bluuebunny/crossref_metadata_2025 huggingface dataset:
Analogist: Out-of-the-box Visual In-Context Learning with Image Diffusion Model
Authors: Gu Zheng , Yang Shiyuan , Liao Jing , Huo Jing , Gao Yang
Published: July 2024

Visual In-Context Learning (ICL) has emerged as a promising research area due to its capability to accomplish various tasks with limited example pairs through analogical reasoning. However, training-based visual ICL has limitations in its ability to generalize to unseen tasks and requires the collection of a diverse task dataset. On the other hand, existing methods in the inference-based visual ICL category solely rely on textual prompts, which fail to capture fine-grained contextual information from given examples and can be time-consuming when converting from images to text prompts. To address these challenges, we propose Analogist, a novel inference-based visual ICL approach that exploits both visual and textual prompting techniques using a text-to-image diffusion model pretrained for image inpainting. For visual prompting, we propose a self-attention cloning (SAC) method to guide the fine-grained structural-level analogy between image examples. For textual prompting, we leverage GPT-4V's visual reasoning capability to efficiently generate text prompts and introduce a cross-attention masking (CAM) operation to enhance the accuracy of semantic-level analogy guided by text prompts. Our method is out-of-the-box and does not require fine-tuning or optimization. It is also generic and flexible, enabling a wide range of visual tasks to be performed in an in-context manner. Extensive experiments demonstrate the superiority of our method over existing approaches, both qualitatively and quantitatively. Our project webpage is available at https://analogist2d.github.io.
Towards Motion Metamers for Foveated Rendering
Authors: Tariq Taimoor , Didyk Piotr
Published: July 2024

Foveated rendering takes advantage of the reduced spatial sensitivity in peripheral vision to greatly reduce rendering cost without noticeable spatial quality degradation. Due to its benefits, it has emerged as a key enabler for real-time high-quality virtual and augmented realities. Interestingly though, a large body of work advocates that a key role of peripheral vision may be motion detection, yet foveated rendering lowers the image quality in these regions, which may impact our ability to detect and quantify motion. The problem is critical for immersive simulations where the ability to detect and quantify movement drives actions and decisions. In this work, we diverge from the contemporary approach towards the goal of foveated graphics, and demonstrate that a loss of high-frequency spatial details in the periphery inhibits motion perception, leading to underestimating motion cues such as velocity. Furthermore, inspired by an interesting visual illusion, we design a perceptually motivated real-time technique that synthesizes controlled spatio-temporal motion energy to offset the loss in motion perception. Finally, we perform user experiments demonstrating our method's effectiveness in recovering motion cues without introducing objectionable quality degradation.
Holographic Parallax Improves 3D Perceptual Realism
Authors: Kim Dongyeon , Nam Seung-Woo , Choi Suyeon , Seo Jong-Mo , Wetzstein Gordon , Jeong Yoonchan
Published: July 2024

Holographic near-eye displays are a promising technology to solve long-standing challenges in virtual and augmented reality display systems. Over the last few years, many different computer-generated holography (CGH) algorithms have been proposed that are supervised by different types of target content, such as 2.5D RGB-depth maps, 3D focal stacks, and 4D light fields. It is unclear, however, what the perceptual implications are of the choice of algorithm and target content type. In this work, we build a perceptual testbed of a full-color, high-quality holographic near-eye display. Under natural viewing conditions, we examine the effects of various CGH supervision formats and conduct user studies to assess their perceptual impacts on 3D realism. Our results indicate that CGH algorithms designed for specific viewpoints exhibit noticeable deficiencies in achieving 3D realism. In contrast, holograms incorporating parallax cues consistently outperform other formats across different viewing conditions, including the center of the eyebox. This finding is particularly interesting and suggests that the inclusion of parallax cues in CGH rendering plays a crucial role in enhancing the overall quality of the holographic experience. This work represents an initial stride towards delivering a perceptually realistic 3D experience with holographic near-eye displays.
Kinetic Simulation of Turbulent Multifluid Flows
Authors: Li Wei , Wu Kui , Desbrun Mathieu
Published: July 2024

Despite its visual appeal, the simulation of separated multiphase flows (i.e., streams of fluids separated by interfaces) faces numerous challenges in accurately reproducing complex behaviors such as guggling, wetting, or bubbling. These difficulties are especially pronounced for high Reynolds numbers and large density variations between fluids, most likely explaining why they have received comparatively little attention in Computer Graphics compared to single- or two-phase flows. In this paper, we present a full LBM solver for multifluid simulation. We derive a conservative phase field model with which the spatial presence of each fluid or phase is encoded to allow for the simulation of miscible, immiscible and even partially-miscible fluids, while the temporal evolution of the phases is performed using a D3Q7 lattice-Boltzmann discretization. The velocity field, handled through the recent high-order moment-encoded LBM (HOME-LBM) framework to minimize its memory footprint, is simulated via a velocity-based distribution stored on a D3Q27 or D3Q19 discretization to offer accuracy and stability to large density ratios even in turbulent scenarios, while coupling with the phases through pressure, viscosity, and interfacial forces is achieved by leveraging the diffuse encoding of interfaces. The resulting solver addresses a number of limitations of kinetic methods in both computational fluid dynamics and computer graphics: it offers a fast, accurate, and low-memory fluid solver enabling efficient turbulent multiphase simulations free of the typical oscillatory pressure behavior near boundaries. We present several numerical benchmarks, examples and comparisons of multiphase flows to demonstrate our solver's visual complexity, accuracy, and realism.
Cricket: A Self-Powered Chirping Pixel
Authors: Nayar Shree K. , Klotz Jeremy , Nanda Nikhil , Fridberg Mikhail
Published: July 2024

We present a sensor that can measure light and wirelessly communicate the measurement, without the need for an external power source or a battery. Our sensor, called cricket, harvests energy from incident light. It is asleep for most of the time and transmits a short and strong radio frequency chirp when its harvested energy reaches a specific level. The carrier frequency of each cricket is fixed and reveals its identity, and the duration between consecutive chirps is a measure of the incident light level. We have characterized the radiometric response function, signal-to-noise ratio and dynamic range of cricket. We have experimentally verified that cricket can be miniaturized at the expense of increasing the duration between chirps. We show that a cube with a cricket on each of its sides can be used to estimate the centroid of any complex illumination, which has value in applications such as solar tracking. We also demonstrate the use of crickets for creating untethered sensor arrays that can produce video and control lighting for energy conservation. Finally, we modified cricket's circuit to develop battery-free electronic sunglasses that can instantly adapt to environmental illumination.
Area ReSTIR: Resampling for Real-Time Defocus and Antialiasing
Authors: Zhang Song , Lin Daqi , Kettunen Markus , Yuksel Cem , Wyman Chris
Published: July 2024

Recent advancements in spatiotemporal reservoir resampling (ReSTIR) leverage sample reuse from neighbors to efficiently evaluate the path integral. Like rasterization, ReSTIR methods implicitly assume a pinhole camera and evaluate the light arriving at a pixel through a single predetermined subpixel location at a time (e.g., the pixel center). This prevents efficient path reuse in and near pixels with high-frequency details.

We introduce
Area ReSTIR
, extending ReSTIR reservoirs to also integrate each pixel's 4D ray space, including 2D areas on the film and lens. We design novel subpixel-tracking temporal reuse and shift mappings that maximize resampling quality in such regions. This robustifies ReSTIR against high-frequency content, letting us importance sample subpixel and lens coordinates and efficiently render antialiasing and depth of field.
TensoSDF: Roughness-aware Tensorial Representation for Robust Geometry and Material Reconstruction
Authors: Li Jia , Wang Lu , Zhang Lei , Wang Beibei
Published: July 2024

Reconstructing objects with realistic materials from multi-view images is problematic, since it is highly ill-posed. Although the neural reconstruction approaches have exhibited impressive reconstruction ability, they are designed for objects with specific materials (e.g., diffuse or specular materials). To this end, we propose a novel framework for robust geometry and material reconstruction, where the geometry is expressed with the implicit signed distance field (SDF) encoded by a tensorial representation, namely
TensoSDF.
At the core of our method is the roughness-aware incorporation of the radiance and reflectance fields, which enables a robust reconstruction of objects with arbitrary reflective materials. Furthermore, the tensorial representation enhances geometry details in the reconstructed surface and reduces the training time. Finally, we estimate the materials using an explicit mesh for efficient intersection computation and an implicit SDF for accurate representation. Consequently, our method can achieve more robust geometry reconstruction, outperform the previous works in terms of relighting quality, and reduce 50% training times and 70% inference time. Codes and datasets are available at https://github.com/Riga2/TensoSDF.
A Heat Method for Generalized Signed Distance
Authors: Feng Nicole , Crane Keenan
Published: July 2024

We introduce a method for approximating the signed distance function (SDF) of geometry corrupted by holes, noise, or self-intersections. The method implicitly defines a completed version of the shape, rather than explicitly repairing the given input. Our starting point is a modified version of the
heat method
for geodesic distance, which diffuses normal vectors rather than a scalar distribution. This formulation provides robustness akin to
generalized winding numbers (GWN)
, but provides distance function rather than just an inside/outside classification. Our formulation also offers several features not common to classic distance algorithms, such as the ability to simultaneously fit multiple level sets, a notion of distance for geometry that does not topologically bound any region, and the ability to mix and match signed and unsigned distance. The method can be applied in any dimension and to any spatial discretization, including triangle meshes, tet meshes, point clouds, polygonal meshes, voxelized surfaces, and regular grids. We evaluate the method on several challenging examples, implementing normal offsets and other morphological operations directly on imperfect curve and surface data. In many cases we also obtain an inside/outside classification dramatically more robust than the one obtained provided by GWN.
Multi-Material Mesh-Based Surface Tracking with Implicit Topology Changes
Authors: Heiss-Synak Peter , Kalinov Aleksei , Strugaru Malina , Etemadi Arian , Yang Huidong , Wojtan Chris
Published: July 2024

We introduce a multi-material non-manifold mesh-based surface tracking algorithm that converts self-intersections into topological changes. Our algorithm generalizes prior work on manifold surface tracking with topological changes: it preserves surface features like mesh-based methods, and it robustly handles topological changes like level set methods. Our method also offers improved efficiency and robustness over the state of the art. We demonstrate the effectiveness of the approach on a range of examples, including complex soap film simulations with thousands of interacting bubbles, and boolean unions of non-manifold meshes consisting of millions of triangles.
Robust Containment Queries over Collections of Rational Parametric Curves via Generalized Winding Numbers
Authors: Spainhour Jacob , Gunderman David , Weiss Kenneth
Published: July 2024

Point containment queries for regions bound by watertight geometric surfaces, i.e., closed and without self-intersections, can be evaluated straightforwardly with a number of well-studied algorithms. When this assumption on domain geometry is not met, such methods are either unusable, or prone to misclassifications that can lead to cascading errors in downstream applications. More robust point classification schemes based on generalized winding numbers have been proposed, as they are indifferent to these imperfections. However, existing algorithms are limited to point clouds and collections of linear elements. We extend this methodology to encompass more general curved shapes with an algorithm that evaluates the winding number scalar field over unstructured collections of rational parametric curves. In particular, we evaluate the winding number for each curve independently, making the derived containment query robust to how the curves are arranged. We ensure geometric fidelity in our queries by treating each curve as equivalent to an adaptively constructed polyline that provably has the same generalized winding number at the point of interest. Our algorithm is numerically stable for points that are arbitrarily close to the model, and explicitly treats points that are coincident with curves. We demonstrate the improvements in computational performance granted by this method over conventional techniques as well as the robustness induced by its application.
DiffCAD: Weakly-Supervised Probabilistic CAD Model Retrieval and Alignment from an RGB Image
Authors: Gao Daoyi , Rozenberszki David , Leutenegger Stefan , Dai Angela
Published: July 2024

Perceiving 3D structures from RGB images based on CAD model primitives can enable an effective, efficient 3D object-based representation of scenes. However, current approaches rely on supervision from expensive yet imperfect annotations of CAD models associated with real images, and encounter challenges due to the inherent ambiguities in the task - both in depth-scale ambiguity in monocular perception, as well as inexact matches of CAD database models to real observations. We thus propose DiffCAD, the first weakly-supervised probabilistic approach to CAD retrieval and alignment from an RGB image. We learn a probabilistic model through diffusion, modeling likely distributions of shape, pose, and scale of CAD objects in an image. This enables multi-hypothesis generation of different plausible CAD reconstructions, requiring only a few hypotheses to characterize ambiguities in depth/scale and inexact shape matches. Our approach is trained only on synthetic data, leveraging monocular depth and mask estimates to enable robust zero-shot adaptation to various real target domains. Despite being trained solely on synthetic data, our multi-hypothesis approach can even surpass the supervised state-of-the-art on the Scan2CAD dataset by 5.9% with 8 hypotheses.
Spatio-Temporal Parallel Transformer Based Model for Traffic Prediction
Authors: Kumar Rahul , Mendes-Moreira João , Chandra Joydeep
Published: July 2024

Traffic forecasting problems involve jointly modeling the non-linear spatio-temporal dependencies at different scales. While graph neural network models have been effectively used to capture the non-linear spatial dependencies, capturing the dynamic spatial dependencies between the locations remains a major challenge. The errors in capturing such dependencies propagate in modeling the temporal dependencies between the locations, thereby severely affecting the performance of long-term predictions. While transformer-based mechanisms have been recently proposed for capturing the dynamic spatial dependencies, these methods are susceptible to fluctuations in data brought on by unforeseen events like traffic congestion and accidents. To mitigate these issues we propose an improvised spatio-temporal parallel transformer (STPT) based model for traffic prediction that uses multiple adjacency graphs passed through a pair of coupled graph transformer-convolution network units, operating in parallel, to generate more noise-resilient embeddings. We conduct extensive experiments on 4 real-world traffic datasets and compare the performance of STPT with several state-of-the-art baselines, in terms of measures like RMSE, MAE, and MAPE. We find that using STPT improves the performance by around

\(10-34\%\)

as compared to the baselines. We also investigate the applicability of the model on other spatio-temporal data in other domains. We use a Covid-19 dataset to predict the number of future occurrences in different regions from a given set of historical occurrences. The results demonstrate the superiority of our model for such datasets.
Electrochemical Detection of Arsenic Using Eco-Friendly Zeolite-Graphite Composite Electrode
Authors: Ait Baha Abdellah , Idouhli Rachid , Tabit Kamal , Kazemian Hossein , Khadiri Mohy Eddine , Abouelfida Abdesselam
Published: July 2024

This paper presents an innovative and stable electrochemical sensor using zeolite and graphite for the reliable and accurate detection of arsenic(V). The zeolite materials were hydrothermally synthesized using coal fly ash and fumed silica by-products under specific environments (1.5 M NaOH, L/(Si/Al) ratio of 40, 120 °C, and 24 h), addressing environmental concerns related to wastes managment. The performance of the zeolite-graphite modified electrode (ZGME) was evaluated using voltammetric and impedance techniques at potentials between 1.2 V and −1.2 V (vs SCE) and a frequency range of 100 kHz to 10 mHz, respectively. The results showed that the incorporation of zeolite for the first time offered notable advantages, such as affordability, simplicity, and improved oxidation-reduction current and peak resolution for arsenic. The ZGME effectively detected As(V) under neutral conditions using a phosphate buffer solution, with a concentration range of 1 × 10−3 to 1 × 10−6 mol.l−1. The sensor achieved a detection limit of 3 μmol.l−1, a quantification limit of 5 μmol.l−1, a sensitivity of 0.28 μA/μM.cm2, and exhibited good reproducibility, opening up new potential for portable zeolite-based electrochemical sensors. Moreover, this research pioneers the use of zeolite as a graphite modifier.
iFQS: An Integrated FCNP‐Q‐Learning‐Based Scheduling Algorithm for On‐Demand Charging in Wireless Rechargeable Sensor Networks
Authors: Ri Man Gun , Kim Chun Hyok , Pak Se Hun , Pong Chol Min
Published: July 2024

In wireless rechargeable sensor networks (WRSNs), charging request nodes (RNs) are characterized by several criteria which are contradictory. Recently, on‐demand charging scheduling schemes, which use two or more multicriteria decision‐making (MCDM) methods, have been proposed. However, these on‐demand charging schemes use a pairwise ratio scale which can magnify the actual pairwise difference between multicriteria and do not take into account the trade‐off between performance metrics. In this paper, we propose a novel on‐demand charging scheduling method using a fuzzy cognitive network process (FCNP) which uses a fuzzy pairwise interval scale to solve these issues. The proposed method, coined as an integrated FCNP‐Q‐learning‐based scheduling (iFQS), first uses FCNP to exactly assign the relative weights to five multicriteria for charging prioritization and to three multicriteria for partial charging time (PCT) determination. Then, in charging path planning with Q‐learning, the BS use these five criteria’s weights to design the reward function and select the most suitable next charging sojourn point. On the other hand, three criteria’s weights are also used to reasonably determine the PCT at charging sojourn points while achieving a desirable trade‐off between charging metrics. The results of the extensive simulation show that the iFQS significantly improves charging performance in comparison with the existing MCDM‐based methods. The network lifetime of the proposed method is 125.1%, 286.7%, and 368.5% longer than FAHP‐VWA‐TOPSIS, fuzzy Q‐charging, and AHP‐TOPSIS, respectively, when the number of nodes is 600.
MR1 Gene and Protein Expression Are Enhanced by Inhibition of the Extracellular Signal-Regulated Kinase ERK
Authors: Constantin Daniel , Nosi Vladimir , Kehrer Natalie , Vacchini Alessandro , Chancellor Andrew , Contassot Emmanuel , Beshirova Aisha , Prota Gennaro , Navarini Alexander , Mori Lucia , De Libero Gennaro
Published: July 2024

The MHC class I–related molecule MR1 is ubiquitously expressed, is highly conserved among mammals, and presents bacterial and endogenous antigens in tumor cells. These features indicate that tumor-specific T cells restricted to MR1 may represent ideal candidates for novel cancer-directed T-cell immunotherapy. The very low expression of the MR1 protein at the cell surface is a potential challenge limiting the possible use of MR1-directed immunotherapies. To overcome this challenge, it is important that understanding of the mechanisms regulating MR1 expression is increased, as little is known about this currently. This study identified ERK1/2 as negative regulators of the MR1 gene and protein expression. Inhibition of ERK1/2 in tumor cells or treatment of BRAF-mutant tumor cells with drugs specific for mutated BRAF increased MR1 protein expression and recognition by tumor-reactive and MR1-restricted T cells. The ERK1/2 inhibition of MR1 was mediated by the ELF1 transcription factor, which was required for MR1 gene expression. The effects of ERK1/2 inhibition also occurred in cancer cell lines of different tissue origins, cancer cell lines resistant to drugs that inhibit mutated BRAF, and primary cancer cells, making them potential targets of specific T cells. In contrast to tumor cells, the recognition of healthy cells was very poor or absent after ERK1/2 inhibition. These findings suggest a pharmaceutical approach to increase MR1 protein expression in tumor cells and the subsequent activation of MR1-restricted T cells, and they have potential therapeutic implications.
Wastewater monitoring system in the textile industry
Authors: Raditya Murry , Pratama I Putu Eka Widya , Bagastyo Arseto Yekti , Nurhayati Ervin , Kalahari Bintang
Published: July 2024

Recently, a problem which often experienced by the Environmental Agency is to monitor the quality of industrial waste. The problem comes from manual monitoring of wastewater and the high cost of laboratory tests for each variable for the waste. This system is intended to develop a wastewater monitoring system considering the state of the environment and technology. This system uses 5 types of sensors to measure the quality of wastewater. The sensor will display measurement data both offline via liquid crystal display (LCD) and online via the website. For the pH sensor test, we obtained an error value approximately of 1.32% and accuracy of 98.68%. For the oxidation reduction potential (ORP) sensor test, we obtained an error value of 1.4% with 98.6% accuracy. We obtained an error value of 0.22% with 99.78% accuracy for the temperature sensor test. For the total dissolved solid (TDS) sensor test, we obtained an error value of 1.02% with 98.98% accuracy. The color sensor is validated using a spectrometer to measure the variation of color in remazol waste concentration. For the Client - Server communication test, the system has a delay of 2 seconds. One of the advantages of using a web server is the system has minimum network traffic.
A comparative analysis of activation functions in neural networks: unveiling categories
Authors: Bouraya Sara , Belangour Abdessamad
Published: July 2024

Activation functions (AFs) play a critical role in artificial neural networks, allowing for the modeling of complex, non-linear relationships in data. In this review paper, we provide an overview of the most commonly used AFs in deep learning. In this comparative study, we survey and compare the different AFs in deep learning and artificial neural networks. Our aim is to provide insights into the strengths and weaknesses of each AF and to provide guidance on the appropriate selection of AFs for different types of problems. We evaluate the most commonly used AFs, including sigmoid, tanh, rectified linear units (ReLUs) and its variants, exponential linear unit (ELU), and SoftMax. For each activation category, we discuss its properties, mathematical formulation (MF), and the benefits and drawbacks in terms of its ability to model complex, non-linear relationships in data. In conclusion, this comparative study provides a comprehensive overview of the properties and performance of different AFs, and serves as a valuable resource for researchers and practitioners in deep learning and artificial neural networks.
Gamma and ultraviolet radiation radiation analysis: an internet of things-based dosimetric study
Authors: Baena-Navarro Rubén , Alcala-Varilla Luis , Torres-Hoyos Francisco , Carriazo-Regino Yulieth , Parodi-Camaño Tobías
Published: July 2024

This study presents the implementation of an internet of things (IoT)-based device for the accurate and continuous measurement of gamma and ultraviolet (UV) radiation in a rural area of Sincelejo, Colombia. The device, calibrated with an error margin below 5%, allowed for the reliable collection of data during the year 2022. An average effective dose rate of gamma radiation of (0.998±0.037) mSv/year was recorded, a value that approaches the recommended limit. Additionally, the inverse square law of radiation was confirmed, observing a decrease in radiation with an increase in altitude. Concurrently, a constant risk of high to extremely high UV radiation exposure was detected throughout the year. These findings emphasize the need for constant monitoring and the implementation of UV protection measures in the region. The integration of IoT in environmental dosimetry has proven to be an invaluable tool for detailed tracking of radiation levels, significantly contributing to the understanding of radiation in rural areas. The exploration of more advanced sensors and data analysis tools in future research is recommended to further improve the accuracy and utility of these devices.
Design and analysis of sustainable photovoltaic solar charging system with battery storage for electric vehicles
Authors: Radwan Eyad , Awada Emad , Nour Mutasim
Published: July 2024

This paper presents a sustainable electric vehicle (EV) charging system that operates in three modes of operation to maximize the yield of photovoltaic (PV) system. The design and analysis of the EV charging system is customized based on the operational or office hours of a corporation. The proposed system incorporates a battery pack capable of providing at least one day of autonomy to overcome the weather conditions during early morning, shading times, or cloudy days. In this study, the perturb and observe (PO) algorithm is modified and used to operate the PV system at maximum power point (MPP) when charging either the EV or the storage battery. The load current, in both cases, is regulated using proportional integral (PI) controllers and pulse width modulation (PWM) switching of DC-DC converter. The proposed system operation is switched between three modes (boost operation for direct charging of EV and discharging of storage battery, and buck operation for charging of storage battery) by a simple event-driven finite state machine (FSM). Simulation results showed excellent tracking behavior of the proposed system when supplying a 5 kW load with variation in solar irradiance between 1000 and 400 W/m2, battery state of charge (SOC) between 40% and 100%, and temperature between 15 to 39 ℃.
Feature selection in P2P lending for default prediction using grey wolf optimization and machine learning
Authors: Sam'an Muhammad , Safuan Safuan , Munsarif Muhammad
Published: July 2024

Online loan services like peer-to-peer (P2P) lending enable lenders to transact without bank intermediaries. Predicting which lenders are likely to default is crucial to avoid bankruptcy since lenders bear the risk of default. However, this task becomes challenging when the P2P lending dataset contains numer- ous features. The prediction performance could be improved if the dataset fea- tures are relevant. Hence, applying feature selection to remove redundant and irrelevant features is essential. This paper introduces a novel wrapper feature selection model to identify the optimal feature subset for predicting defaults in P2P lending. The proposed method includes two main phases: feature selection and classification. Initially, grey wolf optimization (GWO) is used to select the best features in P2P lending datasets. Then, the fitness function of GWO is as- sessed using ten different machine learning (ML) models. Experimental results indicate that the proposed model outperforms previous related work, achieving average accuracy, recall, precision, and F1-score of 96.77%, 80.73%, 97.52%, and 80.06%, respectively.
Application of neural networks ensemble method for the Kazakh sign language recognition
Authors: Amirgaliyev Yedilkhan , Ataniyazova Aisulyu , Buribayev Zholdas , Zhassuzak Mukhtar , Urmashev Baydaulet , Cherikbayeva Lyailya
Published: July 2024

Sign languages are an extremely important means of communication in many cases, especially for deaf and hard of hearing people. But the same gesture can convey different meanings in different countries, so many different sign languages have been developed all over the world. In this study, a convolutional neural network (CNN) model was developed based on an ensemble method containing the ResNet-50 and VGG-19 architectures, which will be able to classify the Kazakh sign language (KSL) consisting of 42 Kazakh alphabet signs (classes). A proprietary data set of 57,708 images for 42 signs of the KSL has been formed. The ensemble model was compared with ResNet-50 and VGG-19 by evaluation metrics such as accuracy, precision, recall, f1-measure, and loss function. The recognition accuracy of the ensemble method reached 95.7%, exceeding the performance of ResNet-50 and VGG-19. The developed method was also tested on test data, where 35 out of 42 gestures were recognized completely correctly. The reliability of the proposed approach and the classification results obtained by using preprocessing methods and data augmentation techniques to expand the data set was confirmed by a computational experiment.
Enhance sentiment analysis in big data tourism using hybrid lexicon and active learning support vector machine
Authors: Saraswati Ni Wayan Sumartini , Ketut Gede Darma Putra I , Sudarma Made , Made Sukarsa I
Published: July 2024

Sentiment analysis is a review analysis process used to determine whether an opinion is neutral, negative, or positive. Sentiment analysis can be done using lexicon-based or machine learning-based approaches. Lexicon can perform sentiment analysis without training data because it is dictionary-based but performs worse than machine learning. Machine learning can perform well in completing sentiment analysis but requires training data so that the model does not experience underfitting. In the case of sentiment analysis on big data, manual labeling of training data is an inefficient job. Support vector machine (SVM) has the opportunity to be used together with the active learning (AL) method to make small training data but still have good performance. This research proposed a hybrid lexicon and AL-SVM method to complete sentiment analysis on big data tourism. This research used polarity from the valence aware dictionary and sentiment reasoner (VADER) lexicon as a reference for the query by user process from the AL-SVM to automate the sentiment analysis process on big data. The experimental results showed that using the hybrid lexicon and AL-SVM increased the sentiment analysis performance compared to the VADER lexicon, SVM, and lexicon SVM, which run separately.
Controlling mobile robot in flat environment taking into account nonlinear factors applying artificial intelligence
Authors: Huong Tran Thi , Ha Pham Thi Thu
Published: July 2024

The article shows how to build and identify intelligent automatic control problems for mobile robots in a flat surface environment at the workplace, with known and unknown obstacles. Research and develop programming and control methods as an operating system for mobile robots robot operating system (ROS). Update map data information, in the operating environment, robot position control process, obstacle overcoming process simultaneous positioning and mapping (SLAM). From there, we aim to calculate and determine the robot's motion trajectory to get a smart path. The positioning trajectory calculation system robots. The authors use actor-critic (AC) algorithm to research and develop control. Research results in simulations, in Gazebo environment and test runs on real mobile robots have shown high-quality practical performance of automatic navigation and control while using this algorithm.
Improving cultural awareness and trust towards m-banking apps in Jordan
Authors: Almuhairat Ahmed , Alti Adel , Alswailim Mohannad
Published: July 2024

Today’s mobile technology is improving our quality of life and changing our lifestyles by providing mobile financial applications that allow us to conduct daily financial transactions anytime and anywhere. As the number of mobile applications increases, great customer training offers innovative solutions suited to different customers’ cultures. Although Jordanian society has limited use of mobile banking applications due to a weak level of cultural awareness among consumers and financial security risks. Hence, we propose a culture-aware trust-based assistant to facilitate mobile banking transactions. It leverages the potential of guidance and behavior controllers to enhance awareness about available banking services while increasing confidence levels between Jordanians users for mobile apps. Particularly, an effective monitoring strategy and customer behavior controller aims to reduce fraud in mobile banking apps. An 18-user empirical study confirms that the completeness of financial culture and trust impact the customer’s attention to mobile banking apps. Therefore, the proposed assistant reaching an average interaction time of 20 seconds while achieving a high confidence rate of 74.05% which validates it is efficacy and practicability.
Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network
Authors: Cahyani Denis Eka , Hariadi Anjar Dwi , Setyawan Faisal Farris , Gumilar Langlang , Setumin Samsul
Published: July 2024

Pneumonia is a condition characterised by the sudden inflammation of lung tissue, which is triggered by microorganisms such as fungi, viruses, and bacteria. Chest X-ray imaging (CXR) can detect pneumonia, but it requires considerable time and medical expertise. Consequently, the objective of this study is to diagnose pneumonia using CXR imaging in order to effectively detect early cases of pneumonitis in children. The study employs the ensemble transfer learning convolutional neural network (ETL-CNN) transfer learning ensemble, which combines multiple CNN transfer learning models. Resnet50-VGG19 and VGG19-Xception are the ETL-CNN models used in this investigation. Comparing ETL-CNN models to CNN transfer learning models such as Resnet50, VGG19, and Xception. Pediatric CXR pneumonia, which consists of a normal and pneumonia image, is the source of these study results. The results of this analysis indicate that Resnet50-VGG19 achieved the highest level of accuracy, 99.14%. Additionally, the Resnet50-VGG19 obtained the highest levels of precision and recall when comparing to other models. Consequently, the conclusion of this study is that the Resnet50-VGG19 model can generate acceptable classification performance for pediatric pneumonia based on CXR. This study improves classification results for performance when compared to earlier studies.
Translation of Mexican sign language into Spanish using machine learning and internet of things devices
Authors: Caballero-Hernandez Héctor , Muñoz-Jiménez Vianney , Ramos-Corchado Marco Antonio
Published: July 2024

This paper describes an application based on internet of things (IoT) and gesture recognition through the MediaPipe library to communicate people with hearing and speech disabilities who use Mexican sign language (MSL) with people who do not have a disability. The system is made up of two modules, on the one hand, there is an Apple watch with the ability to obtain text and voice inputs in Spanish to stand for the translation into sign language through video and images. The system can set up a connection with an application in charge of performing sign recognition by capturing images from a camera, the IoT user can connect so that communication between both modules is carried out bidirectionally through Firebase database. During the development of the experimental tests, the visual recognition module was able to recognize signs belonging to the Spanish alphabet, digits, and greetings, with a 96% of precision, while the IoT device correctly displayed voice translations and text to symbology compared to the MSL.
Improved car detection performance on highways based on YOLOv8
Authors: Sutikno Sutikno , Sugiharto Aris , Kusumaningrum Retno , Wibawa Helmie Arif
Published: July 2024

Car detection on the road through computer vision is crucial for improving safety, as it plays an essential role in spotting nearby vehicles and preventing fatal accidents. Additionally, car detection significantly contributes to the advancement of autonomous vehicles. Previous explorations of car detection using YOLOv5 have revealed weaknesses regarding its resulting mean average precision (mAP). This scenario led to the development of a more advanced version of you only look once (YOLO), namely YOLOv8. Consequently, this study aimed to adopt YOLOv8 for automatic car detection on the road. YOLOv8 is proven to perform better than the previous version. A dataset comprising video frame images was captured on the highway in Semarang, Indonesia. The experiment results indicated that the proposed approach achieved impressive precision, recall, and mAP values, reaching 94.1%, 98.2%, and 98.8%, respectively. The proposed approach enhanced mAP and training time when compared with YOLOv5. Therefore, it was concluded that the proposed method was better suited for real-time car detection.
FiMoDeAL: pilot study on shortest path heuristics in wireless sensor network for fire detection and alert ensemble
Authors: Ifeanyi Akazue Maureen , Efetobore Edje Abel , Okpor Margaret Dumebi , Adigwe Wilfred , Ejeh Patrick Ogholuwarami , Odiakaose Christopher Chukwufunaya , Ojugo Arnold Adimabua , Edim Edim Bassey , Ako Rita Erhovwo , Geteloma Victor Ochuko
Published: July 2024

With the incessant outbreak of fire, the heavy loss to both lives and properties in the society fire has since become a critical issue and challenge that needs our daily attention to be resolved. Loss of lives and properties to fire outbreak in 2021 alone as occurring in major Nigerian markets and residential homes was estimated at over 3 trillion Naira. Our study proposes a wireless sensor network internet of things (IoT) based ensemble to aid the effective monitoring, detection and alerting of residents and fire service departments. With cost as a major issue and the requisite installation of fire and smoke detectors in many houses our ensemble can efficiently integrate into the existing system using the ESP8285-controller to create a comprehensive access control system. The system provides real time monitor and control capabilities that will allow administrators to track and manage fire monitor and detection within a facility. Thus, enhances system's efficiency and performance.
Optimal control of automatic voltage regulator system using hybrid PSO-GWO algorithm-based PID controller
Authors: Bouaddi Abdessamade , Rabeh Reda , Ferfra Mohammed
Published: July 2024

In this paper, a new hybrid optimization algorithm known as particle swarm optimization and grey wolf optimizer (PSO-GWO) based proportional integral derivative (PID) controller is suggested for automatic voltage regulator (AVR) system terminal tracking problem. The main objective of the suggested approach is to reduce crucial performance factors such as rise time, settling time, peak overshoot and peak time of the voltage of the power system in order to improve the AVR system's transient response. This analysis was compared to results obtained from existing heuristic algorithm-based approaches found in the literature, proving the improved PID controller's enhanced performance obtained through the suggested approach. Furthermore, the performance of the tuned controller with respect to disturbance rejection and its robustness to parametric uncertainties were evaluated separately and compared with existing control approaches. According to the obtained comparison results and from all simulations, using MATLAB-Simulink tool, it has been noted that the PID controller optimized using PSO-GWO algorithm has superior control performance compared to PID controllers tuned by ABC, DE, BBO and PSO algorithms. The main conclusion of the presented study highlights that the recommended strategy can be effectively implemented to improve the performance of the AVR system.
Intelligent control strategies for grid-connected photovoltaic wind hybrid energy systems using ANFIS
Authors: Babu Thiruveedula Madhu , Chenchireddy Kalagotla , Kumar Kotha Kalyan , Nehal Vasukul , Srihitha Sappidi , Vikas Marikal Ram
Published: July 2024

This study proposes intelligent control strategies for optimizing the grid integration of photovoltaic (PV) and wind energy in hybrid systems using an adaptive neuro-fuzzy inference system (ANFIS). The ANFIS control aims to enhance grid stability, improve power management, and maximize renewable energy (RE) utilization. The hybrid system's performance is evaluated through simulations, considering various environmental conditions and load demands. Results demonstrate the effectiveness of the proposed ANFIS-based control in dynamically adjusting the power output from PV and wind sources, ensuring efficient grid-connected operation. The findings underscore the potential of intelligent control strategies to contribute to the reliable and sustainable integration of RE into the grid.
Adsorbent from coffee grounds to reduce cadmium concentration in leachate water
Authors: Suaebu Syamsuddin , Rasjid Ashari , Suryadi Iwan
Published: July 2024

Wastewater contains a variety of heavy metals, one of which is cadmium (Cd) which causes adverse effects on both the environment and health. Coffee grounds can be used to reduce Cd in wastewater so this study aims to determine the reduction of Cd levels using coffee grounds. The type of research used is an experiment to see the decrease in Cd levels using carbonized and non-carbonized coffee grounds. The samples used were taken from the leachate water source at one of the landfills in Makassar City. Sampling was done using the grab sampling technique. The results showed that the use of uncarbonized and carbonized coffee grounds was effective in reducing Cd levels in Makassar City Tamangapa Landfill leachate water with an average decrease of 0.048 and 0.023 µg/L. Uncarbonized coffee grounds have a higher ability to reduce Cd in leachate so that it can be used as an alternative in the wastewater treatment process.
Role of Health Care Professionals in the Success of Blood Pressure Control Interventions in Patients With Hypertension: A Meta-Analysis
Authors: Mills Katherine T. , O’Connell Samantha S. , Pan Meng , Obst Katherine M. , He Hua , He Jiang
Published: July 2024

BACKGROUND:

Globally, only 13.8% of patients with hypertension have their blood pressure (BP) controlled. Trials testing interventions to overcome barriers to BP control have produced mixed results. Type of health care professional delivering the intervention may play an important role in intervention success. The goal of this meta-analysis is to determine which health care professionals are most effective at delivering BP reduction interventions.

METHODS:

We searched Medline and Embase (until December 2023) for randomized controlled trials of interventions targeting barriers to hypertension control reporting who led intervention delivery. One hundred articles worldwide with 116 comparisons and 90 474 participants with hypertension were included. Trials were grouped by health care professional, and the effects of the intervention on systolic and diastolic BP were combined using random effects models and generalized estimating equations.

RESULTS:

Pharmacist-led interventions , community health worker–led interventions, and health educator–led interventions resulted in the greatest systolic BP reductions of −7.3 (95% CI, −9.1 to −5.6), −7.1 (95% CI, −10.8 to −3.4), and −5.2 (95% CI, −7.8 to −2.6) mm Hg, respectively. Interventions led by multiple health care professionals, nurses, and physicians also resulted in significant systolic BP reductions of −4.2 (95% CI, −6.1 to −2.4), −3.0 (95% CI, −4.2 to −1.9), and −2.4 (95% CI, −3.4 to −1.5) mm Hg, respectively. Similarly, the greatest diastolic BP reductions were −3.9 (95% CI, −5.2 to −2.5) mm Hg for pharmacist-led and −3.7 (95% CI, −6.6 to −0.8) mm Hg for community health worker–led interventions. In pairwise comparisons, pharmacist were significantly more effective than multiple health care professionals, nurses, and physicians at delivering interventions.

CONCLUSIONS:

Pharmacists and community health workers are most effective at leading BP intervention implementation and should be prioritized in future hypertension control efforts.
Action Interpretation Determines the Effects of Go/No-Go and Approach/Avoidance Actions on Stimulus Evaluation
Authors: Chen Zhang , Van Dessel Pieter
Published: July 2024

Executing go/no-go or approach/avoidance responses toward a stimulus can change its evaluation. To explain these effects, some theoretical accounts propose that executing these responses inherently triggers affective reactions (i.e., action execution), while others posit that the evaluative influences originate from interpreting these responses as valenced actions (i.e., action interpretation). To test the role of action execution and action interpretation in these evaluative effects, we developed a novel training task that combined both go/no-go and approach/avoidance actions orthogonally. Participants either responded or did not respond (i.e., go/no-go) to control a shopping cart on screen, and as a result, either collected or did not collect (i.e., approach/avoidance) certain food items. When the task instructions referred to the go/no-go actions (Experiment 1, N = 148), we observed an effect of these actions. Participants evaluated no-go items less positively than both go and untrained items. No effect of approach/avoidance actions was observed. Contrarily, when the task instructions referred to the approach/avoidance actions (Experiment 2, N = 158), we observed an approach/avoidance effect. Participants evaluated approached items more positively and avoided items less positively than untrained items. No effect of go/no-go actions was observed. This suggests that action interpretation determined whether go/no-go or approach/avoidance actions influenced stimulus evaluation, when the same motor responses were made. Further examination of the role of action interpretation can inform theories of how actions influence stimulus evaluation, and facilitate the use of these interventions in applied settings.