledge Distillation (MGSD) is proposed to help the auxiliary encoder learn context selectively. Specifically, with the guidance of mask labels, MGSD tells the auxiliary encoder where to look and how to learn, thus achieving selective inter-slice context learning and boosting segmentation accuracy. The proposed SICNet achieves a DSC of 74.38 ± 11.99, an HD95 of 9.31 ± 2.68, and an ASSD of 1.46 ± 0.76 on an in-house NPC dataset, outperforming other state-of-the-art segmentation methods.


Human skin detection: An unsupervised machine learning way

Abstract: Researchers have been involved for decades in search of an efficient skin detection method. However, current methods have not overcome the significant challenges of skin detection, such as variation of illumination, various skin tones of different ethnic groups, and many others. This research proposed a clustering and region-growing-based skin detection method to overcome these limitations. Together with significant insight, these methods result in a more effective algorithm. The insight concerns the capability to dynamically define the number of clusters in a collection of pixels organized as images. In Clustering for most problem domains, the number of clusters is fixed prior and does not perform effectively over a wide variety of data contents. Therefore, this research paper proposed a skin detection method that validated the above findings. The proposed method assigns the number of clusters based on image properties and ultimately allows freedom from manual thresholds or other manual operations. The dynamic determination of clustering outcomes allows for greater automation of skin detection when dealing with uncertain real-world conditions.


Implementation of three-qubit Deutsch-Jozsa algorithm with pendular states of polar molecules by optimal control

Abstract: Ultracold polar molecules have gained much attention as a potential candidate for quantum information processing due to their long coherence time and strong dipole–dipole interaction. In the present study, we explore the dynamics of three polar molecules that are mutually coupled and arranged in an equilateral triangle configuration within a gradient electric field. By employing the pendular states of polar molecules as qubits, we simulate the three-qubit Deutsch-Jozsa algorithm via multi-target optimal control. Through the design of optimal microwave pulses with multiple iterations, our approach can accomplish the operations required to complete the algorithm in a single operational step with high fidelity and large transition probability. Furthermore, we compare and analyze the fidelity and average transition probability with respect to iteration numbers for each unitary operation Uf pulse. It is found that by combining the optimized X, H⊗H⊗H, Uf, and H⊗H gate pulses, the Deutsch-Jozsa algorithm in the three-dipole system can be performed well for distinguishing between constant and balanced functions. Our findings could shed some light on the physical realization of multi-particle quantum algorithm based on polar molecules in external electric fields.


CBDC's FinTech Innovations

Abstract: As the global financial landscape continues to transform to technological improvements, the emergence of CBDCs represents a significant turning point in financial systems. The central bank of a country issues and controls CBDCs, which are digital representations of sovereign money. Fintech and CBDCs are closely related because of the building pieces of the CBDC operation, which include blockchain or distributed ledgers, digital currency wallets, and dependable payment gateways. The adoption of Central Bank Digital Currencies (CBDCs) has the potential to have a significant impact on the fintech industry and could, in some cases, result in a fintech boom. The goal of this study is to develop a comprehensive knowledge of CBDCs' integration with fintech, their wide-ranging effects on society, and the potential need for digital detoxification techniques to balance off the unwanted effects of growing digitalization which can lead to financial innovation and personal well-being in the digital era by looking at CBDCs from both a technology and human-centric standpoint.


Geometric constructive network with block increments for lightweight data-driven industrial process modeling

Abstract: Industrial data-driven models may require frequent reconstruction to maintain model performance due to the dynamics, uncertainty, and complexity of industrial processes. The infrastructure of the industrial processes is usually distributed control systems (DCS) with energy-sensitive and resource-constrained. In this context, this article proposes a geometric constructive network with block increments (BI-GCN) to reduce the modeling consumption while achieving comparable accuracy. First, this article proposes a geometric control strategy with block increments, which is capable of adding multiple nodes to the BI-GCN simultaneously. Second, this article demonstrates the universal approximation property of BI-GCN, which in turn guarantees the potential high performance of BI-GCN for modeling tasks. Finally, experiments on benchmark datasets and the grinding process show that BI-GCN can effectively reduce the number of iterations in the modeling process while maintaining comparable accuracy.


Role of Human Resource Management on Strategic Management

Abstract: Strategic human resource management (SHRM) is a field of study and practice that focuses on the alignment of human resource strategies and practices with the overall strategic objectives of an organization. It recognizes the critical role that human capital plays in driving organizational success and competitive advantage. By integrating HR practices with strategic goals, SHRM aims to maximize the performance, engagement, and development of employees, ultimately contributing to the achievement of organizational objectives. A thorough understanding of SHRM is essential for organizations seeking to optimize their human resources and gain a competitive edge in today's dynamic business environment. In this literature review, the authors delve into the existing research and explore the various aspects of SHRM, shedding light on its theoretical foundations, practical applications, and the potential benefits it offers to organizations.


The impact of extreme dust storms on the national photovoltaic energy supply

Abstract: This study analyses the consequences of an extreme dust storm that occurred in March 2022 on the Spanish national photovoltaic (PV) energy supply. This event, indeed, substantially raised the particulate matter concentrations and the aerosol optical depths across the country, seriously affecting the surface radiation and posing a substantial threat not only to individual PV systems but also to the national electricity grid. The research, based on the analysis and the forecast of weather, environmental and electrical data, reveals that such event halved the capacity factor of the national PV fleet over a period longer than two weeks. A peak drop as high as 80% was registered, at national level, on the worst day. This underperformance also affected the market share of PV in the national electricity mix, whose monthly average value fell from the predicted 10.9% to 7.1%. Despite the expectations, however, no significant difference in soiling was found compared to the typical losses, thanks to the occurrence of heavier-than-usual rainfall events. This facilitated the recovery of the national PV capacity, which returned to the expected performance factors as soon as the sky cleared.


Deployment of interpretable machine learning in a water treatment device - feasibility exploration of performance enhancement

Abstract: Machine learning (ML) provides a promising tool for predictive control and unattended operation of water treatment processes, especially in decentralized water supply due to its high variability of water quality and scarcity of manpower. However, model deployment after ML development is a challenge that hinders their implementation. Herein, this study constructed a novel electrocoagulation membrane cathode reactor (ECMCR) as an example of decentralized water treatment due to its highly integratable. Nine ML models were established to determine the ECMCR operational current density (CD) and the Bayesian technique was employed for adaptive hyperparameters tuning of each ML model. Light gradient boosting machine (LightGBM) with turbidity, humic acid (HA), pH, temperature and time as input performed best (R2 = 0.99, MSE = 0.0004) during training, whereas the random forest (RF) was effective and robust for test data (R2 = 0.98, MSE = 0.0056) with same inputs. The mean decrease impurity (MDI) and Shapley Additive exPlanation (SHAP) method indicated that the RF model made predictions based primarily on influent turbidity, which implied a correct "understanding" of the RF model. Finally, the model was deployed to the ECMCR to explore the regulation capability, and the HA removal and turbidity removal of the ECMCR increased by 23.68 % and 23.83 %, respectively. The results demonstrate that ML technology could assist in achieving the desired performance of drinking water treatment devices without manual involvement.


Asymmetric Learning for Graph Neural Network based Link Prediction

Abstract: Link prediction is a fundamental problem in many graph-based applications, such as protein-protein interaction prediction. Recently, graph neural network (GNN) has been widely used for link prediction. However, existing GNN-based link prediction (GNN-LP) methods suffer from scalability problem during training for large-scale graphs, which has received little attention from researchers. In this paper, we first analyze the computation complexity of existing GNN-LP methods, revealing that one reason for the scalability problem stems from their symmetric learning strategy in applying the same class of GNN models to learn representation for both head nodes and tail nodes. We then propose a novel method, called a sym m etric l earning (AML), for GNN-LP. More specifically, AML applies a GNN model to learn head node representation while applying a multi-layer perceptron (MLP) model to learn tail node representation. To the best of our knowledge, AML is the first GNN-LP method to adopt an asymmetric learning strategy for node representation learning. Furthermore, we design a novel model architecture and apply a row-wise mini-batch sampling strategy to ensure promising model accuracy and training efficiency for AML. Experiments on three real large-scale datasets show that AML is 1.7×∼7.3× faster in training than baselines with a symmetric learning strategy while having almost no accuracy loss.


Semi-supervised possibilistic c-means clustering algorithm based on feature weights for imbalanced data

Abstract: The possibilistic c-means clustering (PCM) algorithm improves the robustness of fuzzy c-means clustering (FCM) to noise and outliers by releasing the probabilistic constraint of memberships. The semi-supervised possibilistic c-means clustering (SSPCM) algorithm improves the clustering effect on datasets with imbalanced sizes by introducing a small amount of label information. However, the traditional semi-supervised algorithm still faces the problem of low utilization of supervision information for datasets with large differences in sample sizes. Moreover, the Euclidean distance, which treats features equally, cannot handle feature-imbalanced data. Therefore, this paper proposes a semi-supervised possibilistic c-means clustering algorithm based on feature weights (FW-SSPCM) by introducing the ideas of supervised centers. First, the algorithm introduces the supervised center into the objective function of the SSPCM to improve the utilization rate of supervision information and thus guide the center iteration of small clusters. Second, the feature weighting strategy is introduced in the objective function to adaptively assign feature weights according to the importance of different features in different clusters, thus improving the adaptability of the algorithm to feature-imbalanced datasets. In addition, to improve the robustness of the antinoise effect and retain additional image details, a new image segmentation algorithm based on FW-SSPCM and local information (LFW-SSPCM) is proposed by introducing local spatial information obtained by bilateral filtering. Finally, through clustering experiments on synthetic data, UCI datasets and on color images characteristic of multiple features, including imbalanced sizes, imbalanced features and strong noise injection, the clustering performances of the proposed FW-SSPCM and LFW-SSPCM proposed in this paper are significantly better than those of several related clustering algorithms.


Multi-granularity Knowledge Sharing in Low-resource Neural Machine Translation

Abstract: As the rapid development of deep learning methods, neural machine translation (NMT) has attracted more and more attention in recent years. However, lack of bilingual resources decreases the performance of the low-resource NMT model seriously. To overcome this problem, several studies put their efforts on knowledge transfer from high-resource language pairs to low-resource language pairs. However, these methods usually focus on one single granularity of language and the parameter sharing among different granularities in NMT is not well studied. In this article, we propose to improve the parameter sharing in low-resource NMT by introducing multi-granularity knowledge such as word, phrase and sentence. This knowledge can be monolingual and bilingual. We build the knowledge sharing model for low-resource NMT based on a multi-task learning framework, three auxiliary tasks such as syntax parsing, cross-lingual named entity recognition, and natural language generation are selected for the low-resource NMT. Experimental results show that the proposed method consistently outperforms six strong baseline systems on several low-resource language pairs.


Improving extreme learning machine model using deep learning feature extraction and grey wolf optimizer: Application to image classification

Abstract: The Extreme Learning Machine (ELM) is a highly efficient model for real-time network retraining due to its fast learning speed, unlike traditional machine learning methods. However, the performance of ELM can be negatively impacted by the random initialization of weights and biases. Moreover, poor input feature quality can further degrade performance, particularly with complex visual data. To overcome these issues, this paper proposes optimizing the input features as well as the initial weights and biases. We combine both Convolutional Neural Network (CNN) and Convolutional AutoEncoder (CAE) extracted features to optimize the quality of the input features. And we use our hybrid Grey Wolf Optimizer-Multi-Verse Optimizer (GWO-MVO) metaheuristic for initializing weights and biases by applying four fitness functions based on: the norm of the output weights, the error rate on the training set, and the error rate on the validation set. Our method is evaluated on image classification tasks using two benchmark datasets: CIFAR-10 and CIFAR-100. Since image quality may vary in real-world applications, we trained and tested our models on the dataset’s original and noisy versions. The results demonstrate that our method provides a robust and efficient alternative for image classification tasks, offering improved accuracy and reduced overfitting.


Event-Driven Fringe Projection Structured Light 3-D Reconstruction Based on Time–Frequency Analysis

Abstract: Event-based structured light 3D measurement systems (eSL) overcome intrinsic limitations of frame-based structured light 3D measurement systems (fSL) such as sensor dynamic range, pixel bandwidth, and system optical power. However, most of the existing methods are based on laser scanning, and they are not only limited by the speed of acquisition, but are also very sensitive to noise events. This paper proposes an efficient event-driven fringe projection structured light 3D reconstruction system (eFPSL), aiming for high-precision 3D measurements at increased acquisition speeds. Leveraging a digital light processing fringe projection system, eFPSL enhances the acquisition speed. We further develop a time-frequency analysis algorithm capable of efficiently converting event streams into 2D fringe intensity images with high signal-to-noise ratio and excellent sinusoidality thus mitigating noise event impacts. Moreover, a novel events-count-based shadow mask extraction (eSME) strategy is proposed to eliminate shadow noise. Experimental validations demonstrate eFPSL’s ability to produce higher precision 3D reconstructions, reducing mean absolute error (MAE) by 64% compared to existing eSL methods. In addition, eFPSL impressively safeguards comprehensive 3D results while reducing system power consumption, achieving an 82% reduction in system optical power compared to traditional fSL systems. Most importantly, for high dynamic range (HDR) scenes, eFPSL achieves visual representation and measurement accuracy comparable to eight measurements of fSL systems using only a single measurement, indicating an 87.5% improvement in measurement efficiency. Hence, our experiments confirm eFPSL’s adaptability can extend to HDR scenes involving high-speed motion.


Motion segmentation using Ward’s hierarchical agglomerative clustering for crowd disaster risk mitigation

Abstract: Motion segmentation has gained increasing attention due to its significance for many public surveillance applications, such as behavior understanding and density estimation. Thus ensuring crowd risk mitigation and safety. Although numerous solutions have been put out to deal with this issue, it remains challenging due to factors like scene fluctuations, cluttered backgrounds, and severe occlusions. The present paper proposes a novel framework, spatio-angular-shape Ward linkage (SAS-WL), to segment human motion based on tracked trajectories features in crowd scenes. In the proposed SAS-WL, first the motion features are captured from the crowd video through the generalized-KLT (gKLT) keypoint tracker. The tracklets are then examined to keep only the significant and long-term tracklets. Then, spatio-angular-shape motion features are obtained from those long-term tracklets. Next, Ward's linkage was utilized for performing motion pattern segmentation. In addition, we propose an improved Ward's linkage method and integrate it with the obtained features to increase the quality of the clustering results. One of the significant advantages of the SAS-WL is its capability to address various densities in different crowd scenes. Moreover, the SAS-WL achieves accurate segmentation results. We validated our findings with videos of real-world crowd scenes. We introduce a novel dataset along with its manually annotated ground truth, comprising 21 diverse crowd videos. This dataset aims to enhance the assessment of our framework's performance. Obtaining results on a diversity of challenging crowd scenarios demonstrates that this framework provides precise motion segmentation results.


Generalized GHZ entanglement states for optimal rotation detection of quantum spin systems using majorana representation

Abstract: Abstract A multitude of engineering and scientific applications require the estimation of an unknown axis’ rotation, known as the reference frame alignment problem. These problems can be categorized using the quantum Cramer–Rao bound. Detecting rotations is typically focused on smaller angles, as errors are more significant in this range. Therefore, selecting the optimal combination of measurement techniques based on a quantum entangled state for a particular setup is a critical step. This paper proposes a linear combination of anticoherence and coherence measures based on the generalized GHZ entanglement states. The anticoherence measures, optimizes the spin system’s average fidelity in detecting rotated states, while the overlap between states in any spin number is minimal. The optimal quantum rotosensors are characterized by the minimal average fidelity given by the overlap before and after averaging over all directions. The individual subsystems’ states have substantial overlap with GHZ states, making the generalized GHZ state the entanglement quantum spin-j state with coherence decay rate ϵ beneficial in finding the optimal rotation detection on noncoherent entanglement state.


Time-aware and task-transferable adversarial attack for perception of autonomous vehicles

Abstract: With rapid development of self-driving vehicles, recent work in adversarial machine learning started to study adversarial examples (AEs) for perception of autonomous driving (AD). However, generating practical AEs for the perception module remains a significant challenge. Traditional adversarial attacks tend to focus on a single computer vision task, making it difficult to compromise multiple perception tasks such as object detection and segmentation simultaneously. Additionally, the limited computational resources available on-board and the necessity for online operation pose further obstacles to deploying adversarial attacks on real autonomous driving platforms. To address the aforementioned issues, we propose the Time-aware Perception Attack (TPA), which is a real-time cross-task adversarial attack for perception of autonomous driving. In particular, we propose a novel backbone based adversarial attack method to modify input images to approach Lipschitz Constant Point (LCP), which results in erroneous inferences for all the sub-models in perception module. The novel part of this work is proposing an efficient yet effective LCP approaching algorithm. Comparing to conventional LCP based attacks, which consume significant amount of computation resources and can be only applied on small DNNs, TPA generates AEs on an intermediate layer of surrogate backbone, significantly enhancing the cross-task transferability and accelerates the attack process. Evaluation results on Berkeley Driving Dataset 100k (BDD100k) show that, comparing to the state-of-the-art baselines, the proposed TPA achieves higher attack effectiveness and faster processing speed and outperforms the baselines by a large margin.


GPL-GNN: Graph prompt learning for graph neural network

Abstract: Despite the impressive results achieved in many areas of graph machine learning, through graph representation learning using supervised learning techniques, the limited availability of labeled training data has led to a bottleneck in terms of performance. To address this challenge, transfer learning has been proposed as an effective solution. It involves designing pre-training methods in an unsupervised manner to learn representations, which are then adapted to downstream tasks with limited labeled data. However, transfer learning can suffer from negative transfer when there is a major gap between the objectives of pre-training and the downstream tasks. To overcome these challenges, we introduce a novel framework, graph prompt learning-graph neural network (GPL-GNN), to narrow the gap between different tasks. GPL-GNN employs unsupervised methods, which require no labeled data, and incorporates unsupervised pre-trained structural representations into downstream tasks as prompt information. This information is combined with downstream data to train GNNs adapting them to the downstream tasks, and resulting in more adaptive, task-specific representations. Furthermore, the ability of GPL-GNN to learn graph representations without the constraints of pre-training and fine-tuning for model consistency increases the flexibility in choosing task-specific GNNs. In addition, the introduction of prototype networks as classification heads enables quick adaptation of GPL-GNNs to downstream tasks. Finally, we conduct extensive experiments on a benchmark dataset to demonstrate the effectiveness of GPL-GNN. The code is available in: https://github.com/chenzihaoww/GPL-GNN.


Deep learning-based fusion networks with high-order attention mechanism for 3D object detection in autonomous driving scenarios

Abstract: In the realm of autonomous driving, accurately detecting 3D objects is both vital and challenging. Recently, deep convolutional networks have been successfully implemented in the fusion of LiDAR and camera data, delivering impressive results. However, prevailing approaches tend to concentrate on basic architectural designs and the use of fixed 3D bounding boxes, overlooking the exploration of feature interrelations and the varying scales of 3D objects. In this paper, we propose High-order Attention Mechanism Fusion Networks (HAMFNs) for image expression and multi-scale learning, based on a novel high-order attention mechanism with multi-scale detection and scale linear regression. High-order convolution layers are built for tenser filtering with discriminative representations of the holistic image. Multi-scale query module further characterizes the saliency properties of the 3D objects. Our tests on the nuScenes dataset show that HAMFNs outperform the latest top-performing methods, achieving a 0.7% increase in mean Average Precision (mAP). We further integrated high-order convolutional layers into ResNet-50, ResNet-101, and ResNet-152 architectures, enhancing their performance with minimal parameter increase. The Top-1 error rates were reduced by 1.65%, 1.63%, and 1.60% for each network, respectively.


An effective deep learning adversarial defense method based on spatial structural constraints in embedding space

Abstract: Deep neural networks are highly vulnerable to adversarial samples. Most existing adversarial defense methods do not consider the distribution of adversarial samples. We argue that very few adversarial samples in the natural sample set prevent the deep neural networks from learning a complete and effective representation of the adversarial samples. This causes the spatial structures between the natural and the adversarial samples to be vastly different from that of the input space, thus making the models vulnerable to adversarial attacks. Based on this viewpoint, this paper proposes an effective deep-learning adversarial defense method, which incorporates information about the spatial structures of the natural and the adversarial samples in the embedding space during the training process. This proposed approach improves the deep learning model's generalization to new adversarial samples and achieves the purpose of defending against adversarial attacks. Four deep neural networks with different scales are used and experimentally verified on four typical publicly available image data. The experimental results show that our method effectively improves the defense ability of deep learning models against adversarial attacks.


Stereo vision-based health monitoring method for wind turbine towers

Abstract: Wind turbines are subjected to complex aerodynamic loads, which results in potential sway and tilt, even causing the collision between blades and the tower. Therefore, it is imperative to monitor the condition of the tower to ensure safety. In this paper, we propose a method to monitor the health of wind turbine towers based on stereo vision. Our approach extracts the tower central axis from the image based on the structural information. Then the tower's sway magnitude is measured by binocular intersection, and the tilt angle is computed by exploiting the angle information provided by an inclinometer. Moreover, we detect blade tips using their geometric shape, which can be used to estimate the clearance distance between blades and the tower. The proposed method can simultaneously monitor the tower tilt and sway, while also identifying instances of blade collision with the tower. Experiments demonstrate the accuracy and feasibility of the designed system.


Debiased Visual Question Answering via the perspective of question types

Abstract: Visual Question Answering (VQA) aims to answer questions according to the given image. However, current VQA models tend to rely solely on textual information from the questions and ignore the visual information in the images to get answers, which is caused by bias that is generated during the training phase. Previous studies have shown that bias in VQA is mainly caused by the text modality, and our analysis suggests that question type is a crucial factor in bias formation. To address this bias, we proposed a self-supervised method including the Against Biased Samples(ABS) module that performs targeted debiasing by selecting samples that are prone to bias, and the Shuffle Question types(SQT) module that constructs negative samples by randomly replacing the question types of the samples selected by the ABS, to interrupting the shortcuts from question type to answer. Our approach mitigates the question-to-answer bias without using external annotations, overcoming the prior language problem. Additionally, we designed a new objective function for negative samples. Experimental results indicate that our method outperforms both self-supervised-based and supervised-based state-of-the-art approaches, achieving 70.36% accuracy on the VQA-CP v2 dataset.


Blockchain-based multi-institution collaborative credit evaluation modeling method

Abstract: Multi-institution credit data sharing-aggregation can improve the accuracy of credit evaluation, however, it also encounters the problems of data fraud, and the privacy, compliance of collaborative modeling. Faced with increasingly stringent and comprehensive privacy protection regulations, the potential security risk call into question the current data sharing-aggregation mode. Blockchain has shown that trusted and auditable computing is possible using a decentralized network of peers, and the immutable distributed ledger. Moreover, oblivious transfer (OT) guarantees the confidentiality of cross-institution transmission and calculation. The integration of blockchain with OT is rapidly increasing in computing environment. In this paper, we design and implement a blockchain-OT-based credit evaluation system dubbed as MEChain. Blockchain acts as a distributed ledger which facilitates efficient and credible data sharing-aggregation. Combined the 1-out-of-N OT, data transformation is used for raw data encryption achieving private, secure, and compliant sharing-aggregation operations. The calculation case and security analysis, prototype system implementation, performance and accuracy rate analysis are presented to validate the proposed solution.


Sequential visual and semantic consistency for semi-supervised text recognition

Abstract: Scene text recognition (STR) is a challenging task that requires large-scale annotated data for training. However, collecting and labeling real text images is expensive and time-consuming, which limits the availability of real data. Therefore, most existing STR methods resort to synthetic data, which may introduce domain discrepancy and degrade the performance of STR models. To alleviate this problem, recent semi-supervised STR methods exploit unlabeled real data by enforcing character-level consistency regularization between weakly and strongly augmented views of the same image. However, these methods neglect word-level consistency, which is crucial for sequence recognition tasks. This paper proposes a novel semi-supervised learning method for STR that incorporates word-level consistency regularization from both visual and semantic aspects. Specifically, we devise a shortest path alignment module to align the sequential visual features of different views and minimize their distance. Moreover, we adopt a reinforcement learning framework to optimize the semantic similarity of the predicted strings in the embedding space. We conduct extensive experiments on several standard and challenging STR benchmarks and demonstrate the superiority of our proposed method over existing semi-supervised STR methods.


Hybrid approach of type-2 fuzzy inference system and PSO in asthma disease

Abstract: 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

Abstract: 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

Abstract: 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 & 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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☆

Abstract: 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

Abstract: 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: 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

Abstract: 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: 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

Abstract: 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 & conversion rate (CTCVR) estimation task. Currently, TAFE is deployed in an online platform to provide various traffic services.


Partial multi-label learning via robust feature selection and relevance fusion optimization

Abstract: Partial Multi-Label Learning (PML) is a more practical learning paradigm, in which the labeling information is ambiguated. Most existing PML algorithms rely on assumptions to resolve ambiguity. However, these assumptions do not account for the origin of the noise labeling and therefore fail to address the impact of noise on the learner's performance at the root. In this paper, we will propose a PML method jointly granular ball-based robust feature selection and relevance fusion optimization (PML-GR). Specifically, in the first stage, we construct a granular ball to compute the core-set with weights and then design a feature importance evaluation function to assign weights to each feature in the core-set, resulting in a ranking of feature importance for the PML learner; in the second stage, based on the selected features, a fusion-based objective function is constructed to compute the label confidence by taking into account the joint effect of the global sample similarity and local label relevance. Finally, a multi-label prediction model is learned by fitting the multi-output regressor to the label confidence. The experimental results demonstrate that the proposed method achieves competitive generalization performance by effective feature selection and relevance fusion optimization, which can focus more on discriminative features and minimize the effect of noisy labels during training.


Multi-Semantic Decoding of Visual Perception with Graph Neural Networks

Abstract: Constructing computational decoding models to account for the cortical representation of semantic information plays a crucial role in understanding visual perception. The human visual system processes interactive relationships among different objects when perceiving the semantic contents of natural visions. However, the existing semantic decoding models commonly regard categories as completely separate and independent visually and semantically and rarely consider the relationships from prior information. In this work, a novel semantic graph learning model was proposed to decode multiple semantic categories of perceived natural images from brain activity. The proposed model was validated on the functional magnetic resonance imaging data collected from five normal subjects while viewing 2750 natural images comprising 52 semantic categories. The results showed that the Graph Neural Network-based decoding model achieved higher accuracies than other deep neural network models. Moreover, the co-occurrence probability among semantic categories showed a significant correlation with the decoding accuracy. Additionally, the results suggested that semantic content organized in a hierarchical way with higher visual areas was more closely related to the internal visual experience. Together, this study provides a superior computational framework for multi-semantic decoding that supports the visual integration mechanism of semantic processing.


Identifiability and Consistent Estimation for Gaussian Chain Graph Models

Abstract: The chain graph model admits both undirected and directed edges in one graph, where symmetric conditional dependencies are encoded via undirected edges and asymmetric causal relations are encoded via directed edges. Though frequently encountered in practice, the chain graph model has been largely under investigated in the literature, possibly due to the lack of identifiability conditions between undirected and directed edges. In this article, we first establish a set of novel identifiability conditions for the Gaussian chain graph model, exploiting a low rank plus sparse decomposition of the precision matrix. Further, an efficient learning algorithm is built upon the identifiability conditions to fully recover the chain graph structure. Theoretical analysis on the proposed method is conducted, assuring its asymptotic consistency in recovering the exact chain graph structure. The advantage of the proposed method is also supported by numerical experiments on both simulated examples and a real application on the Standard & Poor 500 index data. Supplementary materials for this article are available online.


Application of Bayesian method for mining-induced tremors: A case study of the Xinjulong coal mine in China

Abstract: Mining-induced tremor is a dynamic phenomenon occurring during the fracturing or slipping of rock and coal mass in coal mines, and the tremor mechanism is one of the key problems in the field of rockburst. The Bayesian inversion method can more accurately analyse the source rupture type under the premise of considering waveform data and model errors. First, we added different noise levels to the waveform and determined filtering parameters based on the spectrum. The rationality of the Bayesian inversion was verified by observing the focal mechanism solution. Finally, a total of fourteen mining-induced tremors were selected, and the parameters of the moment tensor (MT), type of rupture, and fault plane solutions were obtained. The results demonstrate that (1) the tremor events in the roof are mainly tensile/shear failure, and compression failure mainly occurs in the coal seam. (2) Events on the floor are closely related to gob and mining activities. (3) The rupture form of the events near the tectonic region is more complex, and the fault plane solutions (strike, dip, and slip) were obtained by MT conversion. (4) The relation between rockbursts and mining-induced tremors is discussed by analysing events 1# and 14# that occurred in the Xinjulong coal mine. These research findings can provide a reference for the analysis of source parameters and rupture forms of mining-induced tremors.


Versatile correlation learning for size-robust generalized counting: A new perspective

Abstract: Generalized counting has recently emerged to count novel-class objects within a query image, leveraging limited exemplars. Although methods based on exemplar-query pairs matching have made impressive progress, they typically rely on a single correlation representation, regardless of the varying sizes of objects, which limits more accurate counting. In this paper, we introduce a novel and conceptually straightforward perspective to guide the design of our correlation mechanism that enhances the effectiveness of counting size-diversity objects. Our new perspective encompasses three key aspects: (1) Small objects typically exhibit features concentrated within limited spatial regions, underscoring the importance of an effective channel-wise correlation mechanism for small object counting. (2) Large objects tend to possess rich spatial semantics, making an effective spatial-wise correlation mechanism crucial for large object counting. (3) Integrating both channel-wise and spatial-wise correlation mechanisms holds the potential to enhance counting accuracy across different object sizes. Building upon the above perspective, firstly, we propose a simple yet effective Dual-level Channel-wise Correlation (DCC) module that utilizes kernel-wise correlation and distinct correlation to encode global-to-local channel-wise relationships, enhancing small objects counting accuracy. Secondly, we develop a 4D-convolution-based Spatial-aware Correlation (4DSC) module to extract local-to-local spatial correlation in 4D space, promoting large objects counting accuracy. Finally, we combine the proposed DCC and 4DSC to realize our Versatile Correlation Module (VCM) to simultaneously process both small and large objects, providing adaptability to object size diversity. Extensive experiments on the FSC-147 dataset and CARPK dataset demonstrate the effectiveness of the proposed methods and the superior performance of our counting model.


Optimal algorithm for color medical encryption and compression images based on DNA coding and a hyperchaotic system in the moments

Abstract: Currently, visual data security plays a significant role in various fields, especially in medical imaging. Addressing the challenges associated with limited key space and vulnerability to different types of attacks within current encryption schemes, this work proposes an optimal compression-encryption scheme for large medical images that incorporates elements of Archimedes' optimization algorithm, discrete orthogonal Hahn moments, chaotic systems, and DNA coding. The primary aim of this study is to develop an optimal and exceptionally resilient compression-encryption scheme capable of countering various attack types effectively. This approach is structured into three principal phases: a compression phase harnessing the efficiencies of Hahn's discrete orthogonal moments (HMs) in signal and image representation, coupled with the Archimedes optimization algorithm (AO) to ensure optimal tuning of polynomial parameters (a, b) for superior image reconstruction quality. The encryption phase is performed on the compressed image, using hyperchaotic memristive 4-D (HCM-4D), adapted logistics map (ALM) and DNA coding. Initially, the adapted logistics map is responsible for generating random sequences linked to the compressed image. Subsequently, chaotic sequences originating from the hyperchaotic 4-D memristive system govern both random sequences and DNA processes. The optimization phase, facilitated by the AO algorithm, focuses on minimizing the value of the objective function (correlation) on the compressed and encrypted images. Ultimately, the image with the lowest correlation value is designated as the optimal compressed-encrypted representation. The simulation results clearly illustrate the resilience of the AO algorithm when juxtaposed with other optimization algorithms, especially with respect to convergence speed and computational efficiency. On the other hand, the proposed compression approach demonstrated exceptional efficiency in compressing medical images, offering us the possibility to achieve impressive compression ratio (CR) as well as exceptional quality in decompressed images, evident thanks to high PSNR values. In addition, security analyze demonstrate that the proposed compression-encryption benefits from a larger key space and superior resistance against different types of attacks. Furthermore, our approach was subjected to a comparative analysis alongside various encryption method. These comparisons demonstrate that our encryption algorithm surpasses others in terms of both security and effectiveness.


Practical application of interactive AI technology based on visual analysis in professional system of physical education in universities

Abstract: Currently, physical education teaching in universities tends to adopt the traditional model of one teacher for multiple students, which has high requirements for teachers and is difficult to consider students' strengths. On this basis, an interactive system has been established, including three modules: behavior information, user data collection, and behavior evaluation. Taking the 400 m running physical education teaching as an example, Kinectv2 was used to collect students' movements and contours while running, and ORB (Oriented FAST and Rotated BRIEF) feature extraction algorithm was used to extract students' movement features. After importing the data into the interactive system, students and teachers could view it in the system and provide guidance based on the students’ actions. This article took 10 students as examples to test their performance changes in the 400 m running before and after systematic training. The results showed that the evaluation score after receiving systematic instruction increased by 6–7 s compared to the score without receiving instruction, with a significant change. This indicated that the interactive AI (artificial intelligence) system constructed in this article can play a significant role in sports teaching of 400 m running.


Texture-aware and color-consistent learning for underwater image enhancement

Abstract: Texture and color are pivotal factors for evaluating the quality of underwater image enhancement. However, current methods for enhancing underwater images still exhibit deficiencies in the restoration of texture and color. Diverging from previous approaches, we conduct heuristic modeling specifically targeting color and texture, culminating in the proposal of a texture-aware and color-consistent network (TACC-Net). Specifically, it first obtains preliminary enhanced images of more global features to facilitate feature decoupling. Then, we designed a color feature decoupling (CFD) sub-module to decouple the preliminary enhanced images into color-consistent color histograms to generate textured gray images. Finally, we designed a color space adjustment embedding (CSAE) sub-module, which is used to evaluate the similarity between features when embedding color information, and adjust the embedding of color information on textures. The experimental results indicate that our method enhances images and exhibits strong competitiveness in improving visual quality.


MCFP: A multi-target 3D perception method with weak dependence on 2D detectors

Abstract: 3D perception for multi-target in complex traffic environments plays an important role in autonomous driving. Nowadays, some mainstream models use 2D detectors to provide auxiliary information for 3D perception. But it is a challenge that the accuracy of 2D detectors has a sensitive impact on the final results. In this paper, we propose MCFP, a multi-target 3D perception model with weak dependence on 2D detectors, to improve the 3D perception performance. We use the point cloud expansion as the main idea to provide more information about instance objects for model learning. Firstly, the centroid awareness strategy is integrated into the front-end part for downsampling and feature extraction to reduce the loss of object-related information. Secondly, the ball query operation with adaptive radius is designed to obtain more complete object information. Experiments show that our model outperforms baseline models in normal environments, and achieves detection performances close to those of SOTA models in extreme environments, which implies that our model has a strong ability to obtain semantic information and hence enhancing the perception ability. The code and data will be made available at https://github.com/MrSakasky/MCFP.


Guided learning strategy: A novel update mechanism for metaheuristic algorithms design and improvement

Abstract: Meta-heuristic algorithms (MH) are naturally inspired global optimization algorithms. They are often relatively simple and can solve problems in a short period of time, with certain benefits. However, as the problem becomes more complex, the solution that the algorithm can obtain is often not the optimal solution to the problem, which limits its application scenarios. Therefore, improving the performance and solving accuracy of existing algorithms is crucial for expanding their application ability. In traditional optimization algorithms, there are often two concepts, namely exploration and exploitation. Exploration refers to a wide range of discrete search, used to avoid falling into local optima, and exploitation refers to a small range of focused exploration, used to improve algorithm accuracy. How to balance exploration and exploitation is the key to enhancing algorithm performance and problem adaptability. This paper proposes a brand new strategy named Guided Learning Strategy (GLS) to solve above problem. GLS obtains the dispersion degree of the population by calculating the standard deviation of the historical locations of individuals in recent generations, and infers what guidance the algorithm currently needs. When the algorithm is biased towards exploration, it will guide the algorithm to exploit. Otherwise, it will guide the algorithm to explore. It is precisely because this strategy can identify the current needs of the algorithm and provide assistance that it can improve the performance of most algorithms. This article improves three types of algorithms based on evolution (LSHADE_SPACMA), Stochastic Fractal Search (SFS), and Marine Predators Algorithm (MPA) with better performance, and tests their performance on 57 constrained engineering problems and CEC2020. The effectiveness of this strategy has been confirmed and proved for optimization problem. The source codes of the proposed GLS (GLS_MPA) can be accessed by https://github.com/luchenghao2022/Guided-Learning-Strategy


Steered coherence of a central two-qubit system coupled to an XY spin chain

Abstract: We investigate the average steered coherence (ASC) of a central two-qubit system coupled to an XY spin-chain environment. It is shown that there exists subspace in which the initial states are immune to this environment. Outside of this subspace, the decay of the ASC can be significantly suppressed by tuning the interaction of the central qubits and its anisotropy in the z axis, as well as the anisotropy of the spin-spin interaction in the xy plane of the environment and the strength of the transverse magnetic field. For the initial Bell states and their unitary equivalent classes, one can also identify system and environmental parameters for which the ASC oscillates near its maximum even in the long-time interval.


Nonlocal correlations dynamics of two-qubit Heisenberg XYZ states with influence of intrinsic decoherence and y-component of Dzyaloshinskii-Moriya interaction

Abstract: In this paper, we explore nonlocal correlation and Bell non-locality in a two-qubit Heisenberg XYZ model by using Bell-CHSH inequality and uncertainty induced nonlocality (UIN) as well as log-negativity. We consider the effects of spin-qubits XYZ-Heisenberg couplings, the y-component of DM interaction, and intrinsic decoherence. Our results show that spin-qubits Heisenberg couplings have a strong ability to create two-qubit nonlocal correlations, enhanced by combining with DM interactions. The generated Bell-CHSH nonlocality and entanglement conform to the hierarchy principle. The UIN correlation is more resistant to intrinsic decoherence compared to Bell-CHSH inequality and log-negativity. Intrinsic decoherence causes the disappearance of Bell-CHSH non-locality and log-negativity entanglement, while leading to the appearance of partial stationary spin-qubits UIN correlation.


Block-matching and 3D filtering-based denoising of acoustic images obtained through point contact excitation and detection method

Abstract: A novel experimental method based on the Coulomb coupling technique is used for visualizing the interaction of ultrasonic waves in PZT (Lead Zirconate Titanate - (Pb[ZrxTi1-x]O3 (0 ≤ x ≤ 1)) ceramic materials. The method of excitation and detection through a point contact produces representations of propagating wave vectors in the time domain as a series of images. In the acoustic image, several types of noises from different types of sources corrupt the signal as well as the image. As a result, the resolution and quality of the images are reduced. Block matching and 3D filtering (BM3D) algorithm is implemented for the reduction of noises from ultrasonic signals that are in the form of 2D images. Here, we also implemented a custom-designed hybrid filter and conventional total variation (TV-L1) filter on the noisy images to compare the final denoised outputs from the BM3D filter. Comparison metrics like PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index measure) are used for quantitative analysis of those denoised ultrasonic images with respect to their ground truth or almost noise-free counterparts. Based upon those parameter values, the BM3D filter demonstrated the best results for the denoising of the acoustic images acquired from the sample PZT material. This paper discusses and explains the results and proves the successful application of the BM3D algorithm on acoustic image data.


Neural radiance fields for multi-scale constraint-free 3D reconstruction and rendering in orchard scenes

Abstract: Efficiently, accurately, and realistically reconstructing large-scale 3D orchard scenes in a virtual world is an immensely challenging task. This complexity stems from the intricate and expansive of real orchard scenes. Traditional 3D reconstruction and rendering methods have encountered limitations in terms of modeling efficiency and computational costs, hindering the ability to provide users with immersive experiences. In response to these challenges, this study introduces a strategy for 3D scene reconstruction and rendering grounded in implicit neural representation: the NeRF-Ag model. Building upon the baseline NeRF, this model integrates a multi-resolution latent feature encoding technique, notably heightening training efficiency and elevating modeling precision. Furthermore, by means of environmental factor embedding, the model's robustness and practical applicability are further enhanced. The experimental outcomes illustrate that NeRF-Ag attains photo-realistic rendering outcomes across small, medium, and large scales. Moreover, it surpasses NeRF concerning the evaluation metrics of PSNR, SSIM, and LPIPS. Notably, the training speed of NeRF-Ag is roughly 39 times faster than NeRF. In 3D reconstruction tasks, NeRF-Ag showcases enhanced texture detail representation and higher modeling accuracy compared to the COLMAP-based 3D reconstruction method. Additionally, this study accomplishes free-viewpoint rendering of 3D scenes employing NeRF-Ag and provides evidence substantiating the connection between the quantity of training images and the precision of 3D rendering. The conclusions of this study will contribute to supporting and referencing the implementation of immersive visual interactive features within agricultural digital twin systems.


Privacy-preserving recommendation system based on social relationships

Abstract: In the era of internet-based data, recommendation systems are crucial for helping users access personalized content and facilitating business promotion and sales. Recommendation systems based on social relationships have gained popularity due to their ability to use social influence and enhance the impact and credibility of recommendations. However, building a successful recommendation system faces the challenge of protecting user data privacy, as it requires a significant amount of sensitive user data. For this challenge, we propose a privacy-preserving recommendation system based on social relationships in the dual-cloud model. To achieve the secure computation of this system, we also design and improve some underlying secure protocols such as secure equality protocol and secure comparison protocol. Our underlying protocol has the advantages of high efficiency and provable security. As a result, our recommendation system can provide excellent recommendations while ensuring privacy protection. Experiments show that our system can perform secure recommendation calculations on 128-bit data with semi-honest security in approximately 5.5 s.


OrangeStereo: A navel orange stereo matching network for 3D surface reconstruction

Abstract: Three-dimensional (3D) surface reconstruction is crucial to accurately detecting navel orange external qualities such as size and shape, and binocular stereo vision is promising for practical sorting scenarios due to its high frame rates and ease of deployment. For objects with texture-less surface, such as navel oranges, it is very challenging to match the corresponding points using traditional stereo matching algorithms because the detected feature points have high similarity in color as well as structure. Most of the existing deep learning-based stereo matching algorithms focus on the stereo matching performance of the whole image and lack the attention to the matching performance of the main region. Meanwhile, simple background with less texture will bring more uncertainty to the matching of corresponding points, which affects the robustness of the model. The OrangeStereo was proposed based on the GwcNet structure to achieve a better corresponding points matching effect of navel oranges, which can provide more accurate 3D surface information. The Structural Features Extraction (SFE) module was introduced after the initial feature extraction module to extract stable structural features and avoid the limitations of using single convolutional features. The Attention Weights Generation (AWG) module was utilized at matching cost calculation stage to help suppress redundant information and make the network pay more attention to the matching performance of specific regions. The improved loss function assigned a greater weight to the loss of the navel orange region using semantic information from left image to make the network more focused on the matching effect of the navel orange region. An ablation study was conducted to confirm the efficiency of the SFE module, AWG module, and the improved loss function of the algorithm. The ablation study contained five sub-experiments, including the baseline model, adding the SFE and the AWG modules respectively, and whether to add the improved loss function based on the addition of previous two modules. Additionally, PSMNet, GwcNet, RealtimeStereo, and ACVNet, four typical stereo matching methods, were contrasted with the OrangeStereo. The proposed OrangeStereo achieved better performance with EPE, RMSE, bad-1, bad-3, bad-5, and inference time of 0.60 pixels, 3.53 pixels, 2.37 %, 1.83 %, 1.62 %, and 33 ms in the orange region, respectively. The R2 and RMSE for the depth of orange utilizing our method were 0.982 and 0.81 mm, respectively. The proposed algorithm can be employed to obtain accurate depth information of fruits quickly, and it is expected to be used in the 3D reconstruction of fruits in the actual commercial fruit sorting lines to achieve more accurate external quality assessment.


Achieving Consensus in Groups with Low Authoritarianism of Participants in Decision Making

Abstract: The complexity of formalizing negotiation procedures leads to the search for fundamentally new approaches to building mathematical models. The theoretical study of consensus makes it possible to analyze various situations encountered by social groups participating in the group decision-making process, leaving aside specific characteristics of the groups. In the article, we built a mathematical model for a group with low authoritarianism among participants based on modeling using Markov chains. The analysis of the model showed that as the leniency of the group members increases, the time to reach consensus increases exponentially, apparently due to the negotiators' lack of desire to take responsibility for the decision. The differences between the number of negotiations in groups of participants with low authoritarianism are greater as the size of the group is smaller. This indicates a lack of coordination in such groups in the absence of a desire to take responsibility for the decision-making. It was revealed that in a dyad of two absolutely lenient participants, consensus is unattainable. For a group with low authoritarianism among participants, psychological "traps" such as false consensus or groupthink are possible, which can sometimes lead to managerial decisions with serious consequences.