Miracle-dz/UniFAR-FTUs huggingface dataset:
Updated Geology and Vertebrate Paleontology of the Triassic Ntawere Formation of Northeastern Zambia, with Special Emphasis on the Archosauromorphs

ABSTRACT The two vertebrate fossil assemblages from the ? Middle Triassic Ntawere Formation have been known since the 1960s, but little new work has been done since the description of novel taxa in the 1960s and 1970s. Three recent field seasons have increased vertebrate diversity in the upper Ntawere assemblage and expanded biostratigraphic connections between the lower and upper Ntawere assemblages and assemblages in fossiliferous basins across southern Pangea. The upper Ntawere contains hybodontoid sharks, ptychoceratodontid lungfish, large- and small-bodied stereospondyl amphibians (Cherninia, ‘Stanocephalosaurus’ Batrachosuchus, a new taxon), stahleckeriid dicynodonts (Sangusaurus, Zambiasaurus), traversodontid and trirachodontid cynodonts (Luangwa, a new species, Cricodon), and at least four archosauromorphs, including a large loricatan pseudosuchian, a shuvosaurid poposauroid, and silesaurid dinosauriforms (Lutungutali), whereas the lower Ntawere contains the cynodonts Cynognathus and Diademodon and species of the dicynodont Kannemeyeria. The lower and upper Ntawere assemblages have been correlated with the middle and upper subzones of the Cynognathus Assemblage Zone of the Karoo Basin, South Africa, into a network of connections between assemblages in modern day Tanzania, Argentina, Brazil, Namibia, Antarctica, and India. Although lower Ntawere correlations are reinforced by the occurrence of Cynognathus, new observations from the upper Ntawere, in combination with field work in Tanzania, Namibia, and Brazil, have shifted the geographic focus of biostratigraphic connection away from the Karoo later in the Triassic. A recent radiometric date from Argentina from below the horizon correlated with both the Karoo and the lower Ntawere places these, and all higher assemblages, into the Carnian Stage of the Late Triassic.
Nitrogen Metabolism during Anaerobic Fermentation of Actual Food Waste under Different pH Conditions

Acidogenic fermentation can convert food waste (FW) into small molecules of acids and alcohols, and the broth can be used as a carbon source of denitrification in wastewater treatment plants. However, the soluble nitrogen-containing substances generated in fermentation influence the quality of the carbon source, and microbial nitrogen transformation under different pH conditions has rarely been reported. In this study, four FW fermentation systems were operated continuously with a focus on nitrogen transformation, and metagenomic and metatranscriptomic analyses were used to reveal the metabolic pathways. The results showed that approximately 70% of nitrogen existed in solid organic matter, and the dissolution of solid proteins was limited at pH 4.0–5.0. The concentration of soluble nitrogen, encompassing both soluble organic nitrogen and ammonium, remained relatively stable across various pH conditions. However, high pH values promoted the conversion of soluble nitrogen-containing substances to ammonium, and its concentration increased by 122%, 180%, 202%, and 267% at pH 4.00, pH 4.27, pH 4.50, and pH 5.00. Lactobacillus played a crucial role in ammonium production via the arginine deiminase pathway at pH 4.0–4.5, and Prevotella was the key contributor with the assistance of glutamate dehydrogenase at pH 5.0. The findings provide insights into organic nitrogen transformation in acidogenic fermentation for optimizing FW treatment processes.
Evaluating The Effect of Refraction on Underwater Stereo Vision

Underwater binocular stereo vision is a method for the three-dimensional measurement of underwater objects, but its measurement accuracy is affected by the refraction of underwater cameras. The calibration-based refraction compensation method is widely used due to its easy operation and calculation, while the physical model-based method seems to be a higher precision but is not popular enough. In this paper, we propose to compare the calibration-based and the physical model-based refraction correction methods for their best working conditions. We constructed the underwater binocular distance measurement environment and evaluated the calibration-based and the physical model-based method by adjusting the setting of the underwater camera parameters and the calibration mode of the underwater camera. We analyzed the effect of several key factors emphatically, such as the distance between the optical center of the camera and the glass interface (camera-glass distance), the calibration distance of the underwater camera, and the measurement distance of the underwater camera. Results demonstrate that the calibration-based method has smaller errors when the camera-glass distance is smaller, or when the underwater binocular distance measurement is carried out nearby the calibration distance of the underwater camera. In other cases, the physical model method is more suitable.
Fast-DAVAD : Domain Adaptation for Fast Video Anomaly Detection on Resource-Constrained Edge Devices

The development of artificial intelligence of things (AIoT) makes it possible to detect video anomalies on intelligent edge devices with low transmission delay and data privacy. However, the performance of these applications is constrained by the domain shift between the diverse real-world environment and the training data. Despite the existence of many advanced domain adaptation methods, it is difficult to deploy these methods on resource-constrained intelligent edge devices due to their model complexity. In addition, there is a lack of large amounts of labeled real-scenario data to complete model training. In this paper, we propose the Fast-DAVAD, an unsupervised end-to-end lightweight adaptive video anomaly detection framework. It solves the domain shift problem through multi-level adversarial domain adaptation. Furthermore, it achieves short training time and inference latency on resource-constrained edge devices with Residual Unet, and applies a memory module to guarantee detection accuracy. Extensive experiments on public datasets and multiple platforms, such as PC and NVIDIA Jetson, demonstrate that the effectiveness and superiority of Fast-DAVAD compared with state-of-the-art domain adaptation-based video anomaly detection solutions.
Inversion of Deep Facial Templates using Synthetic Data

In this paper, we use synthetic data and propose a new method to reconstruct high-resolution face images from facial templates in a template inversion attack against face recognition systems. We use a pre-trained face generator network to generate synthetic face images, and then learn a mapping from the facial templates to the intermediate latent space of the face generator network. We train our mapping network with a multi-term loss function. During the inference stage, we use our mapping network to map facial templates to the intermediate latent code and then generate high-quality face images using the face generator network. We propose our method for whitebox and blackbox template inversion attacks against face recognition systems. We use our model (trained on synthetic data) to evaluate the vulnerability of state-of-the-art face recognition systems on real face datasets, including Labeled Faces in the Wild (LFW) and MOBIO datasets. Experimental results show the vulnerability of the state-of-the-art face recognition system to our template inversion attack. Our experiments also show that our template inversion method outperforms previous methods in the literature. The source code of our experiments is publicly available to facilitate reproducibility of our work.
A Dual Domain Attention Mechanism for Face Forgery Detection

Recently, deep face forgery detection has been attracting considerable attentions, due to the potential security consequences induced by this type of forgeries. Unfortunately, the existing techniques have not specifically considered the intrinsic differences between the frequency and spatial domain information. To explicitly accommodate different feature representations from different domains, we propose a novel Dual Domain Attention Mechanism (DDAM) for deep face forgery detection. Inspired by digital image processing, we construct a “soft” filter to adaptively filter the frequency information, which is irrelevant to our forgery detection. Besides, we construct a FC-based Attention Module to maintain a receptive field of the entire feature map, to better leverage contextual information from different domains. Extensive experiments demonstrate the effectiveness of the proposed method on widely used datasets.
Hybrid cyber threats detection using explainable AI in Industrial IoT

The rise of new technologies have enabled the combination of multiple cyber threats to become more sophisticated and capable cyber attacks, which can result in significant losses and consequences. This work aims to develop a hybrid threats detection systems using explainable artificial intelligence techniques, preventing the growing number of combined cyber attacks in IoT environment. A multilayer hybrid cyber threats model was proposed to analyse increasingly complicated and capable cyber attacks. Meanwhile, an explainable artificial intelligence (XAI) model was designed to create understanble interpretation for the detection model. The experimental results demonstrate the effectiveness of proposed hybrid cyber threats analysis model and XAI model.
EMYOLO: Enhanced Multispectral YOLO for Robust Object Detection in Adverse Weather

Object detection is a fundamental task in computer vision and has many important applications, such as autonomous driving. However, detecting objects in adverse weather conditions, such as rain, fog, haze, and low light, remains a difficult challenge. In order to address this issue, we propose a novel object detection framework called Enhanced Multispectral YOLO (EMYOLO). EMYOLO utilizes two key modules: the Enhanced module and the Transformer Fusing module, to enhance the representation power and feature representation of both RGB and IR modalities. We evaluate EMYOLO on several benchmark datasets, including LLVIP, TNO, and M3FD, under various weather conditions. Experimental results demonstrate that EMYOLO outperforms several object detection models and maintains a good balance between detection accuracy and model complexity, particularly in adverse weather conditions. Our proposed approach has significant implications for the development of intelligent cars and other applications that rely on object detection in challenging weather conditions.
Multi-Label Weather Image Classification for Embedded Computing Platforms

In this work, we propose a Multi-Label Weather Classification framework (MLWC) designed for embedded computing platforms. The MLWC framework consists of five Convolutional Neural Network (CNN) models, where each one is trained with a specific adverse weather condition, i.e., clear skies, cloudy, rainy, foggy, snowy. In order to develop lightweight CNN models, we have applied the novel Sparse-Split-Parallelism (SSP) framework to DenseNet-121, achieving a model size reduction of 55%. The experiments prove the effectiveness of the multi-label framework, by correctly predicting multiple weather conditions in a single image. In particular, the weather classification models, lightened with the SSP method, achieved accuracy rates of 90.95% on clear skies, 59.24% on cloudy weather, 91.54% on foggy weather, 73.33% on rainy weather, and 95.22% on snowy weather. Following that, their performance is compared with other lightweight models, such as, MobileNetV2, MobileNetV3 and EfficientNetB0, confirming the advantages of the SSP framework. Lastly, the proposed MLWC framework is evaluated on the NVIDIA Jetson Nano board, which achieves multi-label classification results in 4.2 seconds per image. With the accomplished inference time, we prove that the MLWC framework is suitable to be deployed in small embedded platforms that can be used in autonomous driving systems, like vehicles or drones, and maintain a good performance.
Deep Self-Supervised Hashing With Fine-Grained Similarity Mining for Cross-Modal Retrieval

With the efficiency of storage and retrieval speed, the hashing methods have attracted a lot of attention for cross-modal retrieval applications. In contrast to traditional cross-modal hashing by using handcrafted features, deep cross-modal hashing integrates the advantages of deep learning and hashing methods to encode raw multimodal data into compact binary codes with semantic information preserved. Generally speaking, most of the existing deep cross-modal hashing methods simply define the semantic similarity between heterogeneous modalities by counting the number of shared semantic labels (such as, two samples share at least one label, they are similar, otherwise they are dissimilar), which fails to represent the accurate multi-label semantic relations between heterogeneous data. In this paper, we propose a new Deep Self-supervised Hashing with Fine-grained Similarity Mining (DSH-FSM) framework to efficiently preserve the fine-grained multi-label semantic similarity, learning a highly separable embedding space. Specifically, by employing an asymmetric guidance strategy, a novel Semantic-Network is introduced into cross-modal hashing to learn two semantic dictionaries, including the semantic feature dictionary and the semantic code dictionary, which guides the Image-Network and the Text-Network to capture multi-label semantic relevance across different modalities. Based on the obtained semantic dictionary, an asymmetric margin-scalable loss is proposed to obtain fine-grained pair-wise similarity information, which could contribute to the production of similarity-preserving and discriminative binary codes. Besides, two feature extractors with transformer encoders are designed to achieve the Image-Network and Text-Network, which could extract the representative semantic characteristics from raw heterogeneous samples. Extensive experimental results on various benchmark datasets show that our proposed DSH-FSM framework achieves state-of-the-art cross-modal similarity search performance. Compared to the state-of-the-art methods, the results of mAP are significantly improved by 1.9%, 9.1%, and 9.8%, respectively, on the three widely used datasets.
Profiling and Understanding CPU Power Management in Linux

Dynamic Voltage and Frequency Scaling (DVFS) is a popular technique for power management. Understanding the DVFS principles and the current practice of existing DVFS governors embedded in operating systems (OS) is essential. This work aims to provide a deep understanding of DVFS power management through real-time profiling on various Linux platforms, including an Intel-based laptop, an ARM-based Jetson Nano Board, and a Raspberry Pi platform. We first present the theoretical model for dynamic and static power consumption and describe experiments on three platforms to show how frequency affects their power consumption. We then visualize the real-time behaviour of the existing DVFS governors: the Ondemand and Conservative governors in OS kernels on multiple platforms under different parameter settings. Furthermore, we identify issues that may be encountered in actual hardware experiments but are often overlooked by researchers. Examples are coarse-grained frequency levels and the OS interface not reflecting the actual frequency. Finally, we design experiments to explore the relationship between utilization and power. This work can help DVFS algorithm designers to consider the practical aspect of integrating DVFS algorithms into actual systems.
Yaw Motion Control System for Novel Unmanned Water-Powered Aerial Vehicle: Design and Experiments

This paper presents a novel unmanned water-powered aerial vehicle and designs a robust nonlinear yaw motion controller. Over the past five years, researchers have studied autonomous water-powered aerial vehicles to enhance their application in various fields including rescue missions, intricate space exploration, firefighting operations, and more, rather than limiting them to entertainment purposes like their manned counterparts. To enable the water-powered aerial vehicle to operate without riders, numerous drive mechanisms have been proposed to govern its motion. Nonetheless, the existing structures still possess certain minor imperfections. Therefore, this study first introduces a novel unmanned water-powered aerial vehicle (UWAV) that utilizes a nozzle rotation mechanism with some improvements. A mathematical model is derived to describe its rotational dynamic characteristics. A robust nonlinear controller is designed to control the yaw motion of the proposed UWAV. This controller consists of two parts: (1) forming a disturbance estimation through an extended state observer (ESO) to estimate and compensate for disturbances, and (2) implementing an integral super-twisting sliding mode control (ISTSMC) law, which ensures robust tracking and stability of the UWAV. The experiments compare the proposed controller with the standalone ISTSMC. The results demonstrate the feasibility of the designed model and the superiority of the proposed controller in terms of robustness and tracking performance.
Novel Body Biometric for Long-Range Recognition Under Extreme Conditions

The task of video-based human recognition is complicated by many factors such as imaging distortions, imaging range, lack of frames, arbitrary pose, occlusions, air turbulence, and changing clothes. This work presents the first study that utilizes single-frame binary silhouettes and their auxiliary representations for human recognition under extreme distortions. The proposed representation is compact, modular, and robust to distortions, allowing for easy deployability for long-range recognition. Quantitative metrics are reported on long-range dataset such as Briar, demonstrating the robustness of the proposed approach to common challenges of video-based recognition in the wild. The proposed single-frame method is compared against gait techniques using limited frames, outperforming in most cases. Performance is also compared to grayscale images with varying ranges, environments, and changing clothes, where the proposed model outperforms grayscale images. Under consistent conditions, the proposed model still augments the performance of the baseline grayscale model by over 15%.
Meta Combination Framework for 3D Point Cloud Analysis with Distillation

Recent advancements in 3D point cloud analysis have introduced various network architectures to the field. Nevertheless, the absence of a standard framework for describing these networks makes it difficult to systematically compare and analyze them. Furthermore, the network size of these models tends to be large due to the emergence of Large Language Models. We propose a method called MetaPointDistill that employs unified building blocks and model compression techniques to mitigate the aforementioned issues. To explain, when comparing systems, we need a unified framework structure for fair and convenient comparison. At the same time, the scale of this framework cannot be too large, so model compression technology needs to be used. Specifically, we partition the building blocks into Neighborhood Updater, Neighborhood Aggregator, Point Updater, and Spatial Embedding Generator, and explore their optimal combinations. Additionally, we employ knowledge distillation to obtain a model with improved performance and reduced size. We conduct experiments on multiple tasks to evaluate the efficacy of our framework. The results demonstrate that MetaPointDistill outperforms prior state-of-the-art methods by 1.2% mIoU and 0.4% OA while utilizing only 13% and 35% FLOPs on the S3DIS and ScanObjectNN datasets, respectively. Overall, our proposed framework exhibits significant potential for addressing challenges encountered in practical production processes.
LMI-based Robust State Feedback Control of A Water-powered Tethered Drone with Input Saturations

This paper presents the design of a motion control system for a tethered drone propelled by water jets. The drone is intended to be used for aerial operations in water areas. The system consists of a water pump, a flexible hose, and a main body with four rotatable nozzles that can be controlled. The mathematical model highlights the significant effect of the hose on the system's motion, while practical perspectives give the saturation of control signals. To fulfill the system's motion requirements, a state feedback controller is proposed. The controller is synthesized using linear matrix inequalities so that the system robustness is preserved, and the input saturations are satisfied. Simulation studies validate the effectiveness of the proposed control system. In comparison to a cascade proportional-integral-derivative controller, the proposed one provides more stable motion with less control effort.
Predicting Prostate Cancer Risk Stratification Based on PSA and Functional Subsets of Peripheral Lymphocyte

Prostate cancer (PCa) is the second most common cancer among men worldwide, but its diagnosis has long grappled with issues of over-and under-treatment. To address this challenge, novel screening tools and liquid biopsy techniques have emerged in recent years. These methods not only enhance risk stratification for prostate cancer but also reduce patient discomfort and risk. Notably, Prostate-specifc antigen (PSA) screening is no longer the sole choice. In this study, we selected functional subsets of peripheral lymphocyte along with PSA levels to predict the three risk stratifications of PCa. We collected clinical data from 197 prostate cancer patients, subjected it to data cleaning, and used lasso feature selection to choose the optimal feature subset. A decision tree model was employed with ten-fold cross-validation to predict low, intermediate, and high-risk stratifications for PCa. The study yielded an AUC of 0.8688, F1 score of 0.7950, sensitivity of 0.7944, and specificity of 0.9093.
CRNN-Based Abstract Artistic Text Recognition

As an important technique for visual perception, text recognition has been an active and long-standing research topic in the field of computer vision. With the rapid development of deep learning in recent years, significant progress has been made in scene text recognition, leading to the proposal of numerous excellent algorithms. However, limited research has been conducted on the recognition of abstract artistic text, which possess unique application scenarios. In this work, we improved and optimized various aspects of the classical Convolutional Recurrent Neural Network (CRNN), including data training, data preprocessing, and feature extraction. While ensuring the delivery of model’s ability in common text recognition tasks, we have greatly enhanced its end-to-end capability to spot abstract artistic text of arbitrary length. Additionally, we performed extensive experiments on a large number of artistic text recognition, comparing our model with the classical CRNN model and Baidu’s Paddle OCR tool. The results demonstrate the significant advantages of our model in this regard.