Reviewerly/exHarmony huggingface dataset:
A new Markov–Dubins hybrid solver with learned decision trees

In this paper, the applicability of machine learning models and techniques to the Markov–Dubins path planning problem have been explored. Machine learning techniques are already applied to several fields, which range from computer vision, to physics simulation, to item recommendation, to user profiling. This pervasiveness has led to marked improvements in the implementation and support for applying machine learning models, in particular for specialised use cases such as low-power devices, embedded hardware, and real-time applications. On the other hand, the Markov–Dubins path planning problem, which is central in robotic nonholonomic trajectory design, is already covered by established numerical and optimisation techniques. However, the benefits of applying machine learning approaches to this problem remain to be investigated. In particular, there is the need to research potential speed-ups or application domains that would be better solved by a machine learning approach compared to the traditional algorithmic approaches. In this study, we train a state-of-the-art machine learning model in a supervised setting on Markov–Dubins and use it in two different ways: to directly predict the solution, and to filter candidate solutions. Also, a comparison of the quality of these predictions with a state-of-the-art Markov–Dubins solver is made. The results obtained indicate that machine learning approaches are comparable to state-of-the-art solutions: our bare model, directly predicting the solution, appears to be 8.3 times faster than the current standard, sacrificing the accuracy, which amounts to a value close to 92%; the hybrid model that filters the solutions prior to finding the best candidate runs in times that are comparable to the classical solver (58 ms) and has over 98% accuracy. A further comparison with alternative solvers and techniques, such as Optimal Control, NonLinear Programming and Mixed Integer NonLinear Programming has been made, confirming the benefits of the machine learning approach over these, for which the computational times are in the range of seconds. This opens new avenues for interdisciplinary applications of machine learning to more general planning problems (e.g., the same problem in 3D), where the number of possible manoeuvres is large and the computation of each of them requires a considerable computational effort, which makes the brute force trial-and-error infeasible.
Geologic and Geochemical Features of the World-Class Archean Windfall Intrusion-Related Au Deposit, Abitibi Subprovince, Canada

Abstract The Neoarchean Windfall gold deposit, hosted in the Urban-Barry greenstone belt of the Abitibi subprovince (Quebec, Canada), represents an emerging and significant Au deposit with a resource of 7.4 Moz of Au. It is hosted in 2717 Ma bimodal volcanic rocks that are cut by several generations of calc-alkaline quartz-feldspar porphyry dikes separated into (1) a 2697.6 ± 2.6 Ma group spatially related to Au mineralization and (2) a 2697.6 ± 0.4 Ma group that truncates the earlier dikes and the Au mineralization. The Au zones are structurally controlled and localized to faults and fractures proximal to the contacts of the early quartz-feldspar porphyry dikes; these zones form thin, subvertical, and elongate lenses plunging 35° east-northeast. Gold mineralization, present as both free gold and inclusions in pyrite, occurs (1) in gray quartz veins and stockworks with pyrite and subordinate carbonate and tourmaline and (2) in pervasive to patchy sericite-silica-pyrite-carbonate ± tourmaline ± fuchsite alteration zones. The Au mineralization and associated hydrothermal alteration, along with all the host rocks that include postmineralization intrusions, are overprinted by D2 deformational features that include a penetrative fabric, shear zones, and associated folds. The spatial and temporal association of the quartz-feldspar porphyry intrusions with the Au mineralizing event at the Windfall gold deposit, along with its elemental association (Ag, As, Sb, S, Se, Bi, Te, ± Zn, Cu, Pb, Mo, W), suggests an intrusion-related model and contrasts with the more abundant orogenic gold deposits in the Abitibi greenstone belt. This interpretation has important implications both locally and regionally for Au exploration in Archean greenstone terranes.
SEA-net: Sequence attention network for seismic event detection and phase arrival picking

Automatic seismic phase detection and phase arrival time picking are always fundamental tasks in seismology. With the emergence of machine learning, and deep learning in particular, a number of neural network architectures are being used to explore the correlations between seismic waveforms and hidden information of interest. In this paper, we present a sequential attention (SEA) module that can learn both local and long-distance corrections to effectively extract sequential features. This module is lightweight and plug-in-play. Based on the SEA module, a fully convolutional neural network with sequential attention mechanism was constructed to detect seismic events and determine the arrival time of phases. Further experiments on STEAD dataset, in comparison with existing methods, show that our proposed model outperforms other algorithms with a higher F1-score (100% for seismic detection, 98.8% for P arrival picking, and 89.0% for S arrival picking) and fewer residual errors. Comparatively, our model has a quarter of the model parameters and computational complexity as well as a 36.7% reduction in processing time compared to the current state-of-the-art method. This demonstrates that the proposed architecture is effective for seismic sequential learning tasks and, as a result, can serve as an improved alternative backbone.
Fuzzy Photogrammetric Algorithm for City Built Environment Capturing into Urban Augmented Reality Model

Cities are increasingly looking to become smarter and more resilient. Also, the use of computer vision takes a considerable place in the panoply of techniques and algorithms necessary for the 3D reconstruction of urban built environments. The models thus obtained make it possible to feed the logic of decision support and urban services thanks to the integration of augmented reality. This chapter describes and uses Fuzzy Cognitive Maps (FCM) as computing framework of visual features matching in augmented urban built environment modeling process. It is a combination of the achievements of the theory of fuzzy subsets and photogrammetry according to an algorithmic approach associated with the ARKit renderer. In this experimental research work, part of which is published in this chapter, the study area was confined to a portion of a housing estate and the data acquisition tools are in the domain of the public. The aim is the deployment of the algorithmic process to capture urban environments built in an augmented reality model and compute visual feature in stereovision within FCM framework. The comparison of the results obtained with our approach to two other well-known ones in the field, denotes the increased precision gain with a scalability factor.
Blockchain Technology in the Environmental Economics: A Service for a Holistic and Integrated Life Cycle Sustainability Assessment

The application of blockchain technology in the field of environmental economics is still in its inception so it is not sufficiently used in a holistic and integrated life cycle sustainability assessment (HILCSA). The capability of the blockchain to provide a verifiable and transparent record can make it a good tool in environmental economics for an agile reflection in doing business and production. The research is focused on the advantages and challenges in the inclusion of blockchain technology into a holistic life cycle assessment. Based on the existing possibilities of using blockchain technology in environmental economics and life cycle assessments (LCAs), a framework and a model for applying the blockchain in the holistic life cycle sustainability assessment are proposed. A Design Science methodology was used as a research strategy. Particular emphasis in this paper is put on risk management when integrating blockchain methodologies through environmental economics into the life cycle sustainability assessment (LCSA) in order to use all the advantages of the blockchain technology optimally.
Research on Underwater Image Restoration Technology Based on Multi-Domain Translation

Underwater images are crucial in various underwater applications, including marine engineering, underwater robotics, and subsea coral farming. However, obtaining paired data for these images is challenging due to factors such as light absorption and scattering, suspended particles in the water, and camera angles. Underwater image recovery algorithms typically use real unpaired dataset or synthetic paired dataset. However, they often encounter image quality issues and noise labeling problems that can affect algorithm performance. To address these challenges and further improve the quality of underwater image restoration, this work proposes a multi-domain translation method based on domain partitioning. Firstly, this paper proposes an improved confidence estimation algorithm, which uses the number of times a sample is correctly predicted in a continuous period as a confidence estimate. The confidence value estimates are sorted and compared with the real probability to continuously optimize the confidence estimation and improve the classification performance of the algorithm. Secondly, a U-net structure is used to construct the underwater image restoration network, which can learn the relationship between the two domains. The discriminator uses full convolution to improve the performance of the discriminator by outputting the true and false images along with the category to which the true image belongs. Finally, the improved confidence estimation algorithm is combined with the discriminator in the image restoration network to invert the labels for images with low confidence values in the clean domain as images in the degraded domain. The next step of image restoration is then performed based on the new dataset that is divided. In this way, the multi-domain conversion of underwater images is achieved, which helps in the recovery of underwater images. Experimental results show that the proposed method effectively improves the quality and quantity of the images.
Generative Adversarial Network for Overcoming Occlusion in Images: A Survey

Although current computer vision systems are closer to the human intelligence when it comes to comprehending the visible world than previously, their performance is hindered when objects are partially occluded. Since we live in a dynamic and complex environment, we encounter more occluded objects than fully visible ones. Therefore, instilling the capability of amodal perception into those vision systems is crucial. However, overcoming occlusion is difficult and comes with its own challenges. The generative adversarial network (GAN), on the other hand, is renowned for its generative power in producing data from a random noise distribution that approaches the samples that come from real data distributions. In this survey, we outline the existing works wherein GAN is utilized in addressing the challenges of overcoming occlusion, namely amodal segmentation, amodal content completion, order recovery, and acquiring training data. We provide a summary of the type of GAN, loss function, the dataset, and the results of each work. We present an overview of the implemented GAN architectures in various applications of amodal completion. We also discuss the common objective functions that are applied in training GAN for occlusion-handling tasks. Lastly, we discuss several open issues and potential future directions.
Image Translation of Breast Ultrasound to Pseudo Anatomical Display by CycleGAN

Ultrasound imaging is cost effective, radiation-free, portable, and implemented routinely in clinical procedures. Nonetheless, image quality is characterized by a granulated appearance, a poor SNR, and speckle noise. Specific for breast tumors, the margins are commonly blurred and indistinct. Thus, there is a need for improving ultrasound image quality. We hypothesize that this can be achieved by translation into a more realistic display which mimics a pseudo anatomical cut through the tissue, using a cycle generative adversarial network (CycleGAN). In order to train CycleGAN for this translation, two datasets were used, "Breast Ultrasound Images" (BUSI) and a set of optical images of poultry breast tissues. The generated pseudo anatomical images provide improved visual discrimination of the lesions through clearer border definition and pronounced contrast. In order to evaluate the preservation of the anatomical features, the lesions in both datasets were segmented and compared. This comparison yielded median dice scores of 0.91 and 0.70; median center errors of 0.58% and 3.27%; and median area errors of 0.40% and 4.34% for the benign and malignancies, respectively. In conclusion, generated pseudo anatomical images provide a more intuitive display, enhance tissue anatomy, and preserve tumor geometry; and can potentially improve diagnoses and clinical outcomes.
Knowledge Maps as Support Tool for Managing Scientific Competences: A Case Study at a Portuguese Research Institute

In a research organization, finding someone who is an expert in a field and that can take up a given role, defining areas of excellence, or employing a new member all require understanding the competences that are available in-house. This work explores the idea of using knowledge or competence maps as support tools for managing scientific competences. We implemented a use case at the Institute of Electronics and Informatics Engineering of Aveiro, a research institute at the University of Aveiro, but the methodology we proposed can be adapted to virtually any research organization. Knowledge maps are visual representations of information that can be designed with variable granularities with respect to the knowledge assets of an organization. From a research management perspective, knowledge maps support the discovery of research competences and provide an instant overview of a topic by showing the main areas at a glance. This solution explored in this work employed data mining approaches for gathering information from public databases and presenting it using knowledge maps. Other visualization tools, such as bar graphs, tables, filters and search functionalities, were created and integrated into a web platform. When put together, these components could turn the platform into a key component for the administration of a research organization.
Cross-Correlation Fusion Graph Convolution-Based Object Tracking

Most popular graph attention networks treat pixels of a feature map as individual nodes, which makes the feature embedding extracted by the graph convolution lack the integrity of the object. Moreover, matching between a template graph and a search graph using only part-level information usually causes tracking errors, especially in occlusion and similarity situations. To address these problems, we propose a novel end-to-end graph attention tracking framework that has high symmetry, combining traditional cross-correlation operations directly. By utilizing cross-correlation operations, we effectively compensate for the dispersion of graph nodes and enhance the representation of features. Additionally, our graph attention fusion model performs both part-to-part matching and global matching, allowing for more accurate information embedding in the template and search regions. Furthermore, we optimize the information embedding between the template and search branches to achieve better single-object tracking results, particularly in occlusion and similarity scenarios. The flexibility of graph nodes and the comprehensiveness of information embedding have brought significant performance improvements in our framework. Extensive experiments on three challenging public datasets (LaSOT, GOT-10k, and VOT2016) show that our tracker outperforms other state-of-the-art trackers.
Knowledge-Guided Prompt Learning for Few-Shot Text Classification

Recently, prompt-based learning has shown impressive performance on various natural language processing tasks in few-shot scenarios. The previous study of knowledge probing showed that the success of prompt learning contributes to the implicit knowledge stored in pre-trained language models. However, how this implicit knowledge helps solve downstream tasks remains unclear. In this work, we propose a knowledge-guided prompt learning method that can reveal relevant knowledge for text classification. Specifically, a knowledge prompting template and two multi-task frameworks were designed, respectively. The experiments demonstrated the superiority of combining knowledge and prompt learning in few-shot text classification.
RSP-DST: Revisable State Prediction for Dialogue State Tracking

Task-oriented dialogue systems depend on dialogue state tracking to keep track of the intentions of users in the course of conversations. Although recent models in dialogue state tracking exhibit good performance, the errors in predicting the value of each slot at the current dialogue turn of these models are easily carried over to the next turn, and unlikely to be revised in the next turn, resulting in error propagation. In this paper, we propose a revisable state prediction for dialogue state tracking, which constructs a two-stage slot value prediction process composed of an original prediction and a revising prediction. The original prediction process jointly models the previous dialogue state and dialogue context to predict the original dialogue state of the current dialogue turn. Then, in order to avoid the errors existing in the original dialogue state continuing to the next dialogue turn, a revising prediction process utilizes the dialogue context to revise errors, alleviating the error propagation. Experiments are conducted on MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.4 and results indicate that our model outperforms previous state-of-the-art works, achieving new state-of-the-art performances with 56.35, 58.09, and 75.65% joint goal accuracy, respectively, which has a significant improvement (2.15, 1.73, and 2.03%) over the previous best results.
A Flight Parameter-Based Aircraft Structural Load Monitoring Method Using a Genetic Algorithm Enhanced Extreme Learning Machine

High-precision operational flight loads are essential for monitoring fatigue of individual aircraft and are usually determined by flight parameters. To tackle the nonlinear relationship between flight loads and flight parameters for more accurate prediction of flight loads, artificial neural networks have been widely studied. However, there are still two major problems, namely the training strategy and sensitivity analysis of the flight parameters. For the first problem, the gradient descent method is usually used, which is time-consuming and can easily converge to a local solution. To solve this problem, an extreme learning machine is proposed to determine the weights based on a Moore–Penrose generalized inverse. Moreover, a genetic algorithm method is proposed to optimize the weights between the input and hidden layers. For the second problem, a mean impact value (MIV) method is proposed to measure the sensitivity of the flight parameters, and the neuron number in the hidden layer is also optimized. Finally, based on the measured dataset of an aircraft, the proposed flight load prediction method is verified to be effective and efficient. In addition, a comparison is made with some well-known neural networks to demonstrate the advantages of the proposed method.
Domain Adaptation with Contrastive Simultaneous Multi-Loss Training for Hand Gesture Recognition

Hand gesture recognition from images is a critical task with various real-world applications, particularly in the field of human-robot interaction. Industrial environments, where non-verbal communication is preferred, are significant areas of application for gesture recognition. However, these environments are often unstructured and noisy, with complex and dynamic backgrounds, making accurate hand segmentation a challenging task. Currently, most solutions employ heavy preprocessing to segment the hand, followed by the application of deep learning models to classify the gestures. To address this challenge and develop a more robust and generalizable classification model, we propose a new form of domain adaptation using multi-loss training and contrastive learning. Our approach is particularly relevant in industrial collaborative scenarios, where hand segmentation is difficult and context-dependent. In this paper, we present an innovative solution that further challenges the existing approach by testing the model on an entirely unrelated dataset with different users. We use a dataset for training and validation and demonstrate that contrastive learning techniques in simultaneous multi-loss functions provide superior performance in hand gesture recognition compared to conventional approaches in similar conditions.
Online multiple object tracking with enhanced Re‐identification

Abstract In existing online multiple object tracking algorithms, schemes that combine object detection and re‐identification (ReID) tasks in a single model for simultaneous learning have drawn great attention due to their balanced speed and accuracy. However, different tasks require to focus different features. Learning two different tasks in the same model extracted features can lead to competition between the two tasks, making it difficult to achieve optimal performance. To reduce this competition, a task‐related attention network, which uses a self‐attention mechanism to allow each branch to learn on feature maps related to its task is proposed. Besides, a smooth gradient‐boosting loss function, which improves the quality of the extracted ReID features by gradually shifting the focus to the hard negative samples of each object during training is introduced. Extensive experiments on MOT16, MOT17, and MOT20 datasets demonstrate the effectiveness of the proposed method, which is also competitive in current mainstream algorithm.
Equivalence between LC-CRF and HMM, and Discriminative Computing of HMM-Based MPM and MAP

Practitioners have used hidden Markov models (HMMs) in different problems for about sixty years. Moreover, conditional random fields (CRFs) are an alternative to HMMs and appear in the literature as different and somewhat concurrent models. We propose two contributions: First, we show that the basic linear-chain CRFs (LC-CRFs), considered as different from HMMs, are in fact equivalent to HMMs in the sense that for each LC-CRF there exists an HMM—that we specify—whose posterior distribution is identical to the given LC-CRF. Second, we show that it is possible to reformulate the generative Bayesian classifiers maximum posterior mode (MPM) and maximum a posteriori (MAP), used in HMMs, as discriminative ones. The last point is of importance in many fields, especially in natural language processing (NLP), as it shows that in some situations dropping HMMs in favor of CRFs is not necessary.