Reviewerly/exHarmony huggingface dataset:
Comparative Study between Type-1 and Interval Type-2 Fuzzy Systems in Parameter Adaptation for the Cuckoo Search Algorithm

The objective of this work is focused on improving the optimization ability of the cuckoo search algorithm (CS), and, for this reason, a comparison is made between type-1 and interval type-2 fuzzy logic to look for more promising results in the cuckoo search algorithm (CS), and to help performance, we dynamically adjust the alpha parameter. The idea is to enable CS in leaving the local optima, and then be able to reach the global optima. Currently, there are good results in improving the optimization of algorithms through intelligent fuzzy logic computing after finding the best adjustment parameters. The approach is based on finding the ideal rules with their respective linguistic variables to represent the real world as is perceived by humans. The membership functions that the fuzzy system uses are symmetrically defined for reducing the search space, and this symmetry is what makes the algorithm efficient. We plan to test the proposal in future works in the optimal design of control systems. In the present study, we use five benchmark mathematical functions with variation in the number of dimensions to validate the approach and perform the comparison of interval type-2 and type-1 fuzzy systems in parameter adaptation. For the dynamic adjustment of the parameters, we select the alpha parameter, and the values of Pa and Beta are defined based on the analysis of their behavior in previous works.
Research on Small Acceptance Domain Text Detection Algorithm Based on Attention Mechanism and Hybrid Feature Pyramid

In the traditional text detection process, the text area of the small receptive field in the video image is easily ignored, the features that can be extracted are few, and the calculation is large. These problems are not conducive to the recognition of text information. In this paper, a lightweight network structure on the basis of the EAST algorithm, the Convolution Block Attention Module (CBAM), is proposed. It is suitable for the spatial and channel hybrid attention module of text feature extraction of the natural scene video images. The improved structure proposed in this paper can obtain deep network features of text and reduce the computation of text feature extraction. Additionally, a hybrid feature pyramid + BLSTM network is designed to improve the attention to the small acceptance domain text regions and the text sequence features of the region. The test results on the ICDAR2015 demonstrate that the improved construction can effectively boost the attention of small acceptance domain text regions and improve the sequence feature detection accuracy of small acceptance domain of long text regions without significantly increasing computation. At the same time, the proposed network constructions are superior to the traditional EAST algorithm and other improved algorithms in accuracy rate P, recall rate R, and F-value.
Two-Photon Interference of Single Photons from Dissimilar Sources

Entanglement swapping and heralding are at the heart of many protocols for distributed quantum information. For photons, this typically involves Bell-state measurements based on two-photon interference effects. In this context, hybrid systems that combine high rate, ultrastable, and pure quantum sources with long-lived quantum memories are particularly interesting. Here, we develop a theoretical description of pulsed two-photon interference of photons from dissimilar sources to predict the outcomes of second-order cross-correlation measurements. These are directly related to, and hence used to quantify, photon indistinguishability. We study their dependence on critical system parameters such as quantum state lifetime and emission frequency, and quantify the impact of time jitter, pure dephasing, and spectral wandering. We show that for a fixed lifetime of one of the two emitters, for each frequency detuning there is an optimal lifetime of the second emitter that leads to the highest photon indistinguishability. Expectations for different hybrid combinations involving III-V semiconductor quantum dots, color centers in diamond, atom-scale defects in two-dimensional materials and neutral atoms are quantitatively compared for real-world system parameters. Our work provides a theoretical basis for the treatment of dissimilar emitters and enables assessment of which imperfections can be tolerated in hybrid photonic quantum networks.
Improved deep metric learning with local neighborhood component analysis

Deep metric learning aims to learn a discriminative feature space in which features have larger intra-class similarities and smaller inter-class similarities. Most recent studies mainly focus on designing different loss functions or sampling strategies, while ignoring a crucial limitation caused by mini-batch training. We argue that existing mini-batch-based approaches do not explore the global structure similarities among samples in feature space. As a result, instances and their k-nearest neighbors may not be semantically consistent. To this end, we propose a method, dubbed Local Neighborhood Component Analysis (LNCA), to improve deep metric learning. Specifically, LNCA leverages a feature memory bank, storing the feature vectors of all instances, to estimate the global structure similarities and determine the k nearest neighbors of samples in the feature space. Further, in order to refine the local neighborhood components of samples, LNCA introduces a metric to attract the positive neighbors and repulse the negative neighbors simultaneously. LNCA is a plug-and-play module and can be integrated into a general DML framework. Experimental results show that it can boost the generalization performance of existing DML approaches significantly.
Research on the standardization model of data semantics in the knowledge graph construction of Oil&amp;Gas industry

At present, knowledge graphs in both general and vertical fields rarely consider the problem of data semantic standardization in the process of constructing from a global perspective, which brings trouble and loss to the sharing and interoperability of domain information. Therefore, it is very important to construct a set of standardized and semantically consistent knowledge organization model, and to propose a data semantics standardization methodology on this basis, so as to fundamentally solve this serious problem that hinders data sharing. This paper first proposes a model of data semantic standardization theoretically through the research on knowledge organization, concept triangle and concept logic and other theories, combined with ISO 1087 international standard, and proposes the important idea that the key issue of data semantic standardization is conceptual standardization as the core. Then, based on this theoretical model, a comprehensive study and combing of various vocabulary standards in the international Oil&Gas industry was carried out, and the Oil&Gas industry reference vocabulary standardization model (OIRVSM) used to guide the construction of data semantic standards in the oil and gas industry is proposed. Finally, under the guidance of the W3C best practice framework, combined with the proposed model, an operational algorithm for constructing standardized knowledge graph (ACSKG) in Oil&Gas industry and a construction example are given, which verifies the rationality and correctness of data semantic standardization model and vocabulary standard system model. The two models proposed in this paper are innovative. Although they are proposed in the context of Oil&Gas industry, they also have application and reference significance in other fields.
How handheld use is connected to learning-related factors and academic achievement: Meta-analysis and research synthesis

Handhelds (e.g., cell phones, tablets) are promising learning tools, so they are now used in formal classroom settings in many educational institutions around the world. Previous meta-analyses have focused predominantly on the direct effects of handhelds on academic achievement. From a psychological perspective, however, achievement is the outcome of a complex and multifaceted process involving adaptive cognitions and motivation for learning. While these factors are also themselves desirable learning outcomes, previous meta-analyses have neglected the effect of handheld use on these outcomes. This meta-analysis is the first to synthesize how the use of handhelds in formal educational contexts is associated with a broad range of motivational (e.g., intrinsic motivation, self-efficacy) and other learning-related factors (cognitive load, satisfaction with learning, attitude towards learning) beyond academic achievement. The question how handhelds can be used most effectively in learning settings is also addressed by considering studies’ learning designs. We included 59 samples (N = 4259) in 58 studies published between 1998 and 2021. Only studies with an experimental or quasi-experimental research design providing pre- and/or post-test data and comparisons between experimental and if available control groups were included. We found overall moderate to high effect sizes for learning-related factors (gs between .41 and .77) and for academic achievement (g = .71). None of the presumed moderating variables (handheld types, learning designs, students’ age, gender) significantly explained heterogeneity in the respective outcomes. Our findings demonstrate a broad range of positive effects of handheld use thereby implying a multicriterial, sustainable impact on educational trajectories.
Neural Temporal Denoising for Indirect Illumination

Various temporal denoising methods have been proposed to clean up the noise for real-time ray tracing (RTRT). These methods rely on the temporal correspondences of pixels between the current and previous frames, i.e. per-pixel screen-space motion vectors. However, the state-of-the-art temporal reuse methods with traditional motion vectors cause artifacts in motion occlusions. We accordingly propose a novel neural temporal denoising method for indirect illumination of Monte Carlo (MC) ray tracing at 1 sample per pixel. Based on end-to-end multi-scale kernel-based reconstruction, we apply temporally reliable dual motion vectors to facilitate better reconstruction of the occlusions, and also introduce additional motion occlusion loss to reduce ghosting artifacts. Experiments show that our method significantly reduces the over-blurring and ghosting artifacts while generating high-quality images at real-time rates.
Software practitioners’ point of view on technical debt payment

Technical debt (TD) payment refers to the activity of expending maintenance effort and resources to make up for the effects of previous technical compromises. To investigate if software practitioners have paid debt items off in their projects, the practices that have been used for paying off debt items, and the issues that hamper the implementation of these practices. We analyze 653 responses collected by surveying practitioners from six countries about TD payment. Practitioners have not paid off TD items in most cases. We identified 27 reasons for not paying off those items and 32 payment-related practices. Practices are mainly related to internal quality issues, while reasons for not paying TD off are mostly associated with planning and management issues. Lastly, we identified relationships between practices and between reasons, indicating that both can appear in combination. . We use different views to consolidate the set of information on TD payment, extending the conceptual model for TD and organizing the set of practices and reasons into a TD payment map. We believe that the model and the map can support practitioners in planning their TD payment strategy.
Cross-modal co-feedback cellular automata for RGB-T saliency detection

Saliency cellular automata (CA), a temporally evolving model to efficiently locate salient object, has achieved great progress in saliency detection task. However, most the previous CA models originate from RGB data and are thus limited in some extreme scenes. Inspired by the observation that thermal infrared data (T) can overcome the limitation of RGB data themselves in some cases and RGB-T saliency detection has gained more and more attention. In this paper, we contribute a novel RGB-T saliency detection approach via Cross-modal Co-feedback Cellular Automata (C3A). Before this, we firstly present a novel weighted background-based map (WBM) to give each superpixel(image patch) an initial saliency value. Then C3A is proposed to improve the quality of the WBM. Specifically, it firstly establishes two complementary cellular automata (CA) mechanisms dependent on RGB and thermal infrared data, which respectively refine the WBM based on two different perspectives. To bridge RGB-T modalities, an iterative cross-modal co-feedback framework is contributed to optimize constantly their results. In other words, we regard the result of one modality(RGB or T)-induced CA as important feedback to update and optimize another modality(T or RGB)-induced CA during the iteration. Two modalities constantly pull out the useful and confident data to the opposite side, and so two CAs' results are constantly refined until a stable state is generated, we then integrate the results of two modalities-induced CAs into the saliency map. Finally, a novel boundary-guided pixel-wise refinement (BPR) technology is proposed to further overcome the influence of inaccurate superpixel segmentation to the C3A and refine the saliency map generated by our C3A. For fairness, the proposed method is compared with other state-of-the-art methods on three RGB-T datasets, experimental results show the superiority of our model.
Iterative deep neural networks based on proximal gradient descent for image restoration

The algorithm unfolding networks with explainability of algorithms and higher efficiency of Deep Neural Networks (DNN) have received considerable attention in solving ill-posed inverse problems. Under the algorithm unfolding network framework, we propose a novel end-to-end iterative deep neural network and its fast network for image restoration. The first one is designed making use of proximal gradient descent algorithm of variational models, which consists of denoiser and reconstruction sub-networks. The second one is its accelerated version with momentum factors. For sub-network of denoiser, we embed the Convolutional Block Attention Module (CBAM) in previous U-Net for adaptive feature refinement. Experiments on image denoising and deblurring demonstrate that competitive performances in quality and efficiency are gained by compared with several state-of-the-art networks for image restoration. Proposed unfolding DNN can be easily extended to solve other similar image restoration tasks, such as image super-resolution, image demosaicking, etc.
Multi-dimensional constraints-based PPVO for high fidelity reversible data hiding

Reversible data hiding (RDH) based on pixel value ordering (PVO) is an effective information security hiding technique that allows complete recovery of the cover image after data extraction. Current methods mainly adopt PVO-based prediction error histograms to obtain better embedding performance in terms of block prediction or pixel prediction, regardless of the correlation between image textures and different prediction contexts. In this work, we propose a multi-dimensional constraints (MDCs) method to explore prediction mechanism across three dimensions using a pixel-based PVO (PPVO) scheme for high-fidelity RDH. The method involves (a) a local complexity (LC) Constraint that focuses on pixels containing similar local complexity instances with more different quantization intervals, (b) an enclosing location (EL) constraint that discovers the most relevant pixels within the fully enclosed context for prediction error, and (c) an optimal mode (OM) constraint that fuses fully enclosed context adaptively based on optimal search during different subsets of prediction modes. In addition, we introduce the multi-dimensional constraints to determine the prediction context in the PPVO-based RDH method, which helps the prediction mechanism to obtain predictions that are more accurate. The proposed MDCs can be easily applied to existing PPVO-based RDH frameworks. Extensive experiments on test-image data sets show that the proposed method outperforms the current state-of-the-art PPVO approaches.
Continental-scale distribution of niobium and tantalum in catchment sediments throughout China: Prospecting implications from the China geochemical Baselines project

Niobium (Nb) and tantalum (Ta), which are chemical twins with very similar geochemical properties, are critical mineral resources for emerging global industries. Owing to increasing demands, understanding their concentrations and distributions in the pedosphere is essential for assessing potential Nb and Ta resources in China. To address this, we used geochemical data obtained from the China Geochemical Baselines (CGB) project, which began in 2008 and has since then yielded continental-scale datasets. With a density of approximately-one sampling site per 3,000 km2, the CGB project involved catchment sediments from two horizons at each site: a top sample collected at a depth of 0–25 cm and a deep sample collected at a depth greater than 100 cm. In total, 3,394 samples each of top and deep catchment sediments were collected, and Nb and Ta concentrations were measured using inductively coupled plasma mass spectrometry under strict quality control conditions. The Nb concentrations in the top and deep sediment samples varied in the ranges 0.04–89.86 ppm with a median value of 12.71 ppm and 1.20–104.12 ppm with a median value of 12.41 ppm, respectively. Further, the Ta concentrations in the top samples ranged from 0.01 to 26.41 ppm with a median of 0.97 ppm, while those in the deep samples ranged from 0.14 to 26.41 ppm with a median of 0.96 ppm. Furthermore, both the top and deep samples had similar 2.5th, 25th, 50th, 75th, and 97.5th percentile Nb and Ta contents, indicating that Nb and Ta in the catchment sediments underwent similar and consistent formation processes. Our results also indicated that the concentrations of both elements varied widely across regions, and their distributions in the catchment sediments throughout China were predominantly governed by the parent rock, particularly granites and alkaline rocks. Mineralization and sedimentation properties also played roles at a regional scale. Overall, the spatial distributions of Nb and Ta in the top and deep catchment sediments were relatively consistent, with concentrations being higher in southeastern China and lower in northwestern China. Twelve large-scale geochemical Nb-Ta belts were delineated based on continuous anomalous point distributions wherein many well known Nb-Ta deposits are already located. These nationwide Nb and Ta concentrations and distributions provide foundational large-scale data for further exploration and geological studies.
Value activation for bias alleviation: Generalized-activated deep double deterministic policy gradients

It is vital to accurately estimate the value function in Deep Reinforcement Learning (DRL) such that the agent could execute proper actions instead of suboptimal ones. However, existing actor-critic methods suffer more or less from underestimation bias or overestimation bias, which negatively affect their performance. In this paper, we reveal a simple but effective principle: proper value correction benefits bias alleviation, where we propose the generalized-activated weighting operator that uses any non-decreasing function, namely activation function, as weights for better value estimation. Particularly, we integrate the generalized-activated weighting operator into value estimation and introduce a novel algorithm, Generalized-activated Deep Double Deterministic Policy Gradients (GD3). We theoretically show that GD3 is capable of alleviating the potential estimation bias. We interestingly find that simple activation functions lead to satisfying performance with no additional tricks, and could contribute to faster convergence. Experimental results on numerous challenging continuous control tasks show that GD3 with task-specific activation outperforms the common baseline methods. We also uncover a fact that fine-tuning the polynomial activation function achieves superior results on most of the tasks. Codes will be available upon publication.
Video Channel Suppression Method of Unmanned Aerial Vehicles

For this paper, a noise generator based on the ADF435x board was developed and a spectrum analyzer based on Pololu Wixel with OLED. A simple Unmanned Aerial Vehicle (UAV) DJI Tello was used as a test bench, which operates in the 2.4 GHz frequency range. The problem of cooling the stationary UAV was constructively solved during the creation of the experimental model. The study results show that the influence of an external noise generator can lead to significant image distortions, which can lead to piloting errors. And after the limit value of 7.33%, the video signal transmission is stopped, and the control signals may be partially lost.
Soil heavy metal Pb concentration quantitative inversion method based on hyperspectral remote sensing

Abstract Taking a mining area in the Liangshan section of the Anning River Basin as the research area, soil samples were collected, and the collected soil samples were respectively measured for Pb concentration and soil spectral reflectance, and the correlation between soil Pb concentration and soil spectral reflectance was analyzed, using the partial least squares regression method to construct the soil Pb concentration estimation model and carry out the accuracy test. The results show that: (1) The correlation between the original soil spectral curve and the measured value of soil Pb concentration is greatly improved after the first-order differential(FD), second-order differential(SD), reciprocal first-order differential(RTFD) and inverse second-order differential(RTSD) transformation, and there are many characteristic bands. (2) The best model for estimating soil Pb concentration is the model established by second-order differential, whose coefficient of determination (R2) is 0.56 and root mean square error (RMSE) is 1.28. It provides a reference for the retrieval of remote sensing hyperspectral soil Pb concentration in this area.
Industry-track: Challenges in Rebooting Autonomy with Deep Learned Perception

Deep learning (DL) models are becoming effective in solving computer-vision tasks such as semantic segmentation, object tracking, and pose estimation on real-world captured images. Reliability analysis of autonomous systems that use these DL models as part of their perception systems have to account for the performance of these models. Autonomous systems with traditional sensors have tried-and-tested reliability assessment processes with modular design, unit tests, system integration, compositional verification, certification, etc. In contrast, DL perception modules relies on data-driven or learned models. These models do not capture uncertainty and often lack robustness. Also, these models are often updated throughout the lifecycle of the product when new data sets become available. However, the integration of an updated DL-based perception requires a reboot and start afresh of the reliability assessment and operation processes for autonomous systems. In this paper, we discuss three challenges related to specifying, verifying, and operating systems that incorporate DL-based perception. We illustrate these challenges through two concrete and open source examples.