FakeSwarm: Improving Fake News Detection with Swarming Characteristics

The proliferation of fake news poses a serious threat to society, as it can misinform and manipulate the public, erode trust in institutions, and undermine democratic processes. To address this issue, we present FakeSwarm, a fake news identification system that leverages the swarming characteristics of fake news. To extract the swarm behavior, we propose a novel concept of fake news swarming characteristics and design three types of swarm features, including principal component analysis, metric representation, and position encoding. We evaluate our system on a public dataset and demonstrate the effectiveness of incorporating swarm features in fake news identification, achieving an f1-score and accuracy of over 97% by combining all three types of swarm features. Furthermore, we design an online learning pipeline based on the hypothesis of the temporal distribution pattern of fake news emergence, validated on a topic with early emerging fake news and a shortage of text samples, showing that swarm features can significantly improve recall rates in such cases. Our work provides a new perspective and approach to fake news detection and highlights the importance of considering swarming characteristics in detecting fake news.Comment: 9th International Conference on Data Mining and Applications (DMA 2023). Keywords: Fake News Detection, Metric Learning, Clustering, Dimensionality Reductio

BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns

An essential topic in online social network security is how to accurately detect bot accounts and relieve their harmful impacts (e.g., misinformation, rumor, and spam) on genuine users. Based on a real-world data set, we construct behavioral sequences from raw event logs. After extracting critical characteristics from behavioral time series, we observe differences between bots and genuine users and similar patterns among bot accounts. We present a novel social bot detection system BotShape, to automatically catch behavioral sequences and characteristics as features for classifiers to detect bots. We evaluate the detection performance of our system in ground-truth instances, showing an average accuracy of 98.52% and an average f1-score of 96.65% on various types of classifiers. After comparing it with other research, we conclude that BotShape is a novel approach to profiling an account, which could improve performance for most methods by providing significant behavioral features.Comment: CDKP 202

BotTriNet: A Unified and Efficient Embedding for Social Bots Detection via Metric Learning

A persistently popular topic in online social networks is the rapid and accurate discovery of bot accounts to prevent their invasion and harassment of genuine users. We propose a unified embedding framework called BotTriNet, which utilizes textual content posted by accounts for bot detection based on the assumption that contexts naturally reveal account personalities and habits. Content is abundant and valuable if the system efficiently extracts bot-related information using embedding techniques. Beyond the general embedding framework that generates word, sentence, and account embeddings, we design a triplet network to tune the raw embeddings (produced by traditional natural language processing techniques) for better classification performance. We evaluate detection accuracy and f1score on a real-world dataset CRESCI2017, comprising three bot account categories and five bot sample sets. Our system achieves the highest average accuracy of 98.34% and f1score of 97.99% on two content-intensive bot sets, outperforming previous work and becoming state-of-the-art. It also makes a breakthrough on four content-less bot sets, with an average accuracy improvement of 11.52% and an average f1score increase of 16.70%

MedLens: Improve mortality prediction via medical signs selecting and regression interpolation

Monitoring the health status of patients and predicting mortality in advance is vital for providing patients with timely care and treatment. Massive medical signs in electronic health records (EHR) are fitted into advanced machine learning models to make predictions. However, the data-quality problem of original clinical signs is less discussed in the literature. Based on an in-depth measurement of the missing rate and correlation score across various medical signs and a large amount of patient hospital admission records, we discovered the comprehensive missing rate is extremely high, and a large number of useless signs could hurt the performance of prediction models. Then we concluded that only improving data-quality could improve the baseline accuracy of different prediction algorithms. We designed MEDLENS, with an automatic vital medical signs selection approach via statistics and a flexible interpolation approach for high missing rate time series. After augmenting the data-quality of original medical signs, MEDLENS applies ensemble classifiers to boost the accuracy and reduce the computation overhead at the same time. It achieves a very high accuracy performance of 0.96% AUC-ROC and 0.81% AUC-PR, which exceeds the previous benchmark

Learning to Reduce Information Bottleneck for Object Detection in Aerial Images

Object detection in aerial images is a fundamental research topic in the domain of geoscience and remote sensing. However, advanced progresses on this topic are mainly focused on the designment of backbone networks or header networks, but surprisingly ignored the neck ones. In this letter, we first analyse the importance of the neck network in object detection frameworks from the theory of information bottleneck. Then, to alleviate the information loss problem in the current neck network, we propose a global semantic network, which acts as a bridge from the backbone to the head network in a bidirectional global convolution manner. Compared to the existing neck networks, our method has advantages of capturing rich detailed information and less computational costs. Moreover, we further propose a fusion refinement module, which is used for feature fusion with rich details from different scales. To demonstrate the effectiveness and efficiency of our method, experiments are carried out on two challenging datasets (i.e., DOTA and HRSC2016). Results in terms of accuracy and computational complexity both can verify the superiority of our method.Comment: 5 pages, 3 figure

ROS-Dependent Activation of Autophagy through the PI3K/Akt/mTOR Pathway Is Induced by Hydroxysafflor Yellow A-Sonodynamic Therapy in THP-1 Macrophages

Monocyte-derived macrophages participate in infaust inflammatory responses by secreting various types of proinflammatory factors, resulting in further inflammatory reactions in atherosclerotic plaques. Autophagy plays an important role in inhibiting inflammation; thus, increasing autophagy may be a therapeutic strategy for atherosclerosis. In the present study, hydroxysafflor yellow A-mediated sonodynamic therapy was used to induce autophagy and inhibit inflammation in THP-1 macrophages. Following hydroxysafflor yellow A-mediated sonodynamic therapy, autophagy was induced as shown by the conversion of LC3-II/LC3-I, increased expression of beclin 1, degradation of p62, and the formation of autophagic vacuoles. In addition, inflammatory factors were inhibited. These effects were blocked by Atg5 siRNA, the autophagy inhibitor 3-methyladenine, and the reactive oxygen species scavenger N-acetyl cysteine. Moreover, AKT phosphorylation at Ser473 and mTOR phosphorylation at Ser2448 decreased significantly after HSYA-SDT. These effects were inhibited by the PI3K inhibitor LY294002, the AKT inhibitor triciribine, the mTOR inhibitor rapamycin, mTOR siRNA, and N-acetyl cysteine. Our results demonstrate that HSYA-SDT induces an autophagic response via the PI3K/Akt/mTOR signaling pathway and inhibits inflammation by reactive oxygen species in THP-1 macrophages

A bibliometric analysis of the knowledge related to mental health during and post COVID-19 pandemic

ObjectiveCOVID-19 led to a horrific global pandemic, with strict lockdowns and prolonged indoor stays increasing the risk of mental health problems, affecting people of different ages, genders, regions, and types of work to varying degrees. This study provides a bibliometric summary of the knowledge map related to mental health during and post COVID-19 pandemic.MethodsPublications related to mental health during and post COVID-19 pandemic were searched in the Web of Science Core Collection (WoSCC) database through March 19, 2024. After screening the search results, the literature included in the final was first quantitatively analyzed using GraphPad Prism software and then visualized using VOSviewer, CiteSpace, and R (the bibliometrix package).ResultsThe 7,047 publications from 110 countries were included, with the highest number of publications from China and the United States, and the number of publications related to mental health during and post the COVID-19 pandemic increased annually until 2023, after which it began to decline. The major institutions were University of Toronto, University of London, Harvard University, King’s College London, University College London, University of California System, University of Melbourne, Institut National De La Sante Et De La Recherche Medicale (Inserm), Mcgill University, and University of Ottawa; Frontiers in Psychiatry had the highest number of publications, and the Journal of Affective Disorders had the highest number of co-citations; 36,486 authors included, with Xiang, Yu-Tao, Cheung, Teris, Chung, Seockhoon published the most papers, and World Health Organization, Kroenke K, and Wang CY were the most co-cited; epidemiologically relevant studies on mental health related to COVID-19, and the importance of mental health during normalized epidemic prevention and control are the main directions of this research area, especially focusing on children’s mental health; “pandemic,” “sars-cov-2,” “epidemic,” “depression,” “coronavirus anxiety,” “anxiety,” “longitudinal,” “child,” “coronavirus anxiety,” “longitudinal,” “child,” and “coronavirus” are the top keywords in recent years.ConclusionThis comprehensive bibliometric study summarizes research trends and advances in mental health during and after the COVID-19 Pandemic. It serves as a reference for mental health research scholars during and after the COVID-19 pandemic, clarifying recent research preoccupations and topical directions

Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

A particle swarm optimization (PSO)-based least square support vector machine (LS-SVM) method was investigated for quantitative analysis of extraction solution of Yangxinshi tablet using near infrared (NIR) spectroscopy. The usable spectral region (5400–6200 cm-1) was identified, then the first derivative spectra smoothed using a Savitzky–Golay filter were employed to establish calibration models. The PSO algorithm was applied to select the LS-SVM hyperparameters (including the regularization and kernel parameters). The calibration models of total flavonoids, puerarin, salvianolic acid B and icariin were established using the optimum hyperparameters of LS-SVM. The performance of LS-SVM models were compared with partial least squares (PLS) regression, feed-forward back-propagation network (BPANN) and support vector machine (SVM). Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS-SVM method were superior to PLS, BP-ANN and SVM. For PSO-based LS-SVM models, the determination coefficients (R2) for the calibration set were above 0.9881, and the RSEP values were controlled within 5.772%. For the validation set, the RMSEP values were close to RMSEC and less than 0.042, the RSEP values were under 8.778%, which were much lower than the PLS, BP-ANN and SVM models. The PSO-based LS-SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy, which has definite practice significance and application value

Carbon nanotube-supported gold nanoparticles as efficient catalysts for selective oxidation of cellobiose into gluconic acid in aqueous medium

Gold nanoparticles loaded on nitric acid-pretreated carbon nanotubes are efficient for the selective oxidation of cellobiose by molecular oxygen to gluconic acid in aqueous medium without pH control; a gluconic acid yield of 80% has been obtained at 145 degrees C.NSFC [20625310, 20773099, 20873110]; National Basic Research Program of China [2010CB732303, 2005CB221408