Reviewerly/exHarmony huggingface dataset:
Improving Software Defect Prediction in Noisy Imbalanced Datasets

Software defect prediction is a popular method for optimizing software testing and improving software quality and reliability. However, software defect datasets usually have quality problems, such as class imbalance and data noise. Oversampling by generating the minority class samples is one of the most well-known methods to improving the quality of datasets; however, it often introduces overfitting noise to datasets. To better improve the quality of these datasets, this paper proposes a method called US-PONR, which uses undersampling to remove duplicate samples from version iterations and then uses oversampling through propensity score matching to reduce class imbalance and noise samples in datasets. The effectiveness of this method was validated in a software prediction experiment that involved 24 versions of software data in 11 projects from PROMISE in noisy environments that varied from 0% to 30% noise level. The experiments showed a significant improvement in the quality of datasets pre-processed by US-PONR in noisy imbalanced datasets, especially the noisiest ones, compared with 12 other advanced dataset processing methods. The experiments also demonstrated that the US-PONR method can effectively identify the label noise samples and remove them.
Applying Object Detection and Embedding Techniques to One-Shot Class-Incremental Multi-Label Image Classification

In this paper, we introduce an efficient approach to multi-label image classification that is particularly suited for scenarios requiring rapid adaptation to new classes with minimal training data. Unlike conventional methods that rely solely on neural networks trained on known classes, our model integrates object detection and embedding techniques to allow for the fast and accurate classification of novel classes based on as few as one sample image. During training, we use either Convolutional Neural Network (CNN)- or Vision Transformer-based algorithms to convert the provided sample images of new classes into feature vectors. At inference, a multi-object image is analyzed using low-threshold object detection algorithms, such as YOLOS or CutLER, identifying virtually all object-containing regions. These regions are subsequently converted into candidate vectors using embedding techniques. The k-nearest neighbors are identified for each candidate vector, and labels are assigned accordingly. Our empirical evaluation, using custom multi-label datasets featuring random objects and backgrounds, reveals that our approach substantially outperforms traditional methods lacking object detection. Notably, unsupervised object detection exhibited higher speed and accuracy than its supervised counterpart. Furthermore, lightweight CNN-based embeddings were found to be both faster and more accurate than Vision Transformer-based methods. Our approach holds significant promise for applications where classes are either rarely represented or continuously evolving.
A Low-Altitude Obstacle Avoidance Method for UAVs Based on Polyhedral Flight Corridor

UAVs flying in complex low-altitude environments often require real-time sensing to avoid environmental obstacles. In previous approaches, UAVs have usually carried out motion planning based on primitive navigation maps such as point clouds and raster maps to achieve autonomous obstacle avoidance. However, due to the huge amount of data in these raw navigation maps and the highly discrete map information, the efficiency of solving the UAV’s real-time trajectory optimization is low, making it difficult to meet the demand for efficient online motion planning. A flight corridor is a series of unobstructed continuous areas and has convex properties. The flight corridor can be used as a simple parametric representation to characterize the safe flight space in the environment, and used as the cost of the collision term in the trajectory back-end optimization for trajectory solving, which can improve the efficiency of real-time trajectory solving and ensure flight safety. Therefore, this paper focuses on the construction of safe flight corridors for UAVs and autonomous obstacle avoidance algorithms for UAVs based on safe flight corridors, based on a rotary-wing UAV platform, and proposes a polyhedral flight corridor construction algorithm and realizes autonomous obstacle avoidance for UAVs based on the constructed flight corridors.
A Global Structural Hypergraph Convolutional Model for Bundle Recommendation

Bundle recommendations provide personalized suggestions to users by combining related items into bundles, aiming to enhance users’ shopping experiences and boost merchants’ sales revenue. Existing solutions based on graph neural networks (GNN) face several significant challenges: (1) it is demanding to explicitly model multiple complex associations using standard graph neural networks, (2) numerous additional nodes and edges are introduced to approximate higher-order associations, and (3) the user–bundle historical interaction data are highly sparse. In this work, we propose a global structural hypergraph convolutional model for bundle recommendation (SHCBR) to address the above problems. Specifically, we jointly incorporate multiple complex interactions between users, items, and bundles into a relational hypergraph without introducing additional nodes and edges. The hypergraph structure inherently incorporates higher-order associations, thereby alleviating the training burden on neural networks and the dilemma of scarce data effectively. In addition, we design a special matrix propagation rule that captures non-pairwise complex relationships between entities. Using item nodes as links, structural hypergraph convolutional networks learn representations of users and bundles on a relational hypergraph. Experiments conducted on two real-world datasets demonstrate that the SHCBR outperforms the state-of-the-art baselines by 11.07–25.66% on Recall and 16.81–33.53% on NDCG. Experimental results further indicate that the approach based on hypergraphs can offer new insights for addressing bundle recommendation challenges. The codes and datasets have been publicly released on GitHub.
Effect of data preprocessing and machine learning hyperparameters on mass spectrometry imaging models

The self-organizing map (SOM) is a nonlinear machine learning algorithm that is particularly well suited for visualizing and analyzing high-dimensional, hyperspectral time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data. Previously, we compared the capabilities of the SOM with more traditional linear techniques using ToF-SIMS imaging data. Although SOMs perform well with minimal data preprocessing and negligible hyperparameter optimization, it is important to understand how different data preprocessing methods and hyperparameter settings influence the performance of SOMs. While these investigations have been reported outside of the ToF-SIMS field, no such study has been reported for hyperspectral MSI data. To address this, we used two labeled ToF-SIMS imaging datasets, one of which was a polymer microarray dataset, while the other was semisynthetic hyperspectral data. The latter was generated using a novel algorithm that we describe here. A grid-search was used to evaluate which data preprocessing methods and SOM hyperparameters had the largest impact on the performance of the SOM. This was assessed using multiple linear regression, whereby performance metrics were regressed onto each variable defining the preprocessing-hyperparameter space. We found that preprocessing was generally more important than hyperparameter selection. We also found statistically significant interactions between several parameters studied, suggesting a complex interplay between preprocessing and hyperparameter selection. Importantly, we identified interesting trends, both dataset specific and dataset agnostic, which we describe and discuss in detail.
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs

As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e.g., Graph Neural Networks (GNNs). However, graph data are stored separately in multiple distributed parties in some practical scenarios, which may not be directly shared due to conflicts of interest. Hence, federated graph neural networks are proposed to address such data silo issues while preserving each party’s privacy (or client). Nevertheless, different graph data distributions of various parties, which is known as the statistical heterogeneity, may degrade the performance of naive federated learning algorithms like FedAvg. In this article, we propose FedEgo, a federated graph learning framework based on ego-graphs to tackle the challenges above, in which each client will train their local models while also contributing to the training of a global model. FedEgo applies GraphSAGE over ego-graphs to make full use of the structure information and utilizes Mixup for privacy concerns. To deal with the statistical heterogeneity, we integrate personalization into learning and propose an adaptive mixing coefficient strategy that enables clients to achieve their optimal personalization. Extensive experimental results and in-depth analysis demonstrate the effectiveness of FedEgo.
Forming We-intentions under breakdown situations in human-robot interactions

When agents (e.g. a person and a social robot) perform a joint activity to achieve a joint goal, they require sharing a relevant group intention, which has been defined as a We-intention. In forming We-intentions, breakdown situations due to conflicts between internal and "external" intentions are unavoidable, particularly in healthcare scenarios. To study such We-intention formation and "reparation" of conflicts, this paper has a two-fold objective: introduce a general computational mechanism allowing We-intention formation and reparation in interactions between a social robot and a person; and exemplify how the formal framework can be applied to facilitate interaction between a person and a social robot for healthcare scenarios.The formal computational framework for managing We-intentions was defined in terms of Answer set programming and a Belief-Desire-Intention control loop. We exemplify the formal framework based on earlier theory-based user studies consisting of human-robot dialogue scenarios conducted in a Wizard of Oz setup, video-recorded and evaluated with 20 participants. Data was collected through semi-structured interviews, which were analyzed qualitatively using thematic analysis. N=20 participants (women n=12, men=8, age range 23-72) were part of the study. Two age groups were established for the analysis: younger participants (ages 23-40) and older participants (ages 41-72).We proved four theoretical propositions, which are well-desired characteristics of any rational social robot. In our study, most participants suggested that people were the cause of breakdown situations. Over half of the young participants perceived the social robot's avoidant behavior in the scenarios.This work covered in depth the challenge of aligning the intentions of two agents (for example, in a person-robot interaction) when they try to achieve a joint goal. Our framework provides a novel formalization of the We-intentions theory from social science. The framework is supported by formal properties proving that our computational mechanism generates consistent potential plans. At the same time, the agent can handle incomplete and inconsistent intentions shared by another agent (for example, a person). Finally, our qualitative results suggested that this approach could provide an acceptable level of action/intention agreement generation and reparation from a person-centric perspective.
FeConDefense: Reversing adversarial attacks via feature consistency loss

Existing adversarial defense methods often employ adversarial training or data pre-processing techniques to defend against adversarial attacks. However, adversarial training is burdensome as it requires to find a single representation that works for all attack possibilities, and may decrease the network model's classification accuracy after excessive training. While data pre-processing methods focus on eliminating adversarial perturbations by modifying the input samples, they do not consider the internal relationships between reverse perturbations and adversarial examples, resulting in weak specificity of the generated modifications. In this paper, we propose a novel adversarial defense method, named FeConDefense, which aims to reverse adversarial attacks via analyzing the intrinsic features of images. Specifically, we first extract two different features of adversarial examples by respectively using two different network models. Then, we design a novel feature consistency loss to measure the distance between these two features. Finally, we integrate the feature consistency into the contrastive learning to generate reverse perturbation for each adversarial example. Comprehensive experiments on different adversarial attack methods demonstrate that our FeConDefense achieves state-of-the-art results in reversing adversarial perturbations and improving robustness of image classifiers.
Victimization in online gaming-related trade scams: A study among young Danes

This study examines the prevalence and predictors of trade scams related to online video games, which is an emergent field of cyber-victimization. Using self-report data from a representative Danish survey among Danish children and adolescents (n = 1,026) conducted in 2022, we estimate that 36% of participants who are engaged in trading virtual items or game accounts have experienced a trade scam. The study applied lifestyle routine activities theory to explain risk of trade scam victimization, and logistic regressions were used to determine routine activities associated with such risk. Analyses show that game characteristics, use of trading sites, and engagement in risky online activities are associated with elevated risks of experiencing gaming related trade scams.
Handling Concept Drift in Global Time Series Forecasting

Machine learning (ML) based time series forecasting models often require and assume certain degrees of stationarity in the data when producing forecasts. However, in many real-world situations, the data distributions are not stationary and they can change over time while reducing the accuracy of the forecasting models, which in the ML literature is known as concept drift. Handling concept drift in forecasting is essential for many ML methods in use nowadays, however, the prior work only proposes methods to handle concept drift in the classification domain. To fill this gap, we explore concept drift handlingConcept drift handling methods in particular for Global Forecasting Models (GFM) which recently have gained popularity in the forecasting domain. We propose two new concept drift handlingConcept drift handling methods, namely Error Contribution Weighting (ECW)Error Contribution Weighting (ECW) and Gradient Descent Weighting (GDW)Gradient Descent Weighting (GDW), based on a continuous adaptive weighting concept. These methods use two forecasting models which are separately trained with the most recent series and all series, and finally, the weighted average of the forecasts provided by the two models is considered as the final forecasts. Using LightGBM as the underlying base learner, in our evaluationEvaluation on three simulated datasets, the proposed models achieve significantly higher accuracy than a set of statistical benchmarksBenchmarks and LightGBM baselines across four evaluation metrics.
A feature enhanced RetinaNet-based for instance-level ship recognition

Instance-level ship recognition (ISR) has important applications in civil and military fields such as target acquisition, maritime surveillance, and ship situational awareness. Due to the special peculiarities of the marine environment and the rigidity of ships, the changes of viewpoint and scale lead to significant differences in the appearance of ships, which poses a challenge to ISR. Meanwhile, the lack of public datasets for ISR further increases the difficulty of recognition. Considerable work has been conducted on coarse- or fine-grained classification, and little research has been done on ISR. Therefore, a feature enhanced RetinaNet-based for ISR (FERISR) is proposed. First, an attention-aware pyramid network (APN) is designed to enhance the salience features of ship instances and integrate attention-guided features to maximize the use of multilayer information while reducing information redundancy. On this basis, a detail information enhancement module (DFEM) is proposed to refine the fused multi-scale feature maps to improve model performance in scale variations by further enhancing the ship features. At the same time, foggy environment at sea is simulated and the images of foggy and low-light scenario are tested to cope with the impacts caused by low visibility scenes. The ReidDataset is further divided into instance level to make a dataset for ISR(DISR), which is used to discuss the performance of FFRISR on ISR. The generalizability of FFRISR in different scenarios is also discussed. The experimental results demonstrate that FERISR can effectively improve recognition accuracy in viewpoint change, scale change and low visibility scenarios, as well as improve the detection speed. Finally, the effectiveness of APN and DFEM is further verified by ablation experiments.
Density-Based Clustering to Deal with Highly Imbalanced Data in Multi-Class Problems

In machine learning and data mining applications, an imbalanced distribution of classes in the training dataset can drastically affect the performance of learning models. The class imbalance problem is frequently observed during classification tasks in real-world scenarios when the available instances of one class are much fewer than the amount of data available in other classes. Machine learning algorithms that do not consider the class imbalance could introduce a strong bias towards the majority class, while the minority class is usually despised. Thus, sampling techniques have been extensively used in various studies to overcome class imbalances, mainly based on random undersampling and oversampling methods. However, there is still no final solution, especially in the domain of multi-class problems. A strategy that combines density-based clustering algorithms with random undersampling and oversampling techniques is studied in this work. To analyze the performance of the studied method, an experimental validation was achieved on a collection of hyperspectral remote sensing images, and a deep learning neural network was utilized as the classifier. This data bank contains six datasets with different imbalance ratios, from slight to severe. The experimental results outperform the classification measured by the geometric mean of the precision compared with other state-of-the-art methods, mainly for highly imbalanced datasets.
Detecting Tumor Infiltration in Diffuse Gliomas with Deep Learning

Glioblastoma tumor recurrences often occur in brain tissue areas harboring infiltrating tumor cells, resembling healthy tissue in brain imaging. Demarcating infiltrative regions for aggressive resections is critical for improving prognostic outcomes but is challenging in neurosurgery. Herein, a multilayer sigmoid‐activated convolutional neural network (MLS‐CNN) is developed for rapidly distinguishing glioma tumor infiltration in brain tissue histology. Unlike conventional multiclass classifiers, the MLS‐CNN employs sigmoidal activation to accommodate coexisting classes within patch images. 59 811 image patches (25 807 infiltrating edge, 15 178 normal brain, 18 826 cellular tumor) from 73 brain tissue samples are extracted to train the classifier. The model achieves an accuracy of 91.70% (sensitivity: 91.62%; specificity: 91.78%) and area under the curve (AUC) of 0.964 in distinguishing infiltrating edges, outperforming the current state‐of‐the‐art Vision Transformer (ViT) (accuracy: 89.45; AUC: 0.947). The MLS‐CNN is computationally efficient, generating predictions within 11.5 s in comparison to 81.4 s for ViT. The predictions strongly correlate with In Situ Hybridization expression intensities, validating the utility of the MLS‐CNN model in spatial genomics investigations in gliomas. The robust model can therefore serve as an automatic and accurate classifier to help pathologists identify infiltrative glioma for better diagnosis and patient outcomes in brain oncology.
Classification of breast lesions in ultrasound images using deep convolutional neural networks: transfer learning versus automatic architecture design

Abstract Deep convolutional neural networks (DCNNs) have demonstrated promising performance in classifying breast lesions in 2D ultrasound (US) images. Exiting approaches typically use pre-trained models based on architectures designed for natural images with transfer learning. Fewer attempts have been made to design customized architectures specifically for this purpose. This paper presents a comprehensive evaluation on transfer learning based solutions and automatically designed networks, analyzing the accuracy and robustness of different recognition models in three folds. First, we develop six different DCNN models (BNet, GNet, SqNet, DsNet, RsNet, IncReNet) based on transfer learning. Second, we adapt the Bayesian optimization method to optimize a CNN network (BONet) for classifying breast lesions. A retrospective dataset of 3034 US images collected from various hospitals is then used for evaluation. Extensive tests show that the BONet outperforms other models, exhibiting higher accuracy (83.33%), lower generalization gap (1.85%), shorter training time (66 min), and less model complexity (approximately 0.5 million weight parameters). We also compare the diagnostic performance of all models against that by three experienced radiologists. Finally, we explore the use of saliency maps to explain the classification decisions made by different models. Our investigation shows that saliency maps can assist in comprehending the classification decisions. Graphical Abstract
Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting

Solar Photovoltaic (PV) systems represent key and transformative technology at the forefront of the global shift towards sustainable energy solutions [...]
UNIMEMnet: Learning long-term motion and appearance dynamics for video prediction with a unified memory network

As a pixel-wise dense forecast task, video prediction is challenging due to its high computation complexity, dramatic future uncertainty, and extremely complicated spatial-temporal patterns. Many deep learning methods are proposed for the task, which bring up significant improvements. However, they focus on modeling short-term spatial-temporal dynamics and fail to sufficiently exploit long-term ones. As a result, the methods tend to deliver unsatisfactory performance for a long-term forecast requirement. In this article, we propose a novel unified memory network (UNIMEMnet) for long-term video prediction, which can effectively exploit long-term motion-appearance dynamics and unify the short-term spatial-temporal dynamics and long-term ones in an architecture. In the UNIMEMnet, a dual branch multi-scale memory module is carefully designed to extract and preserve long-term spatial-temporal patterns. In addition, a short-term spatial-temporal dynamics module and an alignment and fusion module are devised to capture and coordinate short-term motion-appearance dynamics with long-term ones from our designed memory module. Extensive experiments on five video prediction datasets from both synthetic and real-world scenarios are conducted, which validate the effectiveness and superiority of our proposed method UNIMEMnet over state-of-the-art methods.