Reviewerly/exHarmony huggingface dataset:
Deep learning-based object detection in maritime unmanned aerial vehicle imagery: Review and experimental comparisons

With the advancement of maritime unmanned aerial vehicles (UAVs) and deep learning technologies, the application of UAV-based object detection has become increasingly significant in the fields of maritime industry and ocean engineering. Endowed with intelligent sensing capabilities, the maritime UAVs enable effective and efficient maritime surveillance. To further promote the development of maritime UAV-based object detection, this paper provides a comprehensive review of challenges, relative methods, and UAV aerial datasets. Specifically, in this work, we first briefly summarize four challenges for object detection on maritime UAVs, i.e., object feature diversity, device limitation, maritime environment variability, and dataset scarcity. We then focus on computational methods to improve maritime UAV-based object detection performance in terms of scale-aware, small object detection, view-aware, rotated object detection, lightweight methods, and others. Next, we review the UAV aerial image/video datasets and propose a maritime UAV aerial dataset named MS2ship for ship detection. Furthermore, we conduct a series of experiments to present the performance evaluation and robustness analysis of object detection methods on maritime datasets. Eventually, we give the discussion and outlook on future works for maritime UAV-based object detection. The MS2ship dataset is available at https://github.com/zcj234/MS2ship.
Novel diversified echo state network for improved accuracy and explainability of EEG-based stroke prediction

Echo State Networks (ESNs) are a powerful machine learning technique that can be used for EEG-based stroke prediction. However, conventional ESNs suffer from two main limitations: they are not always accurate, and they are not always interpretable. This paper presents a novel multi-level framework that addresses these limitations. The framework consists of three main components: optimized feature extraction, ensemble learning, and output refinement for improved interpretability. The optimized feature extraction component uses a novel algorithm to extract features from EEG data that are more relevant to stroke prediction. The ensemble learning component uses a diversified Echo State Networks (D-ESN) to combine the predictions of multiple ESNs, which improves the accuracy of the predictions. The output improvement component uses two Explainability techniques, LIME and ELI5, to gain insight into the decision-making of the D-ESN model. These techniques allow users to see how each feature in the dataset contributed to the model's prediction. The framework was evaluated on a well-known EEG dataset from stroke patients. The experimental results showed that the framework significantly outperformed baseline approaches in terms of both accuracy with 95 % and interpretability. These results suggest that the proposed framework has the potential to advance the field of stroke prediction and enable informed decision-making in clinical settings.
Reinforcement learning to achieve real-time control of triple inverted pendulum

This work uses reinforcement learning (RL) to achieve the first-ever data-driven real-time control of an actual, not simulated, triple inverted pendulum (TIP) in a model-free way. A swing-up control task for the TIP is formulated as a Markov decision process with a dense reward function, then conducted in real time by using a model-free RL approach. To increase the sample efficiency of learning, a structure-aware virtual experience replay (VER) method is proposed; it works together with an off-policy actor-critic algorithm. The VER exploits the geometrically-symmetric property of TIPs to create virtual sample trajectories from measured ones, then uses the resulting multifold augmented dataset to effectively train actor and critic networks during the learning process. These structure-infused training data serve to obtain additional information and hence increase the convergence speed of network learning. We combine the proposed VER with a state-of-the-art actor-critic algorithm, and then validate its effectiveness through numerical simulations. Notably, the inclusion of VER amplifies computational efficiency, slashing the requisite trials, training steps, and overall duration by approximately 66.67%. Finally experiments demonstrate the real-time control capability of the proposed approach on an actual TIP system.
Improved coverage path planning for indoor robots based on BIM and robotic configurations

Robotics holds great potential to improve productivity in construction, and coverage path planning (CPP) is an essential capability crucial to various applications, including floor cleaning and environmental monitoring. However, there is still a lack of a comprehensive CPP system that can handle complex indoor conditions and various robotic properties. An improved CPP (ICCP) system that leverages building information modeling (BIM) and robotic configurations for indoor robots is proposed in this study. Firstly, BIM is semantically enriched to generate semantic trapezoidal grid maps (TGMs); Next, a novel concept called "coverage bonus" is incorporated into coverage pattern analysis to enable farsighted decision making; Finally, the coverage sequence is optimized by solving the cluster generalized traveling salesman problem, resulting in routes that minimize both coverage distances and disruptions in indoor activities. Experimental validation shows that the ICPP system can not only attain optimal coverage performance with the highest coverage ratio (97.6%) but also ensure the adherence to indoor coverage rules. Future research will focus on enhancing coverage ratio through tunable hyperparameters, optimizing computation time in ICCP, and expanding the study to multi-robot scenarios.
Selection of a viable blockchain service provider for data management within the internet of medical things: An MCDM approach to Indian healthcare

Health 4.0 is gaining significant attention globally to support better treatment and care for people. Digital technologies such as the IoMT "("Internet of Medical Things"") and blockchain substantially promote quality health services. Literature shows that embedding blockchain in IoMT is an effective way to attain secure and quality healthcare. Selecting a viable blockchain service provider (BSP) becomes a complex task and can be considered an MCDM "("Multi-Criteria Decision-Making"") problem. Earlier studies on BSP selection indicate that complex expressions cannot be well-modelled and methodical estimation of decision parameters by capturing hesitation and interaction of entities is not adequately explored. Driven by the issues, authors put forward a novel MCDM framework with (i) a double hierarchy linguistic structure for data collection in natural expressions; (ii) regret measure for experts' reliability calculation; (iii) a weighted CRITIC approach for criteria weight determination, and (iv) CRADIS-Copeland algorithm for ranking BSPs. Finally, a case example from Recent Indian healthcare is exemplified to demonstrate the framework's applicability. Recently, the Indian healthcare sector launched initiatives/plans as part of Health 4.0 to promote data management for a better quality of treatment by seeking support from digital technologies such as IoMT and blockchains to ensure data privacy and improved customer experience. Sensitivity analysis and comparison with extant methods infer that (i) BC1, BC5, and BC3 are the top three BSPs for the considered problem; (ii) criteria such as privacy aspects, intercommunication capability, global accessibility, and total cost constitute 58% importance in the supposed decision problem; (iii) developed model reduces human intervention/biases; (iv) developed model is robust to weight alteration and yields unique rank orders with both personalised and cumulative ordering of BSPs; and (v) proposed model produces broader rank values that aid in effective backup planning.
FLAD: Adaptive Federated Learning for DDoS attack detection

Federated Learning (FL) has been recently receiving increasing consideration from the cybersecurity community as a way to collaboratively train deep learning models with distributed profiles of cyber threats, with no disclosure of training data. Nevertheless, the adoption of FL in cybersecurity is still in its infancy, and a range of practical aspects have not been properly addressed yet. Indeed, the Federated Averaging algorithm at the core of the FL concept requires the availability of test data to control the FL process. Although this might be feasible in some domains, test network traffic of newly discovered attacks cannot be always shared without disclosing sensitive information. In this paper, we address the convergence of the FL process in dynamic cybersecurity scenarios, where the trained model must be frequently updated with new recent attack profiles to empower all members of the federation with the latest detection features. To this aim, we propose FLAD (adaptive Federated Learning Approach to DDoS attack detection), an FL solution for cybersecurity applications based on an adaptive mechanism that orchestrates the FL process by dynamically assigning more computation to those members whose attacks profiles are harder to learn, without the need of sharing any test data to monitor the performance of the trained model. Using a recent dataset of DDoS attacks, we demonstrate that FLAD outperforms state-of-the-art FL algorithms in terms of convergence time and accuracy across a range of unbalanced datasets of heterogeneous DDoS attacks. We also show the robustness of our approach in a realistic scenario, where we retrain the deep learning model multiple times to introduce the profiles of new attacks on a pre-trained model.
DHGAT: Hyperbolic representation learning on dynamic graphs via attention networks

Hyperbolic graph embedding has garnered significant attention in a wide range of downstream applications. This is because hyperbolic geometry provides a valuable mapping tool, whereby the scale-free or hierarchical properties of complex networks can be naturally reflected as hyperbolic metric properties. Despite this, most advancements have focused on learning hyperbolic representations of static graphs, the potential advantages of hyperbolic metrics in dynamic graphs embedding have not been fully exploited. To fully harness the properties of hyperbolic space, we propose DHGAT1, a dynamic hyperbolic graph attention network with a novel architecture that designs a spatiotemporal self-attention mechanism based on hyperbolic distance. DHGAT maps dynamic graphs into the hyperbolic space, generates spatiotemporal self-attention of nodes, and directly aggregates weighted node representations without the tangent space. This is achieved by using the Einstein gyromidpoints, which reduces distortions and preserves manifold properties. The results of experiments on real-world datasets demonstrate that DHGAT performs exceptionally well in multi-step link prediction tasks, particularly in predicting new links compared to the baselines.
Research on a double image security transmission algorithm of image encryption and hiding

Image encryption is an important research field. In some application scenarios, only encrypting can actually attract the attention of attackers. Therefore, this article provides dual protection for images that require confidentiality. The image is first encrypted and then hidden in the normally transmitted image to avoid attacks from attackers. The encryption adopts a combination of DNA encoding and chaos theory, while the hiding adopts LSB algorithm. The experimental results show that the algorithm has achieved good security effects, and the correlation coefficient analysis has also achieved ideal results.
Dynamics-aware navigation among moving obstacles with application to ground and flying robots

We present a novel method for navigation of mobile robots in challenging dynamic environments. The method, which is based on Nonlinear Model Predictive Control (NMPC), hinges upon a specially devised constraint for dynamics-aware collision avoidance. In particular, the constraint builds on the notion of avoidable collision state, taking into account the robot actuation capabilities in addition to the robot-obstacle relative distance and velocity. The proposed approach is applied to both ground and flying robots and tested in a variety of static and dynamic environments. Comparative simulations with an NMPC using a purely distance-based collision avoidance constraint confirm the superiority of the dynamics-aware version, especially for high-speed navigation among moving obstacles. Moreover, the results indicate that the method can work with relatively short prediction horizons and is therefore amenable to real-time implementation.
Towards a generalized hybrid deep learning model with optimized hyperparameters for malicious traffic detection in the Industrial Internet of Things

Detecting malicious attacks in Industrial Internet of Things (IIoT) is crucial to minimize downtime and financial losses. However, existing deep learning (DL) research faces limitations in reliability and generalization due to reliance on a single dataset. This single dataset approach hinders the effectiveness of the models when applied to new and unseen datasets. This paper addresses the issue using multiple-domain learning, creating a generalized DL framework for IIoT traffic classification. It combines three datasets: Edge-IIoTSet, WUSTL-IIoT-2021, and X-IIoTID. The proposed framework has two stages. The first stage involves the fusion of different datasets into a common feature space. To achieve this, an autoencoder architecture is proposed to match the dimensionality of the datasets into a common feature space. Subsequently, a modified locally linear embedding is used as a manifold alignment to ensure statistical matching of multiple datasets such that the maximum mean discrepancy between multiple datasets is minimized to compare and combine datasets effectively. The second stage presents a hybrid DL model with a convolutional neural network (CNN) and gated recurrent unit (GRU) for IIoT traffic classification. Bayesian optimization fine-tunes hyperparameters. Experiments on combined, single, and cross-datasets show the superiority of the framework. The CNN-GRU model achieved 97.68% accuracy, 97.70% recall, 97.67% precision, and 97.68% F1-score for binary classification on the combined dataset. Transfer learning improved accuracy from 50.95% to 97.80% and F1-score from 48.52% to 97.79% when trained on X-IIoTID and tested on Edge-IIoTSet. Multi-classification accuracy and weighted averages of recall, precision, and F1-score were approximately 98.85%.
A methodological and theoretical framework for implementing explainable artificial intelligence (XAI) in business applications

Artificial Intelligence (AI) is becoming fundamental in almost all activity sectors in our society. However, most of the modern AI techniques (e.g., Machine Learning – ML) have a black box nature, which hinder their adoption by practitioners in many application fields. This issue raises a recent emergence of a new research area in AI called Explainable artificial intelligence (XAI), aiming at providing AI-based decision-making processes and outcomes to be easily understood, interpreted, and justified by humans. Since 2018, there has been an exponential growth of research studies on XAI, which has justified some review studies. However, these reviews currently focus on proposing taxonomies of XAI methods. Yet, XAI is by nature a highly applicative research field, and beyond XAI methods, it is also very important to investigate how XAI is concretely used in industries, and consequently derive the best practices to follow for better implementations and adoptions. There is a lack of studies on this latter point. To fill this research gap, we first propose a holistic review of business applications of XAI, by following the Theory, Context, Characteristics, and Methodology (TCCM) protocol. Based on the findings of this review, we secondly propose a methodological and theoretical framework in six steps that can be followed by all practitioners or stakeholders for improving the implementation and adoption of XAI in their business applications. We particularly highlight the need to rely on domain field and analytical theories to explain the whole analytical process, from the relevance of the business question to the robustness checking and the validation of explanations provided by XAI methods. Finally, we propose seven important future research avenues.
Age, gender and handedness prediction using handwritten text: A comprehensive survey

Handwriting recognition is widely acknowledged as one of the most challenging tasks in pattern recognition and machine learning. This study explores the intriguing field of age, gender and handedness prediction through handwriting analysis by offering valuable insights into its potential applications in forensics, psychology and education. A comprehensive survey has been conducted encompassing an introduction, motivational aspects, background information, sources of data, analysis schemes, survey protocols, reported findings and methodologies used for predicting age, gender and handedness in both Indic and non-Indic scripts. The investigation highlights research gaps and concludes with an in-depth analysis of unresolved issues accompanied by a roadmap for future advancements in this domain. The survey focuses on a systematic examination of eight Indic scripts (Assamese, Bangla, Devanagari, Gurumukhi, Kannada, Malayalam, Tamil and Telugu) and six non-Indic scripts (Arabic, Chinese, Japanese, Persian, Roman and Thai) and it can be concluded that non-Indic scripts attain more accuracy in comparison with Indic scripts. Furthermore, the study mostly focuses on providing a catalog of publicly accessible online datasets featuring diverse handwriting samples from various scripting languages by giving a roadmap for further research in this area.
Emojifying chatbot interactions: An exploration of emoji utilization in human-chatbot communications

The prevalence of chatbots in human–computer communication has significantly increased. Emojis, as a form of emotional disclosure, have gained significant attention for their potential to boost chatbot service satisfaction. However, how and when emoji usage can increase satisfaction toward chatbots is not fully examined. This paper aims to fill this gap and contribute to the rapidly evolving field of human-chatbot communication research. Through three experiments, this paper investigates and explores the role of emojis in enhancing chatbot interactions. The results reveal that emojis heighten chatbot's perceived warmth but do not necessarily augment their competence. This warmth promoting effect leads to boosted service satisfaction and is more apparent when chatbots serve hedonic purposes and are pre-programmed rather than highly autonomous. However, the warmth upshot of emojis is not as potent for chatbots as it is for humans. While this study unravels the intricate pathway of how emojis augment service satisfaction, it also extends the dialogue of the Stereotype Content Model (SCM) and propels the new wave of the Computers Are Social Actors (CASA) paradigm. Thus, this research lays down pathways for further studies in understanding the role of emotionally simulated interactions in automated technologies.
A partition-based problem transformation algorithm for classifying imbalanced multi-label data

Multi-label learning has garnered much research interest due to its wide range of real-world applications. Many multi-label learning methods have been proposed; however, few have addressed the class imbalance problem existing in multi-label data. Even though some studies have taken this issue into account, most of them have ignored the label correlations or only considered random correlations between them. In this study, we propose a novel partition-based imbalanced multi-label learning algorithm, named Multi-label Learning based on Hierarchical Clustering (MLHC), to tackle this problem. MLHC first carries out hierarchical clustering on the original label space to divide it into several disconnected subspaces, each of which contains several labels that are strongly correlated with each other. Then, for each label subspace, we use the problem transformation strategy to convert it into a multi-class problem by binary coding. Any multi-class imbalance learning algorithm can be applied to the transformed multi-class data. Finally, the classification results will be decoded to retrieve the corresponding label subspace, and all label subspace results are combined to show the predicted label vector in the original label space. We conducted experiments not only on thirteen benchmark multi-label datasets but also carried out them on XJTU-SY which is a multi-label engineering application dataset, and the results indicated that our proposed MLHC learning algorithm outperforms several state-of-the-art class imbalance multi-label learning algorithms, demonstrating the effectiveness and necessity of discovering label correlations and transforming the original imbalanced multi-label learning problem into multiple strongly correlated multi-class imbalanced learning problems.
Arithmetic Average Density Fusion-Part III: Heterogeneous Unlabeled and Labeled RFS Filter Fusion

This paper proposes a heterogenous density fusion approach to scalable multisensor multitarget tracking where the inter-connected sensors run different types of random finite set (RFS) filters according to their respective capacity and need.These diverse RFS filters result in heterogenous multitarget densities that are to be fused with each other in a proper means for more robust and accurate detection and localization of the targets.Our approach is based on Gaussian mixture implementations where the local Gaussian components (L-GCs) are revised for PHD consensus, i.e., the corresponding unlabeled probability hypothesis densities (PHDs) of each filter best fit their average regardless of the specific type of the local densities.To this end, a computationally efficient, coordinate descent approach is proposed which only revises the weights of the L-GCs, keeping the other parameters unchanged.In particular, the PHD filter, the unlabeled and labeled multi-Bernoulli (MB/LMB) filters are considered.Simulations have demonstrated the effectiveness of the proposed approach for both homogeneous and heterogenous fusion of the PHD-MB-LMB filters in different configurations.
Manipulating hidden-Markov-model inferences by corrupting batch data

Time-series models typically assume untainted and legitimate streams of data. However, a self-interested adversary may have incentive to corrupt this data, thereby altering a decision maker's inference. Within the broader field of adversarial machine learning, this research provides a novel, probabilistic perspective toward the manipulation of hidden Markov model inferences via corrupted data. In particular, we provision a suite of corruption problems for filtering, smoothing, and decoding inferences leveraging an adversarial risk analysis approach. Multiple stochastic programming models are set forth that incorporate realistic uncertainties and varied attacker objectives. Three general solution methods are developed by alternatively viewing estimation from frequentist and Bayesian perspectives. The efficacy of each method is illustrated via extensive, empirical testing. The developed methods are characterized by their solution quality and computational effort, resulting in a stratification of techniques across varying problem-instance architectures. This research highlights the weaknesses of hidden Markov models under adversarial activity, thereby motivating the need for robustification techniques to ensure their security.