Reviewerly/exHarmony huggingface dataset:
A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

This research presents a novel hybrid sampling technique, implemented at the data level, to effectively address imbalanced and noisy data in classification processes.The proposed technique expertly combines two established methods, namely, the random over sampling (ROS) and neighbourhood cleaning rule (NCL) approaches, to tackle imbalance and noise issues, respectively.The study carried out an empirical evaluation of the proposed approach using crowdsourced text data that primarily emphasized the triple bottom line (TBL) dimension of a smart social, economic, and environmental city.The study used the long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM classification models to validate the efficacy of the proposed hybrid sampling technique and compare its performance with other existing approaches, including ROS oversampling, NCL undersampling, synthetic minority over sampling & tomek links (SMOTE-Tomek), and synthetic minority oversampling and edited nearest neighbours (SMOTE-ENN) hybrid sampling.The results are impressive, with the ROS-NCL hybrid sampling technique achieving high accuracy rates across all three classification models, at 97.71%, 98.01%, and 98.11%, respectively.This approach provides a robust and effective solution for handling impure data and holds great promise in identifying complex data patterns in real-world classification problems.
Sinusoidal Regression Modelling of Vehicular Data Communication Employing NP-CSMA

The objective is to investigate effect of slot probability and its effectiveness in vehicular communication.The work carries out MATLAB simulation covering three different levels of slot probability; low, medium, and high.The goal behind such simulation is to uncover the process that leads to high reduction in throughput.The normal explanation for the reduction is high levels of collisions and re-transmission of data through new attempts.However, this work shows that such process can be characterized and controlled as the work shows through sinusoidal regression, the oscillatory behavior caused by collisions, attempts, retransmission, as a function of slot probability.The simulation results, which covers 20 vehicles at various slot probability levels, shows that there is a change in the data trafficthroughput shape function.This is due to the effect of slot probability and reflects on the level of obtained oscillations.The work also discovered an oscillation pattern that infiltrate and affect communication channels at different levels as a function of collisions and data delivery attempts, which is related to slot probability and data traffic.Such oscillatory pattern should be investigated further and the conditions for such pattern should be eliminated, in order to achieve better utilization of communication channel and higher throughput, with lower attempts and less collisions.This is also related to both slot probability and number of available slots.The obtained results in this work showed that at the upper part of the mid slot probability range and all the way to through the high slot probability part, a noticeable oscillatory behaviour is observed, with almost constant values of average data traffic and throughput values.These effects are accompanied by large acceleration and accumulation of collisions.Thus such new approach, and in conjunction with NP-CSMA, can be used at both the design stage of communication channels and vehicular networks, and during operations.This will enable adaptive slot allocation and dynamic transmission of messages, which will result in collisions reduction and better channel utilization.
Meta-Knowledge Guided Weakly Supervised Instance Segmentation for Optical and SAR Image Interpretation

The interpretation of optical and synthetic aperture radar (SAR) images in remote sensing is general for many tasks, such as environmental monitoring, marine management, and resource planning. Instance segmentation of optical and SAR images, which can simultaneously provide instance-level localization and pixel-level classification of objects of interest, is a crucial and challenging task in image interpretation. Considering that most current methods for instance segmentation of optical and SAR images rely on expensive pixel-level annotation, we develop a weakly supervised instance segmentation (WSIS) method to balance the visual processing requirements with the annotation cost. First, we decompose the prior knowledge of the mask-aware task in WSIS into three meta-knowledge components: fundamental knowledge, apparent knowledge, and detailed knowledge inspired by human visual perception habits of “whole to part” and “coarse to detailed.” Then, a meta-knowledge-guided weakly supervised instance segmentation network (MGWI-Net) is proposed. In this network, the weakly supervised mask (WSM) head can instantiate both fundamental knowledge and apparent knowledge to perform mask awareness without any annotations at the pixel level. The network also includes a mask information awareness assist (MIAA) head, which can implicitly guide the network to learn detailed information about edges through the boundary-sensitive feature of the fully connected conditional random field (CRF), facilitating the instantiation of detailed knowledge. The experimental results show that the MGWI-Net can efficiently generate instance masks for optical and SAR images and achieve the approximate instance segmentation results of the fully supervised method with about one-eighth of the annotation production time. The model parameters and processing speed of our network are also competitive. This study can provide inexpensive and convenient technical support for applying and promoting instance segmentation methods for optical and SAR images.
The Funneling Effect: A prototype implementation of an illusory sense of touch in virtual reality

This paper implements the “funneling illusion” in immersive virtual reality. The aim is to create a richer user experience by incorporating multiple senses, as well as, improving the sense of presence and sense of body ownership. In the future, we will experimentally test the hypothesis that vibrotactile stimuli delivered by two separate handheld VR controllers can elicit an “out-of-the-body” illusory sense of touch in virtual reality.
Image-based Automated Framework for Detecting and Classifying Unmanned Aerial Vehicles

UAVs are expected to be extensively used in many applications related to modern transportation systems, e.g., traffic monitoring, flying police-eye, and flying roadside units. However, it is important to note that UAVs can also be maliciously utilized as a threat to public safety and pose a significant risk to the stability and execution of smart city applications. Detection of flying intruders can be performed using various sensors such as cameras, RADAR, and LIDAR. In this paper, we propose an automated framework for UAV detection and classification using ground cameras. First, we use YOLOv8 for detecting UAVs, and then we use an unsupervised clustering approach to classify the detected objects according to their different categories. The clustering process is performed by extracting the Histogram of Oriented Gradients (HOG) features of the detected UAVs. Afterward, the features are embedded by mapping them into a two-dimensional space where the separation of the classes is possible. To this end, we use the t-distributed stochastic neighbor embedding (t-SNE) approach. Our framework is tested on an anti-intrusion UAV unlabeled dataset where we identify all categories of UAVs within that dataset. Our approach has shown remarkable clustering performance compared to existing machine-learning methods.
The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks

Sensitivity analysis measures the influence of a Bayesian network's parameters on a quantity of interest defined by the network, such as the probability of a variable taking a specific value. In the literature, this influence is often measured by computing the partial derivative with respect to the network parameters. However, this can become computationally expensive in large networks with thousands of parameters. We propose an algorithm combining automatic differentiation and exact inference to calculate the sensitivity measures in a single pass efficiently. It first marginalizes the whole network once, using e.g. variable elimination, and then backpropagates this operation to obtain the gradient with respect to all input parameters. Our method can be used for one-way and multi-way sensitivity analysis and the derivation of admissible regions. Simulation studies highlight the efficiency of our algorithm by scaling it to massive networks with up to 100,000 parameters and investigate the feasibility of generic multi-way analyses. Our routines are also showcased over two medium-sized Bayesian networks: the first modeling the country risks of a humanitarian crisis, the second studying the relationship between the use of technology and the psychological effects of forced social isolation during the COVID-19 pandemic. An implementation of the methods using the popular machine learning library PyTorch is freely available.
On the fractional perturbation theory and optical transitions in bulk semiconductors: Emergence of negative damping and variable charged mass

This study is devoted to the construction of a fractional perturbation theory which represents the basic technique of approximation in quantum mechanics. Our analysis is based on the fractional actionlike variational approach (FALVA) implementation of fractionality. After discussing the basic properties of fractional harmonic oscillator through the fractional Feynman's path integral approach, we have discussed the Rabi oscillations within the context of two-level quantum systems for simulated optical transition. In particular the case α>1 is correlated to the emergence of negative damping and negative probabilities which have been obtained in several quantum out-of-equilibrium systems. The fractional Fermi Golden rule and the fractional Heisenberg's uncertainty principle have been also obtained and their main properties and features have been discussed accordingly. We have discussed optical transitions in bulk semiconductors. It was observed that the fundamental properties of transitions rates are recovered if the charged particle moving inside the semiconductor is variable with time.
Privacy Preserving Face Recognition with Lensless Camera

The widespread adoption of facial recognition technology is a global phenomenon. Facial recognition systems leverage upon image data containing faces. This poses serious threats to user privacy as the data is exposed to potential data breaches. In this paper, we propose a face recognition system that works without compromising user privacy. It utilizes data captured by FlatCam - a lensless camera. FlatCam captures the scene as a sensor measurement that is visually unintelligible. The proposed system preserves user privacy since it works directly on FlatCam’s sensor measurements without the need of FlatCam camera parameters which are required for pixel reconstruction. We propose a frequency domain deep learning solution that computes the DCT of the sensor field at multiple resolutions and organizes it into sub-bands before training a classification network with attention. The multi-resolution DCT subband representation leads to huge performance gains when compared to using the sensor measurement directly for training. Our proposed system was trained and tested on a real lensless camera dataset - the Flat-Cam Face dataset. Privacy of user is preserved during both training and testing. Experimental results demonstrate the effectiveness of our method.
Compensatory Debiasing For Gender Imbalances In Language Models

Pre-trained language models (PLMs) learn gender bias from imbalances in human-written corpora. This bias leads to critical social issues when deploying PLMs in real-world scenarios. However, minimizing bias is limited by the trade-off due to the degradation of language modeling performance. It is particularly challenging to detach and remove biased representations in the embedding space because the learned linguistic knowledge entails bias. To address this problem, we propose a compensatory debiasing strategy to reduce gender bias while preserving linguistic knowledge. This strategy utilizes two types of sentences to distinguish biased knowledge: stereotype and non-stereotype sentences. We assign small angles and distances to pairs of representations of the two gender groups to mitigate bias for the stereotype sentences. At the same time, we maximize the agreement for the representations of the debiasing model and the original model to maintain linguistic knowledge for the non-stereotype sentences. To validate our approach, we measure the performance of the debiased model using the following evaluation metrics: SEAT, StereoSet, CrowS-Pairs, and GLUE. Our experimental results demonstrate that the model fine-tuned by our strategy has the lowest level of bias while retaining knowledge of PLMs.
Learning on Entropy Coded Images with CNN

We propose an empirical study to see whether learning with convolutional neural networks (CNNs) on entropy coded data is possible. First, we define spatial and semantic closeness, two key properties that we experimentally show to be necessary to guarantee the efficiency of the convolution. Then, we show that these properties are not satisfied by the data processed by an entropy coder. Despite this, our experimental results show that learning in such difficult conditions is still possible, and that the performance are far from a random guess. These results have been obtained thanks to the construction of CNN architectures designed for 1D data (one based on VGG, the other on ResNet). Finally, we propose some experiments that explain why CNN are still performing reasonably well on entropy coded data.
Multi-Source Templates Learning for Real-Time Aerial Tracking

Aerial tracking aims at tracking an arbitrary visual object in a video captured by Unmanned Aerial Vehicles (UAV). Due to the scarce computation resources, the deployment of high-consuming state-of-the-art trackers on UAV becomes impractical. On the other hand, lightweight trackers suffer from inferior performance caused by the low sampling frequency and resolution of UAV videos. In this paper, we propose a novel multi-source templates learning method to alleviate the paradox of efficiency and effectiveness for aerial tracking. Besides conventional static and dynamic templates, our work introduces an additional general-object template to learn common feature properties of a general object during training time. To exploit all templates information, a multi-source templates fusion scheme is proposed to capture characteristics of object in low quality UAV video streams. Furthermore, a joint optimization process is employed to enforce the lightness of model while achieving comparable tracking performance. Our experimental results demonstrate an appealing performance trade-off between accuracy and speed. The proposed tracker achieves 200 FPS on GPU, 100 FPS on CPU, and 12 FPS on Nvidia Jetson Xavier NX, respectively. Our code will be released at https://github.com/vpx-ecnu/MSTL.
Virtual reality enabled asynchronous learning modules for fiber optic preform manufacturing education

The demand for skilled workers and novel manufacturing training solutions has increased with the growing demand for fiber optic cables. Web-based simulations can be used for training, and this paper presents an approach for developing a fiber preform manufacturing browser-based VR simulation. Subsequently, a study was conducted to evaluate the effectiveness of the simulation based on learning gains and learner perception of ease of use, usefulness, intention of use, learning outcomes, and workload. A mixed-methods between-subjects study with 63 participants found that the combination of lecture and simulation was significantly better for perceived usefulness and learning outcomes compared to lecture-only or lecture-and-video conditions.
Ontology-Aware Network for Zero-Shot Sketch-Based Image Retrieval

Zero-Shot Sketch-Based Image Retrieval (ZSSBIR) is an emerging task. The pioneering work focused on the modal gap but ignored inter-class information. Although recent work has begun to consider the triplet-based or contrast-based loss to mine inter-class information, positive and negative samples need to be carefully selected, or the model is prone to lose modality-specific information. To respond to these issues, an Ontology-Aware Network (OAN) is proposed. Specifically, the smooth inter-class independence learning mechanism is put forward to maintain inter-class peculiarity. Meanwhile, distillation-based consistency preservation is utilized to keep modality-specific information. Extensive experiments have demonstrated the superior performance of our algorithm on two challenging Sketchy and Tu-Berlin datasets.
Neural Feature Predictor and Discriminative Residual Coding for Low-Bitrate Speech Coding

Low and ultra-low-bitrate neural speech codecs achieved unprecedented coding gain by generating speech signals from compact features. This paper introduces additional coding efficiency in speech coding by reducing the temporal redundancy existing in the frame-level feature sequence via a feature predictor. This predictor produces low-entropy residual representations, and we discriminatively code them based on their contribution to the signal reconstruction. Combining feature prediction and discriminative coding optimizes bitrate efficiency by assigning more bits to hard-to-predict events. We demonstrate the advantage of the proposed methods using the LPCNet as a neural vocoder, resulting in a scalable, lightweight, low-latency, and low-bitrate neural speech coding system. While our approach guarantees strict causality in the frame-level prediction, the subjective tests and feature space analysis show that our model achieves superior coding efficiency compared to the loosely-causal LPCNet and Lyra V2 in the very low bitrates.
Spatiotemporally Consistent HDR Indoor Lighting Estimation

We propose a physically motivated deep learning framework to solve a general version of the challenging indoor lighting estimation problem. Given a single LDR image with a depth map, our method predicts spatially consistent lighting at any given image position. Particularly, when the input is an LDR video sequence, our framework not only progressively refines the lighting prediction as it sees more regions, but also preserves temporal consistency by keeping the refinement smooth. Our framework reconstructs a spherical Gaussian lighting volume (SGLV) through a tailored 3D encoder-decoder, which enables spatially consistent lighting prediction through volume ray tracing, a hybrid blending network for detailed environment maps, an in-network Monte Carlo rendering layer to enhance photorealism for virtual object insertion, and recurrent neural networks (RNN) to achieve temporally consistent lighting prediction with a video sequence as the input. For training, we significantly enhance the OpenRooms public dataset of photorealistic synthetic indoor scenes with around 360k HDR environment maps of much higher resolution and 38k video sequences, rendered with GPU-based path tracing. Experiments show that our framework achieves lighting prediction with higher quality compared to state-of-the-art single-image or video-based methods, leading to photorealistic AR applications such as object insertion.
Geochemical mapping by stream sediments of the NW portion of Quadrilátero Ferrífero, Brazil: Application of the exploratory data analysis (EDA) and a proposal for generation of new gold targets in Pitangui gold district

Quadrilátero Ferrífero (QF) is a worldwide known metallogenetic province and has in its NW portion the Pitangui greenstone belt, one of the new frontiers for gold exploration in that region, presenting important gold deposits, such as Turmalina and São Sebastião. Among several actions of the geological and mineral resources projects carried out by Geological Survey of Brazil (SGB/CPRM) in the QF region, a high-density geochemical survey (1 sample/4 km2) by stream sediments was accomplished in its NW portion to better understand the behavior and distribution of chemical elements along the main geological units, especially those related to gold mineralization (Au, As, Sb, Bi, etc.). The exploratory data analysis (EDA), regarding uni-, bi-, and multivariate analyses, point to an important chemical control of the stream sediments by the geological units and help to describe some lithological features and to understand the behavior and distribution of hydrothermal processes and gold mineralization. This procedure defined the main geochemical associations, including the association As, Sb, Cu and Bi linked to Ni, Cr and Co, interpreted as gold mineralization hosted in the metavolcanosedimentary lithotypes. The staged factor analysis (SFA) considering compositional data (CoDa), logistic function, and fractal model approaches provided the generation of the geochemical mineralization probability index (GMPI), which produce a GMPI map for gold mineralization. The calculations did not take into account the gold concentrations but only the pathfinder elements and the model validation by the prediction-area plot, confirm the high performance of this approach. The gold mineralization map, in addiction to satisfactorily predicting the location of most known gold occurrences, was able to indicate new potential targets for prospecting new gold deposits.
SAR Image Despeckling with Residual-in-Residual Dense Generative Adversarial Network

Deep convolutional neural networks have delivered remarkable aptitude in performing Synthetic Aperture Radar (SAR) image speckle removal tasks. Such approaches are nevertheless constrained in balancing speckle removal and preservation of spatial information, particularly with respect to strong speckle noise. In this paper, a novel residual-in-residual dense generative adversarial network is proposed to effectively suppress SAR image speckle while retaining rich spatial information. A despeckling sub-network composed of residual-in-residual dense blocks with an encoder-decoder structure is devised to learn end-to-end mapping of noisy images onto noise-free images, where the combination of residual-in-residual structure and dense connection significantly enhances the feature representation capability. In addition, a discriminator sub-network with a fully convolutional structure is introduced, and the adversarial learning strategy is adopted to continuously refine the quality of despeckled results. Systematic experimental results on simulated and real SAR images demonstrate that the novel approach offers superior performance in both quantitative and visual evaluation as compared to state-of-the-art methods.