bluuebunny/crossref_metadata_2025 huggingface dataset:
SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration
Authors: Duckworth Daniel , Hedman Peter , Reiser Christian , Zhizhin Peter , Thibert Jean-François , Lučić Mario , Szeliski Richard , Barron Jonathan T.
Published: July 2024

Recent techniques for real-time view synthesis have rapidly advanced in fidelity and speed, and modern methods are capable of rendering near-photorealistic scenes at interactive frame rates. At the same time, a tension has arisen between explicit scene representations amenable to rasterization and neural fields built on ray marching, with state-of-the-art instances of the latter surpassing the former in quality while being prohibitively expensive for real-time applications. We introduce SMERF, a view synthesis approach that achieves state-of-the-art accuracy among real-time methods on large scenes with footprints up to 300 m
2
at a volumetric resolution of 3.5 mm
3
. Our method is built upon two primary contributions: a hierarchical model partitioning scheme, which increases model capacity while constraining compute and memory consumption, and a distillation training strategy that simultaneously yields high fidelity and internal consistency. Our method enables full six degrees of freedom navigation in a web browser and renders in real-time on commodity smartphones and laptops. Extensive experiments show that our method exceeds the state-of-the-art in real-time novel view synthesis by 0.78 dB on standard benchmarks and 1.78 dB on large scenes, renders frames three orders of magnitude faster than state-of-the-art radiance field models, and achieves real-time performance across a wide variety of commodity devices, including smartphones. We encourage readers to explore these models interactively at our project website: https://smerf-3d.github.io.
Cell Space: Augmented Awareness of Intercorporeality
Authors: Lin Rem RunGu , Hu Botao Amber , Ke Koo Yongen , Wu Wei , Zhang Kang
Published: July 2024

Cell Space is an interactive art experience that integrates collocated augmented reality, neurofeedback, and contact improvisation, challenging the conventional boundaries between audience and performer, and blending the physical with the virtual, organic with technological. Inspired by McLuhan's concept of media as an extension of the human body, and The AR Art Manifesto's vision of "we becoming the Media," this project reimagines participants as dynamic cells of a shared bio-digital ecosystem within the built environment. This immersive experience expands the concept of intercorporeality, moving beyond mere physical interaction to a shared bio-digital domain, thereby fostering a deeply interconnected environment of Human-Machine-Space Symbiosis. The paper contributes to the discourse on performing arts by presenting a new paradigm for applying AR and neurofeedback in contact improvisation, creating a new layer of perception and fostering intercorporeal interactions by reinforcing both individual and collaborative intentionality of its participants. Ultimately, Cell Space serves as a reflective mirror to the potential futures of our increasingly interconnected existence, encouraging participants to explore a transformative landscape that resonates with the complex interplay between their physical and digital identities and their surroundings.
Handcrafting in Zero Gravity: Reinventing the Spindle as An Artistic Intervention in Space Research
Authors: Kurbak Ebru
Published: July 2024

This paper explores a methodological approach to art making in techno-scientific environments, by unfolding the conceptual framework and actual implementation of the art project Reinventing the Spindle. The research-based project explores ancient hand spinning and textile handcrafting methods in microgravity with the aim of highlighting weightlessness as a shared commodity and catalyzing critical discussions about the politics of space exploration and colonization. The act of researching marginalized knowledges in actual space research environments is conceived as a decolonial artistic strategy with a performative character. In line with this approach, this paper itself also becomes part of the art project. It reveals both technical and experiential details of experiments conducted on a parabolic flight, with the aim to inform potential researchers from different backgrounds and contribute to greater inclusivity in the field of space research.
Ephemera: Language as a Virus - AI-driven Interactive and Immersive Art Installation
Authors: Huang Jiayang , Huang Yue , Yip David , Guljajeva Varvara
Published: July 2024

In this paper, we introduce the speech-based interactive and immersive installation, Ephemera, as an artistic response to the linguistic taboos encountered in daily communication, prompting audience reflection and thoughts. Within this project, we symbolize the dissemination chain of language through a computational ecosystem. Utilizing the surreal 'virus' as an embodiment of banned words, we employ generative models for visual representation, leverage large language models for communicative agents, and use machine learning for behavioral engines, ultimately simulating a digitally autonomous micro-organism world of forbidden language. We contextualized the speech-to-content generation process to draw the audience's attention to the power and constraints of language. Additionally, we examine AI's comprehension of censored words and ethical considerations. Finally, our artistic project proposes the aphorism "Language as a virus, art as an antibody," offering novel perspectives on language taboos and art-technology intersections.
Virtual Machinations: Using Large Language Models as Neural Computers
Authors: Meijer Erik
Published: July 2024

We explore how Large Language Models (LLMs) can function not just as databases, but as dynamic, end-user programmable neural computers. The native programming language for this neural computer is a Logic Programming-inspired declarative language that formalizes and externalizes the chain-of-thought reasoning as it might happen inside a large language model.
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training
Authors: Liu Yonghao , Li Mengyu , Li Ximing , Huang Lan , Giunchiglia Fausto , Liang Yanchun , Feng Xiaoyue , Guan Renchu
Published: July 2024

Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies have attempted to combine meta-learning with graph neural networks to solve few-shot node classification on graphs. Despite their promising performance, some limitations remain. First, they employ the node encoding mechanism of homophilic graphs to learn node embeddings, even in heterophilic graphs. Second, existing models based on meta-learning ignore the interference of randomness in the learning process. Third, they are trained using only limited labeled nodes within the specific task, without explicitly utilizing numerous unlabeled nodes. Finally, they treat almost all sampled tasks equally without customizing them for their uniqueness. To address these issues, we propose a novel framework for few-shot node classification called
Meta-GPS

\(++\)

. Specifically, we first adopt an efficient method to learn discriminative node representations on homophilic and heterophilic graphs. Then, we leverage a prototype-based approach to initialize parameters and contrastive learning for regularizing the distribution of node embeddings. Moreover, we apply self-training to extract valuable information from unlabeled nodes. Additionally, we adopt S

\({}^{2}\)

(scaling and shifting) transformation to learn transferable knowledge from diverse tasks. The results on real-world datasets show the superiority of Meta-GPS

\(++\)

. Our code is available
here
.
3D Surface-Enhanced Raman Scattering Substrate Based on an Array of Self-Assembled Au@SiO2 Microspheres
Authors: Liang Jiran , Wang Shuai , Yang Guixiang , Liang Xiu , Zhang Dequan , Zhang Chengye , Bai Yu , Lei Dangyuan
Published: July 2024

A quasi-periodic array of 3D gold-nanoparticle-capped SiO2 microspheres (Au@SiO2) was designed and prepared with a facile approach to enhance the Raman signal intensity of adsorbed biomolecules. Through adjusting the thickness and annealing of Au thin films initially deposited on arrays of self-assembled SiO2 microspheres, we were able to control the diameter of Au nanoparticles and their interparticle spacing to produce two types of plasmonic near-field hot spots, locating at the gaps of such densely arranged Au nanoparticles on individual SiO2 microspheres and in the gap regions of neighboring SiO2 microspheres, respectively. Such double near-field enhancement mechanism leads to a surface-enhanced Raman scattering (SERS) enhancement factor up to 3 × 106 for Rhodamine 6G molecules. The SERS signal intensity was highly uniform with a relative standard deviation of 4.5%. This 3D SERS substrate has significant potential for various applications in the field of SERS detection of analytes and wearable biosensing.
Relationship between LDL-cholesterol, small and dense LDL particles, and mRNA expression in a cohort of African Americans
Authors: Diallo Ana , Abbas Malak , Goodney Gabriel , Price Elvin , Gaye Amadou
Published: July 2024

The study investigated the interplay between LDL-C and mRNA expression, focusing on the role of small dense LDL (sdLDL) particles and sex differences. Differential expression analysis identified 1,048 and 284 mRNAs associated with LDL-C and sdLDL levels, respectively. Mediation analysis revealed that sdLDL mediates the relationship between LDL-C and 33 mRNAs involved in immune, inflammatory, and metabolic pathways. These findings highlight the significance of sdLDL in cardiovascular disease risk assessment and underscore sex-specific differences in lipid metabolism.
Investigation of Knee Torque Dynamics in Single‐Leg Transfemoral Prosthetic during Walking Using Mathematical Modeling
Authors: Kameni Nteutse Peguy , Geletu Abebe
Published: July 2024

Transfemoral amputees rely on prosthetic devices to restore their mobility and maintain their independence. However, the design and performance of these devices can significantly affect the amputee’s gait and overall functionality. In this study, we investigate the knee torque dynamics of a single‐leg transfemoral prosthetic during walking using a mathematical model that takes into consideration knee friction force (β2), the horizontal component of the ground reaction force (F1), and the vertical component of the ground reaction force (F2). By carrying out model simulation in the MATLAB tool, along with Simulink, we derived the tracking error between the generated knee torque of the transfemoral prosthesis and the knee torque trajectory for a normal human leg. This error was evaluated using the normalized root mean square error (NRMSE) method. Our results show that when β2 = 0.3 N.s.m−1, F1 = 320 N, and F2 = 800 N, a tracking error of NRMSE = 0.342868% occurs. A comparison between the simulated knee torque and a desired knee torque trajectory for a normal leg study in the literature suggests that the tracking error generated will force the amputee to use higher metabolic energy to minimize discomfort while walking. These findings highlight the importance of optimizing the design and performance of transfemoral prosthetic devices to improve the knee torque dynamics and overall functionality of amputees. Our mathematical model provides a useful tool for future research in this field, which could help develop more effective prosthetic devices for transfemoral amputees.
Parental Opinion toward Problem Behaviors among Preschool Children in Pattani Province, Thailand: A Cross–Sectional Study
Authors: Eso Mayuening , McNeil Rhysa , Awang Halimah
Published: July 2024

The educational performance of children in the three southern border provinces is the lowest in Thailand. This area has been continuously facing unrest, and the COVID‐19 crisis has resulted in increased social inequality in schools. The purposes of this study were to determine the problem behaviors exhibited by preschool children in an early childhood center and investigate the association between those behaviors and their related factors. A total of 140 children, aged 2–4, from three early childhood centers were sampled. The dependent variable of the study was a problem behavior with a binary value. The determinants were the children’s demographic information; their parent’s demographic information; and their behaviors of using a smartphone, watching TV, and spending quality time with the children and parent. The association between outcome and determinants was investigated using multiple logistic regression. The prevalence of problem behavior among preschool children was 44.3%. Three factors were associated with problem behavior: family type, allowing children to use smartphones, and children watching television on average per day. Compared to the overall prevalence, the children in the extended family had a high prevalence of problem behavior (62.5%). The children who were disallowed from using a smartphone demonstrated a high prevalence of problem behavior (79.1%). In contrast, children who watched television for more than 3 hr per day exhibited lower problem behavior (21.6%). The results of this study are beneficial to teachers and parents in monitoring their children’s development. Prompt intervention upon early detection of problems in children fosters their development and facilitates learning to their full potential.
Robust sliding mode observer based-simultaneous state and actuator fault estimation for a class of switching systems
Authors: Elouni Mohamed , Hamdi Habib , Rabaoui Bouali , Rodrigues Mickael , Benhadj Braiek Naceur
Published: July 2024

This paper investigates the state and actuator fault reconstruction problem in a class of switched linear systems subjected to unknown external disturbances according to average dwell time (ADT) technique. First, a robust switched sliding mode observer (SMO) is developed to simultaneously reconstruct the states of the switched system and the actuator faults. A novel and less conservative sufficient stability conditions are then established using the multiple quadratic Lyapunov function technique and the ADT approach. These conditions are formulated as linear matrix inequalities (LMI) to facilitate the design of the SMO. The observer gains matrices are obtained throughout the resolution of LMI using convex optimization techniques. Next, actuator faults are estimated by utilizing the concept of equivalent output injection, achieved through an analysis of the state error dynamics during the sliding motion. Finally, simulation results are considered to illustrate the applicability and efficiency of the developed method. It showcases the rapid and accurate convergence of the estimated system states and actuator fault to the real variables.
Bio-engineered strategies for osteochondral defect repair
Authors: Alnaimat Feras , Owida Hamza Abu , Turab Nidal M. , Al-Nabulsi Jamal I.
Published: July 2024

Due to the absence of blood vessels and nerves, the regenerative potential of articular cartilage is significantly constrained. This implies that the impact of a ruptured cartilage extends to the entire joint. Osteoarthritis, a health condition, may arise due to injury and the progressive breakdown of joint tissues. The progression of osteoarthritis can be accelerated by the extensive degradation of articular cartilage, which is ranked as the third most prevalent musculoskeletal disorder necessitating rehabilitation, following low back pain and fractures. The existing therapeutic interventions for cartilage repair exhibit limited efficacy and seldom achieve complete restoration of both functional capacity and tissue homeostasis. Emerging technological advancements in the field of tissue engineering hold significant promise for the development of viable substitutes for cartilage tissue, capable of exhibiting functional properties. The overarching strategy involves ensuring that the cell source is enriched with bioactive molecules that facilitate cellular differentiation and/or maturation. This review provides a comprehensive summary of recent advancements in the field of cartilage tissue engineering. Additionally, it offers an overview of recent clinical trials that have been conducted to examine the latest research developments and clinical applications pertaining to weakened articular cartilage and osteoarthritis.
Multimodal speech emotion recognition optimization using genetic algorithm
Authors: Michael Stefanus , Zahra Amalia
Published: July 2024

Speech emotion recognition (SER) is a technology that can detect emotions in speech. Various methods have been used in developing SER, such as convolutional neural networks (CNNs), long short-term memory (LSTM), and multilayer perceptron. However, sometimes in addition to model selection, other techniques are still needed to improve SER performance, namely optimization methods. This paper compares manual hyperparameter tuning using grid search (GS) and hyperparameter tuning using genetic algorithm (GA) on the LSTM model to prove the performance increase in the multimodal SER model after optimization. The accuracy, precision, recall, and F1 score improvement obtained by hyperparameter tuning using GA (HTGA) is 2.83%, 0.02, 0.05, and 0.04, respectively. Thus, HTGA obtains better results than the baseline hyperparameter tuning method using a GS.
Current critical review on prediction stroke using machine learning
Authors: Byna Agus , Modi Lakulu Muhammad , Yusuf Panessai Ismail
Published: July 2024

Strokes are a significant health problem because they often lead to long-term disabilities due to delayed diagnoses and insufficient information about the disease. The use of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), has the potential to aid in stroke diagnosis and significantly advance healthcare. This review article critically examines predictive methods for ischemic and hemorrhagic strokes. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was used to identify 79 relevant articles from five databases spanning 2012 to 2022, with IEEE having the highest number of articles and citations. China had the most authors, and the random forest (RF) algorithm showed the most accurate results. A taxonomy categorizing the implementation and usage of ML and DL for stroke prediction was created and includes five focus areas: building, system planning, evaluation, comparison, and analysis. Additional research into other disease features related to stroke is warranted. Decentralized federated learning should also be implemented to collect data from remote locations for early diagnosis and create a single training model.
Energy consumption forecasting: a case study on Bhashan Char island in Bangladesh
Authors: Sarker Md. Tanjil , Ramasamy Gobbi , Al Farid Fahmid , Mansor Sarina , Abdul Karim Hezerul
Published: July 2024

In Bangladesh's distant regions, where dependable access to energy supplies is still an issue, effective energy consumption forecasting is essential for tackling the country's energy problems. In order to anticipate energy consumption in these neglected areas effectively, this study suggests a novel method that combines inverse matrix method (IMM) with linear regression method (LRM). The model produces accurate estimates by using historical data on energy use and relevant factors, such as weather patterns, population dynamics, economic indicators, and seasonal trends. A case study focusing on distant areas in Bangladesh shows how the proposed technique might be applied. The outcomes indicate how well the method captures the complex patterns of energy demand and how it may be used to guide sustainable energy management plans in these outlying regions. This study advances energy planning and resource allocation in areas with a limited supply of energy, paving the way for increased development and efficiency of the energy sector. Any rural or remote area in the globe can use these strategies to predict their short-term power consumption.
Evaluation of steady-state ground resistance by field measurement and CDEGS computation
Authors: Muhammad Usman , Zaid Hadee , Ahmad Nurul Nadia , Mohamad Nor Normiza , Aman Fazlul
Published: July 2024

In addition to the soil resistivity and size of the grounding system, grounding system configuration can influence the steady-state resistance (RDC) of a grounding system. The RDC of four to six configurations in three distinct soil conditions (sites 1 to 3) is measured using the fall-of-potential method and computed using the current distribution, electromagnetic fields, grounding, and soil structure analysis (CDEGS) simulation. The RDC value generally decreases as size increases, i.e., when more rods or tapes are added, except for a little variation subject to the electrode arrangement and soil resistivity. The 3 and 4-parallel configurations perform better on low resistivity soil (site 1), while the grid configurations (2×2- and 3-rod grids) are better on high resistivity soil (site 2). The difference between the measured and computed values at high soil resistivity sites (sites 2 and 3) is large, ranging from 18% to 66% for site 2 and from 35% to 53% for site 3. The difference is lower and more consistent at site 1, where five out of six configurations achieve less than 10%. At all sites, the difference between computed and measured RDCs generally decreases as the area of the electrode increases, except for some cases at site 2.
Integral-proportional derivative approach for brushless direct current motor speed control
Authors: Panjaitan Seno Darmawan , Priyatman Hendro , Supriono Supriono , Frizky Muhammad Revaldi
Published: July 2024

is paper proposed the integral-proportional-derivative (I-PD) as an extension of the conventional proportional-integral-derivative (PID) method that has been used in many brushless direct current (BLDC) applications to control the BLDC motor that can deal with desired speed (reference) changes. It has elucidated a comprehensive comparative analysis between PID, intending to delineate the most efficacious control approach based on a thorough evaluation. This paper scrutinizes four principal methods: proportional-integral (PI), integral-proportional (I-P), PID, and I-PD. Our findings indicate that in the presence of voltage spike constraints, I-P or I-PD emerges as the optimum choice for both four-pole and six-pole motors. Where maximum difference (MaxDiff) is the principal consideration, PI, and I-P are identified as the most suitable methods. Conversely, when the primary objective is to minimize root mean square error (RMSE), PI proves superior for four-pole motors, while PID is preferable for six-pole types. Notably, I-P demonstrates excellent performance in terms of settling time for both motor types. In summation, I-P stands out as the preeminent choice if the objective is to select a singular method that ensures optimal performance across all parameters for a four-pole or six-pole motor.
Deblurring image compression algorithm using deep convolutional neural network
Authors: Menassel Rafik , Gattal Abdeljalil , Kerdoud Fateh
Published: July 2024

There are instances where image compression becomes necessary; however, the use of lossy compression techniques often results in visual artifacts. These artifacts typically remove high-frequency detail and may introduce noise or small image structures. To mitigate the impact of compression on image perception, various technologies, including machine learning and optimization metaheuristics that optimize the parameters of image compression algorithms, have been developed. This paper investigates the application of convolutional neural networks (CNNs) to reduce artifacts associated with image compression, and it presents a proposed method termed deblurring compression image using a CNN (DCI-CNN). Trained on a UTKFace dataset and tested on six benchmark images, the DCI-CNN aims to address artifacts such as block artifacts, ringing artifacts, blurring artifacts, color bleeding, and mosquito noise. The DCI-CNN application is designed to enhance the visual quality and fidelity of compressed images, offering a more detailed output compared to generic and other deep learning-based deblurring methods found in related work.
Implementing trajectory correction strategy through model prediction control for flight vehicle missions
Authors: Irwanto Herma Yudhi , Yusgiantoro Purnomo , Abidin Sahabuddin Zainal , Oktovianus Bura Romie , Andiarti Rika , Eko Putro Idris , Sudiana Oka , Hanif Azizul
Published: July 2024

Modeling a high-speed flying vehicle is imperative to ensure the success of vehicle development missions. Moreover, adherence to research protocols mandates a stepwise approach to testing the vehicle model, encompassing simulation trials using software-in-the-loop simulation (SILS), hardware-in-the-loop simulation (HILS), as well as diverse ground and environmental tests prior to flight testing. This study entailed a collaborative effort between MATLAB/Simulink and LabVIEW to seamlessly integrate the model developed in MATLAB/Simulink into LabVIEW for the implementation of model predictive control (MPC) strategy, aimed at trajectory correction (TC) missions for the vehicle. This MPC strategy was directly applied to the onboard flight control system (OBFCS) of the vehicle. Simulation results indicate the successful control of roll and pitch conditions by OBFCS in both SILS and HILS, ensuring the maintenance of flight conditions in accordance with predicted trajectories despite the presence of simulated disturbances. Notably, the simulation demonstrates the independence or absence of interference between each simultaneous MPC control for roll and pitch adjustments.
Performance comparison of state-of-the-art deep learning model architectures in Indonesian food image classification
Authors: Rasyidi Mohammad Arif , Mardhiyyah Yunita Siti , Nasution Zuraidah , Wijaya Christofora Hanny
Published: July 2024

Food image recognition is essential for developing an elderly-friendly daily food recording application in Indonesia. However, existing datasets and models are limited and do not cover the diversity and complexity of Indonesian food. In this paper, we present a new dataset of 24,427 images of 160 types of Indonesian food with higher variety and quality than previous datasets. We also train and compare the performance of 67 models based on 16 state-of-the-art deep learning architectures on this dataset. We find that efficientnet_v2_l provides the best accuracy of 85.44%, followed by other models such as convnext_large and swin_s. We also discuss the trade-off between model size and performance, as well as the challenges and limitations of food image classification. Our dataset and models can serve as a basis for developing a user-friendly and accurate food recording application for the elderly population in Indonesia.
Image classification of fabric defects using ResNet50 deep transfer learning in FastAI
Authors: Sitompul Erwin , Leonhart Setiawan Vincent , Jaya Tarigan Hendra , Galina Mia
Published: July 2024

One of the most common issues in manufacturing is the inability to persistently maintain good quality, which can lead to product defects and customer complaints. In this research, the novel implementation of deep learning for fabric defect classification in FastAI was proposed. The residual network structure of ResNet50 was trained through transfer learning to classify the data set that contained five classes of fabric images: good, burned, frayed, ripped, and stained. A novel approach to constructing the data set was undertaken by compiling randomly downloaded fabric images within the aforementioned five classes with a broad variety from the internet. The effect of the two splitting methods in dividing the data into training and validation data was investigated. Random splitting divides the data into random class proportions, while stratified splitting maintains the original class proportions. Models were tested offline with unseen data and reached a mean accuracy of 92.5% for the 2-class model and 70.3% for the 5-class model. Based on the attained accuracy and precision, no splitting method was superior to the other. The feasibility of the system’s online implementation was evaluated by integrating a smartphone camera to capture and classify fabric samples, with a mean accuracy of 75.6% for the 5-class model.
Advanced controller design for D-FACTS device in grid-connected photovoltaic system controller
Authors: AlQattan Ali Jaber , Albasri Fadhel , Al-Mosawi Sayed Ali
Published: July 2024

Photovoltaic (PV) solar energy is considered one of the highest renewable energy (RE) resources worldwide. Hence, PV system installation capacity is increasing, triggering new power quality problems in grid systems. Some examples of these problems include unbalanced voltages, fluctuating power levels, harmonic distortions, or reverse power flow. To mitigate the adverse impacts of PV integration on voltage regulation and harmonic distortion in electrical distribution systems, a distribution static synchronous compensation (D-STATCOM) is considered a solution. A simulation study is performed by modeling a power system model with an integrated PV system and D-STATCOM. Two control schemes, proportional-integral (PI) and artificial neural network (ANN), were applied within the internal control of D-STATCOM to enhance the power quality of the power system. Two different inverter configurations were adapted, a sinusoidal pulse width modulation (SPWM) and a hysteresis current controller (HCC). Results are obtained as voltage profiles for all the considered control schemes with different inverter types under different contingency conditions. The performance is also evaluated by control characteristics evaluation for different controllers. The controller ANN has better performance than the PI controller and it can mitigate power quality problems and the impact of the PV integration on voltage regulation and harmonic distortion.
User interface design of a sengkedan concept-based digital test
Authors: Adiarta Agus , Divayana Dewa Gede Hendra , Ariawan I Putu Wisna , Suyasa P. Wayan Arta , Andayani Made Susi Lissia , Wiradika I Nyoman Indhi
Published: July 2024

This study’s purpose was to demonstrate the design of a digital test for the “educational evaluation” course based on the sengkedan (swales) concept that has good quality. This research approach was a development that used the Borg and Gall model with more focus on the design development stage, initial design trials, and revisions. Subjects involved in the initial testing of the digital test user interface design were 42 respondents. The tool used to conduct initial testing of the digital test user interface design is a questionnaire. The data analysis technique used in this research was descriptive quantitative. The results showed user interface design of a sengkedan concept-based digital test for the “educational evaluation” course was quite good. The impact of this research on evaluators in the education field was a positive thing that added to their insights in developing a digital test. The evaluators will finally understand the importance of designing the user interface before finalizing the physical application to minimize errors.
Optical character recognition for Telugu handwritten text using SqueezeNet convolutional neural networks model
Authors: Revathi Buddaraju , Raju B N V Narasimha , Marapatla Ajay Dilip Kumar , Veeramanikanta Kagitha , Dinesh Katta , Supraja Maddirala
Published: July 2024

Optical character recognition (OCR) is a process that recognizes and converts data from scanned images, including both handwritten and printed documents, into an accessible format. The challenges in Telugu OCR arise from compound characters, an extensive character set, limited datasets, character similarities, and difficulties in segmenting overlapping characters. To tackle these segmentation complexities, an algorithm has been developed, prioritizing the preservation of essential features during character segmentation. For distinguishing between structurally similar characters, we used convolutional neural networks (CNN) due to their feature-extracting properties. We have employed the CNN model, the SqueezeNet for feature extraction, resulting in an impressive character recognition rate of 94% and a word recognition rate of 80%.
A model for classifying breast masses in ultrasound images
Authors: Morsy Shereen Ekhlas , Abd-Elsalam Neveen Mahmoud , Al-Saidy Zaid Abdu , Kandil Ahmed Hesham , El-Bialy Ahmed Mohammed , Youssef Abou-Bakr Mohammed
Published: July 2024

The most frequent type of cancer among women is breast cancer. Artificial intelligence (AI) researchers are developing automated systems to assist in the detection and classification of breast cancer. This study explores machine learning (ML) and deep learning (DL) as two AI methods for identifying benign and malignant breast tumors in ultrasound images. The investigation assesses the performance of various computer-aided detection and diagnosis (CAD) systems, which utilize either handcrafted features or deep features extracted from DL models. Furthermore, three models for CAD deep learning-based systems were implemented using convolutional neural networks (CNN), convolutional autoencoders (CAE), and deep features with CNN models, and compared with three traditional ML models based on handcrafted (texture) features. The results indicate that the deep features of the CNN model are promising, achieving a mean accuracy of 95% with a standard deviation of 1.1%.
Microcontroller-based camera with the sound source localization for automated accident detection
Authors: Nazeem Nur Nazifah Adlina Mohd , Hassan Siti Lailatul Mohd , Halim Ili Shairah Abdul , Abdullah Wan Fadzlida Hanim , Sulaiman Nasri
Published: July 2024

This paper is on a microcontroller-based camera controller with sound source localization (SSL). With the rising frequency of highway accidents in Malaysia, there is a pressing need for a reliable detection system. The current approach, involving fixed-angled cameras, necessitates constant human monitoring, proving inefficient. To address this, the study introduces a hybrid camera system incorporating a camera for image capture and a microphone to detect collision sounds. By integrating a pan-tilt (PT) camera controller driven by time difference of arrival (TDOA) inputs, the system can swiftly move toward accident locations. The TDOA method is employed to convert sound arrival time differences into camera angles. The accuracy of the PT camera's rotation angle was analyzed based on the original sound source angle. As a result, this project produced an automated highway monitoring camera system that uses sound SSL to detect car crash sounds on highways. Its PT feature will help cover a large highway area and eliminate blind spots to capture possible accident scenes. The average inaccuracy of the experimental test of the pan and tilt angle of the camera is 19 and 23%, respectively. The accuracy of the pan tilt angle can be increased by adding more analog acoustic sensors.
Usability analysis of marker-based augmented reality application for the microcontroller study
Authors: Beeran Kutty Suhaili , Nazri Muhammad Azim , Anuar Che Nur Shafareen Afera Che , Kassim Murizah , Sulaiman Norakmar Arbain
Published: July 2024

Virtual labs using augmented reality (AR) applications have brought transformation in teaching laboratory courses. However, in the field of engineering laboratories, the use of AR applications is still new. In this work, a marker-based AR application is used as a tool that enables a hands-on learning experience of microcontroller study. The software used to develop this application are Unity3D and Vuforia. C# programming language has also been used to provide the command for the interaction between the interface and the application. After the development of the application was complete, it was tested by a group of electrical engineering students. Then, the students are required to fill out a survey on the system usability scale (SUS) test. The SUS score of this application is 62.5. It was found that the perceived usability of the evaluated AR application as a teaching tool for laboratory courses is acceptable. This response shows the marker-based AR application has the potential to be used as a teaching aid in engineering laboratory courses.
Enhancement performance of the Naïve Bayes method using AdaBoost for classification of diabetes mellitus dataset type II
Authors: Mahendra I Gusti Agung Putu , Wirawan I Made Agus , Gunadi I Gede Aris
Published: July 2024

In using technology, especially in health sciences, machine learning modeling can make it easier to predict disease treatment. Naïve Bayes optimization with AdaBoost is needed because even though Naïve Bayes has the advantage of minimal parameters, its accuracy is susceptible to too many features. AdaBoost is used to overcome sensitivity to an excessive number of features and optimize its ability to handle complex datasets. This research aims to analyze the classification results of the Naïve Bayes method with the help of the AdaBoost method. This data comes from Community Health Centers I, II, and III Mengwi District, Bali Province patient medical records. The classification process uses the Naïve Bayes method and Naïve Bayes with AdaBoost, which is then evaluated using a confusion matrix. Two scenarios were used in testing: Naïve Bayes and AdaBoost-based Naïve Bayes. The algorithm is implemented on the dataset and tested directly using cross-validation. The evaluation results show that the Naïve Bayes method experienced an increase in accuracy of 5.92% at 5-fold and 5.93% at 10-fold on a dataset with 890 data. The addition of the AdaBoost method to diabetes classification has been proven to improve the accuracy performance of the Naïve Bayes method.
Extended Poisson Gaussian-Process Latent Variable Model for Unsupervised Neural Decoding
Authors: Luo Della Daiyi , Giri Bapun , Diba Kamran , Kemere Caleb
Published: July 2024

Dimension reduction on neural activity paves a way for unsupervised neural decoding by dissociating the measurement of internal neural pattern reactivation from the measurement of external variable tuning. With assumptions only on the smoothness of latent dynamics and of internal tuning curves, the Poisson gaussian-process latent variable model (P-GPLVM; Wu et al., 2017) is a powerful tool to discover the low-dimensional latent structure for high-dimensional spike trains. However, when given novel neural data, the original model lacks a method to infer their latent trajectories in the learned latent space, limiting its ability for estimating the neural reactivation. Here, we extend the P-GPLVM to enable the latent variable inference of new data constrained by previously learned smoothness and mapping information. We also describe a principled approach for the constrained latent variable inference for temporally compressed patterns of activity, such as those found in population burst events during hippocampal sharp-wave ripples, as well as metrics for assessing the validity of neural pattern reactivation and inferring the encoded experience. Applying these approaches to hippocampal ensemble recordings during active maze exploration, we replicate the result that P-GPLVM learns a latent space encoding the animal’s position. We further demonstrate that this latent space can differentiate one maze context from another. By inferring the latent variables of new neural data during running, certain neural patterns are observed to reactivate, in accordance with the similarity of experiences encoded by its nearby neural trajectories in the training data manifold. Finally, reactivation of neural patterns can be estimated for neural activity during population burst events as well, allowing the identification for replay events of versatile behaviors and more general experiences. Thus, our extension of the P-GPLVM framework for unsupervised analysis of neural activity can be used to answer critical questions related to scientific discovery.
On the Mathematical Relationship Between Contextual Probability and N400 Amplitude
Authors: Michaelov James A. , Bergen Benjamin K.
Published: July 2024

Accounts of human language comprehension propose different mathematical relationships between the contextual probability of a word and how difficult it is to process, including linear, logarithmic, and super-logarithmic ones. However, the empirical evidence favoring any of these over the others is mixed, appearing to vary depending on the index of processing difficulty used and the approach taken to calculate contextual probability. To help disentangle these results, we focus on the mathematical relationship between corpus-derived contextual probability and the N400, a neural index of processing difficulty. Specifically, we use 37 contemporary transformer language models to calculate the contextual probability of stimuli from 6 experimental studies of the N400, and test whether N400 amplitude is best predicted by a linear, logarithmic, super-logarithmic, or sub-logarithmic transformation of the probabilities calculated using these language models, as well as combinations of these transformed metrics. We replicate the finding that on some datasets, a combination of linearly and logarithmically-transformed probability can predict N400 amplitude better than either metric alone. In addition, we find that overall, the best single predictor of N400 amplitude is sub-logarithmically-transformed probability, which for almost all language models and datasets explains all the variance in N400 amplitude otherwise explained by the linear and logarithmic transformations. This is a novel finding that is not predicted by any current theoretical accounts, and thus one that we argue is likely to play an important role in increasing our understanding of how the statistical regularities of language impact language comprehension.
Preschoolers’ Comprehension of Functional Metaphors
Authors: Zhu Rebecca , Goddu Mariel K. , Zhu Lily Zihui , Gopnik Alison
Published: July 2024

Previous work suggests that preschoolers often misunderstand metaphors. However, some recent studies demonstrate that preschoolers can represent abstract relations, suggesting that the cognitive foundations of metaphor comprehension may develop earlier than previously believed. The present experiments used novel paradigms to explore whether preschoolers (N = 200; 4–5 years; 100 males, 100 females; predominantly White) can understand metaphors based on abstract, functional similarities. In Experiment 1, preschoolers and adults (N = 64; 18–41 years; 25 males, 39 females; predominantly White) rated functional metaphors (e.g., “Roofs are hats”; “Tires are shoes”) as “smarter” than nonsense statements (e.g., “Boats are skirts”; “Pennies are sunglasses”) in a metalinguistic judgment task (d = .42 in preschoolers; d = 3.06 in adults). In Experiment 2, preschoolers preferred functional explanations (e.g., “Both keep you dry”) over perceptual explanations (e.g., “Both have pointy tops”) when interpreting functional metaphors (e.g., “Roofs are hats”) (d = .99). In Experiment 3, preschoolers preferred functional metaphors (e.g., “Roofs are hats”) over nonsense statements (e.g., “Roofs are scissors”) when prompted to select the “better” utterance (d = 1.25). Moreover, over a quarter of preschoolers in Experiment 1 and half of preschoolers in Experiment 3 explicitly articulated functional similarities when justifying their responses, and the performance of these subsets of children drove the success of the entire sample in both experiments. These findings demonstrate that preschoolers can understand metaphors based on abstract, functional similarities.
Adaptation of Multicultural Identity Styles Scale into Russian
Authors: Verbnaya Vladislava V. , Lepshokova Zarina Kh.
Published: July 2024

Background. Integration has been shown as the most adaptive acculturation strategy for ethnic minorities and immigrants. Still, there is a lack of understanding of how exactly an individual manages cultural identities in the process of integration and how this affects the success of adaptation. According to the concept of multicultural identity styles (Ward et al., 2018), an individual can manage their cultural identities either by blending them into a single consistent identity (hybrid identity style) or by switching between identities depending on social context (alternating identity style). This approach is promising in acculturation research and the translation and adaptation of the relevant methodology will allow its use in studies of the adaptation of Russian-speaking immigrants and ethnic minorities. Objectives. The goal is to adapt the Multicultural Identity Styles Scale into Russian. Methods. The authors made a direct and reverse translation of the methodology into Russian and the correction of items after the analysis of cognitive interviews. Then the authors assessed the factor structure of the questionnaire, its reliability, and construct validity based on the socio-psychological study conducted on a sample of 197 Russian Jews. The study was based on the Multicultural Identity Style Scale (Ward et al., 2018). To assess its construct validity, we used the Integration subscale of Acculturation attitudes scale from the MIRIPS (Mutual Intercultural Relations in Multicultural Societies) questionnaire (Berry, 2017), items to measure perceived ethno-national and religious-national identity incompatibility (Verkuyten, Martinovic, 2012) and a technique to measure perceived cultural distance (Galchenko, Van de Vijver, 2007). Results. The adapted version of the Multicultural Identity Styles Scale showed a two-factor structure (hybrid and alternating identity styles), which corresponds to the original scale. High reliability of the scales and construct validity, confirmed by correlations of the questionnaire subscales with other measures and discriminant validity relative to the integration scale were also confiemed. Conclusions. The Multicultural Identity Styles Scale has been successfully adapted into the Russian language and can be used in the studies of adaptation in Russian-speaking immigrants and ethnic minorities.
A Criteria to Reduce Interhospital Transfer of Traumatic Brain Injuries in Greater East Texas
Authors: Murry Jason , Cook Alan D. , Swindall Rebecca J. , Kanazawa Hirofumi , Wadle Carly R. , Mohiuddin Musharaf , Nalbach Stephen V. , Le Tuan D. , Pero Brandi N. , Norwood Scott H.
Published: July 2024

Background

Traumatic brain injury (TBI) due to single-level falls (SLF) are frequent and often require interhospital transfer. This retrospective cohort study aimed to assess the safety of a criteria for non-transfer among a subset of TBI patients who could be observed at their local hospital, vs mandatory transfer to a level 1 trauma center (L1TC).

Methods

We conducted a 7-year review of patients with TBI due to SLF at a rural L1TC. Patients were classified as transfer/non-transfer according to the Brain Injuries in Greater East Texas (BIGTEX) criteria. The primary outcome measure was the occurrence of a critical event defined as deteriorating repeat head computed tomography (CT) scan or neurological status, neurosurgical intervention, or death.

Results

Of the 689 included patients, 63 (9.1%) were classified as non-transfer. Although there were 4 cases with a neurological change and one with a head CT change among the non-transfer group, there were no neurosurgical procedures or deaths. The Cox Proportional Hazard model showed a near 3-fold increased risk of experiencing a critical event if classified as a non-transfer. The multivariable regression model showed patients with an Abbreviated Injury Scale (AIS) of 3 was twice as likely to experience a critical event, with an AIS of 4, three times, and 3 times more likely to be classified to transfer.

Discussion

The BIGTEX criteria identify a subset of patients who can safely be observed at their local hospital. To confirm the safety and efficacy of this transfer criteria recommendation, a prospective study is warranted.