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Parkinson's disease (PD) is a condition that degenerates over time and impairs speech and pronunciation because brain cells have died. This research work aims to predict parkinson disease using the voice features extracted from speech signals recorded from PD individuals with dysphonic speech disorders by employing deep learning algorithms. PD is challenging to diagnose early on in the clinical presentation. To address the issue in machine learning methods, this paper proposes a neural network model by processing speech signals to classify PD using the University of California Irvine (UCI) machine learning repository dataset. Initially, a pre-loss reduction module is created by using pre-sampling to make the dataset balanced by reducing the dimensionality and maintaining the size of the space without influencing the learning process for data preparation. The relevant features are derived using a novel multi-agent salp swarm (MASS) algorithm, and a novel Parkinson classification neural network (PCNN) is proposed to classify Parkinson's patients with high accuracy employing these derived features. The result shows that the models that use MASS-PCNN produce higher classification accuracy of 99.1%, precision of 97.8%, recall of 94.7% and F1-score of 0.995 when paralleled to the existing models. As an outcome, the suggested model will perform superior to common convolutional neural networks. [ABSTRACT FROM AUTHOR]

This paper studies supply chain pricing and production/ordering decisions under carbon tax policy and retailer's stochastic demand. Firstly, a supply chain model with random demand obeying normal distribution is established. Based on this, a stochastic optimization problem with the goal of maximizing expected social welfare is constructed. The multi-agent consensus is used to solve the optimization problem, and the pricing and production/ordering decisions with random demand obeying normal distribution are obtained. Finally, the theoretical results are verified by numerical simulation, and the influence and effectiveness of multi-agent consensus theory on supply chain decision-making are demonstrated in the presence of random demand and sudden failure of an enterprise in the supply chain and further analyze the impact of carbon tax policy on supply chain decision-making. [ABSTRACT FROM AUTHOR]

On most e-commerce websites, there are two crucial factors that customers rely on to assess product quality and dependability: customer reviews provided online and related ratings. Reviews offer feedback to customers about the product's merits, reasons for negative reviews, and feelings of satisfaction or dissatisfaction with the provided service. As for ratings, they express customer opinions about the product's quality as numerical values from one to five (one or two for the worst opinion, three for the neutral opinion, and four or five for the best opinion). Usually, the customer reviews may be inconsistent with their relevant ratings; the customer may write the worst review despite providing a four- or five-star rating or write the best review with only a one- or two-star rating. Due to this inconsistency, customers may need help to identify relevant information. Therefore, it is required to develop a model that can classify reviews as either positive or negative, depending on the polarity of thoughts, to demonstrate if there is an inconsistency between customer reviews and their actual ratings by comparing them with the ratings resulting from the model. This paper proposes an efficient deep learning (DL) model for classifying customer reviews and assessing whether there is inconsistency. The recommended model's performance and stability are examined on a large dataset of product reviews from Amazon e-commerce. The experimental findings showed that the proposed model dominates and significantly outperforms its peers regarding prediction accuracy and other performance measures. [ABSTRACT FROM AUTHOR]

Once realized, autonomous aerial refueling will revolutionize unmanned aviation by removing current range and endurance limitations. Previous attempts at establishing vision-based solutions have come close but rely heavily on near perfect extrinsic camera calibrations that often change midflight. In this paper, we propose dual object detection, a technique that overcomes such requirement by transforming aerial refueling imagery directly into receiver aircraft reference frame probe-to-drogue vectors regardless of camera position and orientation. These vectors are precisely what autonomous agents need to successfully maneuver the tanker and receiver aircraft in synchronous flight during refueling operations. Our method follows a common 4-stage process of capturing an image, finding 2D points in the image, matching those points to 3D object features, and analytically solving for the object pose. However, we extend this pipeline by simultaneously performing these operations across two objects instead of one using machine learning and add a fifth stage that transforms the two pose estimates into a relative vector. Furthermore, we propose a novel supervised learning method using bounding box corrections such that our trained artificial neural networks can accurately predict 2D image points corresponding to known 3D object points. Simulation results show that this method is reliable, accurate (within 3 cm at contact), and fast (45.5 fps). [ABSTRACT FROM AUTHOR]

Real-world traffic flow data often contain missing values, which can limit its usability. Although existing deep learning-based imputation methods have shown promising results by reconstructing observed values, they often overlook certain missing patterns in the dataset and perform worse on filling real missing values. This paper addresses this issue and proposes a novel masking method called PatternwiseMissingMix (M-Mix) for masked modeling-based traffic flow data imputation. M-Mix generates masks by mixing existing missing values in the target datasets to preserve the missing pattern information, thereby enhancing the performance of imputing real missing values. Additionally, a dual-objective loss function is proposed for model optimization, which predicts masked values for higher robustness and reconstructs observed values to maintain semantic correctness. Through extensive experiments on real-world datasets, M-Mix consistently demonstrates superior performance compared to other masking methods. [ABSTRACT FROM AUTHOR]

The positive relationship between knowledge transfer and knowledge innovation is recognized by most scholars, while knowledge heterogeneity, as a knowledge situation of the enterprise organization, affects knowledge transfer within enterprise organizations. Therefore, as a state, knowledge transfer can be regarded as a result of knowledge heterogeneity, and it becomes the intermediary state between knowledge heterogeneity and knowledge innovation. The organizational structure of individuals within an enterprise organization may affect these three elements and their interrelationships. In this paper, data are collected by means of on-the-spot investigation and a network questionnaire for analysis. The results show that the microstructure state of the individual in the enterprise organization plays a moderating role for the first three elements. The lower that the micro-restriction of the individual is, the more favorable the knowledge heterogeneity is for the inverted U-shaped impact of knowledge transfer and knowledge innovation and the more favorable the occurrence of knowledge innovation. [ABSTRACT FROM AUTHOR]

With the rapid development of biometric technology, an increasing number of application scenarios require biometric identification systems for identity authentication, such as cell phones, gate control, and banks. The performance and operational efficiency of bioidentification systems directly impact their effectiveness in application scenarios. Traditional bioidentification technology requires a large number of devices and sites to build bioidentification systems. Bioidentification simulation model technology based on lightweight containers can simulate bioidentification systems in different scenarios through virtualization technology, which can save hardware costs while enabling efficient testing and debugging. In this context, improving the operational efficiency of bioidentification simulation models has become an important research direction. In this paper, efficient scheduling technology for a bioidentification simulation model based on lightweight containers is investigated to explore how to use container technology to improve the operational efficiency and security of the bioidentification simulation model to better serve modern bioidentification applications. For a 4-core 4G light load, the results show that the response time based on the resource scheduling strategy was 30 ms and the throughput was 30 requests per second; in contrast, the response time based on the load scheduling strategy was 45 ms, and the throughput was 27 requests per second. The system performance with the resource scheduling strategy under the same load with identical containers was significantly better than that under the load scheduling strategy. By using lightweight container technology, the operational efficiency of the bioidentification simulation model can be effectively improved while also improving the security of the system. [ABSTRACT FROM AUTHOR]

Personalized learning has gained significant attention in education as a means to cater to the diverse needs of learners and optimize educational outcomes. However, ensuring the efficiency of personalized learning remains a challenge. It requires the ability to accurately analyze and interpret vast amounts of data collected from learners. Traditional analytical approaches often struggle to handle the complexity and heterogeneity of this data, limiting the potential for personalized learning interventions. To address these challenges, this paper proposes a personalized learning efficiency data analysis network (PLEDANet) based on machine learning. First, PLEDANet redesigns a convolutional neural network based on the ResNet structure. The network performs convolutions using multiple convolution kernels of different scales to extract diverse feature information from personalized learning efficiency data. To enhance the extraction and representation of fine-grained differentiated features, PLEDANet introduces a hybrid attention module to combine channel and spatial information among feature maps. Second, PLEDANet designs a hybrid loss function for model training, which consists of the AM-softmax loss and the Center loss. The former increases the inter-class distance of features by imposing a fixed angular margin, while the latter reduces the intra-class distance by constraining the samples and feature centers. Finally, extensive experiments are conducted on PLEDANet. The experimental results validate the superiority of PLEDANet for personalized learning efficiency analysis. [ABSTRACT FROM AUTHOR]

To solve the recommendation accuracy degradation problem encountered in sequential recommendation cases caused by data sparsity—such as short historical user behaviour sequences and limited information—this paper proposes a sequential recommendation model based on multipair contrastive learning with informative augmentation (IA-MPCL). The model aims to better learn user preference representations. Initially, a self-attention network is utilized to maintain the intrinsic relevance of the original sequences and introduce virtual interaction items for short sequences to achieve informative enhancement. Subsequently, multiple positive samples are generated by data augmentation methods to form multiple pairs of positive and negative samples. A multipair contrastive loss is constructed to eliminate the negative impact of fake positive and negative samples on the training process of the self-attention network. Finally, an adaptive loss weighting mechanism is proposed to dynamically regulate the role of the contrastive loss during multitask training. Through comparison experiments involving baseline methods and experiments conducted on datasets with different sparsity levels, the results show that IA-MPCL achieves significant improvements in terms of both recommendation accuracy and data sparsity resistance. [ABSTRACT FROM AUTHOR]

Hypergraph neural network (HGNN) for session-based recommendation (SBR) is quite rare but has been rewarded with promising performance. However, under the hypergraph framework, no works have emphasized the importance of repeat consumption, which is an important consumption pattern in SBR (e.g., people watch movies or listen to music repeatedly over time) and limits the performance of SBR in a beyond graph topology manner. For filling this gap and better recommendation performance, this paper incorporates repeat consumption into HGNNs and proposes a new recommendation machine, named Re-HGNM, which leverages the powerful expressiveness of HGNNs in representation learning and takes repeat consumption into account by considering the relationship between repeat behaviors and modeling repeat recommendations. Specially, in the encoder of Re-HGNM, we process sessions as hyperedges, in which the repeated behaviors are merged, to constitute hypergraph that conveys not only the complex relations between unique items but the relationships between repeat behaviors. After learning item representation by hypergraph convolution, a repeat-explore module is employed to determine whether to make repeat recommendation. In repeat mode, the next click must be in old items; thereby, the shrunk prediction space makes it easier to find the target item. Therefore, in the decoding stage, two decoders correspondingly work under two separate modes. Finally, compared with the current best baseline, Re-HGNM relatively improved P@20 by 58.37%, 8.70%, 1.28%, and 1.16% and MRR@20 by 25.52%, 10.31%, 10.76%, and 2.76% on the Tmall, RetailRocket, Nowplaying, and Diginetica, to be the current state-of-the-art model for SBR. [ABSTRACT FROM AUTHOR]

The metaverse network economy is built on the blockchain protocol network and occurs in the metaverse virtual space by the virtual digital avatar. This is a series of economic activities that produce, exchange, distribute and consume digital products, and interact, integrate and promote with the real economy in real space. The network economic system of the metaverse is gradually formed in the process of adapting to the development of digital economy. While these products and services bring convenience, they are accompanied by more complex and diverse economic risks. Based on transformer serialization analysis, this paper proposes an analytical method of economic risk mechanism of metacosmic network to complete the evaluation of economic risk. First, this work proposes an improved transformer (ITransformer). The probabilistic sparse self-attention (PSSA) and self-attention distillation (SAD) are proposed to improve the performance. This can mine more discriminative temporal features. Secondly, this work designed a dual-stream ITransformer (DSIT) to evaluate the economic risks of the metaverse network via ITransformer. This can process the global and local features in the metaverse economic data with high performance. Finally, this work has performed massive experiments on dual-stream ITransformer pipeline. Experimental results verify the effectiveness and feasibility of DSIT. [ABSTRACT FROM AUTHOR]

Establishing a reliable credit card fraud detection model has become a primary focus for academia and the financial industry. The existing anti-fraud methods face challenges related to low recall rates, inaccurate results, and insufficient causal modeling ability. This paper proposes a credit card fraud detection model based on counterfactual data enhancement of the triplet network. Firstly, we convert the problem of generating optimal counterfactual explanations (CFs) into a policy optimization of agents in the discrete–continuous mixed action space, thereby ensuring the stable generation of optimal CFs. The triplet network then utilizes the feature similarity and label difference of positive example samples and CFs to enhance the learning of the causal relationship between features and labels. Experimental results demonstrate that the proposed method improves the accuracy and robustness of the credit card fraud detection model, outperforming existing methods. The research outcomes are of significant value for both credit card anti-fraud research and practice while providing a novel approach to causal modeling issues across other fields. [ABSTRACT FROM AUTHOR]

Breast cancer, a widespread global disease, represents a significant threat to women's health and lives. Many researchers have proposed computer-aided diagnosis systems for classifying breast cancer. The majority of the approaches primarily utilize deep learning methods, which overlook the crucial necessity of incorporating both local information for precise tumor detection. In addition, available breast cancer datasets are imbalanced in nature. Therefore, this paper presents the hybrid breast network (HBNet) for detection of breast cancer, designed to address two critical challenges: class imbalance and incorporation of both global and local information in achieving precise tumor classification. To overcome the problem of class imbalance, HBNet incorporates the borderline synthetic minority oversampling technique. Simultaneously, it employs a feature fusion approach to combine deep and handcrafted features extracted by utilizing ResNet50 and HOG which incorporates global and local information. Moreover, the proposed method integrates the block-matching and 3D denoising filter to effectively eliminate multiplicative noise that has enhanced the performance of the system. The proposed HBNet is evaluated with BUSI and UDIAT datasets and achieved an average accuracy of 95.824% and 90.37%, respectively. [ABSTRACT FROM AUTHOR]

Successful deployment of convolutional neural networks on resource-constrained hardware platforms is challenging for ubiquitous AI applications. For latency-sensitive scenarios, real-time inference requires model compression techniques such as network pruning to achieve the purpose of inference acceleration. However, many researches focus on hardware-independent filter pruning methods, which cannot balance the contribution of pruned structure to latency and the accuracy drop. Although some pruning methods have introduced latency constraints into the pruning process, most of them are based on look-up tables, which omits the key step of hardware optimization, resulting in significant deviation in latency estimation. In this paper, we propose a novel latency-constrained pruning method, named hardware-in-loop pruning (HILP). It is based on the fast optimal pruning rate search within the layer and layer-wise hybrid pruning, which can prioritize removing the less important layers with considerable latency contributions. The proposed hardware-in-loop pipeline enables the hardware optimization module to be integrated into the entire framework. During pruning, an intermediate network architecture is automatically transformed to a deployable model for accurate latency measurement. The latency-optimized intermediate architecture is then selected by traversing all layers for next progressive step. HILP is generally applicable to any platform that provides a hardware optimization toolchain, such as NVIDIA GPU and Cambricon NPU. We evaluate HILP on both image classification task using ResNet50 with ImageNet and object detection task using YOLOv3 with COCO, HILP can reduce the inference latency of these two networks to 60% and 75%, respectively, within the range of accuracy variation not exceeding 0.6%. Extensive experiment results have proven that HILP is able to achieve a significant advantage in latency-accuracy performance compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Due to the growing demand for clean and sustainable energy sources, there has been an increasing interest in solar cells and photovoltaic panels. Nevertheless, determining the right design parameters to achieve the most efficient energy output that aligns with the energy system's needs can be quite challenging. This complexity arises from the intricate models and the inherent inaccuracies in the available information. To tackle this challenge, this paper introduces the adaptive sine–cosine particle swarm optimization algorithm (ASCA-PSO) as a method for estimating the parameters of solar cells and photovoltaic modules. The ASCA-PSO approach combines the strengths of the SCA and PSO algorithms in a two-tier process. In this process, SCA search agents explore the search space, while the PSO search agents leverage the outcomes derived from SCA exploration. This study evaluates the effectiveness of ASCA-PSO in accurately estimating the parameters of single- and double-diode models using data from two commercial solar cells. The findings are compared with those of cutting-edge methods. It is demonstrated that ASCA-PSO can identify global solutions for multifaceted and intricate objective functions. Furthermore, it proves to be a viable option for designing solar cells even in the presence of noise. [ABSTRACT FROM AUTHOR]

For market players and policy officials, commodity price forecasts are crucial problems that are challenging to address due to the complexity of price time series. Given its strategic importance, corn crops are hardly an exception. The current paper evaluates the forecasting issue for China's weekly wholesale price index for yellow corn from January 1, 2010 to January 10, 2020. We develop a Gaussian process regression model using cross validation and Bayesian optimizations over various kernels and basis functions that could effectively handle this sophisticated commodity price forecast problem. The model provides precise out-of-sample forecasts from January 4, 2019 to January 10, 2020, with a relative root mean square error, root mean square error, and mean absolute error of 1.245%, 1.605, and 0.936, respectively. The models developed here might be used by market players for market evaluations and decision-making as well as by policymakers for policy creation and execution. [ABSTRACT FROM AUTHOR]

One of the most popular techniques for changing the purpose of an image or resizing a digital image with content awareness is the seam-carving method. The performance of image resizing algorithms based on seam machining shows that these algorithms are highly dependent on the extraction of importance map techniques and the detection of salient objects. So far, various algorithms have been proposed to extract the importance map. In this paper, a new method based on Rényi entropy is proposed to extract the importance map. Also, a deep learning network has been used to detect salient objects. The simulator results showed that combining Rényi's importance map with a deep network of salient object detection performed better than classical seam-carving and other extended seam-carving algorithms based on deep learning. [ABSTRACT FROM AUTHOR]

This paper proposes a novel incipient fault estimation and fault-tolerant control approach for wireless power transfer (WPT) systems with disturbances and incipient sensor faults. Firstly, the dynamic models of the WPT system and incipient faults are established. Then, a k-step incipient fault estimation observer method is analyzed for estimating the states and incipient faults of the system. Based on it, a nonlinear dynamic output feedback fault-tolerant controller is devised to ensure the stability of the WPT system when considering incipient faults and disturbances. Further, the designed controller can monitor the system in real time without the knowledge of the system states. Besides, the stability analysis approach is rigorous, and the proof method can also apply to other similar systems. At last, the simulation example shows that the proposed fault diagnosis method is correct and effective. [ABSTRACT FROM AUTHOR]

Microscopic emission estimation based on driving states plays a crucial role in controlling the pollution of on-road vehicles. Existing research has evolved from fitting nonlinear models of driving cycles and emission factors to utilizing neural networks to exploit driving patterns and construct a nonlinear mapping between driving states and emission values. However, due to the small percentage of driving-cycle related to high emissions, it is still challenging to capture the vehicle emission peaks, which lead to the most noteworthy high-emission characteristics being regarded as abnormal disturbances instead. To address the issue, this paper proposes a peak-sensitive microscopic emission estimation framework characterized by sequence-to-sequence learning for on-road vehicles. Sequence-to-sequence learning emphasizes serial pattern mapping from driving sequences to emission sequences to provide more statistical constraints and reduce the estimation uncertainty brought by unstable driving behaviors. Specifically, the framework aims at capturing context features related to high emissions from sequences dynamically in an adaptive and self-learning way and is composed of a driving states embedding module and a dynamic aggregation module. Particularly, an incremental tracking loss (ITL) is proposed to adjust the incremental emissions at adjacent time steps by supervising the differences of the generated sequences, enabling the model to track sudden changes in emissions. Extensive experiments are conducted on the on-board diagnostics (OBD) dataset with 12628 sampling records collected from a heavy-duty diesel vehicle. The results show that the estimation accuracy of our proposed method is significantly better than state-of-the-art methods, and it can effectively capture high-emission peaks. [ABSTRACT FROM AUTHOR]

Power image tampering brings certain security risks to the safe operation of power grids, among which splicing tampering is the most common. Although image tampering detection has received much attention in recent years, relatively little research has been conducted on the practical application to power systems, and the detection results are poor due to the difficulty of learning subtle edge features of tampered regions and the lack of power image tampering dataset for training. In order to effectively detect image tampering regions, this paper proposes a splicing tampering detection model with a Dual-channel Augmented Attentive Dense-convolutional Network (DAAD-Net) structure. The model consists of three main parts: backbone network feature extraction, augmented attention feature extraction, and tampered region detection. Firstly, the backbone network feature extraction module fuses the original tampered image features with the image residual features and transfers them into the backbone network to extract the feature map. Secondly, the augmented attention feature extraction module extracts the tampered region features in the higher and lower layers using hierarchical encoding and decoding operations. Finally, the extracted feature maps of each layer are sent to the tampered region detection module, and by combining the loss of each feature map for optimizing the network parameters. Additionally, we produced a power image tampering dataset containing 552 samples. Experiments demonstrate that the proposed method outperforms current state-of-the-art models, with 1% to 31% improvement in evaluation indicators, and has good robustness to noise and JPEG compression attacks. [ABSTRACT FROM AUTHOR]

Gesture detection has recently attracted a lot of attention due to its wide range of applications, notably in human–computer interaction (HCI). However, when it comes to video-based gesture recognition, elements in the background unrelated to gestures slow down the system's classification rate. This paper presents an algorithm designed for the recognition of large-scale gestures. In the training phase, we utilize RGB-D videos, where the depth modality videos are derived from RGB modality videos using UNET and subsequently employed for testing. However, it's worth noting that in real-time applications of the proposed dynamic hand gesture recognition (DHGR) system, only RGB modality videos are needed. The algorithm begins by creating two dynamic images: one from the estimated depth video and the other from the RGB video. Dynamic images generated from RGB video excel in capturing spatial information; while, those derived from depth video excel in encoding temporal aspects. These two dynamic images are merged to form an RGB-D dynamic image (RDDI). The RDDI is then fed into a modified Xception-based CNN model for the purpose of gesture classification and recognition. In order to evaluate the system's performance, we conducted experiments using the EgoGesture and MSR Gesture datasets. The results are highly promising, with a reported classification accuracy of 91.64% for the EgoGesture dataset and an impressive 99.41% for the MSR Gesture dataset. The results demonstrated that the suggested system outperformed some existing techniques. [ABSTRACT FROM AUTHOR]

The online content, including live streaming and short videos, provides abundant visual and textual product information to users, which offers insights into users' multiple and changeable preferences toward product style, brand, color, etc. These dynamic preferences are helpful to the product recommender system to extract users' purchasing patterns. However, existing sequential recommendation fails to combine with items' multimodal characteristics and dynamic preferences. This paper proposes the Online Content-Based Sequence Recommendation method (named OCBSR), where dynamic preferences and multimodal representation such as visual images, textual descriptions, and item-ID of products are leveraged with convolutional autoencoder to map multimodal data into a unified latent space. We devise a multimodal transformer mechanism to aggregate the multimodal representation. A novel contrastive infomax loss is constructed through contrastive prototype representations and noise-contrastive sequences. The loss enhances the correlation between single-modality and multimodality preferences and learns the contrastive representation. Then, users' dynamic preferences are extracted from the contrastive representation in each time step using the gated recurrent unit with skip connections. Experiments are conducted on two real-life multimodal datasets, H&M and KuaiRec. The hit ratio (HR) and normalized discounted cumulative gain (NDCG) results indicate that our approach noticeably outperforms the current advanced recommenders. The HR increases by above 20% and 25% on H&M and KuaiRec datasets, respectively; the NDCG rises about 12% and 24% on H&M and KuaiRec datasets, respectively. The ablation study verifies each module's effectiveness. This research contributes to online content literature and multimodal sequential recommendation in practice. [ABSTRACT FROM AUTHOR]

Gliomas are the most common and aggressive form of all brain tumors, with a median survival rate of fewer than two years, especially for the highest-grade glioma patient. Accurate and reproducible brain tumor segmentation is essential for an effective treatment plan and diagnosis to reduce the risk of further spread. Automated brain tumor segmentation is challenging because it can appear in the brain with variations in shape, size, and position from one patient to another. Several deep learning architectures have been created to handle automatic segmentation with good performance results on 3D MRI images. However, these architectures are generally large and require high hardware specifications and a large amount of memory and storage. This paper proposes a lightweight modified 3D UNet architecture with an outstanding performance level called Yaru3DFPN. The architecture is built based on the UNet. The block used is ResNet and is modified to use pre-activation strategies and GroupNormalization for batch normalization. In the expanding section, features are arranged into pyramid features. The final output is thresholded using the combining thresholding method. This architecture is light and fast. This proposal was tested using BraTS datasets with the highest dice performance of 80.90%, 86.27%, and 92.02% for ET, TC, and WT areas, respectively. This result outperformed all other comparative architectures and promised to be developed for clinical application. [ABSTRACT FROM AUTHOR]

This paper introduces a cascade one proportional derivative incorporating filter (1PDf)-proportional integral (PI) controller abbreviated as c-1PDf-PI to deal effectively with the speed control issue of brushless DC (BLDC) motors. Two problems exist with implementing this controller such as iterated integral overflow and derivation-based chattering owing to the noise. The former is resolved by using an equivalent expression for the integral operation, while the latter is addressed by putting a first-order filter on the derivative term. To achieve the best performance from the controller, snake optimizer (SO) is fruitfully employed for optimizing the controller parameters without need for expert knowledge/interpretation. Here, a more reasonable cost function to assess the candidate solutions is also described. Simulations and laboratory experiments using DSP of TI TMS320F28335 are performed and the results are presented which show that the reference tracking performance, torque disturbance capability and robustness of the c-1PDf-PI controller have potential. These results are also contrasted by those offered by PI and 1PDf speed control schemes individually, affirming the superior performance of our proposal. As per the results, discussion and observation of this research, we stress that good performance and simplicity are salient advantages of the c-1PDf-PI controller, rendering it a good alternative over the complicated controller designs. [ABSTRACT FROM AUTHOR]

Few-shot semantic segmentation (FSS) is a challenging task that aims to segment new classes in query images with a few annotated support samples. One inherent challenge in FSS is the intra-class variation resulting from the limited availability of support samples and the diversity of query data. Current methods frequently employ prototype-support techniques to tackle this issue. However, it is important to note that a single support prototype shares limited commonalities with query features, and increases the difficulty of accurate segmentation. In this paper, we propose a lightweight and effective framework named Siamese Transformer (SiaT) with a mere 0.68M learnable parameters to enhance commonalities between prototypes and query features. The SiaT framework consists of two key modules: the Siamese Transformer Module (STM) and the Query Activation Module (QAM). The STM integrates two shared Transformer decoders for information propagation to generate two enhanced prototypes. One Transformer decoder facilitates the propagation of target-related information from the query to the original support prototype, while the other propagates foreground information from the support to the initial query prototype. The QAM utilizes the two enhanced prototypes from the STM as support information, engaging with query features through channel-wise allocation and concatenation, termed query activation. Moreover, SiaT showcases competitive performance on the widely used benchmarks PASCAL- 5 i and COCO- 20 i , which demonstrates its effectiveness in addressing the intra-class variation challenge within FSS tasks. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach to achieve finite-time synchronization (FTS) in a certain class of fractional-order complex-valued neural networks (CVNNs) containing reaction-diffusion terms. The proposed method uses intermittent control and provides a theoretical analysis to establish criteria for achieving FTS. This is achieved through new Lyapunov functions based on the proposed system, deriving inequalities in the complex domain. To realize FTS, the study designs complex-valued intermittent controllers for the targeted CVNNs relying solely on the information obtained from the controlled nodes. Moreover, an adaptive controller is introduced to effectively regulate the control gain, and the FTS of CVNNs is analyzed. The effectiveness of the proposed control strategies and derived results is demonstrated by numerical examples. [ABSTRACT FROM AUTHOR]

As a key component of rotating machinery, it is of great significance for the timely diagnosis of bearing weak faults. Stochastic resonance is widely used for its special signal enhancement pattern, and the combination of system parameters determines its actual output effect. Because the social mimic optimization algorithm has the advantages of few parameters, fast convergence speed and strong exploitation capability, it is used to optimize the parameters of stochastic resonance system in this paper. Aiming at the problem that it is easy to fall into local optimum during optimization, inspired by the learning habits of elite, an elite social mimic optimization (ESMO) algorithm is proposed. Its improvement mainly includes five parts: integration of learning efficiency, exchange learning, looking for successors, innovation and breakthrough, elimination mechanism. And its superiority is verified by the comparison of 29 standard benchmark functions and 10 other well-known optimization algorithms. Aiming at the discontinuity of optimization space, the concept of survival rate (surr) is proposed, and the effectiveness of the ESMO algorithm in optimizing discontinuous variables is analyzed and verified. Aiming at the disadvantage that stochastic resonance can only process small frequency signals, a bearing weak fault diagnosis method based on frequency exchange and parameter compensation stochastic resonance is proposed. To verify its application ability in other fault diagnosis methods, a classification and recognition method based on BP neural network is proposed. Finally, the effectiveness and superiority of the ESMO algorithm are verified by simulation signals and bearing experimental data. The above analysis results prove that the ESMO algorithm has certain scientific and engineering application value in the optimization field and engineering application. [ABSTRACT FROM AUTHOR]

Assessing the strength of complex networks is crucial for evaluating their functionality and monitoring system security. Two primary perspectives for measuring network robustness are connectivity and controllability, which quantify how well a network maintains connectivity and controllability after experiencing complete attacks. However, conventional robustness calculation methods that use simulation attacks can be impractical for large-scale networks. Recent research has explored the use of deep learning methods for network robustness prediction, including PCR (short for predictor of controllability robustness), which uses a VGG (short for visual geometry group)-based convolutional neural network (CNN) and a linear filter. However, PCR has limitations, including inaccurate edge importance differentiation and fluctuating output curves. This paper presents ATTRP, an attention-based robustness predictor, which addresses these limitations. ATTRP uses a novel convolutional neural network with channel and spatial attention mechanisms to improve prediction accuracy and a robustness filter based on Savitzky–Golay smoothing to rectify the robustness curve. Experimental studies demonstrate that ATTRP outperforms other predictors, with much lower errors, while its computational speed is far superior to that of traditional attacking simulation. Overall, our research provides a more effective and efficient means of predicting network robustness, with potential applications in monitoring system security. [ABSTRACT FROM AUTHOR]

Optimal portfolio selection—composing a set of stocks/assets that provide high yields/returns with a reasonable risk—has attracted investors and researchers for a long time. As a consequence, a variety of methods and techniques have been developed, spanning from purely mathematics ones to computational intelligence ones. In this paper, we introduce a method for optimal portfolio selection based on multi-objective evolutionary algorithms, specifically Nondominated Sorting Genetic Algorithm-II (NSGA-II), which tries to maximize the yield and minimize the risk, simultaneously. The system, named EvoFolio, has been experimented on stock datasets in a three-years time-frame and varying the configurations/specifics of NSGA-II operators. EvoFolio is an interactive genetic algorithm, i.e., users can provide their own insights and suggestions to the algorithm such that it takes into account users' preferences for some stocks. We have performed tests with optimizations occurring quarterly and monthly. The results show how EvoFolio can significantly reduce the risk of portfolios consisting only of stocks and obtain very high performance (in terms of return). Furthermore, considering the investor's preferences has proved to be very effective in the portfolio's composition and made it more attractive for end-users. We argue that EvoFolio can be effectively used by investors as a support tool for portfolio formation. [ABSTRACT FROM AUTHOR]

In this paper, the optimal formation control issue with collision avoidance and connectivity assurance is investigated for a class of second-order uncertain nonlinear multi-agent systems. First, the neural networks are employed in order to deal with the unknown nonlinear dynamics of the system. Then, an optimal formation control scheme is developed in the framework of the identifier–actor–critic. By constructing a new performance metric function containing collision avoidance and connectivity constraints, it is demonstrated that asymptotic convergence of the tracking error can be achieved under the proposed control scheme. Finally, the effectiveness of the proposed control method is validated by the numerical simulation example. [ABSTRACT FROM AUTHOR]

In this paper, an online event-triggered self-learning scheme based on adaptive dynamic programming (ADP) is developed to address tracking control design for nonlinear systems with constrained input and uncertain disturbance. Firstly, the value function with non-quadratic function is defined for the augmented nominal system, and the constrained robust tracking problem is equivalent to the optimal control for solving the tracking event-triggered Hamilton–Jacobi–Bellman (ETHJB) equation. Then, a single-critic network is developed to obtain the value function and control law related to the solution of the tracking ETHJB equation, greatly reducing approximation errors and computational costs. To alleviate the requirement for the entire state sampling, we propose a triggering rule that ensures system stability while limiting control updates. Theoretical proof demonstrates that the tracking state of the closed-loop system and the weight approximation error of the neural network are uniformly ultimately bounded (UUB). Finally, two examples are provided to validate the availability of the proposed scheme. [ABSTRACT FROM AUTHOR]

Detectors at the Laser Interferometer Gravitational Wave Observatory (LIGO), along with its sister detectors Virgo and Kamioka Gravitational Wave Detector (KAGRA), endlessly collect data to be analyzed in search of gravitational wave signals (GW signals) produced primarily by massive merger events involving colliding black holes and neutron stars. These detectors are the foundation upon which the new field of gravitational wave astronomy is built. However, the algorithms used to sift through this incoming data are extremely computationally expensive. They constantly run the risk of lagging behind the detectors, which would be catastrophic for astronomers searching for merger events, and their inherent rigidity becomes an obstruction to the expansion of these detectors' range and sensitivity. To simplify and expedite the process of detecting and isolating GW signals, this paper presents a neural network (NN) cascade to automatically isolate GW signals in raw LIGO data. Perfecting such a NN system would improve the speed and efficiency of detecting and analyzing GW signals. This study uses a 2-stage cascade of convolutional autoencoders (CAEs) to detect and reconstruct GW signals buried in LIGO data, finding that it is effective at accurately isolating GW signals with very low latency on just one graphics processing unit (GPU). Ultimately, it is found that it is practical and likely beneficial for astronomers to use such a cascade to process incoming LIGO data. [ABSTRACT FROM AUTHOR]

Intelligent swarm optimization algorithms have become increasingly common due to their success in solving real-world problems. Dwarf Mongoose Optimization (DMO) algorithm is a newly proposed intelligent swarm optimization algorithm in recent years. It was developed for continuous optimization problem solutions in its original paper. But real-world problems are not always problems that take continuously variable values. Real-world problems are often problems with discrete variables. Therefore, heuristic algorithms proposed for continuous optimization problems need to be updated to solve discrete optimization problems. In this study, DMO has been updated for binary optimization problems and the Binary DMO (BinDMO) algorithm has been proposed. In binary optimization, the search space consists of binary variable values. Transfer functions are often used in the conversion of continuous variable values to binary variable values. In this study, twelve different transfer functions were used (four Z-shaped, four U-shaped, and four Taper-shaped). Thus, twelve different BinDMO variations were obtained (BinDMO1, BinDMO2, ..., BinDMO12). The achievements of BinDMO variations were tested on thirteen different unimodal and multimodal classical benchmark functions. The effectiveness of population sizes on the effectiveness of BinDMO was also investigated. When the results were examined, it was determined that the most successful BinDMO variation was BinDMO1 (with Z1-shaped transfer function). The most successful BinDMO variation was compared with three different binary heuristic algorithms selected from the literature (SO, PDO, and AFT) on CEC-2017 benchmark functions. According to the average results, BinDMO was the most successful binary heuristic algorithm. This has proven that BinDMO can be chosen as an alternative algorithm for binary optimization problems. [ABSTRACT FROM AUTHOR]

Automatic speech recognition systems based on end-to-end models (E2E-ASRs) can achieve comparable performance to conventional ASR systems while reproducing all their essential parts automatically, from speech units to the language model. However, they hide the underlying perceptual processes modelled, if any, and they have lower adaptability to multiple application contexts, and, furthermore, they require powerful hardware and an extensive amount of training data. Model-explainability techniques can explore the internal dynamics of these ASR systems and possibly understand and explain the processes conducting to their decisions and outputs. Understanding these processes can help enhance ASR performance and reduce the required training data and hardware significantly. In this paper, we probe the internal dynamics of three E2E-ASRs pre-trained for English by building an acoustic-syllable boundary detector for Italian and Spanish based on the E2E-ASRs' internal encoding layer outputs. We demonstrate that the shallower E2E-ASR layers spontaneously form a rhythmic component correlated with prominent syllables, central in human speech processing. This finding highlights a parallel between the analysed E2E-ASRs and human speech recognition. Our results contribute to the body of knowledge by providing a human-explainable insight into behaviours encoded in popular E2E-ASR systems. [ABSTRACT FROM AUTHOR]

The main objective of this research study is to improve the performance of a standalone hybrid power system (SHPS) that consists of photovoltaic modules (PVMs), wind turbines (WTs), battery system (BS), and diesel engine (DE). The emphasis is on optimizing the system's design by incorporating demand response strategies (DRSs). Incorporating these strategies into the system can enhance system performance, stability, and profitability while also reducing the capacity of SHPS components and, consequently, lowering consumers' bills. To achieve this objective, the sizing model incorporates a novel indicator called the load variation factor (LVF). This paper assesses and contrasts various scenarios, including SHPS without DRS, with DRS, and with DRS but no DE. In this article, interruptible/curtailable (I/C) as one of the DRSs is incorporated into the model used for sizing issues. A newly developed optimization algorithm called the mountain gazelle optimizer (MGO) is utilized for the multi-objective design of the proposed SHPS. The utilization of MGO will facilitate achieving the lowest possible values for each of the following: cost of energy (COE), loss of power supply probability (LPSP), and carbon dioxide (CO2) emissions. This work introduces a mathematical model for the entire system, which is subsequently simulated using MATLAB software. The results reveal that among all the scenarios analysed, scenario iii — which has an LVF of 30% — is the most cost-effective. It has the lowest COE, at 0.2334 $/kWh, hence the lowest net present cost (NPC), at 6,836,445.5 $. [ABSTRACT FROM AUTHOR]

The recently proposed semantic-aligned matching detection transformer (SAM–DETR model) accelerates the convergence of the detection transformer (DETR) by mapping object queries into an identical embedding space as the encoder's output feature map. However, SAM–DETR model has the problem of low detection accuracy compared to other DETR variants. We observe that the lower detection accuracy of SAM–DETR model is caused by the insufficient number of sample points and the inaccurate localization of the sample points during re-sampling, which blurs the generated attention map. This paper proposes an object detector based on a feature correction and double sampling DETR (FCDS-DETR) to solve this problem. FCDS-DETR takes SAM–DETR model as a baseline and builds on it by adding a feature correction module and a double sampling mechanism to achieve further improvement in detection accuracy with a limited number of additional parameters without sacrificing convergence speed. Firstly, FCDS-DETR improves the sampling point localization accuracy by adding a feature correction module to model the inter-channel dependence of the feature maps to be sampled. Secondly, the number of sampled points is increased by the double sampling mechanism, and attention fusion is used to fuse the attention weight maps corresponding to the two sets of sampled points to improve the recognizability of the attention weight maps. The experimental results show that the average precision is improved by +0.7 on the COCO dataset compared with the SAM–DETR model, and the number of parameters is increased by only 10.34 % , which improves the detection performance of the model very well. [ABSTRACT FROM AUTHOR]

This paper proposes a decentralized variable impedance control method of modular robot manipulators (MRM) with physical human-robot interaction (pHRI) using Gaussian process-based motion intention estimation. The dynamic model of MRM subsystem is established by using joint torque feedback (JTF) technique. Human limb dynamic model is regarded as mechanical impedance model, and human motion intention is estimated online based on Gaussian process. A variable impedance control method is proposed to make the MRM comply with human motion intention in the process of pHRI. A decentralized sliding mode control strategy is designed to achieve high performance position tracking and compensate the uncertainty of the controller. Based on Lyapunov theory, the uniform ultimately bounded of tracking error of each joint is proved. Finally, the effectiveness of the proposed control method under pHRI is verified by experiments. The experimental results show that the proposed method reduces the position tracking error by ∼ 10% and the interaction force by ∼ 20% compared with the existing control methods. [ABSTRACT FROM AUTHOR]

Unsupervised cross-modality domain adaptation for medical image segmentation has made great progress with the development of adversarial learning-based methods, but the training of adversarial models is considerably complicated. In this paper, we propose a conceptually simple but effective unsupervised domain adaptation method to achieve adaptation on frequency spectrum components of target and source images decomposed by a novel spectrum transfer strategy. Specifically, we replace the high-frequency components of the source domain images with that of the target domain images for details feature adaptation and adjust the low-frequency components by histogram matching for style adaptation. Besides, we propose multi-direction collaborative learning on both target and source domains to further improve the performance. Experimental results demonstrate that our method significantly outperforms state-of-the-art UDA methods for medical image segmentation on two publicly available datasets (cardiac dataset, and abdominal multi-organ dataset) in both CT to MRI and MRI to CT domain adaptation scenarios [ABSTRACT FROM AUTHOR]

This paper proposes a strengthened RIME algorithm to tackle continuous optimization problems. RIME is a newly proposed physical-based evolutionary algorithm (EA) inspired by the soft and hard rime growth process of rime-ice, which has a powerful exploitation ability. But in complex optimization problems, RIME will easily trap into local optima and the optimization will become stagnation. Noticing this issue, we introduce three techniques to the original RIME: (1) Latin hypercube sampling replaces the random generator as the initialization strategy, (2) modified hard rime search strategy, and (3) embedded distance-based selection mechanism. We evaluate our proposed SRIME in 10-D, 30-D, 50-D, and 100-D CEC2020 benchmark functions and eight real-world engineering optimization problems with nine state-of-the-art EAs. Experimental and statistical results show that the introduction of three techniques can significantly accelerate the optimization of the RIME algorithm, and SRIME is a competitive optimization technique in real-world applications. Ablation experiments are also provided to analyze our proposed three techniques independently, and the embedded distance-based selection contributes most to the improvement of SRIME. The source code of SRIME can be found in https://github.com/RuiZhong961230/SRIME. [ABSTRACT FROM AUTHOR]

Representation learning serves as a crucial link between knowledge graphs and neural models. Knowledge graphs are typical symbolic models and require representation learning to obtain vector representations of entities and relationships. Existing efforts focus more on the structural characteristics of knowledge itself, such as the connections between entities and relationships under the same type of knowledge, neglecting the potential value of commonsense knowledge in guiding and constraining. In fact, commonsense knowledge implies the belonging tendency of entities in triplets. In this paper, we propose a novel knowledge representation learning model, CoolGust, which is guided and constrained by commonsense knowledge and can effectively utilize commonsense knowledge to seamlessly guide the belonging relationships of entities and enhance the model performance. Commonsense knowledge is essential for guiding humans to make wise decisions in unknown scenarios. We find that by utilizing commonsense knowledge as guides and constraints for entities, hidden knowledge entanglement structures can be formed in complex network applications, thereby constructing decisions consistent with situational logic. To verify the effectiveness of our model, we conducted experimental verification of two tasks, link prediction, and triple classification, on three public datasets. The experimental results demonstrate the effectiveness and advancedness of our proposed method. [ABSTRACT FROM AUTHOR]

Small object detection is widely used in a variety of fields such as automatic driving, UAV-based object detection, and aerial image detection. However, small objects carry limited information, making it difficult for detectors to detect small objects. In recent years, the development of deep learning has significantly improved the performance of small object detection. This paper provides a comprehensive review to help further the development of small target detection. We summarize the challenges related to small object detection and analyze solutions to these challenges in existing works, including integrating the feature at different layers, enriching available information, balancing the number of positive and negative samples for small objects, and increasing sufficient small object instances. We discuss related methods developed in three application areas, including automatic driving, UAV search and rescue, and aerial image detection. In addition, we thoroughly analyze the performance of typical small object detection methods on popular datasets. Finally, based on the comprehensive review of small object detection methods, we point out possible research directions for future studies. [ABSTRACT FROM AUTHOR]

Question generation (QG) aims to generate natural questions from relevant input. Existing state-of-the-art QG approaches primarily leverage pre-trained language models (PLMs) to encode the deep semantics within the input. Meanwhile, studies show that the input's dependency parse tree (referred to as syntactic information) is promising in improving NLP-oriented tasks. However, how to incorporate syntactic information in PLMs to guide a QG process effectively still needs to be settled. This paper introduces a syntax-guided sentence-level QG model based on prompt learning. Specifically, we model the syntactic information by utilizing soft prompt learning, jointly considering the syntactic information from a constructed dependency parse graph and PLM to guide question generation. We conduct experiments on two benchmark datasets, SQuAD1.1 and MS MARCO. Experiment results show that our model exceeded both automatic and human evaluation metrics compared with mainstream approaches. Moreover, our case study shows that the model can generate more fluent questions with richer information. [ABSTRACT FROM AUTHOR]

Social emotion prediction aims to predict readers' emotion, for example, emotion distributions evoked by documents (e.g., news articles). It makes a significant contribution to social media applications, such as opinion summary, election prediction, and emotions investigation of society. While recent studies have focused on encoding consecutive word sequences in documents using neural network models and leveraging topical information, it is essential to acknowledge the influence of documents sharing similar topics or being related to similar events on evoking readers' emotions. The interactions among documents can significantly impact social emotion prediction. In this paper, we propose a novel approach to model the interactions among documents by constructing a heterogeneous graph. This graph captures the interaction among documents based on global word co-occurrence patterns in a corpus and the emotional scores of words obtained from emotion lexicons. Additionally, we develop heterogeneous graph convolution attention network (HGCA) to embed the heterogeneous graph. This network effectively captures the importance of different neighboring nodes and different node types, enabling comprehensive emotion prediction. Furthermore, we develop Taylor series expansion-based Transformer (Tayformer) to derive initialized node representations that can be co-trained with our graph network while having low memory complexity. Experimental results on four benchmark datasets show the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Knowledge graph (KG) shows great potential in improving recommendation systems. Recent studies have focused on developing end-to-end models based on graph neural networks. However, these previous approaches do not fully utilize collaborative signals, which are the collective information and feedback provided by multiple users in a recommendation system. Specifically, we think that collaborative signals could be better utilized in two points. Firstly, users' collective information leads to differences in node importance in KG, offering opportunities to improve item representation. Secondly, an item's collaborative filtering (CF) feature can reveal the multiple interests of a user, helping to gain fine-grained user representation. In this paper, we highlight the effect of node importance estimation and CF-based item clustering and propose the collaborative signals injection module. We integrate this module in our Collaborative Signals Enhanced Knowledge Graph recommendation (CSEKG) model. CSEKG outperforms state-of-the-art methods like KGIN (Wang et al. in Learning intents behind interactions with knowledge graph for recommendation. CoRR arXiv:2102.07057, 2021) , KGCL (Yang et al. in Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, 2022) , and KGRec (Yang et al. in Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining, 2023) through improving recall@20 by 3.86 % , 5.15 % , and 2.88 % on Last.FM, Amazon-Book and MovieLens-1M, respectively. Furthermore, we flexibly integrate the collaborative signals injection module into existing methods, such as LightGCN, KGAT and KGIN, which improves their performance. Further visualizing recommendation cases show the interpretability benefits brought by collaborative signals. [ABSTRACT FROM AUTHOR]

In multiple-instance learning (MIL), the existing bag encoding and attention-based pooling approaches assume that the instances in the bag have no relationship among them. This assumption is unsuited, as the instances in the bags are rarely independent in diverse MIL applications. In contrast, the instance relationship assumption-based techniques incorporate the instance relationship information in the classification process. However, in MIL, the bag composition process is complicated, and it may be possible that instances in one bag are related and instances in another bag are not. In present MIL algorithms, this relationship assumption is not explicitly modeled. The learning algorithm is trained based on one of two relationship assumptions (whether instances in all bags have a relationship or not). Hence, it is essential to model the assumption of instance relationships in the bag classification process. This paper proposes a robust approach that generates vector representation for the bag for both assumptions and the representation selection process to determine whether to consider the instances related or unrelated in the bag classification process. This process helps to determine the essential bag representation vector for every individual bag. The proposed method utilizes attention pooling and vision transformer approaches to generate bag representation vectors. Later, the representation selection subnetwork determines the vector representation essential for bag classification in an end-to-end trainable manner. The generalization abilities of the proposed framework are demonstrated through extensive experiments on several benchmark datasets. The experiments demonstrate that the proposed approach outperforms other state-of-the-art MIL approaches in bag classification. [ABSTRACT FROM AUTHOR]

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through fivefold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods. [ABSTRACT FROM AUTHOR]

Solar photovoltaic energy is the potential energy in the universe for generating electricity and meeting the required load demand. However, on account of partial shading conditions, the difficult task in the PV system is to track global maxima instead of local maxima and maintain the uninterrupted power supply. To solve this problem, a new metaheuristic algorithm is introduced in this paper such as a heap-based optimizer (HBO). The proposed method is developed in MATLAB/Simulink software. The system is examined under distinct irradiation conditions and compared their performance with other methods. The simulation results reveal that the suggested HBO shows a reliable enhancement as compared to other studied methods with regard to tracking maximum power, convergence time, and settling time. The extracted power efficiencies are 99.85% for case 1, 99.96% for case 2, and 99.92% for case 3. It is found that HBO shows better enrichment than other studied methods. [ABSTRACT FROM AUTHOR]

Space debris poses a potentially catastrophic risk to large-scale LEO constellations due to its substantial cascading effects, thereby rendering efficient and timely debris removal a critical research area in contemporary aerospace engineering. This paper delves into the application of the three-dimensional traveling salesman problem with orbital transfer constraints for space debris removal task planning. To overcome the challenges of sparse optimal solutions and vulnerability to local optima in addressing large-scale space debris removal task planning problems, we introduce a new Hierarchical Exploration Artificial Bee Colony (HEABC) optimization algorithm. Initially, we present an innovative two-step search strategy that employs a short-and-long term hybrid approach to optimize search time utilization and enhance solution quality, thereby addressing the sparsity of optimal solutions in the HEABC algorithm. Subsequently, to mitigate the issue of converging to local optima in high-dimensional encoding-based search problems, we devise a dual population update strategy aimed at preserving the innate evolutionary driving force of the search population. This strategy ensures the continuous updating of the population, even in the absence of intrinsic driving forces. Ultimately, experimental results substantiate that our proposed HEABC algorithm attains superior task planning sequences in a reduced time span and exhibits heightened adaptability in comparison to various traditional search algorithms. This is corroborated by numerical experiments conducted on one publicly available and one STK-generated space debris datasets. [ABSTRACT FROM AUTHOR]

The core premise of cancelable biometrics lies in the creation of a distinct biometric template for every individual, which can be either canceled or regenerated as needed. This process requires the use of a uniquely-defined key during the generation of such template. The generated templates are tailored to be key-specific. This ensures that each distinct key will generate a unique template, while preserving the integrity and security of the original biometric data, ensuring that it remains uncompromised. In this paper, a cancelable biometric system based on electrocardiography (ECG) and photoplethysmography (PPG) signals is introduced. A signal fusion process is implemented for the two traits to generate a single template per user. In order to enhance the security of generated templates, a well-designed permutation stage is implemented according to a user-specific key. The permutation key is obtained through a well-designed look-up table created by the authors. The user verification is conducted on the cancelable template, without the need for any inversion processes. The user verification scheme depends on a two-pronged approach: robust feature extraction followed by the application of a machine learning (ML) classifier. The mel-frequency cepstral coefficients (MFCCs) extraction algorithm is employed for feature extraction due to the low frequency range of the adopted biometric signals and the nonlinearity of the filter bank used for MFCC extraction. Several ML classifiers are adopted to validate the system with cancelable templates without any inversion process. Simulation results with multilayer perceptron (MLP) and logistic regression (LR) classifiers demonstrated superior effectiveness of the proposed authentication framework, with accuracy rates up to 100% and 99.7% on the pulse transit time PPG and BIDMC datasets, respectively. Hence, the proposed system proves effective access control and user verification in the Internet-of-Things (IoT) applications. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) have emerged as powerful tools in diverse remote sensing domains, but their optimization remains challenging due to their complex nature and the large number of parameters involved. Researchers have been exploring more sophisticated methodologies to improve image classification accuracy. In this paper, we introduce a multi-granularity feature encoding ensemble network (MGFEEN) that is designed to fine-tune features at different levels of granularity. The network is trained in a two-step process: First, the output of granularity level i is used as the input for the next level; then, a fully connected layer is added to the pre-trained network to advance to the next level. The effectiveness of the MGFEEN's feature extraction is evaluated by feeding the globally extracted features to a softmax classifier for classification. By applying ensemble learning principles, our proposed MGFEEN achieves more accurate final predictions. We evaluate our model on three widely recognized benchmark datasets: UC-Merced, SIRIWHU, and EAC-Dataset. Notably, on the EAC-Dataset, our results show a significant 0.54% improvement in accuracy over a single-training-network setup, resulting in an impressive 98.70% accuracy level. [ABSTRACT FROM AUTHOR]