Reviewerly/exHarmony huggingface dataset:
Resource Allocation Based on Digital Twin-Enabled Federated Learning Framework in Heterogeneous Cellular Network

Federated learning (FL) allows user devices (UDs) to upload local model parameters to participate in a global model training, which protects UDs' data privacy. Nevertheless, FL still faces challenges such as core network congestion, UDs' limited resources and less efficient mapping between devices and cyber systems. Therefore, in this article, we integrate the digital twin (DT) and the mobile edge computing (MEC) technologies into a hierarchical FL framework in the heterogeneous cellular network scenario. When the UDs are not in the service range of the small base stations (SBSs), the framework allows macro base stations to assist UDs' local computation, thus reducing the transmission delay. It also protects the user privacy and allows more users to join in the training in order to improve the FL accuracy. In addition, we propose a deep reinforcement learning-based scheme to solve the joint optimization problem of dynamic UDs-stations association and resource allocation, thereby minimizing the energy consumption within a limited time delay. Simulation results show that our proposed scheme not only effectively reduces the task transmission failure rate and energy consumption compared with the baseline scheme, but also saves the communication cost through the DT network.
A Method to the Madness: Applying an Intersectional Analysis of Structural Oppression and Power in HCI and Design

With increased focus on historically excluded populations, there have been recent calls for HCI research methods to more adequately acknowledge and address the historical context of racism, sexism, gendered racism, epistemic violence, classism, and so on. In this article, we utilize Black feminist epistemologies to serve as critical frameworks for understanding the historical context that reveals the interconnected systems of power that mutually influence one another to create unequal outcomes or social inequalities for different populations. Leveraging Black feminist thought (BFT) and intersectionality as critical social theories of design praxis, we introduce intersectional analysis of power—a method that enables HCI researchers, designers, and practitioners to identify and situate saturated sites of violence in a historical context and to transform the ways in which they engage with populations that have been historically oppressed. Engaging in self-reflection as researchers, we apply an intersectional analysis of power to co-design technologies with community street outreach workers who address violence in their predominantly Black communities. We: (1) identify the saturated site of violence; (2) identify the intersecting systems of power and who holds power (past and present); (3) describe the “conceptual glue” that binds these intersecting systems together and the assumption(s) that those who hold power are employing to guide their interactions; (4) examine the ways in which Black people are subjugated, surveilled, and/or expected to assimilate to “normative” ways of being and behaving; and (5) identify acts of resistance. This article contributes an alternative to traditional HCI and design methods that falsely perpetuate a lens of neutrality and colorblindness that centers on whiteness, innovation, and capitalism and ignores the history of State-sanctioned violence and structural oppression.
Geoscience Knowledge Graph (GeoKG): Development, construction and challenges

Abstract Big earth data is a cross‐domain of geoscience and information science, which provides a novel perspective for solving geoscience problems. Most contemporary research is driven by data but neglect the potential value of knowledge. As a new scientific language in Geoscience, GeoKG is essential for understanding, representing, and mining geoscience knowledge, and can contribute to the integration of big earth data, geoscience knowledge, and geoscience models. However, research on GeoKG lack spatiotemporal perspectives in knowledge cognition, representation, acquisition and management. To this end, this article first outlines a cognitive mechanism from the human–machine double perspective and categorizes the characteristics and content of geoscience knowledge. To express evolution and complex natural rules, a knowledge representation framework is proposed through ‘state‐process’ and ‘condition‐result’ models. Aiming at multimodal data, a workflow is put forward to acquire knowledge from a small sample, a knowledge graph, a map, and a schematic diagram. Furthermore, we discuss the organization of GeoKG by improving existing data models, developing spatiotemporal correlation indexing and advancing knowledge graph completion. The concrete construction process of GeoKG is analyzed thoroughly in this study, which can support the synthetic analysis of big earth data, promote the development of knowledge engineering and provide a foundation for improving intelligent geoscience.
Challenges of Low-Code/No-Code Software Development: A Literature Review

Low-code/no-code software development is an emerging approach delivering the opportunity to build software with a minimal need for manual coding and enhancing the involvement of non-programmers in software development. Low-code principles allow enterprises to save time and costs through a more rapid development pace and to improve software products quality by bringing closer together business and information technologies as well as promoting automation. Nevertheless, the low-code/no-code approach is a relatively new and continuously progressing domain that requires understanding of existing challenges and identification of improvement directions. In this paper, challenges in the low-code software development process and suggestions for their mitigation are identified and amalgamated with the purpose to deliver insights into the current state of the low-code/no-code development process and identify areas for further research and development.
Witnessing entanglement between two two-level atoms moving inside a leaky cavity under classical control

Based on the quantum memory-assisted entropic uncertainty relation (QMA-EUR), we investigate the Markovian and non-Markovian dynamics of entanglement witness in a two-atom system that asymmetrically are coupled to a leaky cavity with two-photon relaxation. The atoms are two-level systems, they move inside the cavity and interact with a classical driving laser field. We discuss in detail the effects of the translational movement of the atoms, classical driving field and atom-cavity coupling on the protection of entanglement witness. Our results show that with a regular increase in the intensity of the classical driving field, the entanglement region witnessed by lower bound of QMA-EUR increases and the time of entanglement witness gets longer. In the Markovian regime, we find that quantum entanglement between the moving atoms can be witnessed for a longer time by increasing the velocity of the atoms. However, in the non-Markovian regime, higher speeds are required to witness entanglement over long time intervals. In addition, we conclude that the entanglement region witnessed by the lower bound of QMA-EUR can be protected in both the symmetrical and asymmetrical atom-cavity coupling regimes. In the asymmetrical regime, a better protection can be achieved when the memory atom is coupled to the cavity field stronger than the other atom.
Addressing constrained engineering problems and feature selection with a time-based leadership salp-based algorithm with competitive learning

Abstract Like most metaheuristic algorithms, salp swarm algorithm (SSA) suffers from slow convergence and stagnation in the local optima. The study develops a novel Time-Based Leadership Salp-Based Competitive Learning (TBLSBCL) to address the SSA’s flaws. The TBLSBCL presents a novel search technique to address population diversity, an imbalance between exploitation and exploration, and the SSA algorithm’s premature convergence. Hybridization consists of two stages: First, a time-varying dynamic structure represents the SSA hierarchy of leaders and followers. This approach increases the number of leaders while decreasing the number of salp’s followers linearly. Utilizing the effective exploitation of the SSA, the position of the population’s leader is updated. Second, the competitive learning strategy is used to update the status of the followers by teaching them from the leaders. The goal of adjusting the salp swarm optimizer algorithm is to help the basic approach avoid premature convergence and quickly steer the search to the most promising likely search space. The proposed TBLSBCL method is tested using the CEC 2017 benchmark, feature selection problems for 19 datasets (including three high-dimensional datasets). The TBLSBCL was then evaluated using a benchmark set of seven well-known constrained design challenges in diverse engineering fields defined in the benchmark set of real-world problems presented at the CEC 2020 conference (CEC 2020). In each experiment, TBLSBCL is compared with seven other state-of-the-art metaheuristics and other advanced algorithms that include seven variants of the salp swarm. Friedman and Wilcoxon rank-sum statistical tests are also used to examine the results. According to the experimental data and statistical tests, the TBLSBCL algorithm is very competitive and often superior to the algorithms employed in the studies. The implementation code of the proposed algorithm is available at: https://github.com/MohammedQaraad/TBLSBCL-Optimizer.
A multistrategy hybrid adaptive whale optimization algorithm

Abstract To solve the problems of whale optimization algorithm (WOA) with slow convergence speed, low accuracy, and poor global search ability, a multistrategy hybrid adaptive whale optimization algorithm (MHWOA) was proposed. In this algorithm, the logistic–tent hybrid chaos algorithm was used to initialize the population, which could make the population distribution more random and uniform. The opposition-based learning strategy was adopted to expand the population of individuals and complete the population selection. To balance the exploitation phase and exploration phase, a dynamic parameter was constructed based on the sigmoid excitation function, and an active adaptive weight was added to adjust the global and local search, which accelerated the convergence speed also. The perturbation mechanism of the Student T-distribution was introduced with random perturbation to expand the search range and improve the global search ability of the algorithm. In total, 23 benchmark functions were selected to conduct convergence performance and optimization performance experiments of the proposed algorithm. The average value and standard deviation were determined as evaluation indexes. The MHWOA was compared with other improved WOA variants and advanced algorithms. The results showed that the proposed MHWOA had better iterative convergence and optimization performance than different algorithms on the optimization of unimodal functions, multimodal functions, and fixed dimension functions. Meanwhile, the MHWOA was applied to the optimal designs of pressure vessels and springs. The experimental results displayed that the MHWOA obtained better solutions than other meta-heuristic algorithms. This study has practical solid application value, which can be applied to solving various engineering problems.
E-learning as a mean of forming students’ mathematical competence in a research-oriented educational process

The article is devoted to the substantiation of approaches to the effective use of advantages and minimization of disadvantages and losses of e‑learning as a mean of forming mathematical competence of students in the conditions of research-oriented educational process. As a result of the ascertaining experiment, e-learning has certain disadvantages besides its obvious advantages (adaptability, possibility of individualization, absence of geographical barriers, ensuring social equality, unlimited number of listeners, etc.). However, the nature of these drawbacks lies not as much in the plane of opportunity itself as in the ability to use them effectively. On the example of the e-learning course (ELC) “Mathematical Analysis” (Calculus) of Borys Grinchenko Kyiv University, which is developed on the basis of the Moodle platform, didactic and methodical approaches to content preparation and organization of activities in the ELC in mathematics are offered. Given the specifics of mathematics as a discipline, the possibility of using ELCs to support the traditional learning process with full-time learning is revealed, introducing a partially mixed (combined) model. It is emphasized that effective formation of mathematical competence of students by means of e-learning is possible only in the conditions of research-oriented educational environment with active and concerned participation of students and partnership interaction. The prospect of further research in the analysis of e-learning opportunities for the formation of students’ mathematical competence, in particular, research and investigation tools, and the development of recommendations for the advanced training programs of teachers of mathematical disciplines of universities are outlined.
EL-CodeBert: Better Exploiting CodeBert to Support Source Code-Related Classification Tasks

With the development of deep learning and natural language processing techniques, the performance of many source code-related tasks can be improved by using pre-trained models. Of these pre-trained models, CodeBert is a bi-modal pre-trained model for programming languages and natural languages, which has been successfully used in current source code-related tasks. These previous studies mainly use the output vector of CodeBert’s last layer as the code semantic representation for fine-tuning downstream source code-related tasks. However, this setting may miss the valuable representational information, which may be captured by other layers of CodeBert.
Ghostformer: A GhostNet-Based Two-Stage Transformer for Small Object Detection

In this paper, we propose a novel two-stage transformer with GhostNet, which improves the performance of the small object detection task. Specifically, based on the original Deformable Transformers for End-to-End Object Detection (deformable DETR), we chose GhostNet as the backbone to extract features, since it is better suited for an efficient feature extraction. Furthermore, at the target detection stage, we selected the 300 best bounding box results as regional proposals, which were subsequently set as primary object queries of the decoder layer. Finally, in the decoder layer, we optimized and modified the queries to increase the target accuracy. In order to validate the performance of the proposed model, we adopted a widely used COCO 2017 dataset. Extensive experiments demonstrated that the proposed scheme yielded a higher average precision (AP) score in detecting small objects than the existing deformable DETR model.
Dynamic Feature Selection for Solar Irradiance Forecasting Based on Deep Reinforcement Learning

A large volume of data is typically needed to achieve an accurate solar generation prediction. However, not all types of data are consistently available. Various research efforts have addressed this challenge by developing methods that identify the most relevant features for predicting solar generation. However, the optimal features vary with different weather patterns, making it impossible to select a fixed set of optimal features for all weather patterns. This study develops a new framework to accurately predict solar irradiance using dynamically changing optimal features. The developed model first incorporates feature extraction with clustering techniques to identify representative weather data from a dataset. Next, using deep reinforcement learning (DRL), a new feature selection method is developed to yield the minimum features required to accurately forecast solar irradiance from representative data. Benefiting from the model-free nature of DRL, the developed method is adaptive to various weather conditions, and dynamically alters the selected features. Case studies using real-world data have shown that the developed model significantly reduces the volume of data required for accurate irradiance forecasting for different weather patterns.
Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review

Breast cancer is the most common disease among women, with over 2.1 million new diagnoses each year worldwide. About 30% of patients initially presenting with early stage disease have a recurrence of cancer within 10 years. Predicting who will have a recurrence and who will not remains challenging, with consequent implications for associated treatment. Artificial intelligence strategies that can predict the risk of recurrence of breast cancer could help breast cancer clinicians avoid ineffective overtreatment. Despite its significance, most breast cancer recurrence datasets are insufficiently large, not publicly available, or imbalanced, making these studies more difficult. This systematic review investigates the role of artificial intelligence in the prediction of breast cancer recurrence. We summarise common techniques, features, training and testing methodologies, metrics, and discuss current challenges relating to implementation in clinical practice. We systematically reviewed works published between 1 January 2011 and 1 November 2021 using the methodology of Kitchenham and Charter. We leveraged Springer, Google Scholar, PubMed, and IEEE search engines. This review found three areas that require further work. First, there is no agreement on artificial intelligence methodologies, feature predictors, or assessment metrics. Second, issues such as sampling strategies, missing data, and class imbalance problems are rarely addressed or discussed. Third, representative datasets for breast cancer recurrence are scarce, which hinders model validation and deployment. We conclude that predicting breast cancer recurrence remains an open problem despite the use of artificial intelligence.
Improvement of AD-Census Algorithm Based on Stereo Vision

Problems such as low light, similar background colors, and noisy image acquisition often occur when collecting images of lunar surface obstacles. Given these problems, this study focuses on the AD-Census algorithm. In the original Census algorithm, in the bit string calculated with the central pixel point, the bit string will be affected by the noise that the central point is subjected to. The effect of noise results in errors and mismatching. We introduce an improved algorithm to calculate the average window pixel for solving the problem of being susceptible to the central pixel value and improve the accuracy of the algorithm. Experiments have proven that the object contour in the grayscale map of disparity obtained by the improved algorithm is more apparent, and the edge part of the image is significantly improved, which is more in line with the real scene. In addition, because the traditional Census algorithm matches the window size in a fixed rectangle, it is difficult to obtain a suitable window in the image range of different textures, affecting the timeliness of the algorithm. An improvement idea of area growth adaptive window matching is proposed. The improved Census algorithm is applied to the AD-Census algorithm. The results show that the improved AD-Census algorithm has been shown to have an average run time of 5.3% and better matching compared to the traditional AD-Census algorithm for all tested image sets. Finally, the improved algorithm is applied to the simulation environment, and the experimental results show that the obstacles in the image can be effectively detected. The improved algorithm has important practical application value and is important to improve the feasibility and reliability of obstacle detection in lunar exploration projects.
Global Multi-modal 2D/3D Registration via Local Descriptors Learning

Multi-modal registration is a required step for many image-guided procedures, especially ultrasound-guided interventions that require anatomical context. While a number of such registration algorithms are already available, they all require a good initialization to succeed due to the challenging appearance of ultrasound images and the arbitrary coordinate system they are acquired in. In this paper, we present a novel approach to solve the problem of registration of an ultrasound sweep to a pre-operative image. We learn dense keypoint descriptors from which we then estimate the registration. We show that our method overcomes the challenges inherent to registration tasks with freehand ultrasound sweeps, namely, the multi-modality and multidimensionality of the data in addition to lack of precise ground truth and low amounts of training examples. We derive a registration method that is fast, generic, fully automatic, does not require any initialization and can naturally generate visualizations aiding interpretability and explainability. Our approach is evaluated on a clinical dataset of paired MR volumes and ultrasound sequences.
Attentional Feature Fusion for End-to-End Blind Image Quality Assessment

In this paper, an end-to-end blind image quality assessment (BIQA) model based on feature fusion with an attention mechanism is proposed. We extracted the multilayer features of the image and fused them based on the attention mechanism; the fused features are then mapped into score, and the image quality assessment without reference is realized. First, because the human visual perception system hierarchically approaches the input information from local to global, we used three different neural networks to extract physically meaningful image features, and we use modified VGG19 and modified VGG16 to extract the substrate texture information and the local information of the edges, respectively. Meanwhile, we use the resNet50 to extract high-level global semantic information. Second, to take full advantage of multilevel features and avoid monotonic addition in hierarchical feature fusion, we adopt an attention-based feature fusion mechanism that combines the global and local contexts of the features and assigns different weights to the features to be fused, so that the model can perceive richer types of distortion. Experimental findings on six standard databases show that our approach yields improved performance.
An Overview of Wearable Biosensor Systems for Real-Time Substance Use Detection

Wearable biosensors represent an opportunity to improve treatment and research into a variety of diseases, including substance use disorder. They provide continuous, real-time data about the wearer’s condition in their natural environment in an unobtrusive, increasingly capable, and cost-effective way. However, generating clinically relevant insights from high-velocity, noisy, multidimensional data streams requires new approaches in real-time anomaly machine learning (ML). We present a survey of the existing algorithms for substance use monitoring in wearable biosensor data streams and how the advent of 5G and 6G wireless communications will drive further changes in this field. Our work highlights trends that have emerged among the different efforts published to-date as well as identifying ongoing challenges not adequately addressed by existing ML algorithms.