Reviewerly/exHarmony huggingface dataset:
Indoor UAV Object Detection Algorithms On Three Processors: Implementation Test And Comparison

Nowadays Unmanned aerial vehicles (UAV) are used in various applications. One of the domains that they are recently hired on is remote sensing applications which they should detect and identify the object. Till today various algorithms are proposed but among them SIFT, SURF, ORB and BRISK are more famous but based on the author's resources their performance over the different processors with the same target image is not reported before which is aimed at this research paper. The mentioned algorithms are implemented on three types of hardware, Raspberry Pi3, Odroid C2, and Intel NUC, which were selected based on their availability and different applications in the robotic system. In order to compare the images of a common object, the common UAV structure was designed that can carry all the processors to different heights. Python programming language and OpenCV machine vision library have been used to implement each algorithm. After going through the described calibration steps, the performance of each algorithm in terms of accuracy and processing time on each processor with three cases of rotation modes, the scale and deviation are compared and analyzed based on the image obtained from the drone camera. The results obtained in this research show that the Intel NUC processor has a good processing time in all four algorithms, and the maximum accuracy for all cases of rotation, scale and skew belongs to SIFT. In addition to the mentioned comparison, due to the importance of the weight of accessories, a parameter called TW is defined to show the relationship between processing time and processor mass and the results are reported. The obtained results show that the algorithm's performance result in Raspberry Pi3 and Odroid C2 is very close and inferior to the Intel Nuc processor. But TW for Odroid C2 has even better results than Intel Nuc, which can interest UAV designers and professionals who need to use lightweight drones in remote sensing applications.
USCFormer: Unified Transformer With Semantically Contrastive Learning for Image Dehazing

Haze severely degrades the visibility of scene objects and deteriorates the performance of autonomous driving, traffic monitoring, and other vision-based intelligent transportation systems. As a potential remedy, we propose a novel unified Transformer with semantically contrastive learning for image dehazing, dubbed USCFormer. USCFormer has three key contributions. First, USCFormer absorbs the respective strengths of CNN and Transformer by incorporating them into a unified Transformer format. Thus, it allows the simultaneous capture of global-local dependency features for better image dehazing. Second, by casting clean/hazy images as the positive/negative samples, the contrastive constraint encourages the restored image to be closer to the ground-truth images (positives) and away from the hazy ones (negatives). Third, we regard the semantic information as important prior knowledge to help USCFormer mitigate the effects of haze on the scene and preserve image details and colors by leveraging intra-object semantic correlation. Experiments on synthetic datasets and real-world hazy photos fully validate the superiority of USCFormer in both perceptual quality assessment and subjective evaluation. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yz-wang/USCFormer</uri> .
3D Vehicle Object Tracking Algorithm Based on Bounding Box Similarity Measurement

Effectively extracting features from a discrete point cloud is necessary for three-dimensional (3D) vehicle tracking. However, a point cloud data set is large and sparsely distributed, hindering the success of vehicle-tracking algorithms. To solve this problem, this paper proposes a 3D vehicle object tracking algorithm based on bounding box similarity measurement. The algorithm includes state prediction, temporal association, trajectory management, state update, and other processes. Also incorporated is a vehicle object temporal association method based on a siamese encoder. The bounding box is encoded into a high-dimensional space, the feature distance is calculated as the time series association cost, and triplet loss was introduced to urge the encoder to learn the geometric similarity of the truth matching box. A 3D Kalman filter and greedy matching are used to effectuate 3D vehicle object tracking algorithm. The experimental results using a KITTI multi-object tracking dataset show that the proposed algorithm can achieve good vehicle tracking performance. In the case of frame loss of point cloud in the frequency reduction simulation, compared with the benchmark method AB3DMOT, the average multi-object tracking accuracy (AMOTA) and the average multi-object tracking accuracy (AMOTP) of the improved method are increased by 2.62% and 1.41% respectively, and the performance is better in the frame loss scene.
Privacy-Enhancing Face Obfuscation Guided by Semantic-Aware Attribution Maps

Face recognition technology is increasingly being integrated into our daily life, e.g. Face ID. With the advancement of machine learning algorithms, the personal information such as age, gender, and race can be easily deduced from the recorded face images in these applications. This poses a serious privacy threat to individuals who do not want to be profiled, as face images are collected for biometric purposes. Existing methods mostly focus on adding the invisible adversarial perturbations into the images to make automatic inference infeasible. However, the application scenarios of these methods are limited due to the perturbations depending on the specific model. In this paper, we introduce a novel face privacy-enhancing framework by obfuscating the stored faces, which could maintain the data utility (face identity) while protecting the privacy of users (facial attributes). Specifically, we first develop a feature attribution module to discover the identity-related facial parts. Within this module, we introduce a pixel importance estimation model based on Shapley value to obtain a pixel-level attribution map, and then each pixel on the attribution map is aggregated into semantic facial parts, which are used to quantify the importance of different facial parts. Next, we design a privacy-enhancing module to generate the high-quality obfuscated images, which can modify the privacy semantic content and preserve the identity-related information. Using the proposed method, users can choose the single or multiple attributes to be obfuscated without affecting identity matching. Extensive experiments conducted on CelebA-HQ and VGGFace2-HQ benchmarks demonstrate the effectiveness and generalization ability of our method.
Ensemble Transfer Learning Based Cross-Domain UAV Actuator Fault Detection

The actuator is critical for controlling the unmanned aerial vehicle (UAV) attitude. With the development of sensing technology, the convenience of collecting high-quality data by sensors has provided favorable conditions for data-driven-based UAV actuator fault detection methods. However, there are less historical data on a new type of UAV, making it challenging to accurately obtain data-driven fault detection models. The data domain distribution of the new UAV (target domain) is different from that of existing UAVs (source domains), and the basic data-driven methods are difficult to achieve cross-domain fault detection. The transfer learning of a single source domain method can solve this problem, but it has a strong dependence on the single source domain data characteristics. Therefore, we propose the ensemble transfer learning method, which learns multiple UAV data characteristics to address the accuracy degradation issue caused by single-source domain insufficient characteristics and realize cross-domain actuator fault detection with limited data for the new type of UAV. In the method, the basic actuator prediction models are established based on the bidirectional long short-term memory (BiLSTM) algorithm. Then, we combine the transfer learning method to realize the cross-domain fault detection model establishment of the new type of UAV. In addition, a cross-domain similarity metric based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> -score is creatively proposed for the ensemble to avoid excessive dependence on a single-source domain. We conduct experiments based on the ArduPilot open-source simulation platform, and the results show that our proposed method can adaptively obtain more accurate fault detection performance.
Face Mask Recognition Based on Two-Stage Detector

With the number of positive cases of Covid-19 infection is increasing, it is essential for everyone to wear a face mask and prevent the spread of Covid. As people are gathering in a large number at different locations, it is quite important for everyone to wear a face mask and prevent the covid spread. With the increase in the crowd gathering, it is often hard to see who is not wearing a mask. Although various techniques have been proposed earlier for face mask detection, the results have not been effective. This paper proposes region-based deep learning detection techniques for face mask detection using Faster R-CNN. The proposed model uses ResNet-50 as RPN which generates anchors and output region proposals. Later, ROI pooling is used to map the feature map in proposal to target dimensions. Finally, a classifier is used to output the final class and bounding box around the face. The proposed work attained a final mean average precision (mAP) of 45% over 30 epochs and achieved satisfactory performance.
Forecasting online adaptation methods for energy domain

Time series forecasting is crucial for addressing renewable energy optimization challenges, such as minimizing financial costs. Factors like seasonality and human activity induce concept drift in energy-related forecasting tasks. Despite this, the majority of studies concentrate on offline forecasting models, which are unable to accommodate concept drift. In response, we propose a replacement learning-based online adaptation framework for multivariate multi-step time series forecasting in the energy domain. This method tackles concept drift by retraining models from scratch, leveraging historical data via clustering-based sampling and dimensionality reduction. Furthermore, the forecast model's predictions are refined using the proposed correcting factor, which serves to adjust for any inherent bias present in the forecasting model. Additionally, we develop an incremental learning model that adapts to concept drift by building on the previous model, offering a comparative analysis and investigation of incremental learning. Evaluations using synthetic building electricity demand and cooling system electricity demand datasets demonstrate the stability and effectiveness of our proposed replacement learning adaptation, outperforming offline models by over 50% across various dataset characteristics.
Universal Private Estimators

We present universal estimators for the statistical mean, variance, and scale (in particular, the interquartile range) under pure differential privacy. These estimators are universal in the sense that they work on an arbitrary, unknown continuous distribution P over R, while yielding strong utility guarantees except for ill-behaved P. For certain distribution families like Gaussians or heavy-tailed distributions, we show that our universal estimators match or improve existing estimators, which are often specifically designed for the given family and under a priori boundedness assumptions on the mean and variance of P. This is the first time these boundedness assumptions are removed under pure differential privacy. The main technical tools in our development are instance-optimal empirical estimators for the mean and quantiles over the unbounded integer domain, which can be of independent interest.
On Join Sampling and the Hardness of Combinatorial Output-Sensitive Join Algorithms

We present a dynamic index structure for join sampling. Built for an (equi-) join Q --- let IN be the total number of tuples in the input relations of Q --- the structure uses ~O(IN) space, supports a tuple update of any relation in ~O(1) time, and returns a uniform sample from the join result in ~O(INρ* / /max{1, OUT} ) time with high probability (w.h.p.), where OUT and ρ* are the join's output size and fractional edge covering number, respectively; notation ~O(.) hides a factor polylogarithmic to IN. We further show how our result justifies the O(INρ* ) running time of existing worst-case optimal join algorithms (for full result reporting) even when OUT łl INρ*. Specifically, unless the combinatorial k-clique hypothesis is false, no combinatorial algorithms (i.e., algorithms not relying on fast matrix multiplication) can compute the join result in O(INρ*-ε ) time w.h.p. even if OUT łe INε, regardless of how small the constant ε > 0 is.
Dynamic mutual information-based feature selection for multi-label learning

In classification problems, feature selection is used to identify important input features to reduce the dimensionality of the input space while improving or maintaining classification performance. Traditional feature selection algorithms are designed to handle single-label learning, but classification problems have recently emerged in multi-label domain. In this study, we propose a novel feature selection algorithm for classifying multi-label data. This proposed method is based on dynamic mutual information, which can handle redundancy among features controlling the input space. We compare the proposed method with some existing problem transformation and algorithm adaptation methods applied to real multi-label datasets using the metrics of multi-label accuracy and hamming loss. The results show that the proposed method demonstrates more stable and better performance for nearly all multi-label datasets.
A deep-learning–based antifraud system for car-insurance claims

The annual cost of vehicle insurance fraud is estimated to exceed 40 billion dollars. This is an enormous amount considering the number of new vehicles insured yearly. In terms of higher premiums, it implies that insurance fraud incurs an additional annual cost to each U.S. family of $400 to $700, on average. Many frauds can be attributed to previously reported damages, which are submitted a second time to the insurance company. In these cases, it does not suffice to check the customer’s history to identify them: Damaged car panels can be removed from one vehicle and reassembled on another to make an insurance claim on the second car. To deal with these fraud attempts, in this paper, we propose an end-to-end solution to support the special investigation unit of the insurance companies in their antifraud investigations. For each claim, we organize the images sent to the insurance company and analyze them to extract basic vehicle information. Subsequently, we use these images to identify any damage to the bodywork, and, finally, we verify that the damage has not already been processed in previous claims. To the best of our knowledge, this is the first published work that deals with this problem through a pipeline that covers the entire claim management process. To validate our proposal, we compare our solution with other state-of-the-art models for estimating image similarity. Our results show that our solution is, on average, superior by 15.27% in terms of mean average precision (mAP). In addition, we report the challenges faced in scaling such a system in a production environment. This aspect is often ignored, but applying these solutions to industrial settings is of fundamental importance. Our proposed end-to-end system can reduce by up to 18% the number of false positives produced by the damage reidentification system. We show that encapsulating several specialized components and merging their intermediate results leads to a 72% reduction in possible alerts. Finally, to support the discussion and comparison of explanations for this new task, we introduce a new dataset as a benchmark for damage reidentification.
lifeXplore at the Lifelog Search Challenge 2023

Searching substantial data archives of lifeloggers is a challenging task. The Lifelog Search Challenge (LSC) is an annually held competition with the aim of encouraging international teams to develop interactive content retrieval systems capable of searching large lifelog databases. LSC takes place as a live event co-located with the ACM International Conference on Multimedia Retrieval (ICMR), where teams compete against each other by solving retrieval tasks issued by the lifelogger. This paper presents our newest version of lifeXplore, a lifelog retrieval system that has been participating in LSC since 2018. For this year, we significantly redesign the entire system (backend, middleware, and frontend) and integrate free text-search using embeddings from vision transformers trained with large sets of text-image pairs. We present a novel architecture for multi-source search, where results from image embeddings are used together with results from traditional content analysis (for objects, concepts, and recognized text). We also perform intensive analysis of vision transformer models in order to know which one fits best to the requirements of the LSC.
A Multi-Strategy Sparrow Search Algorithm with Selective Ensemble

Aiming at the deficiencies of the sparrow search algorithm (SSA), such as being easily disturbed by the local optimal and deficient optimization accuracy, a multi-strategy sparrow search algorithm with selective ensemble (MSESSA) is proposed. Firstly, three novel strategies in the strategy pool are proposed: variable logarithmic spiral saltation learning enhances global search capability, neighborhood-guided learning accelerates local search convergence, and adaptive Gaussian random walk coordinates exploration and exploitation. Secondly, the idea of selective ensemble is adopted to select an appropriate strategy in the current stage with the aid of the priority roulette selection method. In addition, the modified boundary processing mechanism adjusts the transgressive sparrows’ locations. The random relocation method is for discoverers and alerters to conduct global search in a large range, and the relocation method based on the optimal and suboptimal of the population is for scroungers to conduct better local search. Finally, MSESSA is tested on CEC 2017 suites. The function test, Wilcoxon test, and ablation experiment results show that MSESSA achieves better comprehensive performance than 13 other advanced algorithms. In four engineering optimization problems, the stability, effectiveness, and superiority of MSESSA are systematically verified, which has significant advantages and can reduce the design cost.
A Cluster-based Undersampling Technique for Multiclass Skewed Datasets

Imbalanced data classification is a demanding issue in data mining and machine learning. Models that learn with imbalanced input generate feeble performance in the minority class. Resampling methods can handle this issue and balance the skewed dataset. Cluster-based Undersampling (CUS) and Near-Miss (NM) techniques are widely used in imbalanced learning. However, these methods suffer from some serious flaws. CUS averts the impact of the distance factor on instances over the majority class. Near-miss method discards the inter-class data within the majority of class elements. To overcome these flaws, this study has come up with an undersampling technique called Adaptive K-means Clustering Undersampling (AKCUS). The proposed technique blends the distance factor and clustering over the majority class. The performance of the proposed method was analyzed with the aid of an experimental study. Three multiminority datasets with different imbalance ratios were selected and the models were created using K-Nearest Neighbor (kNN), Decision Tree (DT), and Random Forest (RF) classifiers. The experimental results show that AKCUS can attain better efficacy than the benchmark methods over multiminority datasets with high imbalance ratios.
Image Denoising Method Relying on Iterative Adaptive Weight-Mean Filtering

Salt-and-pepper noise (SPN) is a common type of image noise that appears as randomly distributed white and black pixels in an image. It is also known as impulse noise or random noise. This paper aims to introduce a new weighted average based on the Atangana–Baleanu fractional integral operator, which is a well-known idea in fractional calculus. Our proposed method also incorporates the concept of symmetry in the window mask structures, resulting in efficient and easily implementable filters for real-time applications. The distinguishing point of these techniques compared to similar methods is that we employ a novel idea for calculating the mean of regular pixels rather than the existing used mean formula along with the median. An iterative procedure has also been provided to integrate the power of removing high-density noise. Moreover, we will explore the different approaches to image denoising and their effectiveness in removing noise from images. The symmetrical structure of this tool will help in the ease and efficiency of these techniques. The outputs are compared in terms of peak signal-to-noise ratio, the mean-square error and structural similarity values. It was found that our proposed methodologies outperform some well-known compared methods. Moreover, they boast several advantages over alternative denoising techniques, including computational efficiency, the ability to eliminate noise while preserving image features, and real-time applicability.
Hybrid firefly particle swarm optimisation algorithm for feature selection problems

Abstract Feature selection techniques play a vital role in the processes that deal with enormous amounts of data. These techniques have become extremely crucial and necessary for data mining and machine learning problems. Researchers have always been in a race to develop and provide libraries and frameworks to standardise this procedure. In this work, we propose a hybrid meta‐heuristic algorithm to facilitate the problem of feature selection for classification problems in machine learning. It is a python based, lucid and efficient algorithm geared towards optimising and striking a balance between the number of features selected and accuracy. The proposed work is a binary hybrid of existing meta‐heuristic algorithms, the particle swarm optimisation (PSO) algorithm, and the firefly algorithm (FA) such that it blends the best of each algorithm to provide an optimised and efficient way of solving the said problem. The suggested approach is assessed against six datasets from different domains that are publicly available at the UCI repository to demonstrate its validity. The datasets are Breast cancer, Iris, WBC, Mushroom, Glass ID, and Abalone. This approach has also been evaluated against similar, such evolutionary‐based approaches to prove its superiority. Various metrics such as accuracy, precision, recall, f1 score, number of selected features, and run time have been analysed, measured, and compared. The hybrid firefly particle swarm optimisation algorithm is found to be suitable for feature selection problems.