Reviewerly/exHarmony huggingface dataset:
Recursive posterior Cramér–Rao lower bound on Lie groups

Authors:
raw_author_name: Clément Chahbazian
raw_affiliation_string: MBDA France, 92350 Le Plessis-Robinson, France; ONERA, The French Aerospace Lab, 91120 Palaiseau, France
raw_author_name: Karim Dahia
raw_affiliation_string: ONERA, The French Aerospace Lab, 91120 Palaiseau, France
raw_author_name: Nicolas Merlinge
raw_affiliation_string: ONERA, The French Aerospace Lab, 91120 Palaiseau, France
raw_author_name: Bénédicte Winter-Bonnet
raw_affiliation_string: MBDA France, 92350 Le Plessis-Robinson, France
raw_author_name: Aurélien Blanc
raw_affiliation_string: MBDA France, 92350 Le Plessis-Robinson, France
raw_author_name: Christian Musso
raw_affiliation_string: ONERA, The French Aerospace Lab, 91120 Palaiseau, France

Abstract: The recursive posterior Cramér–Rao Lower Bound (CRLB) introduced by Tichavsky et al. (1998) is an efficient tool to estimate the minimum variance of an estimator. Lately, filters and observers on matrix Lie groups have raised interest in control and nonlinear estimation. However, the recursive CRLB applies to variables belonging to the Euclidean space, and a recursive formulation on matrix Lie groups was not introduced yet. Hence, this paper derives a recursive formula of the Fisher information matrix on Lie groups, which reveals to be a natural extension of the information matrix on the Euclidean space. This generic result is applied to nonlinear Gaussian systems on Lie groups and tested on a navigation problem. The proposed recursive CRLB is consistent with state-of-the-art filters and shows a representative behavior with respect to the estimation errors. This paper provides a simple method to recursively compute the minimal variance of an estimator on matrix Lie groups. It enables to assess the behavior of an estimator, which is fundamental to implement robust algorithms.
BiPR-RL: Portrait relighting via bi-directional consistent deep reinforcement learning

Authors:
raw_author_name: Yukai Song
raw_affiliation_string: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, PR China
raw_author_name: Guangxin Xu
raw_affiliation_string: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, PR China
raw_author_name: Zhijun Zhang
raw_affiliation_string: The School of Automation Science and Engineering, South China University of Technology, Guangzhou, PR China

Abstract: Portrait relighting via deep reinforcement learning could conduct portrait relighting by sequentially predicting local light editing strokes, simulating image editing by artists using brush strokes. It is a locally effective, scale-invariant and interpretable method, which yields state-of-the-art results in relighting wild portrait images. However, it still has problems due to the lack of supervision information on intermediate processes, which makes the reinforcement learning agent not receive sufficient feedback during the exploration process. This will cause incorrect strokes in the relighting. To further optimize the relighting effect, we take advantage of the fact that the light editing actions are invertible and the inverse actions can generate a backward sequence of states. The backward state sequence also provide useful supervision signals as they convey information about the future states given the action choices. Therefore, we propose to combine the forward and the backward state sequence predictions to improve the learning efficiency for portrait relighting. Using the bi-directional state sequences, we design corresponding bi-directional consistent rewards to guide the model to explore the actions with higher accuracy to maximize the performance of the proposed method. The proposed approach is used for portrait relighting tasks based on both SH-lighting and reference images. The results show that our method has further improved the performance by avoiding wrong strokes. Meanwhile, our method performs better than SOTA methods in producing locally effective relighting images for wild portrait images.
A novel partition strategy for efficient implementation of 3D Cellular Genetic Algorithms

Authors:
raw_author_name: Martín Letras
raw_affiliation_string: Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Bayern, 91058, Germany
raw_author_name: Alicia Morales-Reyes
raw_affiliation_string: Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Puebla, 72840, Mexico
raw_author_name: René Cumplido
raw_affiliation_string: Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Puebla, 72840, Mexico
raw_author_name: Claudia Feregrino-Uribe
raw_affiliation_string: Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Puebla, 72840, Mexico

Abstract: Solving optimization problems while fulfilling real-time constraints requires high algorithmic and processing performance. Cellular Genetic Algorithms (cGAs) have been competitive at difficult single objective combinatorial and continuous domain problems. Moreover, it has been demonstrated that structural properties in cGAs, such as population topology dimension, local neighborhood configuration and ad-hoc selection mechanisms, allow not only further algorithmic improvement but also, these characteristics can be combined at hardware level for acceleration. In this article, a novel partition strategy to exploit 3D cGAs population dynamics on a 2D processing array using Field Programmable Gate Arrays (FPGAs) as the target processing platform is presented. The proposed architecture fits as an optimization module within an embedded system where real-time constraints must be fulfilled. Therefore, it is important to find an optimal trade-off between hardware resources usage and searching time. Overall results demonstrate that the proposed architecture can run up to 90 MHz when tackling continuous benchmark functions. Moreover, speed-up of up to three and two orders of magnitude are achieved in comparison to a single CPU and a parallel GPU respectively.
Identifying Expert Behavior in Offline Training Datasets Improves Behavioral Cloning of Robotic Manipulation Policies

Authors:
raw_author_name: Qiang Wang
raw_affiliation_string: School of Electrical and Electronic Engineering, University College Dublin, Ireland
raw_author_name: Robert McCarthy
raw_affiliation_string: Robert McCarthy is with CeADAR - Ireland&#x0027;s Centre for Applied AI, University College Dublin, Ireland
raw_author_name: David Cordova Bulens
raw_affiliation_string: School of Electrical and Electronic Engineering, University College Dublin, Ireland
raw_author_name: Francisco Roldan Sanchez
raw_affiliation_string: School of Electronic Engineering, Dublin City University, Ireland
raw_author_name: Kevin McGuinness
raw_affiliation_string: School of Electronic Engineering, Dublin City University, Ireland
raw_author_name: Noel E. O'Connor
raw_affiliation_string: School of Electronic Engineering, Dublin City University, Ireland
raw_author_name: Stephen J. Redmond
raw_affiliation_string: School of Electrical and Electronic Engineering, University College Dublin, Ireland

Abstract: This paper presents our solution for the Real Robot Challenge III, aiming to address dexterous robotic manipulation tasks through learning from offline data. In this competition, participants were given two types of datasets for each task: expert and mixed. Each expert dataset is collected by a high-skill policy, whereas the mixed dataset is collected using both expert and non-expert policies. We found that the vanilla behavioural cloning (BC) can learn a very proficient policy with minimal human intervention when trained on expert datasets. Notably, BC outperformed even the most advanced offline reinforcement learning (RL) algorithms. However, when applied to mixed datasets, the performance of BC deteriorates; similarly, the performance of offline RL algorithms is also less than satisfactory. Upon examining the provided datasets, it was apparent that each mixed dataset contained a significant proportion of expert data, which should enable the training of a proficient BC agent. However, the expert data is not labelled in the datasets. As a result, we propose a classifier to identify the pattern of the expert behaviour within a mixed dataset and then utilize it to isolate the expert data. To further boost the BC performance, we take advantage of the geometric symmetry of the arena to augment the training dataset through mathematical transformations. Ultimately, our submission outperformed that of all other participants. Our solution scripts are available on our website: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wq13552463699/</uri> Real-Robot-Challenge-III-Winning-Solution.
Behind the Bait: Delving into PhishTank's hidden data

Authors:
raw_author_name: Affan Yasin
raw_affiliation_string: School of Software, Northwestern Polytechnical University, Xian 710072, Shaanxi, China
raw_author_name: Rubia Fatima
raw_affiliation_string: School of Software, Tsinghua University, Beijing, China
raw_author_name: Javed Ali Khan
raw_affiliation_string: Department of Computer Science, School of Physics, Engineering & Computer Science, University of Hertfordshire, Hatfield, UK
raw_author_name: Wasif Afzal
raw_affiliation_string: School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden

Abstract: Phishing constitutes a form of social engineering that aims to deceive individuals through email communication. Extensive prior research has underscored phishing as one of the most commonly employed attack vectors for infiltrating organizational networks. A prevalent method involves misleading the target by employing phishing URLs concealed through hyperlink strategies. PhishTank, a website employing the concept of crowd-sourcing, aggregates phishing URLs and subsequently verifies their authenticity. In the course of this study, we leveraged a Python script to extract data from the PhishTank website, amassing a comprehensive dataset comprising over 190,0000 phishing URLs. This dataset is a valuable resource that can be harnessed by both researchers and practitioners for enhancing phish- ing filters, fortifying firewalls, security education, and refining training and testing models, among other applications.
Research on efficient detection network method for remote sensing images based on self attention mechanism

Authors:
raw_author_name: Jing Li
raw_affiliation_string: College of Information Enginering, Henan Mechanical & Electrical Vocational College, Xinzheng 451192, China; College of Information Enginering, Henan University of Science Technology, Luoyang 471000, China

Abstract: Remote sensing images are widely used in aerial drones, satellites, and other fields. However, traditional object detection methods face challenges of low efficiency and accuracy due to the diverse scales, numerous target types, and complex backgrounds of remote sensing images. To address these issues, this paper proposes a remote sensing image detection network based on self-attention, which adaptively learns the relative importance of pixels to achieve more precise and efficient object detection in remote sensing images, meeting the demands of large-scale remote sensing image processing. Firstly, through end-to-end training, the object detection network is optimized as a whole to directly output detection results from the input remote sensing images, eliminating the need for additional intermediate steps. This not only reduces the impact of information loss and inconsistency but also simplifies the entire detection process. Secondly, we integrate the Faster R-CNN architecture as the foundation, combining region extraction and object classification into a unified process. Lastly, we embed self-attention mechanisms at different levels of the Faster R-CNN to progressively extract multi-scale and multi-level feature information, enhancing the network's ability to learn the correlation of information from different positions in the image, automatically capturing the relationships between objects, and improving the accuracy of object detection. This significantly reduces redundant computation, making it more efficient for large-scale remote sensing image processing. Experimental verification demonstrates that this approach outperforms traditional methods in terms of detection accuracy and efficiency, better addressing the particularities of remote sensing images, and providing an efficient and precise solution for aerial drone and satellite image processing. Remote sensing image detection has become one of the research hotspots in the field of remote sensing, holding significant theoretical significance and practical application value.
Geochemical baselines of potentially toxic elements in Brunei-Muara soils, Brunei Darussalam: Towards environmental assessment and risk management

Authors:
raw_author_name: Basilios Tsikouras
raw_affiliation_string: Universiti Brunei Darussalam, Faculty of Science, Geosciences Programme, Jalan Tungku Link, BE 1410 Gadong, Brunei Darussalam
raw_author_name: Elena Ifandi
raw_affiliation_string: Universiti Brunei Darussalam, Faculty of Science, Geosciences Programme, Jalan Tungku Link, BE 1410 Gadong, Brunei Darussalam
raw_author_name: Chun-Kit Lai
raw_affiliation_string: Fortescue Metals Group Ltd., 87 Adelaide Terrace, East Perth, WA 6004, Australia
raw_author_name: Mohamed Ragab Shalaby
raw_affiliation_string: Universiti Brunei Darussalam, Faculty of Science, Geosciences Programme, Jalan Tungku Link, BE 1410 Gadong, Brunei Darussalam
raw_author_name: Md Aminul Islam
raw_affiliation_string: Universiti Brunei Darussalam, Faculty of Science, Geosciences Programme, Jalan Tungku Link, BE 1410 Gadong, Brunei Darussalam

Abstract: We present a comprehensive study on the presence of potentially toxic elements (PTEs) in the topsoil and subsoil of Brunei-Muara District in Brunei Darussalam. Descriptive statistics were employed to analyze the data, and the threshold values for PTEs in both soil layers were determined. Adopting the Tukey's inner fence method, we recommend baselines for PTEs. The study delved into the distribution patterns and potential sources of PTEs in Brunei-Muara, and we identified traffic and agricultural activities as significant contributors to the presence of As, Cr, Cu, and Pb. Industrial and agricultural areas exhibit spatial associations with Mn, Ni and Zn. Cadmium (Cd) was determined to be negligible in Brunei-Muara's soils. Furthermore, geochemical mapping unveiled highly acidic soil conditions in the region, characterized by variations in pH between the topsoil (2.13–8.00) and subsoil (1.89–6.76) layers that generally facilitate mobility of heavy metals. We employed Principal Component Analysis on the log10-transformed data to examine the behavior and mechanisms governing the mobility and retention of PTEs in the soil. This helped us to better characterize the element distribution within the baseline and the recognition of three additional processes enriching the topsoil and subsoil in PTEs. Overall, this study offers valuable insights into the distribution, sources, and behavior of PTEs in the soils of the Brunei-Muara District, which can greatly aid environmental monitoring and management endeavors.
Stable Minimum Solution Calibration Method Using 2-D Laser Radar and Camera

Authors:
raw_author_name: Meng Peng
raw_affiliation_string: School of Computer and Communication, Hunan Institute of Engineering, Xiangtan, China
raw_author_name: Qin Wan
raw_affiliation_string: School of Computer and Communication, Hunan University, Changsha, China
raw_author_name: Jiale He
raw_affiliation_string: School of Computer and Communication, Hunan Institute of Engineering, Xiangtan, China

Abstract: Two-dimensional laser radar and cameras are widely used to perceive external environments in unmanned vehicle navigation, target recognition, mobile robot obstacle avoidance, and simultaneous localization and mapping. Herein, the two sensors must be calibrated first to fuse laser data and image. The minimum solution method for calibration is one of the most popular ones but has some shortcomings, such as poor numerical stability and accuracy. Therefore, we present a new stable minimum solution calibration method, which introduces the RP3P algorithm to solve the perspective three-point problem (P3P) and improve the mechanisms of optimal solution selection. First, according to the P3P problem constructed using three checkerboards, the enhanced RP3P algorithm is used to improve the stability of the solution. Second, an error measurement model based on region and boundary constraints is proposed to evaluate the deviation of the multiple solutions to select the optimal solution. Simulation and actual experimental results show that the proposed algorithm can significantly improve the percentage of valid solutions and calibration accuracy compared with the algorithms in the literature. In the simulation experiments, compared with Francisco’s, Hu’s, and Zhou’s methods, the percentage of valid solutions of our method is improved by 7%-33%, 6%-10%, and 5%-9% under different noise levels. Furthermore, the accuracy of the calibration result of our calibration method obtained significant performance improvements.
Double-Layer InSb Photonic Crystal Structure for Enhanced Nonreciprocal OTS and Its Potential in Extending the Measurement Range of Bivariate Sensors

Authors:
raw_author_name: Hai-Feng Zhang
raw_affiliation_string: College of Electronic and Optical Engineering &#x0026; College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing, China

Abstract: In this paper, the dependence of optical Tamm state (OTS) to the refractive index of the interface medium is used to form nonreciprocal absorption peaks after introducing the double InSb layers on both sides of the structure. And compared to the introduction of a single layer, the energy coupling between the double InSb layers is stronger, which greatly improves the absorption performance. The enhancement of the incident angle can significantly regulate the polarization separation of the absorption peak in different directions, and the degree of nonreciprocity will also be affected by temperature and magnetic field. In the opposite direction, the relevant absorption peaks show a differentiated linear range for refractive indexes and incident angles, thereby increasing the measurement range (MR) of dual variable detection. For positive incidence, the MR, sensitivity (S) and detection limit (DL) of refractive index detection are 0.52 THz/RIU, 2.6-3.4 RIU, and 5.5×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> RIU, respectively. The corresponding indicators of angle measurement are successively 0.0074 THz/degree, 15-40 degrees, and 0.039 degrees. For negative incidence, the performance indexes of refractive index measurement are 0.81 THz/RIU, 2.1-2.6 RIU, and 3.25×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> RIU in turn. The angle detection characteristics are 0.01 THz/degree, 40-76 degrees, and 0.047 degrees. By comparison, the MR of the refractive index sensor is extended by at least 62.5%, while that of the angle sensor rises by 69.4%. We hope that the proposed design is beneficial to the improvement of sensor performances.
A Low-Complexity Air-Digit-Writing Recognition Method Based on Adaptive Trajectory Learning Using MIMO Radar

Authors:
raw_author_name: Zhaocheng Yang
raw_affiliation_string: State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Luntao Zhuang
raw_affiliation_string: State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Ping Chu
raw_affiliation_string: State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Jianhua Zhou
raw_affiliation_string: State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China

Abstract: Touchless hand gesture recognition is one of the hottest areas of the human-computer interaction. In this paper, we propose a low-complexity air-digit-writing recognition method based on adaptive trajectory learning using a frequency modulated continuous wave multiple-input-multiple-output (MIMO) radar. The core idea of the proposed method is the adaptive learning trajectory, which includes the rough trajectory estimation, the effective trajectory interception, and the fine trajectory processing. Firstly, the rough trajectory estimation is sequentially conducted by the clutter suppression, range object detection, direction of arrival estimation, angle object detection and centroid estimation. This could reduce the effective feature dimension and further the complexity of the following network and real-time system. Secondly, to remove the interference of redundant actions, the effective trajectory interception employs the proposed double adaptive thresholds gradient valley search approach to detect the start and end of the hand gestures. Thirdly, the fine trajectory processing is performed by the position-shift, normalization and resampling to eliminate the differences of hand gestures, including the position, size and speed, and improve the generalization ability and accuracy. Moreover, in order to further reduce the computational complexity, a single-layer long short-term memory network is constructed for identification. Finally, the method is deployed on a commonly used micro unit control to realize real-time system. The experimental results show that the proposed method can achieve an average accuracy of 99.6% in train and test spilt test, 98.24% in leave one subject out test, 97.4% in real-time test on trained people, and 97% in real-time test on untrained people.