Miracle-dz/UniFAR-FTUs huggingface dataset:
Passive OS Identification in Imbalanced Dataset

Identification of the Operating System (OS) by network traffic analysis plays an important role in network security management. With the development of encrypted technologies, the use of machine learning algorithms has gained popularity due to their great identification performance. However, these algorithms often require a large amount of training data and struggle to perform well on imbalanced datasets, which is typical in real network scenarios. To address this issue, this study employs passive identification and resampling techniques to process the dataset, significantly improving the accuracy of the model. To deal with the imbalanced dataset, we introduce a combination of undersampling and oversampling techniques. Then, we apply a deep learning model to accomplish OS identification. Furthermore, we conduct experiments to compare the performance of different resampling techniques. The experimental results show that the proposed method has a better performance compared with other methods.
ELAVL1 ameliorates sevoflurane induced neurotoxicity by inhibiting NLRP3 inflammasome activation

Purpose: To investigate the role of embryonic lethal-abnormal vision like protein 1 (ELAVL1) in sevoflurane-induced neurotoxicity. Methods: A cell model was established in HT22 cells by exposing them to 3 % sevoflurane for 12 h. The HT22 cells were transfected with siRNA ELAVL1 (si-ELAVL1) or siRNA negative control (si-NC) to knockdown ELAVL1 expression. Enzyme-linked immunosorbent assay (ELISA) was performed to assess the levels of proinflammatory factors in HT22 cell culture supernatants. Cell apoptosis was analyzed using flow cytometry while apoptosis-related proteins and NLRP3 inflammasome pathway proteins were determined by western blot. Results: ELAVL1 was upregulated in sevoflurane-exposed HT22 cells. Sevoflurane exposure also resulted in inflammation, apoptosis and activation of NLRP3 inflammasome of HT22 cells. Importantly, knockdown of ELAVL1 inhibited inflammation and apoptosis in HT22 cells caused by sevoflurane through the inhibition of IL-6, IL-1β and TNF-α production and bys regulating Bax and Bcl-2 protein expression. Furthermore, knockdown of ELAVL1 suppressed NLRP3 inflammasome activity as reflected by the inhibition of the expression of caspase 1, ASC, IL-1β and IL-18. Conclusion: The findings of this study suggest that the knockdown of ELAVL1 alleviates the inflammation and apoptosis of HT22 cells induced by sevoflurane which impeded the activation of NLRP3 inflammasome, suggesting that ELAVL1 would make a good therapeutic target for sevoflurane-caused neurotoxicity.
A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus

Introduction Seasonal influenza A H3N2 viruses are constantly changing, reducing the effectiveness of existing vaccines. As a result, the World Health Organization (WHO) needs to frequently update the vaccine strains to match the antigenicity of emerged H3N2 variants. Traditional assessments of antigenicity rely on serological methods, which are both labor-intensive and time-consuming. Although numerous computational models aim to simplify antigenicity determination, they either lack a robust quantitative linkage between antigenicity and viral sequences or focus restrictively on selected features. Methods Here, we propose a novel computational method to predict antigenic distances using multiple features, including not only viral sequence attributes but also integrating four distinct categories of features that significantly affect viral antigenicity in sequences. Results This method exhibits low error in virus antigenicity prediction and achieves superior accuracy in discerning antigenic drift. Utilizing this method, we investigated the evolution process of the H3N2 influenza viruses and identified a total of 21 major antigenic clusters from 1968 to 2022. Discussion Interestingly, our predicted antigenic map aligns closely with the antigenic map generated with serological data. Thus, our method is a promising tool for detecting antigenic variants and guiding the selection of vaccine candidates.
Self Generated Soccer Highlights Using Inflated 3D Convolutional Neural Network

Soccer being an international sport have a huge fanbase from all around the world in different time zones and nowadays in this fast-paced world it is difficult to catch up with every match. So, to remain up-to-date fans watch highlight video instead to save up the time. To automate this process video summarization techniques can be used with the help of Deep Learning Algorithms. Our main goal was to develop a model that can summarize an entire soccer match by processing the whole video using Inflated 3D Convolutional Neural Network. Inflated 3D Convolutional Neural Network is used to identify action and motion from throughout the match due to its spatial and temporal features. We used SoccerNet dataset to train our model which has total 500 complete soccer matches which is around 764 hours long in total. We split our data in the ratio of [0.85, 0.1, 0.05] as training, validation, and testing data. Our aim was to generate small clips that highlight the major events such as goals, substitutions, and penalties. These small videos are merged using Video Overlay method and will generate text in between the highlighted video to give that realistic experience. Accuracy of our model is 95%.
Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images

This paper proposes an efficient algorithm for edge-preserving filtering with multiple guidance images, so-called multilateral filtering. Multimodal signal processing for sensor fusion is increasingly important in image sensing. Edge-preserving filtering is available for various sensor fusion applications, such as estimating scene properties and refining inverse-rendered images. The main application is joint edge-preserving filtering, which can preferably reflect the edge information of a guidance image from an additional sensor. The drawback of edge-preserving filtering lies in its long computational time; thus, many acceleration methods have been proposed. However, most accelerated filtering cannot handle multiple guidance information well, although the multiple guidance information provides us with various benefits. Therefore, we extend the efficient edge-preserving filters so that they can use additional multiple guidance images. Our algorithm, named decomposes multilateral filtering (DMF), can extend the efficient filtering methods to the multilateral filtering method, which decomposes the filter into a set of constant-time filtering. Experimental results show that our algorithm performs efficiently and is sufficient for various applications.
Improved Object Detection in Forward-Looking Sonar Images with Attention-YOLOv8 and Denoising Algorithms

The utilization of forward-looking sonar (FLS) has become increasingly significant for detecting underwater objects. However, despite its widespread use, target recognition tasks for underwater unmanned vehicles (UUVs) are still predominantly carried out through postprocessing techniques. This can be attributed to the limitations of computing devices deployed on UUVs, which have restricted computational capabilities due to power constraints. Furthermore, conventional object detection approaches designed for natural images are inadequate when applied to sonar images with high levels of noise. To address these challenges and enable UUVs to autonomously detect underwater objects, we propose an innovative solution that involves the integration of an improved version of YOLOv8 with attention mechanism, allowing the model to obtain more feature information while reducing the number of parameters required. Additionally, we developed a comprehensive preprocessing module consisting of removal, contrast enhancement, and noise filtering techniques. Our experimental results demonstrate that the proposed method achieves high accuracy, robustness, and can be efficiently implemented on hardware processors. Our code is available on https:/github.com/wangzitao7777/underwater-detection.
THE CONSERVATION OF COASTAL PLAIN FLORA IN NOVA SCOTIA

Although Nova Scotia's rare coastal plain plant species are now among the most endangered plants in Canada they remained little more than a curiosity until the early 1980s. Renewed scientific interest and research since that time has shown that the species, while sometimes locally abundant, are extremely limited in their distribution and are vulnerable to ecosystem changes. Their requirement for widespread protective mechanisms, including the protec- tion of all available populations if they are to remain viable, has been the subject of several governmental and non-governmental efforts. This paper discusses ongoing attempts to conserve and protect these endangered species and con- cludes with a discussion of future needs and opportunities. Over seventy years ago, the Grey Herbarium Expedition from Harvard University traveled to Nova Scotia, Canada, to investigate the flora of the province (Fernald 1921). The southwestern area was of particular interest, as it had been largely overlooked by earlier botanists. Perhaps the most interesting discoveries of the two-year project were the thriving populations of Plymouth gentian (Sabatia kennedyana Fern.), pink Coreopsis (Coreopsis rosea Nutt.), and long-leaved Panic grass (Panicum longifolium Torr.) on the shores of Lake Vaughn, Yarmouth County by Fernald (1921). These species are representatives of the distinct plant association known as the "coastal plain element. " It is found on the Atlantic coastal plain of North America, the now exposed portion of the continental shelf extending from the Caribbean to the Cape Cod area of Massachusetts (Fig. 1) and is a relic of a more widespread flora once growing on formerly
The impact of legal systems on CEO compensation and bank stability: a cross-country study

Purpose Remuneration policies may differ from country to country, and their effect on bank stability could be due to the legal framework. Therefore, this study aims to investigate how the legal system impacts the relationship between CEO compensation and bank stability across countries. Design/methodology/approach To test the study hypotheses, the authors use panel data of 74 banks operating in ten OECD countries during the period 2009–2016 and apply the generalized moments method regression model to better remediate the endogeneity problem. Findings The findings confirm that a country’s banking regulations significantly affect its bank stability. Common law countries have less bank stability than civil law countries. This result can be interpreted by the fact that, in common-law countries, banks’ CEO are strongly protected by the law, so they allocate a large part of bank assets to risky loans to improve their variable remuneration. Practical implications The research can help policymakers understand bank stability in one country. Any legal reform would require prior knowledge of how risk-taking may arise in executive compensation. Originality/value The contribution is to explain the controversial effect of executive compensation on bank stability in the framework of legal theory. The authors argue that regulators should monitor compensation structures and that the country’s legal origin of law shapes the CEO compensation structure and is a determinant of bank stability. To the best of the authors’ knowledge, there are no studies exploring this field. So, this study tries to shed more light on the dark side of CEOs’ behavior when undertaking risky projects to maximize their remuneration.
Research on the Influence of Multi-GCC Function Feature Fusion on the Accuracy of Time Delay Estimation

The localization of sound sources using the time difference of arrival (TDOA) method heavily relies on accurately estimating the time delay through a microphone array. However, the accuracy of this estimation is often compromised by back-ground noise and reverberation. To address this challenge, this paper introduces a new method for time delay estimation of sound source localization, utilizing a convolutional neural network and a multi-generalized cross-correlation feature fusion (MGCCFF) model. The MGCCFF method constructs a fused feature matrix by employing multiple generalized cross-correlation functions, each possessing distinct time-delay estimation characteristics. This feature matrix is then inputted into a convolutional neural network comprising convolutional and fully connected layers for further processing. The time difference of arrival (TDOA) is subsequently estimated through classification methods. In this study, the proposed MGCCFF-based approach is compared and validated against existing methods through simulations conducted under various signal-to-noise ratio (SNR) and reverberation conditions. The results demonstrate that the MGCCFF method improves TDOA estimation accuracy by approximately 3.36% when compared to existing methods. Besides, the simulation comparison underscores the efficacy of this method, particularly in challenging acoustic environments characterized by background noise and reverberation.
SASTDGCN: Self-Attention Based Spatial-Temporal Double Graph Convolutional Networks for Traffic Flow Forecasting

As a core technology of Intelligent Transportation System, traffic flow forecasting has a wide range of applications. Existing methods typically utilize graph neural network (GNNs) and temporal neural networks (TNNs) to model spatial and temporal dependencies. However, these works still have following limitations: 1) Most methods model spatial and temporal dependencies in a static manner, which limits the ability to learn dynamic traffic patterns; 2) TNNs have difficulty in extracting the temporal dependencies in global fields. To this end, in this paper, we propose a Self-Attention Based Spatial-Temporal Double Graph Convolutional Networks (SASTDGCN) for traffic flow forecasting. Specifically, we construct temporal correlation graph to represent the dependencies among timestamps. Then, we design a spatial-temporal self-attention module to generate dynamic spatial and temporal adjacency matrices for capturing the dynamic spatial-temporal correlations. Furthermore, graph convolution module is proposed to extract the spatial patterns using Graph Convolutional Network (GCN) and capture temporal patterns using Relational Graph Convolutional Network (R-GCN). To the best of our knowledge, this is the first time RGCN has been used to model temporal dynamics in traffic flow forecasting. Finally, to validate the performance of the proposed SASTDGCN, we conduct extensive experiments on three realworld traffic datasets. Experimental results show our model outperforms the baseline methods.
Optimization of Performance Management for Commercial Companies by Integrating ROF and Light GBM Algorithms

Performance management in commercial companies is related to the operation and development of enterprises, and as a core competitiveness of enterprises, it can promote the achievement of strategic goals. Research and analyze the key performance indicator method, target management method, balanced scorecard, and 360 degree performance evaluation method, combined with the current situation and future development direction of performance management in commercial companies, and establish performance evaluation indicators and standards based on enterprise goals to promote the achievement of enterprise strategic goals. After comparing the datasets of random forest model, rotating forest model, and classification model based on rotating forest and Light GBM, it was found that the ROF -LGB model accounted for 75% and 66.7% of the micro average gap and macro average gap, respectively, with an accuracy of 82.6%. It has been proven that the classification model based on rotating forest and Light GBM has high accuracy and stability.
Closed Loop Subspace Identification of a Real Time Non Square MIMO System

When creating model-based control systems, the process model is extremely important. Here, uses a system identification method to create the process model. There are two different ways that this identification process develops: the open loop and closed loop approaches. We’ve set up a conical tank system on a laboratory scale using a non-square MIMO architecture as part of the project. Both with and without feedback techniques yield the system function for this conical tank system. We discuss the reasoning behind using these two methods. We use our laboratory-scale conical tank system to test the open loop system identification method, and we build a PI controller based on the knowledge we obtained from this process. We next use our conical tank system to implement the PI controller in real-time, and we record the data for closed-loop identification. We use a modified version of the N4SID algorithm to conduct the locked in kind of technique computation, which provides exceptional model achievement than the CVA method. The proposed / suggested improved N4SID identification technique is suitable for type of non square system matrix bsased MIMO systems.
Analysis of Multimodal Biosignals during Surprise Conditions Correlates with Psychological Traits

Surprise can simultaneously represent bad or good, pleasant or unpleasant, with the same experiences since understanding how humans' physiological qualities link with their emotional or mental health is required. We conducted quantitative research to concisely correlate mental stress and emotional issues by measuring brain activity, breathing, and heart rate in real time while executing specialized audio-visual stimulation to elicit a surprise event. We evaluated the frequency and temporal domain characteristics to determine if physiological measurements matched biochemical metrics and subjective stress assessments during the elicit surprise condition experiment. We discovered that the brain is still preferable to most in recognizing a human's psychological changes over a short period of time. The temporal (T3) (r = 0.544, p = 0.005) and frontal (Fz) (r = 0.519, p = 0.008) regions were shown to correlate with salivary amylase activity. In comparison to other channels, there was a negative association between stress perception and the occipital site (O1, r = -0.618, p = 0.001). We also found that heart rate variability activity correlates with arousal perception. By looking at specific multimodal biosignals, it is possible to understand human psychological traits by recording specific physiological signals for daily mental health monitoring.
An Electronically Tunable Capacitance Multiplier Structure Using DTMOS Technique for the Low Frequency Applications

In this study, a capacitor multiplier based on Multiple Output-Voltage Difference Transconductance Amplifier (MO-VDTA) is built by employing DTMOS transistors. The proposed MO-VDTA based capacitor multiplier is composed of three stages, one of which is operated in the subthreshold region. The proposed structure works with ±0.9V and ±0.3V supply voltages and can be safely tunable for the low frequency operations where the large capacitor values are needed. The proposed multiplier is tested in a 2nd order low-pass filter, while the cut-off frequency of proposed filter from “12Hz” up to “40Hz” and total power consumption of the proposed filter is given around 700µW. Also, multiplication factor can be changed from “40000” to “120000” by the virtue of bias currents. In order to verify the multiplier performance, Monte-Carlo, THD and noise analysis are given in conjunction with the filter results. Performance comparison with the previous studies is presented as well.
Land Cover Classification Using Multi-Frequency SAR over Semi-Arid and Forested African Landscapes

The potential of using multi-frequency Synthetic Aperture Radar (SAR) for land cover classification is becoming a reality, with multiple SAR satellites utilising different frequencies currently in orbit and more missions planned for the future. This study looks at combining SAR frequencies from L-band (ALOS PALSAR), C-band (ENVISAT ASAR) and X-band (TerraSAR-X) to find the optimum combination of the SAR data for land cover classification of forested and semi-arid ecoregions in Africa. The study site for forested areas is in Cameroon and the semi-arid study site is in Tanzania. Data from both the wet and dry seasons are available. Random forest models, with different combinations of input variables, are compared. Models with the top 30 variables are chosen from the mean decrease accuracy and mean decrease Gini variable importance measures, and compared with the classification accuracies using support vector machines. Some of the findings are that L-band is the best single frequency for land cover classifications for both ecoregions, with X-band the best single frequency if only forested regions are considered. Texture measures lead to an increase of between 15-25% overall classification accuracy compared to using only backscatter coefficients. The recommended dual-frequency combination are LX-bands, although L-band data give overall classification accuracies very close to LX-bands. The use of images from LCX data only marginally improves the classification accuracy from LX-images and L-band images. The benefit from acquisition of all three frequencies would therefore rarely outweigh the cost of acquiring and processing data from all three frequencies. The transferability of the random forest models to an additional geographic site did not produce satisfactory results, however the transferability of the random forest models to additional season data did give satisfactory results. The Kullback-Leibler divergence class difference measure showed potential to give an indication of transferability of the models, although refinement remains necessary.
Cross-Domain Fake News Detection Based on Coarse-Fine Grained Environments Reflecting Public Expectation

Prevalence of fake news has seriously disrupted the information ecosystem and undermined social stability and public trust. It stimulated the development of automatic fake news detection method to tackle this dilemma. Most of the methods can be divided into two types, content-based and propagation-based methods. However, these methods overlook the historical background information of the news events contained in the target news in varying degrees. The environment constructed by the historical context of events related to a news event can reflect the direction of the public’s expectations of the current event with respect to future developments. The expectation can be used to study the direction of fake news that has not been fully explored. Therefore, considering the influence of public expectation, we propose a general fake news detection method based on cross-domain coarse-fine grained environments referred to as CFGE including three parts. Specifically, we first construct a cross-domain coarse-fine grained environment with the news related to the domains which were contained in the target news. Then the coarse-grained embedding and the fine-grained embedding of cross-domain environment are extracted respectively by the proposed environment information capture modules. Finally, based on the gate fusion method, coarse-grained embedding and fine-grained embedding are fused to predict fake news. Extensive experimentation substantiates the superiority of CFGE compared to alternative models, further affirming the efficacy of coarse-fine grained environments.