Reviewerly/exHarmony huggingface dataset:
A Cross-Level Interaction Network Based on Scale-Aware Augmentation for Camouflaged Object Detection

Camouflaged object detection (COD), with the task of separating the camouflaged object from its color/texture similar background, has been widely used in the fields of medical diagnosis and military reconnaissance. However, the COD task is still a challenging problem due to two main difficulties: large scale-variation for different camouflaged objects, and extreme similarity between the camouflaged object and its background. To address these problems, a cross-level interaction network based on scale-aware augmentation (CINet) for the COD task is proposed. Specifically, a scale-aware augmentation module (SAM) is firstly designed to perceive the scales information of the camouflaged object by calculating an optimal receptive field. Furthermore, a cross-level interaction module (CLIM) is proposed to facilitate the interaction of scale information at all levels, and the context of the feature maps is enriched accordingly. Finally, with the purpose of fully utilizing these features, we design a dual-branch feature decoder (DFD) to strengthen the connection between the predictions at each level. Extensive experiments performed on four COD datasets, e.g., CHAMELEON, CAMO, COD10K, and NC4K, demonstrate the superiority of the proposed CINet compared with 21 existing state-of-the-art methods.
Analytics-driven complaint prioritisation via deep learning and multicriteria decision-making

Complaint analysis is an essential business analytics application because complaints have a strong influence on customer satisfaction (CSAT). However, the process of categorising and prioritising complaints manually can be extremely time consuming for large companies. In this paper, we propose a framework for automatic complaint labelling and prioritisation using text analytics and operational research techniques. The labelling step of the training set is performed using a simple weighting approach from the multiple-criteria decision-making (MCDM) literature, while transformer-based deep learning (DL) techniques are used for text classification. We define two priority classes, namely, urgent complaints and other claims, and develop a system for automatic complaint categorisation. Our experimental results show that excellent predictive performance can be achieved with state-of-the-art text classification models. In particular, BETO, a bidirectional encoder representations from transformers (BERT) model trained on a large Spanish corpus, reaches an accuracy (ACCU) and area under the curve (AUC) of 92.1% and 0.9785, respectively. This positive result translates into a successful complaint prioritisation scheme, which improves CSAT and reduces the churn rate.
Causal maps in the analysis and unsupervised assessment of the development of expert knowledge: Quantification of the learning effects for knowledge management purposes

This study proposes an application of cognitive maps in the representation of cognitive structures of the experts and assessment of their development/modification as a result of a (computer or expert system-assisted) learning process. It strives to identify information needed for the guidance of the process of creation and management of expert knowledge by formal modeling tools. Changes in experts’ cognitive structures are assumed to stem from individual and collaborative (group-level) learning. The novel approach to assessing the outcomes of learning reflected as changes in the cognitive structures of experts or groups of experts, modeled by cognitive maps, does not assume any correct or desired outcome of the learning process to be known in advance. Instead, it identifies and analyses the changes in (or robustness of) the constituents of the cognitive maps from different points of view and allows for quantifying and visualizing the actual effect of the learning. The proposed methodology can identify changes in cognitive diversity, causal structures in terms of causal relations and concepts, and the perceived importance of strategic issues over the learning period. It can also detect which cause–effect relationships have appeared/disappeared considering the pre-/post-mapping design. Thus it provides an exploratory account on the changes in the cognitive structures of the expert(s) as a result of learning. The applicability of the proposed methods is illustrated in the assessment of the learning outcomes of a group of 71 graduate students who participated in an eight-week business simulation task. The results of the empirical analysis confirm the viability of the proposed methodology and indicate that the students’ understanding of the utilized concepts and associated relationships in the decision-making process improved throughout the learning activity, ultimately showing that the course learning has considerably improved students’ perception and knowledge. Based on the results, it can be concluded that the proposed approach has the potential to be effective in assessing learning outcomes in a teaching-learning activities.
Cross-level collaborative context-aware framework for medical image segmentation

Efficient and accurate medical image segmentation is necessary for pathological evaluation and disease diagnosis in clinical practice. In recent years, the U-shaped encoder–decoder structure has achieved good performance in various medical image segmentation tasks. It is a neural network model similar to the letter U, which can gradually reduce the spatial dimension of the feature map to capture high-level semantic information in the feature encoding stage, and can make up for the lack of target detail information in the feature decoding stage to decouple the final segmentation results. However, the U-shaped encoder–decoder structure still suffers from semantic asymmetry and global semantic dilution problems, which are exacerbated during decoding, due to the limitation of the framework's fixation. In this paper, we propose a cross-level collaborative context-aware framework (C3-Net) to address the aforementioned issues in medical image segmentation. The main contributions of this research include: (i) To address the inherent problems in the U-shaped structure, we propose the C3-Net to explore the differences between cross-level contextual information effectively; (ii) To achieve detailed information preservation and semantic information enhancement, we present a Channel Scale-aware Context Enhancement (CSCE) module to enhance low-level contextual features from the global and local scales of the channel dimension; (iii) A Spatial Pyramid Context Alignment (SPCA) module is designed to extract and align pyramid features, thus obtaining accurate global contextual features at the network's top; (iv) We propose a Cross-Level Collaborative Context Refinement (CCCR) module to enable efficient collaboration between cross-level contextual features to simultaneously achieve semantic alignment and global semantic enhancement. The outcomes of implementing the proposed method on three publicly available datasets (Synapse, ACDC, and GlaS) and one private dataset (VMICH) show that the proposed method outperforms all competing approaches on four datasets. Our C3-Net achieves new state-of-the-art performance on four image segmentation tasks with Dice scores of 85.26%, 92.10%, 87.20%, and 87.61%, respectively.
Single image deraining using scale constraint iterative update network

Rain streaks, which can be viewed as a type of noise, have the potential to destroy the structural integrity of objects within an image, leading to a dramatic degradation in the performance of advanced vision algorithms. Therefore, it is important to develop an effective deraining algorithm. There are three challenges in the current deraining task: First, it is difficult to separate rain streaks from the image background because of the complexity of rain types. Second, most of the existing methods use encoder–decoder-based structures, but the models are designed to be too bulky and inflexible to accommodate complex rain streaks. Third, many models perform the same operation for all rain types, which can reduce the generalizability of the model. To address these challenges, we propose a scale constraint iterative update network which divides the network structure into feature extraction block, multi-scale constraint block, and iterative update block. In the feature extraction block, the deep mixed attention is used to extract complex rain streak features, and the mixed convolution block is used to extract the background details. The multi-scale constraint block aggregates contextual features across different scales, while the Global GateUnit captures long-range dependencies. We followed a coarse-to-fine pipeline. In the iterative update block, the feature maps are updated based on features from different scales, gradually refining the image. The final optimized result is obtained by performing learnable up-sampling. During the whole iteration process, the parameters are shared to reduce the complexity of the model while maintaining the performance. Additionally, We also designed a dynamic inference mechanism, which dynamically selects the number of iterations for different types of rain streaks, effectively enhancing the generalization of the model. We conducted extensive experiments on synthetic and real-world rain datasets, as well as a raindrop dataset. The results of our experiments demonstrate that our method outperforms existing deraining methods in terms of effectiveness and generalization ability.
A nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy

As a new intelligent optimization algorithm, the African vultures optimization algorithm (AVOA) has been widely used in various fields today. However, when solving complex multimodal problems, the AVOA still has some shortcomings, such as low searching accuracy, deficiency on the search capability and tendency to fall into local optimum. In order to alleviate the main shortcomings of the AVOA, a nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy (HWEAVOA) is proposed. Firstly, the Henon chaotic mapping theory and elite population strategy are proposed to improve the randomness and diversity of the vulture's initial population; Furthermore, the nonlinear adaptive incremental inertial weight factor is introduced in the location update phase to rationally balance the exploration and exploitation abilities, and avoid individual falling into a local optimum; The reverse learning competition strategy is designed to expand the discovery fields for the optimal solution and strengthen the ability to jump out of the local optimal solution. HWEAVOA and other advanced comparison algorithms are used to solve classical and CEC2022 test functions. Compared with other algorithms, the convergence curves of the HWEAVOA drop faster and the line bodies are smoother. These experimental results show the proposed HWEAVOA is ranked first in all test functions, which is superior to the comparison algorithms in convergence speed, optimization ability, and solution stability. Meanwhile, HWEAVOA has reached the general level in the algorithm complexity, and its overall performance is competitive in the swarm intelligence algorithms.
Human–machine Teaming with Small Unmanned Aerial Systems in a MAPE-K Environment

The Human Machine Teaming (HMT) paradigm focuses on supporting partnerships between humans and autonomous machines. HMT describes requirements for transparency, augmented cognition, and coordination that enable far richer partnerships than those found in typical human-on-the-loop and human-in-the-loop systems. Autonomous, self-adaptive systems in domains such as autonomous driving, robotics, and Cyber-Physical Systems, are often implemented using the MAPE-K feedback loop as the primary reference model. However, while MAPE-K enables fully autonomous behavior, it does not explicitly address the interactions that occur between humans and autonomous machines as intended by HMT. In this article, we, therefore, present the MAPE-K HMT framework, which utilizes runtime models to augment the monitoring, analysis, planning, and execution phases of the MAPE-K loop to support HMT despite the different operational cadences of humans and machines. We draw on examples from our own emergency response system of interactive, autonomous, small unmanned aerial systems to illustrate the application of MAPE-K HMT in both a simulated and physical environment, and we discuss how the various HMT models are connected and can be integrated into a MAPE-K solution.
Fractional-Order Echo State Network Backstepping Control of Fractional-Order Nonlinear Systems

Classical backstepping control of fractional-order nonlinear systems (FONSs) needs to calculate fractional derivatives of virtual control inputs recursively, which usually results in the “explosion of complexity” problem. This article proposes a command-filtering adaptive control scheme based on fractional-order echo state networks (FOESNs) for strict-feedback uncertain FONSs. Finite-time command filters are introduced to estimate instead of calculating the complicated fractional-order derivatives of virtual control inputs, which not only has the advantages of the conventional command-filters, but also guarantees the finite-time convergent property. An FOESN with stronger approximation ability is presented to approach uncertainties. Compared with conventional adaptive neural network or fuzzy control, the proposed method with similar computational complexity has more design freedom and better approximation effect. The effectiveness of the proposed control framework is verified by two simulation examples.
Updatable Siamese tracker with two-stage one-shot learning

Offline-trained Siamese networks have realized very promising tracking precision and efficiency. However, the performance is still limited by the drawbacks in online update. Traditional strategies cannot tackle the irregular variations of object and the sampling noise, so it is quite risky to adopt them to update Siamese trackers. In this paper, we present a two-stage one-shot learner by exploring the learning scheme of Siamese network, which reveals there are two key issues during online update, i.e., feature fusion and feature comparison. Based on this finding, we propose an updatable Siamese tracker by introducing two independent transformers (SiamTOL). Concretely, a Cross-aware transformer is designed to combine the features of the initial and the dynamic templates, while a Decoder-favored transformer is exploited to compare the fusing template and the search region. By combining these transformers, our tracker is able to adequately model the feature dependencies between multi-frame object samples. Extensive experimental results on several popular benchmarks well manifest that the proposed approach achieves the leading performance, and outperforms other state-of-the-art trackers.
Overview of modulation techniques for spatially structured-light 3D imaging

The modulation and projection of patterns are the cornerstones of spatially structured-light three-dimensional (3D) imaging. However, an overview of this field is lacking. This paper comprehensively reviews the modulation techniques for spatially structured-light 3D imaging. First, the frameworks and the state-of-the-art status of all the mainstream methods are introduced. Then, side-by-side comparisons are performed from different perspectives to illustrate their respective characteristics. In particular, experiments to compare the shape reconstruction quality of different methods were conducted. Beyond academic research, the applications and off-the-shelf products in the industry are reviewed. Finally, our views on the demands and trends in the field are presented.
Product consumptions meet reviews: Inferring consumer preferences by an explainable machine learning approach

Inferring consumers' preferences provides a better understanding of their purchase behavior, which is very important for business success, e.g., recommendation systems and targeted advertising. In this paper, we propose an explainable machine learning approach, namely Multi-view Latent Dirichlet Allocation (MVLDA), to infer and interpret consumer preferences. In the proposed model, we assume that there exists a downstream relationship between consumers' motivations and their purchase behaviors. We model this downstream relationship by linking two types of topics (i.e., textual topics for motivations and product-related topics for purchase behaviors), to quantify and explain consumers' choices. We validate our modeling framework using a real-world dataset collected from the online retailer Amazon. The experimental results show that the proposed model identifies a set of high-quality textual topics but also interprets its effect on consumer choices based on product-related topics. In addition, we demonstrate that the proposed model quantifies the consumer's preferences. The proposed model yields interesting insights about user preferences, and provides several important managerial implications, e.g., e-commerce platforms, brand managers, and marketers.
MMDS: A secure and verifiable multimedia data search scheme for cloud-assisted edge computing

With the rapid development and popularity of Internet of Things (IoT) technology, IoT devices can generate MultiMedia Big Data (MMBD) as multimedia devices. MMBD is often encrypted and stored on an untrusted cloud server. Searchable encryption is an effective way which can finish a controllable keyword search of ciphertext. However, there are two limitations to these existing works. On the one hand, it is hard to independently protect the data privacy of each IoT device. On the other hand, when faced with an untrusted cloud server, it is difficult to implement more flexible fine-grained verification for search results. To overcome these limitations, we propose a secure and verifiable MultiMedia Data Search (MMDS) scheme for cloud-assisted edge computing. To protect the data of IoT devices independently, we designed a secure, flexible, and efficient keyword search mechanism based on bilinear pairings. To achieve a more flexible and practical search result verification mechanism, we design a fine-grained verification algorithm combining blockchain and hashing techniques. We have conducted performance evaluations and security proof and analysis for MMDS scheme. Finally, we applied MMDS scheme to solve real problems in intelligent multimedia systems. Security proof and analysis prove the security of MMDS. Performance analyses and evaluations further confirm that MMDS is efficient and feasible for practical applications.
Self-distillation and self-supervision for partial label learning

As a main branch of weakly supervised learning paradigm, partial label learning (PLL) copes with the situation where each sample corresponds to ambiguous candidate labels containing the unknown true label. The primary difficulty of PLL lies in label ambiguities, most existing researches focus on individual instance knowledge while ignore the importance of cross-sample knowledge. To circumvent this difficulty, an innovative multi-task framework is proposed in this work to integrate self-supervision and self-distillation to tackle PLL problem. Specifically, in the self-distillation task, cross-sample knowledge in the same batch is utilized to refine ensembled soft targets to supervise the distillation operation without using multiple networks. The auxiliary self-supervised task of recognizing rotation transformations of images provides more supervisory signal for feature learning. Overall, training supervision is constructed not only from the input data itself but also from other instances within the same batch. Empirical results on benchmark datasets reveal that this method is effective in learning from partially labeled data.
A hybrid framework using explainable AI (XAI) in cyber-risk management for defence and recovery against phishing attacks

Phishing and social engineering contribute to various cyber incidents such as data breaches and ransomware attacks, financial frauds, and denial of service attacks. Often, phishers discuss these attack vectors in dark forums. Further, the probability of phishing attacks and the subsequent loss suffered by the firm are highly correlated. In this context, we propose a hybrid framework using explainable AI techniques to assess cyber-risks generated from correlated phishing attacks. The first phase computes the probability of expert phishers within a community of similar attackers with varying expertise. The second phase calculates the probability of phishing attacks upon a firm even after it has invested in IT security and adopted regulatory steps. The third phase categorises phishing and genuine URLs using various machine-learning-based classifiers. Next, it estimates the joint distribution of phishing attacks using an exponential-beta distribution and quantifies the expected loss using Archimedean Copula. Finally, we offer recommendations for firms through the computation of optimal investments in cyber-insurance versus IT security. First, based on the risk attitude of a firm, it can use this explainable-AI (XAI) framework to optimally invest in building security into its enterprise architecture and plan for cyber-risk mitigation strategies. Second, we identify a long-tail phenomenon demonstrated by the losses suffered during most cyber-attacks, which are not one-off incidents and are correlated. Third, contrary to the belief that cyber-insurance markets are ineffective, it can guide financial firms to design realistic cyber-insurance products.
Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels

Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets where the labeling effort is entrusted to the clients. While most existing FL approaches assume high-quality labels are readily available on users’ devices, in reality, label noise can naturally occur in FL and is closely related to clients’ characteristics. Due to scarcity of available data and significant label noise variations among clients in FL, existing state-of-the-art centralized approaches exhibit unsatisfactory performance, whereas prior FL studies rely on excessive on-device computational schemes or additional clean data available on the server. We propose FedLN , a framework to deal with label noise across different FL training stages, namely FL initialization, on-device model training, and server model aggregation, able to accommodate the diverse computational capabilities of devices in an FL system. Specifically, FedLN computes per-client noise level estimation in a single federated round and improves the models’ performance by either correcting or mitigating the effect of noisy samples. Our evaluation on various publicly available vision and audio datasets demonstrates a 22% improvement on average compared to other existing methods for a label noise level of 60%. We further validate the efficiency of FedLN in human-annotated real-world noisy datasets and report a 4.8% increase on average in models’ recognition performance, highlighting that FedLN can be useful for improving FL services provided to everyday users.