Reviewerly/exHarmony huggingface dataset:
CNN with New Spatial Pyramid Pooling and Advanced Filter-Based Techniques: Revolutionizing Traffic Monitoring via Aerial Images

The escalation in vehicular traffic, in conjunction with global population growth, has precipitated heightened road congestion, augmented air pollution, and a rise in vehicular accidents. Over the past decade, the global vehicular count has witnessed a substantial surge. In this context, traffic monitoring emerges as a paramount challenge, especially within developing nations. This research introduces an innovative system for vehicle detection and categorization aimed at intelligent traffic monitoring. The system utilizes a convolutional neural network-based U-Net model for the segmentation of aerial images. After segmentation, the outputs are further analyzed for vehicle identification. This vehicle detection utilizes an advanced spatial pyramid pooling (ASPP) mechanism which refines the spatial partitions of the image and captures intricate details, enhancing the accuracy and precision of the detection process. Detected vehicles are then categorized into distinct subcategories. For the effective management and control of high-density traffic flow, the extended Kalman filter (EKF) technique is employed, thereby reducing the reliance on human oversight. In experimental evaluations, our proposed model exhibits exemplary vehicle detection capabilities across the German Aerospace Center (DLR3K) and the Vehicle Detection in Aerial Imagery (VEDAI) datasets. Potential applications of the system encompass vehicle identification in traffic streams, traffic congestion assessment, intersection traffic density analysis, differentiation of vehicle types, and pedestrian pathway determination.
Multi-Head Self-Attention-Enhanced Prototype Network with Contrastive–Center Loss for Few-Shot Relation Extraction

Few-shot relation extraction (FSRE) constitutes a critical task in natural language processing (NLP), involving learning relationship characteristics from limited instances to enable the accurate classification of new relations. The existing research primarily concentrates on using prototype networks for FSRE and enhancing their performance by incorporating external knowledge. However, these methods disregard the potential interactions among different prototype networks, and each prototype network can only learn and infer from its limited instances, which may limit the robustness and reliability of the prototype representations. To tackle the concerns outlined above, this paper introduces a novel prototype network called SACT (multi-head self-attention and contrastive-center loss), aimed at obtaining more comprehensive and precise interaction information from other prototype networks to bolster the reliability of the prototype network. Firstly, SACT employs a multi-head self-attention mechanism for capturing interaction information among different prototypes from traditional prototype networks, reducing the noise introduced by unknown categories with a small sample through information aggregation. Furthermore, SACT introduces a new loss function, the contrastive–center loss function, aimed at tightly clustering samples from a similar relationship category in the center of the feature space while dispersing samples from different relationship categories. Through extensive experiments on FSRE datasets, this paper demonstrates the outstanding performance of SACT, providing strong evidence for the effectiveness and practicality of SACT.
ESA-GCN: An Enhanced Graph-Based Node Classification Method for Class Imbalance Using ENN-SMOTE Sampling and an Attention Mechanism

In recent years, graph neural networks (GNNs) have achieved great success in handling node classification tasks. However, as data explosively grows in various industries, the problem of class imbalance becomes increasingly severe. Traditional GNNs tend to prioritize majority class nodes when dealing with imbalanced class distributions, which fail to adequately capture the features of minority class nodes, leading to significant difficulties and challenges in data classification. To address issues such as inaccurate edge generation during graph data oversampling, insufficient representation of minority classes, and the presence of noisy samples, this paper proposes the ESA-GCN model. The advantages of this model are as follows: (i) it employs the ENN-SMOTE comprehensive sampling method to balance the dataset by reducing majority class nodes and increasing minority class nodes; (ii) the ENN algorithm reduces the classifier’s error rate and improves performance stability by removing low-quality and noisy data; (iii) an attention mechanism is introduced during the edge generation phase between new nodes and original nodes, fully considering the mutual relationships between nodes and concentrating on a subset of key information with high weights, thereby significantly improving classification accuracy while reducing model parameters and computational complexity. Experiments conducted on three public datasets (Cora, Citeseer, and PubMed) demonstrate that the ESA-GCN has achieved significant results in class imbalance node classification tasks.
Modernizing Titrations in the Undergraduate Laboratory: No More Burets and End Point Confusion, Just a Top-Loading Balance and a Smartphone

This study introduces a modernized approach to classical volumetric titrations for the undergraduate chemistry curriculum. Leveraging smartphones and digital top-loading balances, we performed mass-based titrations and determined end points using digital color measurements. Our aim was to make classical wet chemistry more accessible and engaging for students. Our approach will also enable color-blind and visually impaired students to actively engage in routine laboratory procedures, such as titrations, and perform equally as well as their peers. Traditionally, titrations have relied upon manual visual detection of indicator color changes, some of which can be challenging for inexperienced students. This study used a complexometric titration with EDTA as the titrant and Eriochrome Black T (EBT) as the indicator to determine the hardness of water samples. Students used their phones/tablets to capture images of the titration process/progress, and the color digitization application (app) converted the color of the solution to red, green, and blue (RGB) values. These values were used to plot the titration curve, obtain the first and second derivatives, and determine the end point. The study demonstrated the approach was effective in determining water hardness. It showed good reproducibility and accuracy compared to the classical volumetric titrations, reducing the need for a continuous stock of glass burets. The method has the potential for broader applications and could easily be adapted for fieldwork, making it invaluable for Citizen Science type projects. The study highlights the growing role of smartphones in science education and analysis. Such technology can allow students the opportunity to bridge the gap between theory and practice and encourage creativity in experimental design.
An Ensemble Method for the Analysis of Small Biomedical Data based on a Neural Network Without Training

To enhance the accuracy of analyzing short datasets, this paper proposes a novel ensemble learning method that utilizes a single the General Regression Neural Network (GRNN). The core idea behind this method is the synthesis of additional pairs of vectors with different signs around each current vector from the test sample. This is achieved by employing the method of random symmetric perturbations and averaging the prediction outputs for the current vector and all synthesized vectors in its vicinity. Implementing this approach leads to a significant increase in prediction accuracy for short datasets. It achieves error compensation for each pair of addi-tional vectors with different signs and also for the overall prediction result of the current vector and all additional pairs of synthetic vectors created for it. The effectiveness of the proposed method is validated through modeling on a small real-world biomedical dataset, and the optimal parameters have been selected. Comparative analysis with existing GRNN-based me¬thods demonstrates a substantial improvement in accuracy.
What does data literacy means for you (as an educator)nowadays?

Despite progress in data literacy frameworks associated with a critical discussion of datafication, educators are still perplexed when it comes to working with these issues in their everyday teaching practice. In part, this is due to the complexity of the data infrastructures that permeate educational practice itself. In this context, it seems particularly appropriate to understand the discursive phenomena, the construction of professional practise and therefore the educators’ positionings around the issues of datafication in general, and the development of critical data literacy, namely, “postdigital positinings”.&#x0D; This paper proposes a collaborative autoethnographic analysis of the professional experiences of the three authors, as educators.&#x0D; As women with complex migrant identities, with roots in the Global South and at the same time, bearers of European métisages, our pathways meet at the crossover of an international project in which we develop materials and design educational activities. Our history lies on an intersectional basis that allows us to express rich positionalities, full of examples and resources that can be resounding notes for the construction of agentic educational practices in this field of post-digital forces.
Target-Aware Bi-Transformer for Few-Shot Segmentation

Traditional semantic segmentation tasks require a large number of labels and are difficult to identify unlearned categories. Few-shot semantic segmentation (FSS) aims to use limited labeled support images to identify the segmentation of new classes of objects, which is very practical in the real world. Previous researches were primarily based on prototypes or correlations. Due to colors, textures, and styles are similar in the same image, we argue that the query image can be regarded as its own support image. In this paper, we proposed the Target-aware Bi-Transformer Network (TBTNet) to equivalent treat of support images and query image. A vigorous Target-aware Transformer Layer (TTL) also be designed to distill correlations and force the model to focus on foreground information. It treats the hypercorrelation as a feature, resulting a significant reduction in the number of feature channels. Benefit from this characteristic, our model is the lightest up to now with only 0.4M learnable parameters. Furthermore, TBTNet converges in only 10% to 25% of the training epochs compared to traditional methods. The excellent performance on standard FSS benchmarks of PASCAL- $$5^i$$ and COCO- $$20^i$$ proves the efficiency of our method. Extensive ablation studies were also carried out to evaluate the effectiveness of Bi-Transformer architecture and TTL.
Skeleton-Based Action Recognition with Combined Part-Wise Topology Graph Convolutional Networks

Graph Convolutional Network (GCN) has achieved promising performance in skeleton-based action recognition by modeling skeleton sequences as spatio-temporal graphs. However, most existing methods only focus on the overall characteristics of the skeleton, thus lacking fine-grained exploration of human body parts semantics. In this paper, we propose a novel Combined Part-wise Topology Graph Convolutional Network (CPT-GCN), including SPT-GC, TPT-GC, and STPT-GC modules, to refine the spatio-temporal topology from the spatial, temporal, and spatio-temporal perspectives, respectively. Specifically, SPT-GC aggregates spatial features by combining global topology and partial correlations. TPT-GC combines the overall motion trend and the motion details of parts to capture temporal dynamics. STPT-GC establishes a spatio-temporal dependency, focusing on exploiting the implicit spatio-temporal information in motions. Ultimately, the effectiveness of CPT-GCN is demonstrated through experiments on two large-scale datasets: NTU RGB+D 60 and NTU RGB+D 120.
Depth Optimization for Accurate 3D Reconstruction from Light Field Images

Because the light field camera can capture both the position and direction of light simultaneously, it enables us to estimate the depth map from a single light field image and subsequently obtain the 3D point cloud structure. However, the reconstruction results based on light field depth estimation often contain holes and noisy points, which hampers the clarity of the reconstructed 3D object structure. In this paper, we propose a depth optimization algorithm to achieve a more accurate depth map. We introduce a depth confidence metric based on the photo consistency of the refocused angular sampling image. By utilizing this confidence metric, we detect the outlier points in the depth map and generate an outlier mask map. Finally, we optimize the depth map using the proposed energy function. Experimental results demonstrate the superiority of our method compared to other algorithms, particularly in addressing issues related to holes, boundaries, and noise.
GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene Flow

Scene flow plays a pivotal role in the 3D perception task by capturing motion information between consecutive frames. However, occlusion phenomena are commonly observed in dynamic scenes, whether from the sparsity data sampling or real-world occlusion. In this paper, we focus on addressing occlusion issues in scene flow by the semantic self-similarity and motion consistency of the moving objects. We propose a GMA3D module based on the transformer framework, which utilizes local and global semantic similarity to infer the motion information of occluded points from the motion information of local and global non-occluded points respectively, and then uses the Offset Aggregator to aggregate them. Our module is the first to apply the transformer-based architecture to gauge the scene flow occlusion problem on point clouds. Experiments show that our GMA3D can solve the occlusion problem in the scene flow, especially in the real scene. We evaluated the proposed method on the occluded version of point cloud datasets and get state-of-the-art results on the real-world KITTI dataset. To testify that GMA3D is still beneficial to non-occluded scene flow, we also conducted experiments on non-occluded version datasets and achieved promising performance on FlyThings3D and KITTI. The code is available at https://github.com/O-VIGIA/GMA3D .
INNOVATION IN DATA STORAGE TECHNOLOGIES: FROM CLOUD COMPUTING TO EDGE COMPUTING

In an era where data is the new gold, understanding the evolution and future trajectory of data storage technologies is crucial. This paper delves into the transformative journey from traditional storage methods to contemporary paradigms like cloud and edge computing, underpinned by the burgeoning influence of Big Data, IoT, AI, and machine learning. The study's aim is to provide a comprehensive analysis of these technologies, assessing their development, efficacy, and the challenges they face in meeting the escalating demands of data storage.&#x0D; The methodology employed is a meticulous synthesis of literature reviews, case studies, and comparative analyses. This approach facilitates an in-depth exploration of the historical evolution of data storage, the paradigm shifts from cloud to edge computing, and the interplay between technological advancements and user demands. The study also scrutinizes the security concerns inherent in these technologies and identifies strategic directions for future research. Key findings reveal that while cloud computing has revolutionized data storage with its scalability and flexibility, edge computing emerges as a vital solution to latency and bandwidth limitations. The integration of AI and machine learning is identified as a pivotal factor in enhancing the efficiency and intelligence of data storage systems. However, this integration presents unique challenges, necessitating innovative solutions. Conclusively, the study recommends a continued focus on innovation in data storage technologies, emphasizing the development of integrated, secure, and efficient solutions. Future research should particularly explore the potential of AI and machine learning in overcoming current limitations.&#x0D; The paper's scope encompasses a comprehensive overview of the current state and future potential of data storage technologies, making it a valuable resource for researchers, technologists, and policymakers in the field.&#x0D; Keywords: Data Storage Technologies, Cloud Computing, Edge Computing, Big Data, Internet of Things (IoT), Artificial Intelligence (AI).
Full Quaternion Matrix-Based Multiscale Principal Component Analysis Network for Facial Expression Recognition

To acquire a more discriminative feature of facial expression, we propose a multi-scale principal component analysis network based on full quaternion matrix representation. Firstly, the structure feature and color components of facial image constitute a full quaternion matrix. Subsequently, two-staged quaternion principal component analysis is employed to learn convolutional filters. Among them, the feature maps of both stages are activated via nonlinear function. With binarization and coding, the local histograms are stacked together and fed to the classifier for expression matching. Experiments conducted on the RafD, MMI, NVIE, and KDEF datasets have demonstrated that the proposed method achieves higher recognition accuracy than several existing algorithms.
Interpretable Single-dimension Outlier Detection (ISOD): An Unsupervised Outlier Detection Method Based on Quantiles and Skewness Coefficients

A crucial area of study in data mining is outlier detection, particularly in the areas of network security, credit card fraud detection, industrial flaw detection, etc. Existing outlier detection algorithms, which can be divided into supervised methods, semi-supervised methods, and unsupervised methods, suffer from missing labeled data, the curse of dimensionality, low interpretability, etc. To address these issues, in this paper, we present an unsupervised outlier detection method based on quantiles and skewness coefficients called ISOD (Interpretable Single dimension Outlier Detection). ISOD first fulfils the empirical cumulative distribution function before computing the quantile and skewness coefficients of each dimension. Finally, it outputs the outlier score. This paper’s contributions are as follows: (1) we propose an unsupervised outlier detection algorithm called ISOD, which has high interpretability and scalability; (2) massive experiments on benchmark datasets demonstrated the superior performance of the ISOD algorithm compared with state-of-the-art baselines in terms of ROC and AP.
Threshold/Multi Adaptor Signature and Their Applications in Blockchains

Adaptor signature is a variant of digital signatures and useful for fair excheng in financial applications such as cryptocurrencies, to name a few, off-chain transaction protocols, atomic swaps and other privacy-enhancing mechanisms. However, similar to normal digital signatures, an adaptor signature also suffers from the loss of the secret key and single-point failure, which is insufficient in practice. In this paper, we address this constraint by introducing two new concepts as enhancements: multi-adaptor signatures and threshold adaptor signatures. First, we propose the formal security models for multi-adaptor signature and threshold adaptor signature. Then, we present specific schemes for these two primitives based on the commonly used blockchain signature scheme Schnorr and the post-quantum signature scheme Dilithium, respectively. Furthermore, we provide security proofs for these four schemes. Finally, we demonstrate interesting applications for blockchains, such as oracle-based conditional payment and n to n atomic swap.
Towards Agrirobot Digital Twins: Agri-RO5— A Multi-Agent Architecture for Dynamic Fleet Simulation

In this paper, we propose a multi-agent-based architecture for a Unity3D simulation of dynamic agrirobot-fleet-coordination methods. The architecture is based on a Robot Operating System (ROS) and Agrobots-SIM package that extends the existing package Patrolling SIM made for multi-robot patrolling. The Agrobots-SIM package accommodates dynamic multi-robot task allocation and vehicle routing considering limited robot battery autonomy. Moreover, it accommodates the dynamic assignment of implements to robots for the execution of heterogeneous tasks. The system coordinates task assignment and vehicle routing in real time and responds to unforeseen contingencies during simulation considering dynamic updates of the data related to the environment, tasks, implements, and robots. Except for the ROS and Agrobots-SIM package, other crucial components of the architecture include SPADE3 middleware for developing and executing multi-agent decision making and the FIVE framework that allows us to seamlessly define the environment and incorporate the Agrobots-SIM algorithms to be validated into SPADE agents inhabiting such an environment. We compare the proposed simulation architecture with the conventional approach to 3D multi-robot simulation in Gazebo. The functioning of the simulation architecture is demonstrated in several use-case experiments. Even though resource consumption and community support are still an open challenge in Unity3D, the proposed Agri-RO5 architecture gives better results in terms of simulation realism and scalability.
A Level Set-Based Model for Image Segmentation under Geometric Constraints and Data Approximation

In this paper, we propose a new model for image segmentation under geometric constraints. We define the geometric constraints and we give a minimization problem leading to a variational equation. This new model based on a minimal surface makes it possible to consider many different applications from image segmentation to data approximation.
A Closer Look at Reward Decomposition for High-Level Robotic Explanations

Explaining the behaviour of intelligent agents learned by reinforcement learning (RL) to humans is challenging yet crucial due to their incomprehensible proprioceptive states, variational intermediate goals, and resultant unpredictability. Moreover, one-step explanations for RL agents can be ambiguous as they fail to account for the agent's future behaviour at each transition, adding to the complexity of explaining robot actions. By leveraging abstracted actions that map to task-specific primitives, we avoid explanations on the movement level. To further improve the transparency and explainability of robotic systems, we propose an explainable Q-Map learning framework that combines reward decomposition (RD) with abstracted action spaces, allowing for non-ambiguous and high-level explanations based on object properties in the task. We demonstrate the effectiveness of our framework through quantitative and qualitative analysis of two robotic scenarios, showcasing visual and textual explanations, from output artefacts of RD explanations, that are easy for humans to comprehend. Additionally, we demonstrate the versatility of integrating these artefacts with large language models (LLMs) for reasoning and interactive querying.