Reviewerly/exHarmony huggingface dataset:
DWS-YOLO: A Lightweight Detector for Blood Cell Detection

Peripheral blood cell detection is an essential component of medical practice and is used to diagnose and treat diseases, as well as to monitor the progress of therapies. Our objective is to construct an efficient deep learning model for peripheral blood cell analysis that achieves an optimized balance between inference speed, computational complexity, and detection accuracy. In this article, we propose the DWS-YOLO blood detector, which is a lightweight blood detector. Our model includes several improved modules, including the lightweight C3 module, the increased combined attention mechanism, the Scylla-IoU loss function, and the improved soft non-maximum suppression. Improved attention, loss function, and suppression enhance detection accuracy, while lightweight C3 module reduces computation time. The experiment results demonstrate that our proposed modules can enhance a detector’s detection performance, and obtain new state-of-the-art (SOTA) results and excellent robustness performance on the BCCD dataset. On the white blood cell detection dataset (Raabin-WBC), the proposed detector’s generalization performance was confirmed to be satisfactory. Our proposed blood detector achieves high detection accuracy while requiring few computational resources and is very suitable for resource-limited but efficient medical device environments, providing a reliable and advanced solution for blood detection that greatly improves the efficiency and effectiveness of peripheral blood cell analysis in clinical practice.
Hardware-Aware Evolutionary Explainable Filter Pruning for Convolutional Neural Networks

Abstract Filter pruning of convolutional neural networks (CNNs) is a common technique to effectively reduce the memory footprint, the number of arithmetic operations, and, consequently, inference time. Recent pruning approaches also consider the targeted device (i.e., graphics processing units) for CNN deployment to reduce the actual inference time. However, simple metrics, such as the $$\ell ^1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msup> </mml:math> -norm, are used for deciding which filters to prune. In this work, we propose a hardware-aware technique to explore the vast multi-objective design space of possible filter pruning configurations. Our approach incorporates not only the targeted device but also techniques from explainable artificial intelligence for ranking and deciding which filters to prune. For each layer, the number of filters to be pruned is optimized with the objective of minimizing the inference time and the error rate of the CNN. Experimental results show that our approach can speed up inference time by 1.40× and 1.30× for VGG-16 on the CIFAR-10 dataset and ResNet-18 on the ILSVRC-2012 dataset, respectively, compared to the state-of-the-art ABCPruner.
Identifying galaxy cluster mergers with deep neural networks using idealized compton-y and X-ray maps

Abstract We present a novel approach to identify galaxy clusters that are undergoing a merger using a deep learning approach. This paper uses massive galaxy clusters spanning 0 ≤ z ≤ 2 from The Three Hundred project, a suite of hydrodynamic re-simulations of 324 large galaxy clusters. Mock, idealised Compton-y and X-ray maps were constructed for the sample, capturing them out to a radius of 2R200. The idealised nature of these maps mean they do not consider observational effects such as foreground or background astrophysical objects, any spatial resolution limits or restriction on X-ray energy bands. Half of the maps belong to a merging population as defined by a mass increase ΔM/M ≥ 0.75, and the other half serve as a control, relaxed population. We employ a convolutional neural network architecture and train the model to classify clusters into one of the groups. A best-performing model was able to correctly distinguish between the two populations with a balanced accuracy (BA) and recall of 0.77, ROC-AUC of 0.85, PR-AUC of 0.55 and F1 score of 0.53. Using a multichannel model relative to a single channel model, we obtain a 3% improvement in BA score, and a 6% improvement in F1 score. We use a saliency interpretation approach to discern the regions most important to each classification decision. By analysing radially binned saliency values we find a preference to utilise regions out to larger distances for mergers with respect to non-mergers, greater than ∼1.2R200 and ∼0.7R200 for SZ and X-ray respectively.
Fairness-Aware Graph Neural Networks: A Survey

Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks. Despite this success, GNNs suffer from fairness issues that arise as a result of the underlying graph data and the fundamental aggregation mechanism that lies at the heart of the large class of GNN models. In this article, we examine and categorize fairness techniques for improving the fairness of GNNs. We categorize these techniques by whether they focus on improving fairness in the pre-processing, in-processing (during training), or post-processing phases. Furthermore, we discuss how such techniques can be used together whenever appropriate, and highlight the advantages and intuition as well. We also introduce an intuitive taxonomy for fairness evaluation metrics including graph-level fairness, neighborhood-level fairness, embedding-level fairness, and prediction-level fairness metrics. In addition, graph datasets that are useful for benchmarking the fairness of GNN models are summarized succinctly. Finally, we highlight key open problems and challenges that remain to be addressed.
Geochemical Characteristics of Trace Elements of Hot Springs in the Xianshuihe–Xiaojiang Fault Zone

Trace elements in hot-spring waters are indicators for tracing hydrochemical processes, such as the deep circulation of geothermal water, the degree of water–rock interaction, redox reactions, the contamination of shallow water by thermal water, etc. In this study, 74 hot springs in the Xianshuihe–Xiaojiang Fault Zone (XSHF-XJF) were chosen for an investigation of the geochemical characteristics of trace elements using classic geochemical tools and multivariate statistical analysis. The results indicated (1) the hot-spring waters were mainly derived from atmospheric precipitation as indicated by δD and δ18O values that generally increased with decreasing elevations; (2) the high concentrations of B, As, Fe, and Mn in the waters, as well as the values of the Water Quality Index (WQI), indicated that the water quality was spatially heterogeneous and the hot-spring waters are not entirely suitable for drinking; and (3) B/Cl ratios showed that hot springs have different reservoir sources in the XSHF and XJF, respectively. The leaching of surrounding rock during water circulation contributed predominantly to the trace elements of the study’s waters. Diversity of lithology was the main factor affecting their concentrations. In addition, deep circulation controlled by the fault could influence trace-element enrichment. Our results offer a guide for the exploration and use of geothermal resources.
Sign-to-Text Translation from Panamanian Sign Language to Spanish in Continuous Capture Mode with Deep Neural Networks

Convolutional neural networks (CNN) have provided great advances for the task of sign language recognition (SLR). However, recurrent neural networks (RNN) in the form of long–short-term memory (LSTM) have become a means for providing solutions to problems involving sequential data. This research proposes the development of a sign language translation system that converts Panamanian Sign Language (PSL) signs into text in Spanish using an LSTM model that, among many things, makes it possible to work with non-static signs (as sequential data). The deep learning model presented focuses on action detection, in this case, the execution of the signs. This involves processing in a precise manner the frames in which a sign language gesture is made. The proposal is a holistic solution that considers, in addition to the seeking of the hands of the speaker, the face and pose determinants. These were added due to the fact that when communicating through sign languages, other visual characteristics matter beyond hand gestures. For the training of this system, a data set of 330 videos (of 30 frames each) for five possible classes (different signs considered) was created. The model was tested having an accuracy of 98.8%, making this a valuable base system for effective communication between PSL users and Spanish speakers. In conclusion, this work provides an improvement of the state of the art for PSL–Spanish translation by using the possibilities of translatable signs via deep learning.
New Algorithm for Evaluation of Online Courses Based on Quality Matters Rubric Using Fuzzy Soft Expert Sets

The field of instructional technology has experienced significant growth in recent times. Due to the rapid shift towards online courses, the technology-based learning system is facing challenges in ensuring quality and assurance. The aim of this study was to develop online course evaluation tools by proposing a new algorithm to assess the success of the provided online courses and address quality assurance issues. The proposed algorithm is based on quality matters (QM) attributes and the use of fuzzy soft expert sets (FSESs). One key advantage of the proposed algorithm is that it incorporates experts’ opinions, which significantly contributes to achieving the study objective. The proposed algorithm was successfully implemented using the ASP.NET programming language. It resulted in the development of an EOC-FSES prototype system. The experimental evaluation of the prototype system confirms that it requires low effort and achieves high levels of performance, satisfaction, and behavioral intention to use. This paper includes several recommendations and suggestions.
Virtual reality natural experiences for mental health: comparing the effects between different immersion levels

Abstract Virtual nature is an innovative approach for promoting mental health. The purpose of this study was to compare the effects on mental health outcomes between two immersion levels of virtual reality natural experiences. The study design was a cluster trial. Healthy adults were allocated to two experimental groups. Identical pre-recorded 360° videos of natural scenes and sounds were played on the two virtual reality devices, one with a higher immersive level via a head-mounted display and the other one with a lower immersive level via a smartphone. The intervention was conducted for 30 min per session, once a week for 12 weeks. Data were collected by self-reported questionnaires at the baseline and post-intervention. In total, 54 participants completed the interventions. A significantly greater effect was revealed on improving happiness, self-rated health, and physical, mental, social, and environmental quality of life, and ameliorating distress, depression, and somatization in participants who experienced the higher immersive level compared to participants who experienced the lower immersive level. Virtual reality natural experiences with high immersion are recommended to promote mental health.
A Comprehensive Exploration of Outlier Detection in Unstructured Data for Enhanced Business Intelligence Using Machine Learning

Due to the rapid growth of online data, it is evident that social informatics faces a significant obstacle. The task of effectively utilizing this abundance of information for business intelligence purposes and extracting valuable insights from it across diverse and heterogeneous platforms presents a daunting challenge. Coordinating AI with business knowledge stands apart as an essential worry in the ongoing scene. Customarily, exceptions were many times excused as boisterous information, bringing about the deficiency of relevant data. This paper highlights the need to rethink how outliers are handled and shed light on the primary research challenges in this mining subfield. It presents a thorough scientific categorization of different Business Knowledge strategies and diagrams their ongoing application areas. Also, the paper talks about future exploration bearings and proposals to overcome any barrier concerning oddities in information examination, consequently empowering more successful business methodologies. This work plans to improve the usage of tremendous web-based information hotspots for better business insight results.
Simulation of robot arm system control using fuzzy logic

This study explains a robot arm system control simulation using a fuzzy logic controller. The robot arm is a nonlinear system and is not easily controlled using a conventional control system since the mathematical model of the system is not easy to obtain. Considering these problems, this final project presents a simulation of a fuzzy logic controller on a robot arm system. The aim of this control is to move the robot arm at an angular speed thus the robot gets the desired torque or the right position. Determining fuzzy rules greatly influences system stability. This control network produces output in the form of torque while the robot arm system calculates the next torque from the robot. The best output results were found on the triangular curve at response 2 because of the difference in values 0.01 on the final angle obtained.
Calculation and Analysis of Key Parameters of Underwater Optical Imaging System

When photographing objects underwater, it is important to utilize an optical window to isolate the imaging device from the water. The properties of the entire imaging system will change, and the imaging quality will decrease due to the refraction impact of the water and the window. The theoretical calculation method for air imaging is no longer relevant in this context. To analyze the unique rule, this research derives the formulas for key parameters of underwater imaging systems under paraxial circumstances. First, the optical window is modeled, then the formula for the optical window’s focal length in the underwater environment is derived, and the change rule for the focal length of various window forms underwater is condensed. For the ideal imaging system using a domed optical window, the equivalent two-optical group model of the imaging system is established, and the formula for calculating the focal length, working distance, and depth of field of the underwater imaging system is derived through paraxial ray tracing. The accuracy of the formula is verified through the comparative analysis of the formula calculation results and the Zemax modeling simulation results. It provides an important theoretical basis for the in-depth study of underwater imaging technology.
Document Storage and Verification System Using Blockchain Technology

A banking or identity provider can set up a customer identification data verification process between reliant parties with the use of the electronic know your customer (e-KYC) system. Due to its high degree of accessibility and availability and its efficient resource usage, the majority of banks choose to implement their e-KYC system on the cloud. All of the KYC procedures used by banks rely on encryption, which is a cumbersome process that may cause consumer data to be disclosed to unaffiliated financial institutions. Blockchain technology can be used to increase the efficiency of this system because it can automate a lot of human labor and is impervious to attacks of all kinds. The distributed ledger and immutable blockchain block make the perfect addition to the KYC process. The use of smart contacts can automate the identification of fraud. Any kind of KYC can be used to store information related to KYC identity. Consequently, financial institutions can establish a shared private blockchain on their premises for the purpose of document validation. This allows the user to maintain control over their private documents while also simplifying the process for banks to obtain the records needed for compliance
Digital Renaissance in Education: Unveiling the Transformative Potential of Digitization in Educational Institutions

Abstract This paper examines the ongoing trend of digitization within educational institutions, analyzing the potential opportunities that lie ahead. As educational landscapes evolve, embracing digitization offers institutions a pathway to unlock a myriad of prospects. This research explores the current state of digitization in educational settings, highlighting the opportunities that arise from the integration of digital technologies. The driving forces behind significant changes in higher education include escalating operating costs, the emergence of micro and nano degrees, the surge in online learning, the influx of digitally-native students, and the overarching wave of digital transformation. Students are increasingly leveraging digital technologies to showcase a diverse array of tools, integrating structured learning environments into the fabric of higher education teaching. This research provides a comprehensive overview of digital transformation in higher education, evaluating the potential of digitalization and forecasting its prospects in the near future. Key concepts such as digital transformation, virtual reality (VR) technology, artificial intelligence (AI), and Technology, Entertainment, Design (TED) courses are examined as practical and indispensable elements shaping the future of education. Through a meticulous analysis, it becomes evident that digitalization marks only the initial phase in a broader process wherein institutions harness new technologies not merely to replicate old tasks but to redefine educational paradigms. Furthermore, the paper emphasizes that digital transformation necessitates the infusion of new management practices capable of harnessing the full potential of information and communication technologies (ICT). The journey towards the digital transformation of educational institutions involves a profound shift in mindset, encompassing innovative approaches to teaching, learning, and administration. As institutions embrace this transformative journey, they position themselves to create enhanced value for students and forge dynamic collaborations with partners in the evolving landscape of higher education.
A High-Resolution Aftershock Catalog for the 2014 Ms 6.5 Ludian (China) Earthquake Using Deep Learning Methods

A high-resolution catalog for the 2014 Ms 6.5 Ludian aftershocks was constructed based on the deep learning phase-picking model (CERP) and seismic-phase association technology (PALM). A specific training strategy, which combines the advantages of the conventional short–long window average energy ratio algorithm (STA/LTA) and AI algorithms, is employed to retrain the CERP model. The P- and S-wave phases were accurately detected and picked on continuous seismic waveforms by the retained AI model. Hypoinverse and HypoDD were utilized for the precise location of 3286 events. Compared to the previous results, our new catalog exhibits superior performances in terms of location accuracy and the number of aftershock events, thereby enabling a more detailed depiction of the deep-seated tectonic features. According to the distribution of aftershocks, it can be inferred that (1) the seismogenic fault of the Ludian earthquake is the NW-trending Baogunao–Xiaohe Fault, (2) the Ludian aftershocks interconnected with the discontinuous NW-trending Baogunao–Xiaohe Fault, and they also intersected with the Zhaotong–Ludian Fault. (3) This suggests that the NE-trending Zhaotong–Ludian Fault may have been intersected by the NW-trending Baogunao–Xiaohe Fault, indicating that the Baogunao–Xiaohe Fault is likely a relatively young Neogene fault.
Enhanced high-spatial resolution radiographic images based on COTS CMOS image sensors applied to wood dendrochronology and densitometry

The imaging of wood samples using X-rays for the study of dendrochronology and densitometry is predominantly conducted through analog films subsequently digitized or, in more advanced instances, with expensive flat-panel systems characterized by resolutions on the order of tenths of microns. For this reason, this work proposes as an alternative the utilization of Commercial-Off-The-Shelf (COTS) complementary metal–oxide–semiconductor (CMOS) image sensors as affordable direct detectors for the acquisition of 10.2 μm resolution radiographic images, which represents sufficient resolution to observe the small structures formed by wood cells called tracheids. The raw images are subsequently processed to mitigate the impact of non-uniformities inherent in pixel response, including the total dose effects, and artifacts present in the image generated by irregularities and defects in the materials utilized to support the wood samples. Moreover, an exhaustive study of the sensor response over different x-ray tube voltage was performed, comparing the results obtained with a simple calculation model and finding a negligible contribution of the photons above the 20 keV. The model was developed based on diverse analyses of geometry and composition of the sensor, such as Focus Ion Beam, Scanning Electron Microscopy, and Time-of-Flight Secondary Ion Mass Spectrometry, and takes into account the X-ray beam attenuations and the photon interaction with the sensor. Finally, this model enables the estimation of the transmission based on the density and thickness of the wood samples.
Analysis of strong coupling constant with machine learning and its application

Abstract In this work, we investigate the nature of the strong coupling constant and related physics. Through the analysis of accumulated experimental data from around the world, we employ the ability of machine learning to unravel its physical laws. The result of our efforts is a formula that captures the expansive panorama of the distribution of the strong coupling constant across the entire energy range. Importantly, this newly derived expression is very similar to the formula derived from the Dyson-Schwinger equations based on the framework of Yang-Mills theory. By introducing the Euler number, $e$, into the functional formula of the strong coupling constant at high energies, we have successfully solved the puzzle of the infrared divergence, which allows for a seamless transition of the strong coupling constant from the perturbative to the non-perturbative energy regime. Moreover, the obtained ghost and gluon dressing function distribution results confirm that the obtained strong coupling constant formula can well describe the physical properties of the non-perturbed regime. In addition, we investigate the QCD strong coupling constant result of the Bjorken sum rule $\Gamma_1^{p-n}$ and the quark-quark static energy $E_0(r)$, and find that the global energy scale can effectively interpret the experimental data. The results presented in this work shed light on the puzzling properties of quantum chromodynamics and the intricate interplay of strong coupling constants at both low and high energy scales.