Reviewerly/exHarmony huggingface dataset:
Adaptive Markov IMM Based Multiple Fading Factors Strong Tracking CKF for Maneuvering Hypersonic-Target Tracking

Hypersonic targets have complex motion states and high maneuverability. The traditional interactive multi-model (IMM) has low tracking accuracy and a slow convergence speed. Therefore, this paper proposes a strong tracking cubature Kalman filter (CKF) adaptive interactive multi-model (AIMM) based on multiple fading factors. Firstly, this paper analyzes the structure of the CKF algorithm, introduces the fading factor of the strong tracking algorithm into the covariance matrix of the time update and measurement update, and adjusts the filter gain online and in real time, which can reduce the decline infilter accuracy caused by model mismatch. Secondly, Singer model, “current” statistical (CS) model, and Jerk model are selected in the model set of IMM and introduced singular value decomposition (SVD) decomposition to solve the problem that Cholesky decomposition cannot be performed in the CKF due to the model dimension expansion. Last, an adaptive algorithm for the Markov matrix in the IMM is proposed. The transition probability was adaptively modified by the value of the model likelihood function to enhance the proportion of matching models. The simulation results show that the proposed algorithm enhanced the proportion of matching models in the IMM and improved the tracking accuracy by 16.51% and the convergence speed by 37.5%.
IoT-Based Discomfort Monitoring and a Precise Point Positioning Technique System for Smart Wheelchairs

The Internet is becoming increasingly important in our daily lives, allowing people to exchange and receive a wide variety of data. It can be utilized in a variety of ways for maximum benefit. For example, the concept of the Internet of Things (IoT) suggests that objects can be linked to the Internet. Based on this concept, in this paper, we describe the creation of modern smart-wheelchair accessories. These make the wheelchair simple to use, suitable for the elderly, and foldable. A health monitoring accessory is one of the critical functions. The Internet of Things is central to the concept of an electric-powered smart wheelchair. Residential communication networks connect electrical appliances and services, enable monitoring, and provide access from which to control various devices. The controls of a smart wheelchair comprise three essential components: a smart device that connects to the wheelchair, an Internet network, and a microcontroller. The results of our tests enable remote operation of the electric-powered wheelchair; command and control are excellent. Most significantly, our method provides consumers with an extra stage of security.
Energy Productivity of Microinverter Photovoltaic Microinstallation: Comparison of Simulation and Measured Results—Poland Case Study

From 2010 in Europe and from 2019 in Poland, the development of a significant number of photovoltaic (PV) microinstallations have been observed; for example, 1 million PV installations were built in Poland, September 2019–May 2022. A PV microinstallation is typically designed for a household (single-family house). Their capacity and energy productivity should be aligned with energy consumption in a given period, for example, a year (prosumer rules in Poland). The aim of this study is to verify the possibilities and accuracy of the use of PV energy production calculation methods in Polish conditions. The existing methods of calculating the energy produced may be inaccurate due to non-adaptation to terrain conditions, place, wind conditions, representativeness of PV panels in the installation, and many other factors. In the study, the HDKR (Hay, Davies, Klucher, Reindl) method was used based on data from the 0.25-degree (both longitude and latitude) mesh of ERA5 weather conditions. Then, the theoretical energy productivity from PV panels was calculated based on broadly used methods like those used in HOMER software. Statistical verification was done to compare the obtained energy production results from 10 PV panels with real results from microinstallations (energy productivity from each panel measured). The representativeness of the analysis period (one year) over the years was also checked using statistical methods. This is the first study to evaluate energy production from a microinverter installation in real conditions in Central Eastern Europe.
Feature augmentation based on manifold ranking and LSTM for image classification

Image classification is a critical topic due to its wide application and several challenges associated. Despite the huge progress made last decades, there is still a demand for context-aware image representation approaches capable of taking into the dataset manifold for improving classification accuracy. In this work, a representation learning approach is proposed, based on a novel feature augmentation strategy. The proposed method aims to exploit available contextual similarity information through rank-based manifold learning used to define and assign weights to samples used in augmentation. The approach is validated using CNN-based features and LSTM models to achieve even higher accuracy results on image classification tasks. Experimental results show that the feature augmentation strategy can indeed improve the accuracy of results on widely used image datasets (CIFAR10, Stanford Dogs, Linnaeus5, Flowers102 and Flowers17) in different CNNs (ResNet152, VGG16, DPN92). The results indicate gains up to 20% and show the potential of the developed approach in achieving higher accuracy results for image classification.
Image copy-move forgery detection based on dynamic threshold with dense points

Copy-move tampering is one of the most popular tampering techniques at present. The tampered region of the image has good fusion with the original image, which increases the difficulty of detection. After years of research, the current detection method based on key points still has the following problems: 1) Failure to achieve forgery detection of small areas/self-similar areas/smooth areas, 2) Lack of reasonable feature point extraction methods, 3) The various stages of copy-move forgery detection (CMFD) work are relatively independent and lack close connections, 4) A fixed threshold is used as the region of interest similarity metric in the matching and localization stages. The failure to consider tampered images and the diversity of tampered regions leads to the limited detection capability of the algorithm. Considering the actual situation of tampering with the picture, to solve the above problems, we propose a copy-move forgery detection method based on the dynamic threshold. First, we determine the point extraction strategy in each super pixel block according to the size of the simple line interface calculation (SLIC) super pixel block and the Weber local descriptor (WLD) descriptor to ensure the reasonable allocation of feature points and reduce unnecessary points. These key points are then characterized by the scaling, flip and rotation invariants of the fractional general Jacobi-Fourier moments (FJFMs). Then, the matching and mismatch filtering thresholds of each feature point are determined through the WLD and SLIC features, and the SLIC feature is used to replace the distance threshold to improve the detection accuracy of small manufacturing areas. Finally, based on the matching results and SLIC features, an effective positioning method is proposed to improve the speed and accuracy of positioning. Experimental results show that the proposed algorithm is superior to the classic methods in recent years in terms of time and accuracy.
GraphA: An efficient ReRAM-based architecture to accelerate large scale graph processing

Graph analytics is the basis for many modern applications, e.g., machine learning and streaming data problems. With an unprecedented increase in data size of many emerging domains such as the social networks, which generate a lot of images and documents, real-time big data processing is crucial. Graph processing on traditional computer architectures faces irregular memory accesses that lead to significant data movements and waste a large amount of energy and time. ReRAM-based Processing-in-memory (PIM) is a novel technology that addresses the memory wall problem. Additionally, it provides a high parallelism level and a significant reduction in energy consumption with negligible leakage power. In this paper, we propose a ReRAM-based PIM architecture, named GraphA, which includes a novel reordering algorithm and mapping data to ReRAM Graph Engines (RGE) that cause RGEs to be used with high utilization. Furthermore, we present a compressed format, a memory layout, and proper graph partitioning for graph traversal to eliminate extra communication and useless computation. Moreover, we investigate the computation patterns of graph processing to find a suitable preprocessing model for the proposed GraphA architecture based on reorganizing classified supernode graphs and offering a runtime execution that fits it. Evaluations of GraphA on various real-world graphs show an average performance enhancement and energy saving of 5.3x and 6.0x, respectively, compared with the state-of-the-art GraphR architecture.
Single-Stage Underwater Target Detection Based on Feature Anchor Frame Double Optimization Network

The shallow underwater environment is complex, with problems of color shift, uneven illumination, blurring, and distortion in the imaging process. These scenes are very unfavorable for the reasoning of the detection network. Additionally, typical object identification algorithms struggle to maintain high resilience in underwater environments due to picture domain offset, making underwater object detection problematic.This paper proposes a single-stage detection method with the double enhancement of anchor boxes and features. The feature context relevance is improved by proposing a composite-connected backbone network. The receptive field enhancement module is introduced to enhance the multi-scale detection capability. Finally, a prediction refinement strategy is proposed, which refines the anchor frame and features through two regressions, solves the problem of feature anchor frame misalignment, and improves the detection performance of the single-stage underwater algorithm.We achieved an effect of 80.2 mAP on the Labeled Fish in the Wild dataset, which saves some computational resources and time while still improving accuracy. On the original basis, UWNet can achieve 2.1 AP accuracy improvement due to the powerful feature extraction function and the critical role of multi-scale functional modules. At an input resolution of 300 × 300, UWNet can provide an accuracy of 32.4 AP. When choosing the number of prediction layers, the accuracy of the four and six prediction layer structures is compared. The experiments show that on the Labeled Fish in the Wild dataset, the six prediction layers are better than the four.The single-stage underwater detection model UWNet proposed in this research has a double anchor frame and feature optimization. By adding three functional modules, the underwater detection of the single-stage detector is enhanced to address the issue that it is simple to miss detection while detecting small underwater targets.
Generative image inpainting with enhanced gated convolution and Transformers

Image inpainting is widely used to fill the damaged or masked area in an image with realistic visual contents. However, most existing inpainting methods have limitations in reconstructing global structures for large-scale damaged areas. This paper proposes a novel high-quality generative image inpainting method by decomposing the generative network into three modules. First, an enhanced gated convolution is introduced to extract shallow features by making full use of the input mask and the gating mechanism. Second, a U-net-like deep semantic structure modeling module is presented by leveraging the Transformers’ strong ability of long-distance modeling and CNNs’ rich texture patterns learning abilities. Finally, a reconstruction module is employed to generate the inpainted result by combining shallow textural features and deep structural features. Extensive experiments on public datasets demonstrate that the proposed image inpainting method is able to produce high-quality visual effects and many quantitative results of the proposed method are superior to those of the compared state-of-the-art methods. • A novel image inpainting framework for high-quality image inpainting. • An enhanced gated convolution for the extraction of shallow features. • A deep semantic structure modeling module based on Transformers.
OAuch: Exploring Security Compliance in the OAuth 2.0 Ecosystem

The OAuth 2.0 protocol is a popular and widely adopted authorization protocol. It has been proven secure in a comprehensive formal security analysis, yet new vulnerabilities continue to appear in popular OAuth implementations.
Elysium: Context-Aware Bytecode-Level Patching to Automatically Heal Vulnerable Smart Contracts

Fixing bugs is easiest by patching source code. However, source code is not always available: only 0.3% of the ∼ 49M smart contracts that are currently deployed on Ethereum have their source code publicly available. Moreover, since contracts may call functions from other contracts, security flaws in closed-source contracts may affect open-source contracts as well. However, current state-of-the-art approaches that operate on closed-source contracts (i.e., EVM bytecode), such as EVMPatch and SmartShield, make use of purely hard-coded templates that leverage fix patching patterns. As a result, they cannot dynamically adapt to the bytecode that is being patched, which severely limits their flexibility and scalability. For instance, when patching integer overflows using hard-coded templates, a particular patch template needs to be employed as the bounds to be checked are different for each integer size (i.e., one template for uint256, another template for uint64, etc.).
Wavelet transform theory: The mathematical principles of wavelet transform in gamma spectroscopy

The objective of the proposed work was focused in presenting a novel algorithm for processing gamma-ray spectra based on theory of wavelet. We discuss the mathematical principles of applying wavelet transform (WT) method in processing γ-ray spectra and removing fluctuations and background from spectrum. The wavelet algorithm can apply accurately to detect most of peaks and determine net area under peaks. The proposed approach is able to detect most weak peaks in the searching processes. Wavelet method with some kind of discretization known as discrete wavelet transform (DWT) has been applied to filter noise and background detected with the spectrum. The effectiveness of the proposed algorithm is demonstrated via simulated spectra with different peaks containing backgrounds and with some measured gamma ray spectra. These analyzed results prove that the proposed DWT routine gives precise results for filtering and removing noise and background.
Ontopanel: A Tool for Domain Experts Facilitating Visual Ontology Development and Mapping for FAIR Data Sharing in Materials Testing

Abstract In recent years, the design and development of materials are strongly interconnected with the development of digital technologies. In this respect, efficient data management is the building block of material digitization and, in the field of materials science and engineering (MSE), effective solutions for data standardization and sharing of different digital resources are needed. Therefore, ontologies are applied that represent a map of MSE concepts and relationships between them. Among different ontology development approaches, graphical editing based on standard conceptual modeling languages is increasingly used due to its intuitiveness and simplicity. This approach is also adopted by the Materials-open-Laboratory project (Mat-o-Lab), which aims to develop domain ontologies and method graphs in accordance with testing standards in the field of MSE. To suit the actual demands of domain experts in the project, Ontopanel was created as a plugin for the popular open-source graphical editor diagrams.net to enable graphical ontology editing. It includes a set of pipeline tools to foster ontology development in diagrams.net, comprising imports and reusage of ontologies, converting diagrams to Web Ontology Language (OWL), verifying diagrams using OWL rules, and mapping data. It reduces learning costs by eliminating the need for domain experts to switch between various tools. Brinell hardness testing is chosen in this study as a use case to demonstrate the utilization of Ontopanel .
Analysis of a Soldering Motion for Dozing State and Attention Posture Detection

Recently, the governments promote the employment of persons with physical, intellectual, mental or other disabilities and employers must ensure safety in the workplace. However, technical training for the person with disabilities requires explanation of detailed work procedures and the burden on the trainer is increased by monitoring to prevent accidents. In this paper, in order to solve these problems, we presents the analysis of soldering motion for dozing state and attention posture detection based on object detection and posture estimation. Also, we show the experimental results for dozing state and attention posture detection during soldering iron. The experimental results show that the proposed system can detect the dozing state with high accuracy.
Towards lithology mapping in semi-arid areas using time-series Landsat-8 data

Previous remote sensing studies have used optical images to map lithology, commonly based on a single-date product to extract spectral diagnostic features or other geology-related features to aid interpretation. Yet, the performance of time-series or multi-date optical images in lithology mapping has rarely been discussed. In this study, we employed time-series (TS) Landsat-8 products to map the lithology in a semi-arid area of Xinjiang, China. First, we extracted TS reflectance from Landsat-8 multispectral data, and then extracted TS surface moisture, greenness, and brightness via tasseled cap transformation (TCT), a method for transforming the spectral bands of optical images into components (brightness, greenness, and moisture) that can be physically interpreted. Moreover, the land surface temperature (LST) was directly extracted from Landsat-8 Collection 2 products. In total, five surface parameters (TS reflectance, TS moisture, TS greenness, TS brightness, and TS LST), were collected. Then, the rank-based non-parametric Kruskal Wallis rank-sum statistical test was applied, which demonstrated a significant difference between the TS surface parameters among different rock units. Furthermore, different combinations of the TS surface parameters were stacked separately and served as different input features for the random forest classifier. Finally, the performance of TS surface parameters in mapping lithology was evaluated. The classification results showed that (1) TS reflectance outperformed single-date reflectance for mapping rock units; (2) the combination of TS brightness, greenness, and moisture gave a comparable classification result, relative to TS reflectance; (3) TS LST performed best when only one single surface parameter was used to map lithology, and (4) the combined use of LST and all three TCT components achieved the highest accuracy (85.26%) with a kappa coefficient of 0.77. Although surface parameters derived from rock units changed inconspicuously or irregularly over time, these variations captured by TS Landsat-8 data enabled the improvement of the classification accuracy. Overall, our study illustrates the great potential and benefits of using TS Lansat-8 data to map rock units in arid domains.
Three-stream interaction decoder network for RGB-thermal salient object detection

Salient Object Detection (SOD) has witnessed remarkable improvement during the past decade. However, RGB-based SOD methods may fail for real-world applications in some extreme environments like low-light conditions and cluttered backgrounds. Thermal (T) images can capture the heat radiation from the surface of the objects and overcome such extreme situations. Therefore, some researchers introduce the T modality to the SOD task. Existing RGB-T SOD methods fail to explicitly explore multi-scale complementary saliency cues from dual modalities and lack the full explorations of individual RGB and T modalities. To deal with such problems, we propose the Three-stream Interaction Decoder Network (TIDNet) for the RGB-T SOD task. Specifically, the feature maps from the encoder branches are fed to the three-stream interaction decoder for in-depth saliency exploration, catching the single modality and multi-modality saliency cues. For single modality decoder streams, Contextual-enhanced Channel Reduction units (CCR) firstly reduce the channel dimension of feature maps from RGB and T modalities, reducing the computational burden and discriminatively enriching the multi-scale information. For the multi-modality decoder stream, Multi-scale Cross Modality Fusion (MCMF) unit is proposed to explore the complementary multi-scale information from RGB and T modalities. Then Internal and Multiple Decoder Interaction (IMDI) units further dig the specified and complementary saliency cues from the three-stream decoder. Three-stream deep supervision has been deployed on each feature level to facilitate the training strategy. Comprehensive experiments show our method outperforms fifteen state-of-the-art methods in terms of seven metrics. The codes and models are available at https://github.com/huofushuo/TIDNet.
A new method to promptly evaluate spatial earthquake probability mapping using an explainable artificial intelligence (XAI) model

Machine learning (ML) models have been extensively used in several geological applications. Owing to the increase in model complexity, interpreting the outputs becomes quite challenging. Shapley additive explanation (SHAP) measures the importance of each input attribute on the model’s output. This study implemented SHAP to estimate earthquake probability using two different types of ML approaches, namely, artificial neural network (ANN) and random forest (RF). The two algorithms were first compared to evaluate the importance and effect of the factors. SHAP was then carried out to interpret the output of the models designed for the earthquake probability. This study aims not only to achieve high accuracy in probability estimation but also to rank the input parameters and select appropriate features for classification. SHAP was tested on earthquake probability assessment using eight factors for the Indian subcontinent. The models obtained an overall accuracy of 96 % for ANN and 98 % for RF. SHAP identified the high contributing factors as epicenter distance, depth density, intensity variation, and magnitude density in a sequential order for ANN. Finally, the authors argued that an explainable artificial intelligence (AI) model can help in earthquake probability estimation, which then open avenues to building a transferable AI model.