Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Wheat Husk ASH and Calcined Eggshell as Potential Glass Former

Numerous agricultural byproducts, such as rice husk and straw, bagasse from sugar cane, palm kernel shell, wheat husk and straw, corn cobs, etc, are highly desired for the production of renewable energy and are seen as potential raw materials for high-value products. Because they can be used to extract quality silica and Calcium oxide for borosilicate glass production, this research has demonstrated that these wastes have a significant end value. X-ray diffraction (XRD) spectroscopy, Fourier transform infrared spectroscopy (FT-IR), and X-ray fluorescence spectroscopy (XRF) were used to characterize the calcined waste eggshell and wheat husk ash for crystal type, compound identification, and chemical composition. The findings demonstrated that the amount of silica and calcium oxide obtained from agricultural waste could be a suitable alternative source for making glass, with calcined eggshells having a calcium oxide content of 91.7% and wheat husk ash having a silica content of 71.3%. The potential for utilizing the CaO and amorphous silica in the formation of glass is thus intriguing.
Explaining Face Recognition Through SHAP-Based Pixel-Level Face Image Quality Assessment

Biometric face recognition models are widely used in many different real-world applications. The output of these models can be used to make decisions that may strongly impact people. However, an explanation of how and why such outputs are derived is usually not given to humans. The lack of explainability of face recognition models leads to distrust in their decisions and does not encourage their use. The performance of face recognition models is influenced by the quality of the input image. In case the quality of a face image is too low, the face recognition system will reject it to avoid compromising its performance. The quality is evaluated by Face Image Quality (FIQ) approaches, which assigned quality scores to the input images. Pixel-level face image quality (PLFIQ) increases the explainability of quality scores by explaining face image quality at the pixel level. This allows the users of face recognition systems to spot low-quality areas and allows them to make guided corrections. Previous works introduced the concept of PLFIQ and proposed evaluation procedures. This work proposes a new way of computing PLFIQ values depending on given FIQ methods using Shapley Values. They score the contribution of each pixel to the overall image quality evaluation. Therefore, Integrating Shapley Values increases the explainability of the FIQ models. Results show that using these methods leads to significantly better and more robust PLFIQ values estimates and thus provide better explainability.
StyleAU: StyleGAN based Facial Action Unit Manipulation for Expression Editing

Facial expression editing has a wide range of applications, such as emotion detection, human-computer interaction, and social entertainment. However, existing expression editing methods either fail to allow for fine-grained editing, resulting in unnatural and unrealistic facial expressions, or generate artifacts and blurs, leading to poor image quality. In this paper, we propose a novel framework called StyleAU, which is based on StyleGAN and facial action units, to address these problems. Our framework leverages the pre-trained StyleGAN prior knowledge to enable action unit editing of the face in the StyleGAN latent space, allowing precise expression editing. In addition, we use an encoder to extract multi-scale content features to achieve high-fidelity image reconstruction. Our approach qualitatively and quantitatively outperforms competing methods for action unit manipulation and expression editing.
Language Models Fine-Tuning for Automatic Format Reconstruction of SEC Financial Filings

The analysis of financial reports is a crucial task for investors and regulators, especially the mandatory annual reports (10-K) required by the SEC (Securities and Exchange Commission) that provide crucial information about a public company in the American stock market. Although SEC suggests a specific document format to standardize and simplify the analysis, in recent years, several companies have introduced their own format and organization of the contents, making human-based and automatic knowledge extraction inherently more difficult. In this research work, we investigate different Neural language models based on Transformer networks (Bidirectional recurrence-based, Autoregressive-based, and Autoencoders-based approaches) to automatically reconstruct an SEC-like format of the documents as a multi-class classification task with 18 classes at the sentence level. In particular, we propose a Bidirectional fine-tuning procedure to specialize pre-trained language models on this task. We propose and make the resulting novel transformer model, named SEC-former, publicly available to deal with this task. We evaluate SEC-former in three different scenarios: 1) in terms of topic detection performances; 2) in terms of document similarity (TF-IDF Bag-of-words and Doc2Vec) achieved with respect to original and trustable financial reports since this operation is leveraged for portfolio optimization tasks; and 3) testing the model in a real use-case scenario related to a public company that does not respect the SEC format but provides a human-supervised reference to reconstruct it.
Multistage Enhancement Network for Tiny Object Detection in Remote Sensing Images

With the rapid advances in deep learning techniques, remote sensing object detection (RSOD) has achieved remarkable achievements in recent years. However, tiny object detection remains unsatisfactory and suffers from two main drawbacks: 1) the high sensitivity of IoU for location deviation in tiny objects and 2) the poor-quality feature representations of tiny objects. To address the aforementioned problems, we propose a multistage enhancement network (MENet) that achieves the instance-level and feature-level enhancement of tiny objects from different stages of the detector. Since the IoU-based label assignment drastically deteriorates the positive samples for tiny objects, we first propose a central region (CR)-based label assignment to substitute it in the region proposal network (RPN). The CR label assignment regards the anchors that fall into the CR of ground-truth boxes as positive samples, which provides more positive samples for tiny objects. Then, we design a gated context aggregation (GCA) module that selectively aggregates valuable context information to enhance the feature representation of tiny objects. Additionally, we devise a positive RoI (pRoI) feature generator in the region convolutional neural network (R-CNN) to generate a rich diversity of high-quality pRoI features for tiny objects. We conduct extensive experiments on AI-TOD and SODA-A datasets, and the results demonstrate the effectiveness of our proposed method.
Polarity Classification of Twitter Data Using Machine Learning Approach

Sentiment analysis has become very important nowadays because there are lots of social media platforms peoples are using to express their opinion. Twitter is one of the most popular social media platforms which is used for microblogs. People use to express their opinion on current affairs, and there is a challenge for researchers to classify the sentiment accurately. In this research study, we proposed a greatly efficient technique for the detection of fake news on covid-19. The data set of fake news is taken from the corpus and executes the NLP cycle. In this research, we applied five machine learning to predict the sentiment of fake or real news. Support Vector Machine, Logistic Regression, KNN, Decision Trees, and Random Forest are machine learning classifiers used in this research, and results are compared.
Synthetic Face Generation Through Eyes-to-Face Inpainting

This study introduces a new technique for generating synthetic faces using eyes-to-face inpainting methods. The proposed method can synthesize a face image using a combination of the eyes of two different individuals and use it as an input for inpainting, demonstrating its vast potential for various applications in biometrics. Despite minor biases in age and gender, our method proved effective in training reliable age- and gender-detection models using the generated datasets. We also addressed the challenge of training face recognition models using synthetic datasets, and the results demonstrated satisfactory accuracy across four benchmark face recognition datasets. This method could be particularly beneficial for underrepresented groups, for whom there is a scarcity of face samples in biometric datasets.
Image Segmentation with Contrastive Learning for Plant Time-series Images with Priori Distance Embedding

Current plant phenotyping studies have focused extensively on plant time-series image studies utilizing deep learning. Such study images are easier to obtain but costly to annotate, and contrast learning is one method for efficient label training. The growth of plants is slow, their image sequences change little over time, and their semantic information is simple. Previous contrast pre-training models struggle to distinguish between positive samples from different enhanced views of the same image and similar negative samples from different images. For this reason, this paper proposes a contrastive learning method with a priori distance embedding (PDE) for plant time-series images. Different phenological stages of plants correspond to different semantic information in the images. The method converts this essential domain knowledge into a priori distances between image pairs and performs comparative learning pre-training; the weights can then be transferred to the semantic segmentation task. Based on this method, the results of experiments on time-series images of cherries demonstrate that the PDE comparative learning method can be effectively applied to the pre-training of semantic segmentation of plant time-series images, and has a wide range of potential applications in computer vision plant phenotypic research.
The target plant concept-a history and brief overview

: The target plant concept originated with morphological classification of conifer nursery stock in the 1930s, and the concept was enhanced through physiological research and seedling testing towards the end of the century. Morphological grading standards such as shoot height, stem diameter, and root mass are the most common use of the target plant concept, and some physiological grading standards are also being operationally implemented by nursery workers and seedling users. Since 2000, the concept has been expanded to include all types of plant materials, including seeds, cuttings, or wildlings, as well as traditional nursery stock. Because these native plant materials are being outplanted on harsh, severely disturbed sites, this more comprehensive native plant materials concept also involves environmental conditions on the project site.
Real-time Pedestrian Detection Using Resource Constrained Embedded Platforms – A Review

Autonomous Vehicles (AV) are currently limited to the sensors fitted on the vehicle itself to detect pedestrians instead of working collaboratively with the road side units (RSUs). If Resource Constrained Embedded Platforms (RCEPs) were capable of achieving real-time pedestrian detection, they could be deployed at busy traffic intersections to assist AVs. Currently, high-end GPU platforms are required to achieve real-time performance, but their size and cost mean they are not viable for deployment at busy traffic intersections. Therefore, this study performs a comparative analysis as to the ability of RCEPs to achieve real-time pedestrian detection using Convolutional Neural Network (CNN) architectures. Several CNN architectures were trained on a custom pedestrian dataset, with Tiny Yolov4 achieving 62% mean average precision (mAP). When deployed, this analysis indicates that some RCEPs are capable of achieving real-time pedestrian detection, benchmarked at 30FPS, with Tiny YOLOv4 achieving 40.48FPS on the NVIDIA Jetson Nano. The Raspberry Pi 4 paired with the Google Edge TPU also exceeded the real-time threshold, achieving 51.22FPS using MobileNetV2-SSD. Both models were quantized at FP16 and UINT8, which inherently have lower accuracy in favour of faster inferencing. Our analysis evaluates the real-time performance of the selected embedded devices when hosting lightweight CNNs, as well as offering numerous future research directions to improve the pedestrian detection.
An Efficient Marine Species Classifier via Knowledge Distillation and Hybrid Attention

Marine species recognition is a crucial task in ocean exploration. Despite the impressive performance of deep-learning-based classification methods, these approaches often suffer from high complexity in terms of model parameters, storage size, and FLOPs. This complexity leads to slow inference and high consumption of computational resources, which presents a significant challenge for ocean engineering. To address this challenge, we propose a lightweight framework for marine species recognition based on knowledge distillation and the attention mechanism. Our method introduces a relatively complex teacher classifier with the backbone of ResNet-18, and an attention module to enhance its performance. We design the student classifier using only a few convolutional layers, thereby significantly reducing the number of parameters. Extensive experiments demonstrate that the student network achieves competitive performance to other representative lightweight models such as ShuffleNetv2 and MobileNetv3-small, while its model parameters are only 1.624% of the MobileNetv3-small.
Morphophysiological responses of cowpea to irrigation with saline water and application of bovine biofertilizer

Cowpea crop has great economic importance, mainly in the Northeast, where it is a food source for several families. In this context, the objective was to evaluate the effect of levels of electrical conductivity on irrigation water as a function of the application of biofertilizer in the growth and production of cowpea. The experimental design was completely randomized, presenting a factorial scheme of 4 x 2, corresponding to four levels of electrical conductivity of the irrigation water (ECw), 1; 2; 3 and 4 dS m-1 and the presence and absence of bovine biofertilizer application in four replicates. Plant height, number of leaves, stem diameter, number of lateral branches, main branch length, root dry weight, shoot and total number, pod number per plant, mass dry weight of the pod, the length and mean diameter of the pod, the number of grains per plant, the number of grains per pod, the mass of grains and the mass of 100 grains. The increase of ECw inhibits vegetative and productive growth of cowpea and the use of enriched bovine biofertilizer attenuates the effects of saline stress on cowpea, favoring its development.
Fourier Feature Activated Neural-field Modelling For Human Avatar Representation

In this paper, we introduce a novel neural representation method called Fourier Feature Activated Neural-field Modelling (FFANM) for 3D models. FFANM is designed to accurately and efficiently capture the surface geometry of 4D human avatars, which has numerous applications in the fields of remote conferencing, livestreaming marketing, short video web-cast, VR/AR, and video games and movie industry. While existing neural modelling methods are capable of representing either low-frequency or high-frequency surface geometry, they cannot do so simultaneously, resulting in poor overall representation quality. To address this limitation, we propose FFANM, which incorporates position encoding and periodic activation to leverage its Fourier properties for better representation of high-frequency information, while maintaining smooth surfaces free from noise. Our experiments demonstrate that FFANM outperforms state-of-the-art methods both quantitatively and qualitatively in terms of overall model reconstruction quality and high-frequency geometry details representation. Finally, We apply our proposed method, FFANM, in the 4D human avatar digitization pipeline and Metaverse application, and its superior visual performance demonstrates its capability in dynamic scenarios.
Mixed-Precision Architecture for GPU Tensor Cores

NVIDIA introduced a new generation of graphics processing unit (GPU), called Volta, to meet the growing demand for higher performance in Deep Neural Networks (DNNs). Volta GPUs exploit a dedicated hardware unit, called Tensor Core (TC), to accelerate multiplication of matrices. While TCs offer significant computational horsepower for deep learning applications, they increase the power budget of DNNs. In this work, we exploit error resiliency property of DNNs and propose a technique to reduce energy of TCs. In particular, we propose an approximate architecture with the flexibility of switching between exact and approximate operating modes. In approximate mode, TCs offer significant energy saving at the cost of lower accuracy. To mitigate the impact of approximation on accuracy, we propose a mixed-precision architecture where a combination of exact and approximate units is used to reduce error in DNNs. The mixed-precision architecture provides the flexibility for the software stack to tune the level of approximation to satisfy a desired inference accuracy in DNNs. We evaluate the proposed architecture using DNNs selected from a wide range of application domains and show that the mixed-precision architecture achieves 40% energy saving while maintaining accuracy of DNNs.
DESNTC: Transformer-Based Double Shift Network for Breast Histopathological Image Classification

Multiclass classification of breast cancer histopathology images based on deep learning methods has significant clinical implications. It can not only improve the diagnostic efficiency of pathologists but also greatly reduce the diagnostic subjectivity of pathologists. Most of the existing studies use convolutional neural networks for binary classification of benign and malignant lesions, while 8 cancer types classification is less studied. However, the study of multiclass classification is useful for clinicians to accurately distinguish different subtypes of histopathology images for accurate treatment. To this end, we propose a network architecture namely DESNTC in this work based on a transformer structure that fuses shift and convolution operations. The proposed network fully combines the ability of the transformer to extract global information with the ability of the convolution operation to mine local information and enhances the capture of channel information with the shift operation. This is the great application of the transformer model to the task of multiclass classification of breast cancer histopathological images. In this paper, studies for 8 cancer types are conducted on the BreakHis dataset. The proposed model achieves the best results among the existing studies, demonstrating the effectiveness of the approach for smart assistant diagnosis of breast cancer histopathological images.
Towards detecting TV customer frustration using Hidden Markov Models

Customer engagement has long been a major field of study in the market for digital products because a better understanding of customer behaviour enables service providers to optimise their offerings and retain the customers who are at risk of leaving. Process mining has been widely used in this context, as it is important to mine the changes in customer behaviour throughout the lifespan to observe how customers navigate the product or service. The application of Hidden Markov Models (HMM) to BT TV set-top-box (STB) customer viewing data is the main topic of this paper. We use continuous state values derived from event sequences to construct derived features from the source data. We then employ a sliding window-based approach to create features and facilitate smoothing of the outcomes. In order to improve the Internet Protocol Television (IPTV) service, the goal of this study is to explore the methodologies currently used for evaluating customer engagement. We suggest a workable strategy for identifying TV STB customer frustration. Also, we have discovered some intriguing insights regarding the inferred Gaussian distributions of four previously selected features concerning customers' behaviour in TV navigation journeys over time using the hidden states produced by the HMM, with one of the hidden states providing indicators of customer frustration.
Child Restraint Use Survey (LATCH Use and Misuse): Coding Manual

The purpose of this project was to collect quantitative data and other information concerning the restraint use of children in passenger vehicles. In particular, the project was conducted to obtain information regarding whether LATCH technology (Lower Anchors and Tethers for Children) was being used in vehicles equipped with LATCH, and whether LATCH technology was being used correctly in the installation of child restraint systems (CRSs). The study also sought information about drivers’ knowledge of booster seats and their purposes, as well as drivers’ knowledge about the new LATCH technology for CRSs. In addition, if LATCH equipment was available in the vehicles, information about drivers’ likes and dislikes about LATCH technology was obtained, and whether there were preferences for using safety belts to secure the CRS to the vehicle over use of lower anchors. Additionally, the study sought to identify relationships between selected driver demographic characteristics (age, gender, race/ethnicity) and restraint use of children; as well as the relationship that variables such as the demographics of other occupants in the vehicle, vehicle type, occupant seating position, and restraint type available for the seating position had on restraint use. This Coding Manual provides information about data collected in 7 States from 1,182 vehicles that were transporting 1,728 children ages 12 and under. Data are organized into two sets—a Vehicle data set and an Occupant data set. This Manual provides a description of the 126 variables in the Vehicle data set and the 91 variables in the Occupant data set to be used for statistical analysis purposes.
Improving Domain Generalization in Contrastive Learning using Adaptive Temperature Control

Self-supervised pre-training with contrastive learning is a powerful method for learning from sparsely labeled data. However, performance can drop considerably when there is a shift in the distribution of data from training to test time. We study this phenomenon in a setting in which the training data come from multiple domains, and the test data come from a domain not seen at training that is subject to significant covariate shift. We present a new method for contrastive learning that incorporates domain labels to increase the domain invariance of learned representations, leading to improved out-of-distribution generalization. Our method adjusts the temperature parameter in the InfoNCE loss – which controls the relative weighting of negative pairs – using the probability that a negative sample comes from the same domain as the anchor. This upweights pairs from more similar domains, encouraging the model to discriminate samples based on domain-invariant attributes. Through experiments on a variant of the MNIST dataset, we demonstrate that our method yields better out-of-distribution performance than domain generalization baselines. Furthermore, our method maintains strong in-distribution task performance, substantially outperforming baselines on this measure.