Reviewerly/exHarmony huggingface dataset:
Performance of precision poverty alleviation through circular economy based on RS-SVM model in the context of rural revitalization

With the implementation of the rural revitalization strategy, the precision poverty alleviation through circular economy has become an important means of promoting rural development. In order to study the performance of circular economy precision poverty alleviation in rural revitalization, a performance research method based on Support Vector Machines is proposed. During the process, the rough set was introduced for the hidden feature analysis, and a precise poverty alleviation performance indicator system was established. Afterwards, the performance evaluation model was constructed and the genetic algorithm was introduced for parameter optimization. Finally, the performance of the research method was tested. The experimental results show that the research method only requires 15 evolutions to achieve a fitness value of approximately 0.021. In the test of the calculation results, the research method only had 2 points on the test set that differed by more than 0.2 from the standard value. In the F1 test, the research method increased to approximately 97.6 after 200 iterations on the test set. When conducting empirical analysis, the evaluation results obtained by the research method are basically consistent with the expert evaluation results. These results indicate that the research method has good performance and can accurately evaluate the performance of precision poverty alleviation, providing reference for poverty alleviation work.
Supervised Machine-Generated Text Detectors: Family and Scale Matters

This work studies the generalization capabilities of supervised Machine-Generated Text (MGT) detectors across model families and parameter scales of text generation models. In addition, we explore the feasibility of identifying the family and scale of the generator behind an MGT, instead of attributing the text to a particular language model. We leverage the AuTexTification corpus, comprised of multi-domain multilingual human-authored and machine-generated text, and fine-tune various monolingual and multilingual supervised detectors for Spanish and English. The results suggest that supervised MGT detectors generalize well across scales but are limited in cross-family generalization. Contrariwise, we observe that MGT family attribution is practical and effective, while scale attribution has some limitations. Code and results are available here .
Role of Geographers in the Analysis and Modeling of the Spread of Communicable Diseases (Malaria &amp; COVID-19) in Ambon City: A Spatial Approach for Epidemiological Analysis

This study explores the role of geographers in analyzing the distribution of infectious diseases (malaria and COVID-19) in Ambon City with a spatial approach in epidemiological analysis. The method used in this review is a comparative descriptive study with a qualitative approach using secondary data from relevant sources. This research will review the role of geographers in analyzing and modeling the distribution of infectious diseases (malaria &amp; COVID-19) in Ambon City from previous research. This research integrates geographic and health data to understand the pattern of spread and environmental factors that influence disease. Through case mapping, environmental factor analysis, and modeling of future trends, this research illustrates the important contribution of geographers in infectious disease control and prevention efforts at the local level. Interdisciplinary collaboration plays a key role in this approach, which ultimately supports more informed and effective decision-making in addressing these health challenges.
An Empirical Comparison of Malicious Insiders and Benign Insiders

ABSTRACTMalicious insiders continue to pose a significant threat to organizations. With their knowledge, privilege, and access to organizational resources, malicious insiders can attack the organization easier than outsiders, bypassing security measures. Current research about malicious insiders’ traits is often based on a limited number of cases and lacking empirical validation. With few exceptions, most research focuses on the effects of individual traits without investigation of their interactions. To identify the effects of these traits and their interactions, this study employs text mining to analyze 133 real-world malicious insider cases by comparing how the media portray malicious insiders to how the media portray benign insiders. This study sheds light on the predictive power of common traits of malicious insiders. Also, the interaction effects of some traits indicate that although they are not significant at the unary level, their co-occurrence differentiates malicious insiders with benign insiders as portrayed in the media.KEYWORDS: Malicious insidersinsider threatpersonality disorderstext mining Disclosure statementNo potential conflict of interest was reported by the author(s).ConsentThis study is waived by the IRB committee.
Sketchffusion: Sketch-Guided Image Editing with Diffusion Model

Sketch-guided image editing aims to achieve local finetuning of the image based on the sketch information provided by the user while maintaining the original status of the unedited areas. Due to the high cost of acquiring human sketches, previous works mostly relied on edge maps as a substitute for sketches. However, sketches possess more rich structural information. In this paper, we propose a sketch generation scheme that can preserve the main contours of an image and closely adhere to the actual sketch style drawn by the user. Simultaneously, current image editing methods often face challenges such as image distortion, training cost, and loss of fine details in the sketch. To address these limitations, We propose a conditional diffusion model (SketchFFusion) based on the sketch structure vector. We evaluate the generative performance of our model and demonstrate that it outperforms existing methods.
Improving Generalization in Facial Manipulation Detection Using Image Noise Residuals and Temporal Features

The high visual quality of modern deepfakes raises significant concerns about the trustworthiness of digital media and makes facial tampering detection more challenging. Although current deep learning-based deepfake detectors achieve excellent results when tested on deepfake images or image sequences generated using known methods, generalization—where a trained model is tasked with detecting deepfakes created with previously unseen manipulation techniques—is still a major challenge. In this paper, we investigate the impact of training spatial and spatio-temporal deep learning network architectures in the image noise residual domain using spatial rich model (SRM) filters on generalization performance. To this end, we conduct a series of tests on the manipulation methods of the FaceForensics++, DeeperForensics-1.0 and Celeb-DF datasets, demonstrating the value of image noise residuals and temporal feature exploitation in tackling the generalization task.
Color Learning for Image Compression

Deep learning based image compression has gained a lot of momentum in recent times. To enable a method that is suitable for image compression and subsequently extended to video compression, we propose a novel deep learning model architecture, where the task of image compression is divided into two sub-tasks, learning structural information from luminance channel and color from chrominance channels. The model has two separate branches to process the luminance and chrominance components. The color difference metric CIEDE2000 is employed in the loss function to optimize the model for color fidelity. We demonstrate the benefits of our approach and compare the performance to other codecs. Additionally, the visualization and analysis of latent channel impulse response is performed.
Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS Instance Segmentation

This paper presents a deep learning framework for medical video segmentation. Convolution neural network (CNN) and transformer-based methods have achieved great milestones in medical image segmentation tasks due to their incredible semantic feature encoding and global information comprehension abilities. However, most existing approaches ignore a salient aspect of medical video data - the temporal dimension. Our proposed framework explicitly extracts features from neighbouring frames across the temporal dimension and incorporates them with a temporal feature blender, which then tokenises the high-level spatio-temporal feature to form a strong global feature encoded via a Swin Transformer. The final segmentation results are produced via a UNet-like encoder-decoder architecture. Our model outperforms other approaches by a significant margin and improves the segmentation benchmarks on the VFSS2022 dataset, achieving a dice coefficient of 0.8986 and 0.8186 for the two datasets tested. Our studies also show the efficacy of the temporal feature blending scheme and cross-dataset transferability of learned capabilities. Code and models are available at https://github.com/SimonZeng7108/Video-SwinUNet.
Microseismic Events Recognition via Joint Deep Clustering With Residual Shrinkage Dense Network

Recognition of microseismic events is the primary task of microseismic monitoring. Aiming at the low signal-to-noise ratio (SNR) of weak microseismic events and the high cost of labeling them, an unsupervised learning method for recognizing microseismic events is proposed. The method first recognizes microseismic events from monitoring data segments by simultaneous deep clustering and then performs a second clustering to further pick the first arrival times of the detected microseismic events by multistage deep clustering. The networks in this two-step clustering framework are built on a newly designed residual shrinkage dense block (RSDB). To better suppress the noise in microseismic data, RSDB adds a densely connected hybrid dilated convolution and an improved threshold module to the deep residual shrinkage network. The autoencoder built by the RSDB and U-Net architecture is combined with simultaneous deep clustering and multistage deep clustering to recognize microseismic events and their first arrival times, respectively. Finally, tests on the synthetic data and field microseismic data demonstrate the feasibility and superiority of the proposed method.
A Semisupervised Just-In-Time Learning Framework Based on Local Label Propagation and Its Application to Industrial Virtual Sensing

Just-In-Time Learning (JITL) has proven to be an effective tool for developing virtual sensors for complex industrial processes. However, most of the existing JITL-based virtual sensing methods are supervised, whereas labeled samples within a local area are usually particularly scarce. As a result, the performance of these JITL-based virtual sensors are unable to reach the fullest potentials. To this end, a semisupervised JITL (SsJITL) framework is proposed based on local label propagation (LLP), which is referred to as “LLP-SsJITL. ” In the LLP-SsJITL, a robust semisupervised relevant neighborhood dataset of the query sample is first formed by accounting for both spatially and temporally relevant information of the query sample. Then, with the aid of the technique of label propagation on the constructed neighborhood of the query sample, a semisupervised objective function is developed, which transforms the estimation task into an analytical model-free optimization problem. By doing so, abundant unlabeled data are fully exploited such that the shortcomings of the supervised JITL resulting from the scarcity of labeled samples are remedied. Two real-life industrial processes are used to evaluate the performance of the LLP-SsJITL framework, showing significantly improved performance by using the LLP-SsJITL framework.
NAS-Kernel: Learning Suitable Gaussian Kernel for Remote-Sensing Object Counting

The purpose of object counting is to estimate the number of specific kinds of objects in a given image. In remote sensing imagery, challenges arise in object counting due to issues like scale variations and complex backgrounds. Existing density map-based object counting methods have achieved satisfactory performance in some general scenarios ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., crowd counting and vehicle counting) and have become the mainstream methods. These density map-based counting methods use a fixed Gaussian kernel in the density map generation stage, thus they are not well adapted to the challenges such as scale variations present in remote sensing scenes. In this letter, we propose to use the strategy of neural architecture search (NAS-Kernel) to select appropriate Gaussian kernels corresponding to objects of different scales in the Gaussian density map generation stage. NAS-Kernel is a plug-and-play algorithm that can be used in other density map-based counting methods. In addition, a contextual path aggregation feature fusion strategy is proposed to fuse multi-scale feature information. The ablation experiments verify that the proposed method can significantly improve the performance of baseline. Experimental results on the four sub-datasets of RSOC show that the proposed method achieves state-of-the-art performance. On the Building sub-dataset, the proposed method achieves 18% and 12% lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) than the existing methods.
An Efficient and Lightweight Deep Learning Model for Human Activity Recognition on Raw Sensor Data in Uncontrolled Environment

Human activity recognition (HAR) is the process of identifying daily living activities using a set of sensors and optimal learning algorithms. It is a convoluted process, as there is no straightforward way to associate human action with the induced sensor data. Most of the work on HAR is done on highly augmented and pre-processed data. It optimizes performance but introduces intense data pre-processing and feature engineering overhead in real-time activity recognition. This article proposes an efficient and lightweight CNN-long short term memory (LSTM) model for enhanced activity classification on raw sensor data in an uncontrolled environment. It used convolution cum memory functionalities of the CNN-LSTM layer to extract spatial and temporal features for distinguished feature analysis and also classifies different human activities using dense classification layers. A state-of-the-art inbuilt smartphone sensor-based HAR dataset is also generated for six daily living activities in an uncontrolled environment for getting real-world activity data. With zero data pre-processing and augmentation, our proposed 1-D convolution (Conv1D)-based CNN-LSTM model out-completed all the incorporated conventional models and achieved the highest accuracy of 98% in optimized computational time. Also, the loss on real-world test data was minimum, with the loss perimeter advantage of 68% and 158% with LSTM and artificial neural network (ANN) models, respectively.
Multimodal fusion for sensorimotor control in steering angle prediction

Efficient reasoning about the spatial and temporal structure of the environment is crucial for perception in autonomous driving, particularly in an end-to-end approach. Although different sensor modalities are employed to capture the complex nature of the environment, they each have their limitations. For example, frame-based RGB cameras are susceptible to variations in illumination conditions. However, these limitations at the sensor level can be addressed by complementing them with sensor fusion techniques, enabling the learning of efficient feature representations for end-to-end autonomous perception. In this study, we address the end-to-end perception problem by fusing a frame-based RGB camera with an event camera to improve the learned representation for predicting lateral control. To achieve this, we propose a convolutional encoder–decoder architecture called DRFuser. DRFuser encodes the features from both sensor modalities and leverages self-attention to fuse the frame-based RGB and event camera features in the encoder part. The decoder component unrolls the learned features to predict lateral control, specifically in the form of a steering angle. We extensively evaluate the proposed method on three datasets: our collected Dataset, Davis Driving dataset, and the EventScape dataset for simulation. The results demonstrate the generalization capability of our method on both real-world and simulated datasets. We observe qualitative and quantitative improvements in the performance of the proposed method for predicting lateral control by incorporating the event camera in fusion with the frame-based RGB camera. Notably, our method outperforms state-of-the-art techniques on the Davis Driving Dataset, achieving a 5.6% improvement in the root mean square error (RMSE) score.
Understanding public opinions on Chinese short video platform by multimodal sentiment analysis using deep learning-based techniques

Purpose With the rapid development of short videos in China, the public has become accustomed to using short videos to express their opinions. This paper aims to solve problems such as how to represent the features of different modalities and achieve effective cross-modal feature fusion when analyzing the multi-modal sentiment of Chinese short videos (CSVs). Design/methodology/approach This paper aims to propose a sentiment analysis model MSCNN-CPL-CAFF using multi-scale convolutional neural network and cross attention fusion mechanism to analyze the CSVs. The audio-visual and textual data of CSVs themed on “COVID-19, catering industry” are collected from CSV platform Douyin first, and then a comparative analysis is conducted with advanced baseline models. Findings The sample number of the weak negative and neutral sentiment is the largest, and the sample number of the positive and weak positive sentiment is relatively small, accounting for only about 11% of the total samples. The MSCNN-CPL-CAFF model has achieved the Acc-2, Acc-3 and F1 score of 85.01%, 74.16 and 84.84%, respectively, which outperforms the highest value of baseline methods in accuracy and achieves competitive computation speed. Practical implications This research offers some implications regarding the impact of COVID-19 on catering industry in China by focusing on multi-modal sentiment of CSVs. The methodology can be utilized to analyze the opinions of the general public on social media platform and to categorize them accordingly. Originality/value This paper presents a novel deep-learning multimodal sentiment analysis model, which provides a new perspective for public opinion research on the short video platform.
Application of ChatGPT for an English Learning Platform

This paper aims to explore the potential of ChatGPT to serve as a comprehensive English learning platform for public school context by analyzing previous literature and current AI-based chatbots. To this end, the study investigated the potential of ChatGPT through four research questions: First, what are the educational possibilities of English learning chatbots?; Second, what are the possibilities and limitations of English learning chatbots when applied in classroom lessons?; Third, how can ChatGPT be utilized in classroom lessons?; Fourth, how can ChatGPT be utilized when applied to an intelligent adaptive learning model? The main conclusions of this discussion are as follows: First, the capabilities of AI-based English learning chatbots rely on natural language processing abilities, conversation modeling, providing dynamic feedback to learners, and offering personalized learning experiences which are lack in existing AI chatbots. Second, conventional English learning chatbots have been utilized in school education as free conversation apps, debate-specific bots, and integrated learning platforms. Third, ChatGPT has the potential to be integrated into a language learning platform and applied in formal learning environments. Finally, this paper proposes a plan to utilize ChatGPT in the context of an intelligent adaptive learning model.
A Multiscale Residual Solver for Total Variation Models

This paper proposes a multiscale residual solver for total variation regularized models. The proposed solver has three important properties. First, the proposed algorithm is theoretically guaranteed to converge. Second, the proposed method can numerically reach the same global optimal solution as the classical methods. This fact is confirmed on image datasets. Third, the proposed solver is faster than the classical methods. For high resolution images, our solver can be three orders of magnitude faster. The proposed method can be adopted in the applications where total variation regularization is imposed, such as denoising, smoothing, optical flow, and inpainting.
Pyramid Masked Image Modeling for Transformer-Based Aerial Object Detection

Two obstacles, the scarcity of annotated samples and the difficulty in preserving multi-scale hierarchical representations, hinder the advancement of vision Transformer-based aerial object detection. The emergence of self-supervised learning has inspired some solutions to the first issue. However, most solutions focus on single-scale features, conflicting with solving the second issue. To bridge this gap, this paper proposes a novel pyramid masked image modeling (MIM) framework, termed PyraMIM, for self-supervised pretraining in aerial scenarios. Without manual annotation, PyraMIM enables establishing pyramid representations during pretraining, which can be seamlessly adapted to downstream aerial object detection for performance improvement. Experimental results demonstrate the effectiveness and superiority of our method.
Task-Agnostic Open-Set Prototype for Few-Shot Open-Set Recognition

In few-shot open-set recognition (FSOSR), a network learns to recognize closed-set samples with a few support samples while rejecting open-set samples with no class cue. Unlike conventional OSR, the FSOSR considers more practical open worlds where a closed-set class can be selected as an open-set class in another testing (task) and vice versa. Existing FSOSR methods have commonly represented the open set with task-dependent extra modules. These modules decently handle the varied closed and open classes but accompany inevitable complexity increase. This paper shows that a single open-set prototype can represent open-set samples when it satisfies a specific relation in metric space: closest to open-set, and simultaneously second nearest to close-set. We propose a task-agnostic open-set prototype with distance scaling factors and design loss terms. We extensively analyze the proposed components to demonstrate their importance. Our method achieves state-of-the-art results on miniImageNet and tieredImageNet, respectively, without task-dependent extra modules.
DiffusionSTR: Diffusion Model for Scene Text Recognition

This paper presents Diffusion Model for Scene Text Recognition (DiffusionSTR), an end-to-end text recognition framework using diffusion models for recognizing text in the wild. While existing studies have viewed the scene text recognition task as an image-to-text transformation, we rethought it as a text-text one under images in a diffusion model. We show for the first time that the diffusion model can be applied to text recognition. Furthermore, experimental results on publicly available datasets show that the proposed method achieves competitive accuracy compared to state-of-the-art methods.