Reviewerly/exHarmony huggingface dataset:
Lithological Classification Using Densely Connected Convolution Network on Landsat-9 and Aster Datasets in a Semi-Arid Environment

Remote sensing-based geological interpretation provides insight from numerous sensors. That notwithstanding, high-dimensional feature learning requires full-scale diagnosis of intrinsic geological features in remotely sensed datasets. This study proposes a multi-sensor fusion approach. Integrating Landsat-9 and ASTER spectral features with DenseNet for lithological classification, displaces RF and KNN by 2.12 % and surpasses ResNet50 and Xception with a classification accuracy of 83.89%. Furthermore, this study underscores the effectiveness of merging new and old generation sensors for geological interpretation.
FedCCE: A class-level contribution explainable federated learning based on comparable prototypes collaboration for multi-site medical image classification

Data-driven models often require a large amount of data for sufficient training. Federated learning (FL) is a machine learning framework that can effectively help multiple sites utilize data for machine learning modeling while meeting the requirements of privacy protection. However, datasets from different sites are usually Non-Independent and Identically Distributed (Non-IID), which affects the model’s performance. Additionally, different stakeholders involved in healthcare desire a fair compensation method to directly assess the contribution of each hospital’s local data. Therefore, we propose a class-level contribution explainable federated learning based on comparable prototypes collaboration for multi-site medical image classification. Practically, a two-step training strategy is proposed for further improving local model’s performance. We apply the proposed method to three datasets and the experiments demonstrate its effectiveness by comparing with standard FL strategies and competitive personalized FL approaches.
UniMOS: A Universal Framework For Multi-Organ Segmentation Over Label-Constrained Datasets

Machine learning models for medical images can help physicians diagnose and manage diseases. However, due to the fact that medical image annotation requires a great deal of manpower and expertise, as well as the fact that clinical departments perform image annotation based on task orientation, there is the problem of having fewer medical image annotation data with more unlabeled data and having many datasets that annotate only a single organ. In this paper, we present UniMOS, the first universal framework for achieving the utilization of fully and partially labeled images as well as unlabeled images. Specifically, we construct a Multi-Organ Segmentation (MOS) module over fully/partially labeled data as the basenet and designed a new target adaptive loss. Furthermore, we incorporate a semi-supervised training module that combines consistent regularization and pseudo-labeling techniques on unlabeled data, which significantly improves the segmentation of unlabeled data. Experiments show that the framework exhibits excellent performance in several medical image segmentation tasks compared to other advanced methods, and also significantly improves data utilization and reduces annotation cost. Code and models are available at: https://github.com/lw8807001/UniMOS.
Health and Senior Care Video Moment Localization With Procedure Knowledge Distillation

With the aging population, health and senior care are becoming to be crucial issues for the whole world. Because the number of healthcare professionals is far from fulfilling increasing patients’ and seniors’ needs, seeking services from non-professional healthcare staff, such as home caregivers, is indispensable. Methods that support locating video moments with natural language queries can improve the normalization of operations for the non-professional healthcare staff and reduce their time expenditure on specific action moment retrieval. Addressing this problem, we propose a cross-modal neural network model for effective health and senior care video localization. Our model learns procedures in the video reference and uses procedure knowledge to improve the model’s localization performance. We conduct experiments on a dataset for health and senior care video localization and an open-accessible dataset about medical instruction. Experiment results show procedure knowledge can remarkably improve the model’s capacity for video moment localization. We hope our dataset and method could promote the development of cross-modal research and application for health and senior care.
Image Captioning with Prompts Generation

Vision-language multimodal pretraining models are extremely valuable in multimodal tasks in that they can align the two modalities through cross-modal pretraining tasks, alleviating the semantic divide between vision and language to some extent. For example, the large-scale pretraining model CLIP performs outstandingly on the zero-sample image classification task, demonstrating strong visual-linguistic representations learned from large-scale web-collected graphic data. However, few approaches have investigated the generalization of CLIP's cross-modal knowledge to other downstream tasks. Based on this we propose a module for generating prompts that leverages cross-domain prior knowledge in pre-trained large models and use the visually-perceived textual knowledge extracted from the large model as a guidance for captioning. We conducted extensive experiments on the MS-COCO dataset and our model demonstrated great effectiveness.
Contribution of CEEMDAN Decomposition in Enhancing the Forecast of Short-Term Global Solar Irradiation

Given the rapid growth and development of solar energy in recent years, accurate forecasting of solar output has become one of the most critical and formidable challenges in the modern power system. This paper introduces an approach for short-term global solar irradiance forecasting, combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) with Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models. The CEEMDAN decomposition is employed to decompose original data series, extracting crucial features for forecasting. The model's performance is evaluated on two distinct sites in Algeria, characterized by Mediterranean and desert climates. Statistical tests reveal a significant enhancement in nRMSE values, with approximately 14.09% and 7.86% improvements for the ANN_CEEMDAN model and about 17.52% and 8.97% enhancements for the MLR_CEEMDAN model, specifically for the first hour of forecast.
Outdoor Study of the Efficiency of Photovoltaic Power Plants with Sun Tracking in a Hot Desert Climate

Using solar tracking PV systems as an alternative to traditional fixed PV installations on inclined surfaces has become a viable option for increasing the electric output of PV silicon technology. This paper examines the performance of power plants with and without sun tracking in real outdoor conditions. The goal is to investigate the effects of climate change on their behavior and efficiency. The findings demonstrate that the dual-axis tracking photovoltaic system generates more energy than the static photovoltaic system (16.72% and 13.66 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) in a clear sky with few clouds and a cloudy day, respectively. The energy yield produced by a two-axis sun tracking system, a one-axis tracking PV system, and a fixed tilt angle on days with little to no cloud cover and a clear sky is 25318.55 kWh-23395.09 kWh, 24766.94 kWh-22707.58 kWh, and 22275.15 kWh-20042,84 Wh, respectively.
Incorporating Granularity Bias as the Margin Into Contrastive Loss for Video Captioning

Video captioning models easily suffer from long-tail distribution of phrases, which makes captioning models prone to generate vague sentences instead of accurate ones. However, existing debiasing strategies tend to export external knowledge to build dependency trees of words or refine frequency distribution by complex losses and extra input features, which lack interpretability and are hard to train. To mitigate the impact of granularity bias on the model, we introduced a statistical-based bias extractor. This extractor quantifies the information content within sentences and videos, providing an estimate of the likelihood that a video-sentence pair is affected by granularity bias. Furthermore, with the growing trend of integrating contrastive learning methods into video captioning tasks, we use a bidirectional triplet loss to get more negative samples in a batch. Subsequently, we incorporate the margin score into the contrastive learning loss, establishing distinct training objectives for head and tail sentences. This approach facilitates the model's training effectiveness on tail samples. Our simple yet effective loss, incorporating Granularity bias, is referred to as the Margin-Contrastive Loss (GMC Loss). The proposed model demonstrates state-of-the-art performance on MSRVTT with a CIDEr of 57.17, and MSVD, where CIDEr reaches up to 138.68.
Unveiling the Black Box: Enhancing Trust and Accuracy in Social Media Rumour Detection Through Explainable Machine Learning

This study evaluates machine learning classifiers; Logistic Regression, Random Forest, SVM, Passive Aggressive Classifiers, Decision Trees, KNN, Naïve Bayes, and Gradient Boosting Machines for detecting rumors on social media, utilizing datasets from FakeNewsNet and Politifact. While these traditional models are effective, their lack of transparency hinders user trust. To address this, we integrate Explainable AI (XAI) techniques; LIME and SHAP, to enhance interpretability and reliability. Our findings, based on rigorous evaluation metrics, reveal the tradeoffs between accuracy and transparency. The study demonstrates how XAI techniques uncover feature impacts on model decisions, contributing significantly to user trust in AI systems. This balanced approach, combining robust machine learning with XAI, advances our understanding of rumour detection in the dynamic social media landscape. It suggests a trajectory towards more transparent, adaptive, and user-trusted AI solutions, highlighting the need for continuous model updating and ethical considerations in future research.
A Knowledge Distillation-Based Approach for Dynamic Graph Representation Learning

Dynamic graphs are prevalent data structures in the real world, providing powerful modeling capabilities. However, popular methods for dynamic graph representation learning often rely on graph data. Models typically require input of the entire graph's adjacency matrix and aggregate information based on node neighborhoods. This leads to challenges in model deployment and slow inference speeds. This paper combines knowledge distillation with dynamic graph representation learning, transferring graph-related knowledge learned by the teacher model to a student model. The link prediction experimental results demonstrate that introducing knowledge distillation yields a well-performing and deployable student model comparable to the teacher model. Additionally, the student model overcomes its reliance on graph data, eliminating the process of aggregating node neighborhood information through the input adjacency matrix, thereby improving inference speed.
RF Fingerprint Recognition Method Based on Lightweight Deep Learning for Doppler Resilience

Radio Frequency (RF) Fingerprint Recognition has emerged as an area of significant promise within the research community. However, its effective deployment in scenarios characterized by vehicular movement or pedestrian traffic remains a challenge, primarily due to the Doppler effect inducing frequency shifts in RF signals. To counter this, a novel Channel-Independent Spectrum Construction methodology has been proposed, aimed at mitigating the influence of Doppler frequency shifts. This study integrates this innovative approach with advanced data augmentation techniques, thereby pioneering an enhanced methodology for bolstering the resistance of RF signal fingerprint features against Doppler interference. Employing sophisticated deep learning algorithms, this research effectively extracts complex, nonlinear characteristics, thereby capturing the nuances of Doppler variations present within signals. Furthermore, this paper conducts a comprehensive comparison between traditional Convolutional Neural Networks, Deep Residual Networks, and our optimized lightweight PruMobileNet model, an adaptation of MobileNetV1. The empirical results from our study underscore the robustness of our approach in handling Doppler channels, while simultaneously achieving a paradigm shift towards low-cost, environmentally sustainable communication through model lightweighting. Notably, this methodology substantially boosts inference speed and computational efficiency, establishing a new benchmark for reliability in RF fingerprint recognition, particularly in environments with low-speed Doppler effects.
Machine Learning Techniques for Forecasting Daily Global Solar Radiation Ahead

In this paper, we propose a comparative study to perform one-day-ahead forecasting of global solar radiation. Several supervised learning techniques are employed as regression methods, using a dataset of meteorological parameters and predictions of meteorological variables recorded in Malaga city, Spain, under different weather conditions. Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP) have produced very similar results, with an NRMSE of 14.41%, NMAE of 9.94%, the highest correlation coefficient at 0.87, and a remarkable improvement (S) of 37.59%. Furthermore, an optimization procedure regarding the input characteristics considered in the learning methods has led us to reduce the number of attributes from fourteen to three.
A Diffusion Probabilistic Model Based on Multi-Residualattention For Medical Image Segmentation

Diffusion models are currently among the most attention- grabbing generative models, achieving revolutionary applications across multiple domains and demonstrating exceptional performance. In the application of conditional diffusion models to alter specific regions of the output image to match desired features, most methods employ V AE encoders for feature extraction from conditional images. However, V AE encoders face challenges of difficult training and large parameter sizes; simultaneously, the denoising UNet in diffusion models often leads to blurred edges in sampled images. To effectively address these issues, We have introduced ResAtMedDiff, an innovative diffusion model specifically designed for the field of medical image processing, offering a novel approach to enhance the accuracy and efficiency of image analysis. The ResAtMedDiff model consists of two core training networks: the MEUNet conditional encoder and the RsAtUNet encoder-decoder. MEUNet is responsible for efficiently encoding the conditional images and then fusing these encodings with noise image features across multiple scales. This fusion strategy significantly enhances the ability of conditional features to guide the generation of noise images. The MEUN et encoder blends the features of the original conditional image with the Gaussian noise image features of the corresponding layers of the RsAtUN et encoder, followed by the RsAtUNet decoder decoding these fused features. This process achieves the generation of image segmentation masks guided by the features of the original image as medical prior conditions, As a result, this significantly enhances the precision of lesion area segmentation in medical imaging. The experimental findings reveal that the ResAtMedDiff model surpasses existing state-of-the-art segmentation techniques in its performance, particularly in the segmentation of thyroid nodules in ultrasound images and brain tumors in MRI scans, demonstrating its superior efficacy and precision.
SUFFI-GPSC: Sufficient Geometry Problem Solution Checking with Symbolic Computation and Logical Reasoning

Logical reasoning and computational solving skill play a vital role in geometry problem solving (GPS). The development of large-scale language models has facilitated the progress of artificial intelligence (AI) systems capable of solving math word problems and proving theorems. These models have achieved remarkable results across various geometry problem datasets, attracting significant attention in natural language processing community. However, it remains a challenge to effectively evaluate the logical correctness in the process of solution steps answered by the model due to the diversity of solution approaches. Therefore, we propose a powerful approach that can check the logical sufficiency of the solving process of given geometry problems step by step with symbolic computation and logical reasoning, called Sufficient Geometry problem solution checking (Suffi-GPSC). Suffi-GPSC divides the tasks into two categories, one focuses on equation expressions and checks whether the target equations are true with symbolic computation under the preconditions that contain a large number of equations. The other focuses on geometric relationships and checks whether the goal relation can be deduced within limited steps with logical reasoning by considering known facts as conditions and axiomatic knowledge as conditional rules. Inspired by the mainstream, a theorem predictor based on transfer text-to-text transformer(T5) has been designed to generate the theorem sequences for a more efficient and rational reasoning process. Extensive experiments on the Geometry3K dataset demonstrate that Suffi-GPSC achieves remarkable improvements.
A Ship Damage Identification Method Based on Multi-Feature Fusion

In the process of maritime transportation, accidents such as collision, hitting the reef and accidental attack will cause ship damage, and ship damage identification is an important work of ship target damage assessment. In this paper, a ship damage identification method based on multi-feature fusion is proposed. Firstly, the method combining the spectral residual method and DFT method is used to enhance the ship damage enhancement and preliminary location of the damage area and extract the significant features of the corresponding area. Secondly, the damage model with multiple features is fused to realize the accurate detection of damage. The analysis results of experimental data show that this algorithm can identify different morphological damage of ships in 0.2m~2m resolution multi-resolution image data, and the overall detection rate is 83.20%. It has a good ability of quantitative assistant evaluation of target situation.
Research on Image Recognition Algorithm Based on Spiking Neural Networks

With the continuous development of artificial intelligence and computer vision technology, Artificial Neural Networks (ANNs) have achieved remarkable results in image recognition tasks. However, with the development of deep learning technology, the number of layers of neural networks is increasing and the training power consumption of the models is huge. Spiking Neural Network (SNN) is a kind of neural network that simulates the working mechanism of biological nervous system, which is characterized by the fact that the information transfer between neurons occurs in the form of pulses, and it has higher computational efficiency, stronger spatio-temporal information processing capability, and better bio-interpretability compared with traditional artificial neural networks. This paper analyzes the technical advantages of spiking neural network for image recognition, simulation experiments on spiking neural network in terms of data preprocessing, spiking coding, gradient substitution with reference to the classical image recognition model, and puts forward the proposed measures for the image recognition algorithm based on spiking neural network based on the experimental results.
A Blockchain Phishing Scam Detection Method Based on Ethereum Transaction Subgraph

In recent years, the field of blockchain technology has witnessed significant growth, particularly in its extensive application within the financial sector. However, this progress has also brought a concerning issue whereby an increasing number of users are becoming vulnerable to phishing scams. Many extant studies are focused on this issue; nonetheless, a paucity of emphasis has been placed on the comprehensive characterization of Ethernet transaction data from multiple perspectives which enhances the precision and generalization capabilities of the model. Consequently, within the context of this paper, we introduce an innovative detection model. In an effort to mitigate overhead while ensuring the acquisition of substantial information, we initially preprocess Ethernet transaction data and establish transaction subgraphs. Then, we leverage a graph autoencoder to amalgamate local structural information, global structural information, and node features within the graph, culminating in the detection of phishing scams. Our model significantly enhances detection accuracy by exploiting multi-view features. We implemented this method on the dataset from TSGN, and the results show that the accuracy of our method is 95.39%, which is at least 4.07% higher than other related methods.
Bidirectional Training for Generating Math Word Problems Using Pre-Trained Model and Prompt

Math word problems (MWPs) are a vital type of exercise that greatly enhances students' mathematical proficiency and problem-solving abilities. This paper aims to explore the challenging task of automatically generating MWPs, which necessitates the integration of mathematical knowledge, natural language processing, and logical reasoning. We propose an innovative approach that utilizes bidirectional training and prompt construction to fine-tune pre-trained language models. We evaluate our framework on multiple datasets and metrics, demonstrating its effectiveness and superiority over baselines. Our framework introduces a novel tool and perspective for mathematical education and research, creating new possibilities for generating various types of MWPs.
Efficient and Verifiable Blockchain Data Query Scheme Based on External Database

Although the development of blockchain is getting faster and faster, and the application scenarios are becoming more and more extensive, it still faces problems such as low efficiency of data query and single query function. The database has a powerful and mature data management system, which has a natural advantage in solving these problems. Therefore, based on the method of external database, this paper extracts blockchain data and stores it off-chain, and introduces third-party service providers to provide users with diversified query services. To ensure that users can obtain correct and complete query results from service providers, this paper uses an authentication data structure to design a result verification scheme, which supports users to further verify the results after obtaining the query results. The experimental results show that this scheme can provide users with efficient query service and users can verify the results.