Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Depth Cross-Calibration of Gaze Tracker and LiDAR Systems

Discovering where a driver looks and what they look at while driving can provide many benefits to improve driving safety in Advanced Driver Assistance Systems (ADAS). Toward this goal, we utilized a research vehicle equipped with a remote gaze tracker installed inside the vehicle and facing the driver and a LiDAR mounted on the roof of the vehicle. We introduce a novel approach for the cross-calibration of the gaze tracker with the LiDAR's system. We also propose a method to estimate the driver's Point of Gaze in the sparse LiDAR point cloud. In order to provide more information about the driver's attention, we introduce a technique to calculate the driver's visual attention area in the 3D space. We empirically demonstrate that our proposed multi-depth calibration-calibration approach yields excellent results to find the transformation matrix between a remote gaze tracker and LiDAR coordinate systems.
Enhancing Deepfake Image Detection with Deep Convolutional Neural Networks

The availability of image editing software such as Adobe Photoshop or GIMP has made picture alteration so widespread these days. Finding these phony photos is a must for exposing image-based cyber crimes. Due to its ubiquity, images produced with a digital camera or smartphone are typically saved in the JPEG format. The 8×8 pixel-sized, independently compressed mage grids used by the JPEG technique are used. Images that haven't been altered have a comparable inaccuracy level. Due to a similar number of faults throughout the whole image, each block should degrade at roughly the same rate during resaving operations. Error Level Analysis was used to determine that the compression ratio of the false image was different from the actual images.
KBEE: Linux Kernel Vulnerability Exploitability Exploration

The development of kernel fuzzing tools has greatly accelerated the discovery of Linux kernel errors. Fuzzing tools can automatically generate bug reports. The bug reports can guide kernel analysts in assessing the exploitability of bugs and thus prioritizing bug fixes. However, it is easy to underestimate the exploitability of bugs just by the currently exhibited bug behavior. Through different paths or being in different kernel states, a bug may exhibit a variety of bug behaviors that hide different exploitability. We propose a state-driven kernel fuzzing technique to explore the different error behaviors that kernel defects may bring. Unlike traditional coverage-oriented fuzzing, we aim at exploring different states of defective code segments such that bugs exhibit different behaviors, which in turn facilitate the evaluation of exploitability. To evaluate the effectiveness of the techniques we proposed, a KBEE tool is implemented and applied to 50 kernel bugs. Experiments show that KBEE finds a larger number of kernel bugs and is more efficient compared to the state-of-the-art tool GREBE.
Open-Circuit Fault Diagnosis of Fault-Tolerant DAB Converter for Improving Smart Transformers Reliability using 1-D CNN

This paper addresses the challenges brought about by rapid population growth and increasing demand for electrical energy. It focuses on diagnosing open-circuit faults (OCFs) in the dual active bridge (DAB) converter, a crucial component of smart transformers (STs) in smart grids (SGs). The DAB converter’s reliability is paramount for the safe operation of power electronic systems, as power semiconductor devices are prone to failures, particularly those causing OCFs. Traditional model-based diagnosis methods have limitations, which led to the exploration of data-driven alternatives such as the convolutional neural network (CNN). The proposed 1-D CNN model displays exceptional performance, surpassing other notable machine and deep learning techniques in accuracy, recall, precision, and F1-score. The confusion matrix analysis confirms the 1-D CNN model’s superior predictive abilities in accurately distinguishing fault types, especially in challenging situations. In summary, the research improves STs’ reliability and performance in SGs by effectively diagnosing OCF in the DAB converter.
LiDAR-Based Point Cloud Data Processing Application Research

LiDAR technology is widely used for point cloud data acquisition in geographic mapping, ecological surveying, etc., which facilitates the research. The PointNet model is a pioneering representative of deep learning technology and semantic segmentation of 3D point cloud, which improves the network generalization ability through a large number of trainings, but at the same time there are problems such as loss of detail data, complex scene recognition accuracy lower, and incomplete local feature extraction. Attention mechanism is a technique in deep learning that enables the network to focus on valid data and discard invalid data in the process of image processing. The attention mechanism can make the local features of point cloud data under deep learning more distinctive, so that the local features can be extracted more completely and accurately. In this paper, we propose to use PointN et network, combined with our own collected dataset for semantic segmentation experiments. And the attention mechanism is introduced to improve the recognition ability of Point Net network as well as the accuracy of segmentation.
Predictive Multimodal Wireless Localization Algorithm on a Two-Dimensional Plane based on MATLAB

Given the issue of positioning accuracy in high-rise buildings and urban areas within the Global Navigation Satellite System (GNSS), the current approach primarily involves utilizing a combination of GNSS positioning methods to address these challenges. Consequently, the problem of mobile device positioning within cellular communication systems has become a widely researched focus. This paper reviews existing indoor positioning algorithms by establishing a base station network and designing a series of scenarios involving random mobile terminals. Various algorithms, such as TOA-LLOP, TOA-CHAN, TDOA-CHAN, TDOA-TAYLOR, RSSI-K-Nearest-Neighb or, RSSI-Weighted-K-Nearest-Neighbors, and RSSI-Nearest-Neighbors, were simulated, compared, and subjected to in-depth analysis. Through comparing these algorithms and their system performance, we delve into their advantages and limitations. Based on the screening results, we simulated a fusion algorithm, which significantly enhanced positioning accuracy by 29% under specific conditions.
TP-GDM: Trajectory Prediction-based Game Decision-Making Model and A Case Study on Merging

The driving task of autonomous vehicles in urban poses a significant challenge, especially in the merging scenarios with strong interaction and high requirement of real-time. However, the existing driving decision-making methods generally separate the motion prediction of surrounding vehicles from the decision-making of ego vehicle, which is not aligned with human drivers and may generate unreasonable merging behavior. To address this challenge, a merging decision-making framework is proposed, which involves a Mixer based Trajectory Prediction Model (Mixer-TPM) and a Trajectory Prediction-based Game-theory Decision-Making model (TP-GDM). The decision-making model takes into account the future trajectory of interactive vehicles as an input feature and ensures future safety. Finally, both Mixer-TPM and TP-GDM are validated using public naturalistic driving datasets. The results indicate that the Mixer-TPM can improves prediction accuracy significantly compared to the state-of-the-arts and has better computational efficiency. Additionally, TP-GDM shows a high level of similarity to human drivers.
Study of the contextual factors of a project affecting the build performance in Continuous Integration

Continuous Integration (CI) has a significant and substantial impact on software engineering. CI optimize the development processes, enhance the code quality and facilitate a collaborative environment resulting in the effective release of high quality software features to the end users. The performance and outcome of CI build is a major concern in CI environment. In this paper we address the impact of contextual factors of software projects that can influence the performance of build in Continuous Integration/Continuous Deployment (CI/CD) pipeline. We studied 588 large-scale GitHub projects that uses the Travis CI build system. An experimental study is conducted on these projects to leverage the history of prior build success and failures. Three ML models are trained on the data. The aim of the study was to identify the contextual factors of a project, influencing the performance of build. We used Logistic Regression, Decision Tree and Extreme Gradient Boost model to predict the build outcome. We identified that the contextual factors has a significant impact and the study will further helps to develop techniques to preemptively detect when a CI build environment is most likely to trigger a failure.
Tackling Airspace Congestion: A Scalable and Robust Framework for End-to-End UAS Traffic Management

We present an end-to-end air traffic management framework for Unmanned Aircraft Systems (UAS) operations that is both scalable and robust. Our approach involves defining congestion in the airspace and developing a congestion-based cost map that can mitigate potential congestion while adhering to regulations and guidelines. Each UAS operation leverages a recursively updated cost map in solving the path planning problem, providing scalability of the framework. Additionally, we introduce a time-critical controller to enhance the robustness of mission execution. Empirical evidence confirms the feasibility and effectiveness of our method, achieving significant reductions in both cumulative and maximum levels of airspace congestion.
Evaluating Side-Channel Attack Vulnerabilities in Post-Quantum CRYSTALS-Kyber Hardware Based on Simple Power Analysis

CRYSTALS-Kyber is a new algorithm that the NIST recently selected to standardize public-key encryption and key establishment. Therefore, studies are needed to evaluate the side-channel attack resistance of CRYSTALS-Kyber implementations. This paper presents a simple power analysis of a CRYSTALS-Kyber hardware implementation with the security parameter k=3. Since hardware implementations perform computations in parallel, the power consumption of each operation is difficult to quantify. The entire power consumption trace was identified using 6,072,500 samples during the CCAKEM implementation of Kyber. A significant part of the message encoding power consumption occurred during decapsulation. These findings show that existing hardware implementations of CRYSTALS-Kyber require effective countermeasures to efficiently resist side-channel attacks.
Sentiment Analysis With N-Gram Preprocessing for Online-Shopping Reviews in Indonesian Language

Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. This condition motivates the need for analyzing how the users/customers’ opinions on using such platform. Sentiment analysis, as a process of detecting, extracting, and classifying users’ opinions and attitudes toward specific topics, is a good tool for the required analysis. This study aims to evaluate the performance of machine learning approach which combined with N-Gram technique in doing sentiment analysis. The dataset used in this study comes from scraping reviews in Bahasa Indonesia regarding the Shopee Apps. In this study, $\mathrm{N}=2$ for the N-Gram was employed in the preprocessing process. Our main goal is to investigate whether the performance of machine learning in doing sentiment analysis can be improved by adding the N-Gram technique in its preprocessing. This work applied the Naive Bayes Classifier and k-Nearest Neighbor with $K=11$ as the machine learning algorithms. The best accuracy in this study was achieved by Naive Bayes Classifier after applying N-Gram Terms $(N=2)$ with Split Validation (8:2), which is $\mathbf{97.26\%}$.
Research on Monitoring Authority Management of Intelligent Ship Electromechanical System Based on Soundprints

Applying voiceprint technology to the marine field, its robustness is only important, this paper establishes a voiceprint recognition model based on TDNN-Transformer, which improves its robustness compared with the traditional voiceprint recognition model, and adapts more to the real cabin environment. The highest correct result of 98.43 was achieved on the zhvoice speech dataset, and the stable correct rate reached about 95%. A virtual test platform for voice interaction was written, which was interconnected with the turbine simulator via UDP protocol to realize the virtual testing of control commands, and the experimental results show that voiceprint recognition has enough in robustness to be applied to the field of ship turbines in the virtual test environment.
Evaluation of Wet Tropospheric Delay for HY-2C Altimeter Based on GNSS and ERA5 Data

HY-2C satellite equipped with a three-channel Calibrated Microwave Radiometer (CWR) for Wet Tropospheric Delay (WTD) correction for satellite altimetry was launched by China on 21 September 2020. WTD is one of the range errors in satellite measurements of Sea Surface Height (SSH), so verifying the accuracy of CMR WTD inversion is important to obtain high precision SSH. However, there are few researches on the evaluation of BY -2C CWR WTD at present. The objective of this study is to evaluate the accuracy of HY-2C CMR WTD using Global Navigation Satellite Systems (GNSS) and ECMWF Reanalysis vS (ERAS) data. The results of the evaluation in the coastal ocean show that there are some anomalies in CMR WTD and anomaly rate is about 3% for distances to the coast from 40 to 200 km. The results of the evaluation in the open ocean show that there are some anomalies with spare spatial distribution and anomaly rate is about 1% over the open ocean beyond 40km offshore. The accuracy of HY-2C CMR WTD are evaluated in this study, and the abnormal data in BY -2C CMR WTD products over the ocean especially in coastal ocean that need to be improved.
Crossover Dimensionality Reduction and Multi-Branch Deep Learning for Enhanced Hyperspectral Image Classification

The amalgamation of deep learning techniques has led to significant advancements in hyperspectral image (HSI) classification. However, the innate 3D cube structure of HSIs poses a distinctive challenge, demanding a novel strategy for efficient spectral data utilization in classification. This study centers on HSI classification using a recently validated deep-learning approach. Challenges in HSI classification include dimensionality, data redundancy, and computational costs, while CNN-based methods dominate due to architectural constraints. To overcome these hurdles, we present a pioneering "Crossover Dimensionality Reduction and Multi-branch Deep Learning" (CDMD) model for hyperspectral image classification. This approach involves a multi-branch deep learning model combined with Factor Analysis and MNF for crossover feature extraction, with optimal features selected from each technique. Experimental results underscore the CDMD model's superiority over existing methods, underscoring its potential for enhancing HSI classification outcomes. Notably, the CDMD model delivers outstanding performance on benchmark datasets — Salinas Scene (SC), and Kennedy Space Center (KSC) — achieving impressive overall accuracy rates of 99.35% and 99.18% using just 5% of the training data.
Non-Uniform Background Noise Suppression Method Based on Improved Total Variation Model for Wide-Field Imaging System

A wide-field surveillance system with a long exposure time has a stronger detectability for dim space targets. However, with the increase in exposure time and working temperature, complex non-uniform background noise containing hot pixels of the detector cannot be ignored, seriously affecting the background and imaging quality. This article studies and proposes a high-performance denoising method, which does not use any prior knowledge of the target and can automatically remove noise from the image. This method is based on an improved total variation model to remove hot pixels and other background mixed noise in wide-field system images. Firstly, using the idea of the traditional local contrast method (LCM), we utilize the significant difference in grayscale values between contaminated pixels and neighboring pixels to detect impulse noise, such as the hot pixels in the image. And then, we designed an improved adaptive maximum filtering algorithm to remove unwanted contamination, which protected target information from being lost and optimized pixels that were attacked by impulse noise. Finally, the total variation algorithm is used to eliminate residual background noise, the detector’s readout noise, and non-uniform response. The method proposed in this article can effectively filter out hot pixels and non-uniform background noise while preserving the details of target edges. We conducted experiments on a large number of simulated and original images. For star maps captured in long exposure mode, the method proposed in this article has obvious advantages over several competing algorithms. The experimental results show that, compared to competitive algorithms, the algorithm proposed in this article improves PSNR by at least 13.1%, SSIM by at least 0.4%, IEF by at least 5 times, and IQI by at least 9.2%. At the same time, the algorithm in this article achieved a moderate level of computation time.
Prediction of treatment response in lupus nephritis using density of tubulointerstitial macrophage infiltration

Background Lupus nephritis (LN) is a common disease with diverse clinical and pathological manifestations. A major challenge in the management of LN is the inability to predict its treatment response at an early stage. The objective of this study was to determine whether the density of tubulointerstitial macrophage infiltration can be used to predict treatment response in LN and whether its addition to clinicopathological data at the time of biopsy would improve risk prediction. Methods In this retrospective cohort study, 430 patients with LN in our hospital from January 2010 to December 2017 were included. We used immunohistochemistry to show macrophage and lymphocyte infiltration in their biopsy specimens, followed by quantification of the infiltration density. The outcome was the treatment response, defined as complete or partial remission at 12 months of immunosuppression. Results The infiltration of CD68+ macrophages in the interstitium increased in patients with LN. High levels of CD68+ macrophage infiltration in the interstitium were associated with a low probability of treatment response in the adjusted analysis, and verse vice. The density of CD68+ macrophage infiltration in the interstitium alone predicted the response to immunosuppression (area under the curve [AUC], 0.70; 95% CI, 0.63 to 0.76). The addition of CD68+cells/interstitial field to the pathological and clinical data at biopsy in the prediction model resulted in an increased AUC of 0.78 (95% CI, 0.73 to 0.84). Conclusion The density of tubulointerstitial macrophage infiltration is an independent predictor for treatment response in LN. Adding tubulointerstitial macrophage infiltration density to clinicopathological data at the time of biopsy significantly improves risk prediction of treatment response in LN patients.
A New olefin Derivative from Ficus esquiroliana Levl.

: A new olefins derivative, ficuole A ( 1 ) and two known compounds ( 2−3 ) were obtained from the ethyl acetate extract of the Ficus esquiroliana Levl . The structures were elucidated using comprehensive spectroscopic methods, and the absolute configurations were defined by comparing the observed optical rotation with the standard values. All compounds were tested for α -glucosidase inhibitory activities. Compounds 1 and 3 against α -glucosidase with the IC 50 values of 876.5 and 934.6 μ M, respectively.
Breaking News System of At-Bat Results From Sports Commentary via Speech Recognition

The proliferation of over-the-top (OTT) systems has led to a tremendous amount of video content over the Internet that users can watch them regardless of time or location restrictions. For sports games, highlight videos allow users to check the results of games without watching or listening in real-time. Despite these advances, the number of OTT companies broadcasting sports games in real-time is rising. This demonstrates that sports games are valuable video content to view in real-time. However, sports games generally last long, making continuous viewing difficult. To address this issue, X’s posts have been employed to support sports viewing to users. However, X’s limits on reading and posting and its introduction of paid features, an alternative to X need to be found. In this paper, we propose a novel approach as an alternative to X, using sports commentary to keep track of the state of a game in real-time. In order to classify players’ at-bat results from sports commentary, we combine pre-trained speech recognition and language models. In experiments, we used various pre-trained speech recognition models to create a dataset for fine-tuning, fine-tuned several pre-trained language models using this dataset, and compared the models to identify the best combination of pre-trained speech recognition and language models for classifying at-bat results. Moreover, by using both the speech recognition models and the fine-tuned language models, we compared in terms of real-time performance and classification accuracy across multiple games to verify their effectiveness.
Southern Forestry Smoke Management Guidebook

A system for predicting and modifying smoke concentrations from prescription fires is introduced. While limited to particulate matter and the more typical southern fuels, the system is for both simple and complex applications. Forestry smoke constituents, variables affecting smoke production and dispersion, and new methods for estimating available fuel are presented. KEIYWORDS: Air quality, pollution, prescribed burning, smoke management, particulate matter, smoke concentrations, emission factors, components of forestry smoke, fuel loading.
Reforestation of Cunninghamia lanceolata changes the relative abundances of important prokaryotic families in soil

Over the past decades, many forests have been converted to monoculture plantations, which might affect the soil microbial communities that are responsible for governing the soil biogeochemical processes. Understanding how reforestation efforts alter soil prokaryotic microbial communities will therefore inform forest management. In this study, the prokaryotic communities were comparatively investigated in a secondary Chinese fir forest (original) and a reforested Chinese fir plantation (reforested from a secondary Chinese fir forest) in Southern China. The results showed that reforestation changed the structure of the prokaryotic community: the relative abundances of important prokaryotic families in soil. This might be caused by the altered soil pH and organic matter content after reforestation. Soil profile layer depth was an important factor as the upper layers had a higher diversity of prokaryotes than the lower ones (p < 0.05). The composition of the prokaryotic community presented a seasonality characteristic. In addition, the results showed that the dominant phylum was Acidobacteria (58.86%) with Koribacteraceae (15.38%) as the dominant family in the secondary Chinese fir forest and the reforested plantation. Furthermore, soil organic matter, total N, hydrolyzable N, and NH4+−N were positively correlated with prokaryotic diversity (p < 0.05). Also, organic matter and NO3-−N were positively correlated to prokaryotic abundance (p < 0.05). This study demonstrated that re-forest transformation altered soil properties, which lead to the changes in microbial composition. The changes in microbial community might in turn influence biogeochemical processes and the environmental variables. The study could contribute to forest management and policy-making.