Reviewerly/exHarmony huggingface dataset:
Multiscale Image Splitting Based Feature Enhancement and Instance Difficulty Aware Training for Weakly Supervised Object Detection in Remote Sensing Images

Weakly supervised object detection (WSOD) has a great practical value in remote sensing image (RSI) interpretation because the instance-level annotations are not required.The multiple instance learning based methods are mainstream, and two problems should be addressed.First of all, the majority of methods usually detect discriminative parts rather than the whole object.Secondly, the quantity of easy instances is much greater than that of hard instances, which restricts the improvement of WSOD methods.To address the first problem, a multi-scale image splitting based feature enhancement (MSFE) module is proposed.The MSFE module splits the input RSI in multiple scales, afterwards, the spatial attention maps (SAMs) are generated from the feature maps of each proposal corresponding to different splitting scales, and are used to calculate the maximum spatial attention map (MSAM).Each SAM is required to approach MSAM, which enforces the MSFE module to learn the feature maps which can highlight the whole object for each positive proposal.To address the second problem, an instance difficulty aware training (IDAT) strategy is proposed.The difficulty of each instance can be quantitatively measured, and is used as the weight of each instance in the training loss.Consequently, the hard instances will be focused in the training process.The ablation study demonstrates the validity of MSFE module and IDAT strategy.The comparisons with 9 advanced methods on two RSI benchmarks further validate the overall effectiveness of our method.
YOLOv7 Optimization Model Based on Attention Mechanism Applied in Dense Scenes

With object detection technology, real-time detection of dense scenes has become an important application requirement in various industries, which is of great significance for improving production efficiency and ensuring public safety. However, the current mainstream target detection algorithms have problems such as insufficient accuracy or inability to achieve real-time detection when detecting dense scenes, and to address this problem this paper improves the YOLOv7 model using attention mechanisms that can enhance critical information. Based on the original YOLOv7 network model, part of the traditional convolutional layers are replaced with the standard convolution combined with the attention mechanism. After comparing the optimization results of three different attention mechanisms, CBAM, CA, and SimAM, the YOLOv7B-CBAM model is proposed, which effectively improves the accuracy of object detection in dense scenes. The results on VOC datasets show that the YOLOv7B-CBAM model has the highest accuracy, reaching 87.8%, 1.5% higher than that of the original model, and outperforms the original model as well as other models with improved attention mechanisms in the subsequent results of two other different dense scene practical application scenarios. This model can be applied to public safety detection, agricultural detection, and other fields, saving labor costs, improving public health, reducing the spread and loss of plant diseases, and realizing high-precision, real-time target detection.
Design of A Finite-Time Adaptive Controller for Image-Based Uncalibrated Visual Servo Systems with Uncertainties in Robot and Camera Models

Aiming at the time-varying uncertainties of robot and camera models in IBUVS (image-based uncalibrated visual servo) systems, a finite-time adaptive controller is proposed based on the depth-independent Jacobian matrix. Firstly, the adaptive law of depth parameters, kinematic parameters, and dynamic parameters is proposed for the uncertainty of a robot model and a camera model. Secondly, a finite-time adaptive controller is designed by using a nonlinear proportional differential plus a dynamic feedforward compensation structure. By applying a continuous non-smooth nonlinear function to the feedback error, the control quality of the closed-loop system is improved, and the desired trajectory of the image is tracked in finite time. Finally, using the Lyapunov stability theory and the finite-time stability theory, the global finite-time stability of the closed-loop system is proven. The experimental results show that the proposed controller can not only adapt to the changes in the EIH and ETH visual configurations but also adapt to the changes in the relative pose of feature points and the camera's relative pose parameters. At the same time, the convergence rate near the equilibrium point is improved, and the controller has good dynamic stability.
Enhancing eID card mobile-based authentication through 3D facial reconstruction

In recent years the number of credentials and digital identities needed to access online services is exploding, increasing the risk of fraud and unauthorized access to personal data.Biometric verification techniques can help to build more secure authentication systems; in particular face recognition algorithms have reached near perfect accuracy under ideal conditions. In Europe 60% of population owns an electronic identification (eID) document which contains biometric data, and almost all member states provide tools to verify the identity of a user who holds an eID card; however, the adoption of biometric authentication schemes is still limited. Reasons for this low adoption are privacy concerns, about the storage or transmission of personal images, and the degradation of performance of facial recognition algorithms with 2D images acquired "in the wild" (real world condition with inconsistency in lighting and positioning, unclear or occluded facial features, noise from the background). In this paper a new authentication scheme is proposed, exploiting images acquired "in the wild" and 3D facial reconstruction techniques which can help in improving the quality of the probed images, as changes in lighting or position have limited impact on facial shape. Several experiments have been conducted in order to investigate the performance of the proposed method, using the Bosphorus and the NIST Face Recognition Grand Challenge (FRGC) datasets. In particular, one of the most accurate ML face recognition algorithms has been selected to compare the performance of 2D snapshot data, obtained from 3D back-projection, against 2D uncontrolled images acquired "in the wild". We show that using 3D facial reconstructions significantly improve face recognition performance, allowing for a more secure and robust authentication system.
Master–Slave Control System for Virtual–Physical Interactions Using Hands

Among the existing technologies for hand protection, master-slave control technology has been extensively researched and applied within the field of safety engineering to mitigate the occurrence of safety incidents. However, it has been identified through research that traditional master-slave control technologies no longer meet current production and lifestyle needs, and they have even begun to pose new safety risks. To resolve the safety risks exposed by traditional master-slave control, this research fuses master-slave control technology for hands with virtual reality technology, and the design of a master-slave control system for hands based on virtual reality technology is investigated. This study aims to realize the design of a master-slave control system for virtual-physical interactions using hands that captures the position, orientation, and finger joint angles of the user's hand in real time and synchronizes the motion of the slave interactive device with that of a virtual hand. With amplitude limiting, jitter elimination, and a complementary filtering algorithm, the original motion data collected by the designed glove are turned into a Kalman-filtering-algorithm-based driving database, which drives the synchronous interaction of the virtual hand and a mechanical hand. As for the experimental results, the output data for the roll, pitch, and yaw were in the stable ranges of -0.1° to 0.1°, -0.15° to 0.15°, and -0.15° to 0.15°, respectively, which met the accuracy requirements for the system's operation under different conditions. More importantly, these data prove that, in terms of accuracy and denoising, the data-processing algorithm was relatively compatible with the hardware platform of the system. Based on the algorithm for the virtual-physical interaction model, the authors introduced the concept of an auxiliary hand into the research, put forward an algorithmic process and a judgement condition for the stable grasp of the virtual hand's, and solved a model-penetrating problem while enhancing the immersive experience during virtual-physical interactions. In an interactive experiment, a dynamic accuracy test was run on the system. As shown by the experimental data and the interactive effect, the system was satisfactorily stable and interactive.
Diagnosis and Treatment Knowledge Graph Modeling Application Based on Chinese Medical Records

In this study, a knowledge graph of Chinese medical record data was constructed based on graph database technology. An entity extraction method based on natural language processing, disambiguation, and reorganization for Chinese medical records is proposed, and dictionaries of drugs and treatment plans are constructed. Examples of applications of the knowledge graph in diagnosis and treatment prediction are given. Experimentally, it is found that the knowledge graph based on the graph database is 116.7% faster than the traditional database in complex relational queries.
Enhancing Zero-Energy Building Operations for ESG: Accurate Solar Power Prediction through Automatic Machine Learning

Solar power systems, such as photovoltaic (PV) systems, have become a necessary feature of zero-energy buildings because efficient building design and construction materials alone are not sufficient to meet the building’s energy consumption needs. However, solar power generation is subject to fluctuations based on weather conditions, and these fluctuations are higher than other renewable energy sources. This phenomenon has emphasized the importance of predicting solar power generation through weather forecasting. In this paper, an Automatic Machine Learning (AML)-based method is proposed to create multiple prediction models based on solar power generation and weather data. Then, the best model to predict daily solar power generation is selected from these models. The solar power generation data used in this study was obtained from an actual solar system installed in a zero-energy building, while the weather data was obtained from open data provided by the Korea Meteorological Administration. In addition, To verify the validity of the proposed method, an ideal data model with high accuracy but difficult to apply to the actual system and a comparison model with a relatively low accuracy but suitable for application to the actual system were created. The performance was compared with the model created by the proposed method. Based on the validation process, the proposed approach shows 5–10% higher prediction accuracies compared to the comparison model.
Learning Adversarially Robust Object Detector with Consistency Regularization in Remote Sensing Images

Object detection in remote sensing has developed rapidly and has been applied in many fields, but it is known to be vulnerable to adversarial attacks. Improving the robustness of models has become a key issue for reliable application deployment. This paper proposes a robust object detector for remote sensing images (RSIs) to mitigate the performance degradation caused by adversarial attacks. For remote sensing objects, multi-dimensional convolution is utilized to extract both specific features and consistency features from clean images and adversarial images dynamically and efficiently. This enhances the feature extraction ability and thus enriches the context information used for detection. Furthermore, regularization loss is proposed from the perspective of image distribution. This can separate consistent features from the mixed distributions for reconstruction to assure detection accuracy. Experimental results obtained using different datasets (HRSC, UCAS-AOD, and DIOR) demonstrate that the proposed method effectively improves the robustness of detectors against adversarial attacks.
Adaptive Feature Attention Module for Robust Visual–LiDAR Fusion-Based Object Detection in Adverse Weather Conditions

Object detection is one of the vital components used for autonomous navigation in dynamic environments. Camera and lidar sensors have been widely used for efficient object detection by mobile robots. However, they suffer from adverse weather conditions in operating environments such as sun, fog, snow, and extreme illumination changes from day to night. The sensor fusion of camera and lidar data helps to enhance the overall performance of an object detection network. However, the diverse distribution of training data makes the efficient learning of the network a challenging task. To address this challenge, we systematically study the existing visual and lidar features based on object detection methods and propose an adaptive feature attention module (AFAM) for robust multisensory data fusion-based object detection in outdoor dynamic environments. Given the camera and lidar features extracted from the intermediate layers of EfficientNet backbones, the AFAM computes the uncertainty among the two modalities and adaptively refines visual and lidar features via attention along the channel and the spatial axis. The AFAM integrated with the EfficientDet performs the adaptive recalibration and fusion of visual lidar features by filtering noise and extracting discriminative features for an object detection network under specific environmental conditions. We evaluate the AFAM on a benchmark dataset exhibiting weather and light variations. The experimental results demonstrate that the AFAM significantly enhances the overall detection accuracy of an object detection network.
Efficient variational quantum circuit structure for correlated topological phases

We propose an efficient circuit structure of variational quantum circuit Ans\"atze used for the variational quantum eigensolver (VQE) algorithm in calculating gapped topological phases on the currently feasible noisy intermediate-scale quantum computers. An efficient circuit Ansatz should include two layers: the initialization layer and the variational layer. In the initialization layer, a fixed-depth circuit state with a compatible entanglement structure to the target topological phase is constructed. The circuit state is further adjusted subsequently to capture the details of the local correlations, which is dictated with the Hamiltonian, in the parametrized variational layer. Based on this strategy, we design a circuit Ansatz to investigate the symmetry-protected topological Haldane phase in a nonexactly solvable alternating spin-$\frac{1}{2}$ Heisenberg chain by VQE calculations. Main characterizations of the Haldane phase, including the long-ranged string order, the fourfold nearly degenerate ground states associated with four different localized edge-mode patterns for the system with open boundaries, and the twofold degeneracy of the entanglement spectrum, are all observed for the optimized shallow circuit state with only one-depth variational layer both in numerical simulations and on real quantum computers. We further demonstrate that the computational capacity (i.e., expressibility) of this quantum circuit Ansatz is determined not by the system size but only by the intrinsic correlation length of the system, thus implying that the scalable VQE calculation is possible.
Social and Financial Sustainability of Real Estate Investment: Evaluating Public Perceptions towards Blockchain Technology

Real estate markets play a crucial role in the economy, providing opportunities for investment and housing. However, there are several challenges in both direct and indirect investment mechanisms affecting its social and financial sustainability. These challenges include high costs, lengthy processes, limited transparency, and restricted investor control. Additionally, the dominance of large investors in the market intensifies these issues, creating barriers to smaller investors. This raises concerns around social inequality and sustainability among small investors, that represent, in number, the largest share of investors. Blockchain technology has emerged as a possible solution to address these issues in the real estate sector, with the potential to improve its long term social and financial sustainability. Features such as smart contracts and tokenization can enhance efficiency, transparency, security, and accessibility in property transactions. In the case of smart contracts, these enable self-executing and automated agreements, and tokenization allows for fractional ownership and increased liquidity. To assess the knowledge and perceptions of professionals in the real estate sector and evaluate the possible impact of the technology in the market, a survey-based methodology was followed. It targeted individuals actively involved in the industry, including professionals from real estate investment companies and real estate agencies. The data revealed that most professionals in the Portuguese real estate market have little to no knowledge about blockchain technology. Yet, those who possess knowledge recognize the potential benefits it can bring to the industry. This lack of awareness can be attributed to the relatively recent emergence of blockchain and its limited discussion within the real estate sector.
Signatures of Superradiance as a Witness to Multipartite Entanglement

Generation and detection of entanglement is at the forefront of most quantum information technologies. There is a plethora of techniques that reveal entanglement on the basis of only partial information about the underlying quantum state, including entanglement witnesses. Superradiance refers to the phenomenon of highly synchronized photon emission from an ensemble of quantum emitters that is caused by correlations among the individual particles and has been connected by Dicke himself to the presence of multipartite entangled states. We investigate this connection in a quantitative way and discuss whether or not signatures of superradiance from semiconductor nanolasers, manifesting themselves as a modification of the spontaneous-emission time, can be interpreted as a witness to detect entanglement in the underlying state of the emitters.
Preference-Based Multi-Robot Planning for Nuclear Power Plant Online Monitoring and Diagnostics

AbstractCurrent preventative maintenance paradigms in nuclear power plants carry several costly risks and challenges associated with component downtime and the need for human data collection. Preventative maintenance may be enabled by an online monitoring system that accurately assesses component condition and identifies potential faults. We present an approach for autonomous online monitoring and multiagent planning for robotic data collection. Under the occurrence of a fault, we utilize a machine learning model to form an initial guess of its nature, which we then refine by selectively measuring certain variables to gain additional information via a situation-aware variable selection model. To generate a multi-robot plan to conduct these measurements, we develop a preference-based planning framework within a linear temporal logic–based planning approach that prioritizes collecting data from the most important features. Finally, we demonstrate our approach on a case study using a simulated nuclear power plant circulating water system, showing fault diagnostic performance as well as simulated robot data collection.Keywords: Multi-robot task planninglinear temporal logiconline monitoringfault diagnosisnuclear power plant operation NomenclatureA== alphabet of Boolean formulasA== agent transition systemaA== agent actionaG== team automaton actionaP== product automaton actioncA== agent action costcG== team automaton action costcP== product automaton action costcβ== team cost weight vectorcˆβ== augmented team cost vectorE== set of graph edgesea,r== decision variable for agent-action pairF== set of accepting final states in NFAF== nondeterministic finite automatonG== graphG== team automatonN== number of agentso== ordering weightP== product automatonq== NFA stateR== real numbersR== temporal logic releasesA== agent statesG== team automaton statesP== product automaton stateU== temporal logic untilV== set of graph vertices◯== temporal logic next∧== logical and∨== logical or♢== temporal logic eventually¬== logical not→== logical implication□== temporal logic alwaysGreekα== team preference cost weightβ== sequence in team automatonδ== NFA transition relationϵ== team makespan cost weightζ== switch transitionκˆ== team cost functionλ== labeling functionπ== atomic propositionσ== temporal sequenceϕ== linear temporal logic specificationψ== preference costAcknowledgmentsThe authors would like to thank the U.S. Department of Energy, Office of Nuclear Energy, Distinguished Early Career Program for funding this work, award number DE-NE0009306.Disclosure StatementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Office of Nuclear Energy [DE-NE0009306].
Amplitude and inter-event time statistics for the island volcanoes Stromboli, Mount Etna, Yasur, and Whakaari

Detailed analyses of past major and minor seismo-volcanic events can help to understand the eruptive behavior of volcanoes and the underlying physical and chemical processes. Catalogs of these eruptions and, specifically, seismo-volcanic events may be generated using continuous seismic recordings at stations in the proximity of volcanoes. Here, we apply a recently-developed automated approach Adaptive-Window Volcanic Event Selection Analysis Module (AWESAM) to seismic data from Stromboli (Italy), Mount Etna (Italy), Yasur (Vanuatu) and Whakaari (New Zealand). We perform an inter-event time analysis to identify characteristic patterns in the events’ recurrence time and the volcanic activity. Using this identical approach for all volcanoes, we were able to discover that despite their differing types and activity, they exhibit similar statistical behaviors. For Whakaari, we noticed a bimodal inter-event time distribution for large events. Since this observation is based on single station data, further in-depth investigations are needed once more data is available in future. We also derive a new amplitude-frequency relationship from seismo-volcanic events. With this relation, we can confirm a change in slope for large events at Stromboli, which is based on 10 years of data. Additionally, we apply a classification model to events from Stromboli to differentiate between low-period (LP) events and high-frequency (HF) events and found an alternating behavior in the frequency of these events before and after the two paroxysms in 2019.
Motion segmentation in Moving Camera Videos using Velocity Guided Optical Flow Normalization

An obstacle avoidance system is a key feature of a robot navigation system. A capable avoidance system should consider obstacles movements into account. This study proposes a new approach for detecting motion from a video captured by a moving camera, a similar scenario happens in a robot use case. The process starts from acquiring two successive video frames and computes its optical flow image using gmflownet. Then, the semantic segmentation mask, as well as the estimated depth map, are also generated. After that, camera velocity is estimated based on the optical flow of points belonging to static objects. Next, the velocity information is used for optical flow normalization. The normalized optical flow is then fed to a DeeplabV3 network to obtain a motion mask. Finally, the motion and semantic mask are fused in the postprocessing stage to obtain the final mask. Experiments on video data indicate that the performance of the proposed method exceeds that of the standard one indicated by the average precision, recall, and IoU of non-zero results of 0.814, 0.719, and 0.6222, respectively.