Reviewerly/exHarmony huggingface dataset:
Neuro-Symbolic Reasoning for Multimodal Referring Expression Comprehension in HMI Systems

Authors:
raw_author_name: Aman Jain
raw_affiliation_string: The University of Tokyo, Tokyo, Japan
raw_author_name: Anirudh Reddy Kondapally
raw_affiliation_string: The University of Tokyo, Tokyo, Japan
raw_author_name: Kentaro Yamada
raw_affiliation_string: Honda R &D Co. Ltd., Tokyo, Japan
raw_author_name: Hitomi Yanaka
raw_affiliation_string: The University of Tokyo, Tokyo, Japan

Abstract: Abstract Conventional Human–Machine Interaction (HMI) interfaces have predominantly relied on GUI and voice commands. However, natural human communication also consists of non-verbal communication, including hand gestures like pointing. Thus, recent works in HMI systems have tried to incorporate pointing gestures as an input, making significant progress in recognizing and integrating them with voice commands. However, existing approaches often treat these input modalities independently, limiting their capacity to handle complex multimodal instructions requiring intricate reasoning of language and gestures. On the other hand, multimodal tasks requiring complex reasoning are being challenged in the language and vision domain, but these typically do not include gestures like pointing. To bridge this gap, we explore one of the challenging multimodal tasks, called Referring Expression Comprehension (REC), within multimodal HMI systems incorporating pointing gestures. We present a virtual setup in which a robot shares an environment with a user and is tasked with identifying objects based on the user’s language and gestural instructions. Furthermore, to address this challenge, we propose a hybrid neuro-symbolic model combining deep learning’s versatility with symbolic reasoning’s interpretability. Our contributions include a challenging multimodal REC dataset for HMI systems, an interpretable neuro-symbolic model, and an assessment of its ability to generalize the reasoning to unseen environments, complemented by an in-depth qualitative analysis of the model’s inner workings.
Generalized Lorenz-Mie theory and simulation software for structured light scattering by particles

Authors:
raw_author_name: Ming Jian Cheng
raw_affiliation_string: School of Physics, China
raw_author_name: Yuan Cong Cao
raw_affiliation_string: School of Physics, China
raw_author_name: Huan Zhang
raw_affiliation_string: School of Physics, China

Abstract: Structured light refers to an optical field with modulated phase and amplitude, characterized by distinct spatial patterns. It has applications in optical manipulation, 3D imaging, remote sensing, and communications. The Generalized Lorenz-Mie Theory (GLMT) extends foundational Mie theory to accommodate complex structured lights, enabling precise characterization of structured light-particle interactions. GLMT has emerged as a central theoretical framework for analyzing interactions between spherical particles and arbitrary structured light. This paper introduces ABSphere, simulation software utilizing GLMT to model structured light-spherical particle interactions. It then comprehensively reviews representative structured lights, including Laguerre–Gaussian, Bessel, and Airy beams, elucidating their interactions with spherical particles. Understanding structured light scattering behavior is crucial for elucidating underlying interaction mechanisms with spherical particles. The paper also emphasizes the significance of modeling structured light scattering by particles and discusses future directions for ABSphere software. Through continuous theoretical refinements and advancements, deeper understanding of structured light-particle interaction mechanisms can be achieved, enabling innovations in optical applications and technologies.
OPTIMASI KINERJA LINEAR REGRESSION, RANDOM FOREST REGRESSION DAN MULTILAYER PERCEPTRON PADA PREDIKSI HASIL PANEN

Authors:
raw_author_name: Evita Fitri
raw_affiliation_string: Universitas Nusa Mandiri
raw_author_name: Siti Nurhasanah Nugraha
raw_affiliation_string: Universitas Nusa Mandiri

Abstract: Rice yield prediction is a significant challenge in the context of climate uncertainty and farmland variation. Erratic weather factors, along with land differences, make this prediction more complex. This research aims to address these issues using a machine learning approach. The method used involves three machine learning models namely Linear regression, Random Forest Regression, and ANN with MultiLayer Perceptron algorithm as well as the evaluation matrix RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error). This research focuses on testing the accuracy of the three models in the face of uncertain seasonal conditions and variations in agricultural land. The results showed that the MultiLayer Perceptron prediction model gave the best results with an error value of 0.094. The random forest regression method ranks second with an error value of 0.510, followed by Linear regression with an error value of 0.281. The importance of outlier testing in the model development process can be seen from the significant improvement in the performance of the MultiLayer Perceptron model. This research contributes to the development of a more reliable and dependable rice yield prediction system, especially in the midst of uncertain climatic conditions. Machine learning models, particularly MultiLayer Perceptron, can be an effective solution to increase agricultural productivity and reduce risks associated with weather changes and land variations.
Large-Scale Clustering on 100 M-Scale Datasets Using a Single T4 GPU via Recall KNN and Subgraph Segmentation

Authors:
raw_author_name: Junjie Liu
raw_affiliation_string: Zhejiang University, Hangzhou, China
raw_author_name: Rongxin Jiang
raw_affiliation_string: Zhejiang University, Hangzhou, China
raw_author_name: Xuesong Liu
raw_affiliation_string: Zhejiang University, Hangzhou, China
raw_author_name: Fan Zhou
raw_affiliation_string: Zhejiang University, Hangzhou, China
raw_author_name: Yaowu Chen
raw_affiliation_string: Zhejiang University, Hangzhou, China
raw_author_name: Chen Shen
raw_affiliation_string: Alibaba DAMO Academy, Hangzhou, China

Abstract: Abstract Despite the promising progress that has been made, large-scale clustering tasks still face various challenges: (i) high time and space complexity in K-nearest neighbors (KNN), which is often overlooked by most methods, and (ii) low recall rate caused by simply splitting the dataset. In this paper, we propose a novel framework for large-scale clustering tasks named large-scale clustering via recall KNN and subgraph segmentation (LS-RKSS) to perform faster clustering with guaranteed clustering performance, which embraces the ability of handling large-scale data up to 100 million using a single T4 GPU with less than 10% of the running time. We propose recall KNN (RKNN) and subgraph segmentation (SS) to effectively address the primary challenges in large-scale clustering tasks. Firstly, the recall KNN is proposed to perform efficient similarity search among dense vectors with lower time and space complexity compared to traditional exact search methods of KNN. Then, the subgraph segmentation is proposed to split the whole dataset into multiple subgraphs based on the recall KNN. Given the recall rate of RKNN based on traditional exact search methods, it is theoretically proved that dividing the dataset into multiple subgraphs using recall KNN and subgraph segmentation is a more reasonable and effective approach. Finally, clusters are generated independently on each subgraph, and the final clustering result is obtained by combining the results of all subgraphs. Extensive experiments demonstrate that LS-RKSS outperforms previous large-scale clustering methods in both effectiveness and efficiency.
Phantom in the opera: adversarial music attack for robot dialogue system

Authors:
raw_author_name: Sheng Li
raw_affiliation_string: National Institute of Information and Communications Technology, Japan
raw_author_name: Jiyi Li
raw_affiliation_string: University of Yamanashi, Japan
raw_author_name: Yang Cao
raw_affiliation_string: Hokkaido University, Japan

Abstract: This study explores the vulnerability of robot dialogue systems' automatic speech recognition (ASR) module to adversarial music attacks. Specifically, we explore music as a natural camouflage for such attacks. We propose a novel method to hide ghost speech commands in a music clip by slightly perturbing its raw waveform. We apply our attack on an industry-popular ASR model, namely the time-delay neural network (TDNN), widely used for speech and speaker recognition. Our experiment demonstrates that adversarial music crafted by our attack can easily mislead industry-level TDNN models into picking up ghost commands with high success rates. However, it sounds no different from the original music to the human ear. This reveals a serious threat by adversarial music to robot dialogue systems, calling for effective defenses against such stealthy attacks.
Prioritizing factors for the adoption of IoT-based smart irrigation in Saudi Arabia: a GRA/AHP approach

Authors:
raw_author_name: Abdoh Jabbari
raw_affiliation_string: College of Computer Sciences and Information Technology, Jazan University, Saudi Arabia
raw_author_name: Tawseef Ahmed Teli
raw_affiliation_string: Department of Computer Applications, Higher Education Department, India
raw_author_name: Faheem Masoodi
raw_affiliation_string: Department of Computer Sciences, University of Kashmir, India
raw_author_name: Faheem Ahmad Reegu
raw_affiliation_string: College of Computer Sciences and Information Technology, Jazan University, Saudi Arabia
raw_author_name: Mueen Uddin
raw_affiliation_string: College of Computing and Information Technology, University of Doha for Science and Technology, Qatar
raw_author_name: Ashwag Albakri
raw_affiliation_string: Department of Computer Science, College of Computer Science Information Technology, Jazan University, Saudi Arabia

Abstract: The irrigation sector in the Kingdom of Saudi Arabia (KSA) confronts a range of obstacles, such as scarce water resources, the elevated salinity and alkalinity of irrigation water, inefficient irrigation practices, and inter-sectoral competition for water resources. These challenges have led to diminishing agricultural yields and abandonment of arable lands. Internet of Things (IoT)-based irrigation systems present a promising remedy for these issues. By curbing water wastage and ensuring precise water delivery to crops, IoT-based irrigation systems offer a viable solution to the challenges entrenched in traditional irrigation methodologies in KSA. However, the widespread implementation of an IoT-based Smart Irrigation System (I-SIMS) poses a multifaceted and intricate challenge in KSA. This study is focused on the identification of the factors and challenges through a systematic review and ranking of the challenges/factors that exert a significant influence on the adoption of I-SIMS. Ranking aids in determining the importance of various alternatives. It enables locating the best options that support the required objectives in complex decision situations. The study employs both Grey Relational Analysis (GRA) and Analytical Hierarchical Process (AHP) methodologies to prioritize these factors. The study’s conclusive findings indicate that among the challenges, technical expertise and security measures emerge as the foremost concerns that demand attention.
Extracting informational cues between initial coin offering projects and the public

Authors:
raw_author_name: Soo Il Shin
raw_affiliation_string: Department of Information Systems and Security Coles College of Business Kennesaw State University Kennesaw Georgia USA
raw_author_name: Joo Baek Kim
raw_affiliation_string: Department of Information and Technology Management Sykes College of Business University of Tampa Tampa Florida USA
raw_author_name: Sung‐Byung Yang
raw_affiliation_string: Department of Business Administration School of Management Kyung Hee University Seoul South Korea

Abstract: Abstract An initial coin offering (ICO) seeks to raise initial investments for companies by selling crypto tokens to individuals interested in crowdfunding. As ICOs have gained popularity, a lack of information regarding startup companies' prior financial status and business health has led to information asymmetry between potential investors and ICO projects. This study explores the informational cues necessary to bridge this gap. Employing signaling theory, we elucidate the presence of information asymmetry and conduct two case analyses to identify relevant informational cues. Twitter data from two ICO projects (Stratis and NEO), which achieved the highest return on investment at the time of data collection, are extracted and analyzed to identify informational themes. Our findings reveal multiple and distinctive themes of informational cues for each ICO project, demonstrating that transmitted signals vary across different stages and are generated by both the coin publisher and the public. This research illuminates the flow of informational cues between ICO initiators and potential investors on social media. By analyzing online communication threads using qualitative methods, this study significantly contributes to the understanding of information asymmetry in the context of ICOs.
ggVennDiagram: Intuitive Venn diagram software extended

Authors:
raw_author_name: Guangchuang Yu
raw_affiliation_string: Department of Bioinformatics, School of Basic Medical Sciences Southern Medical University Guangzhou China

Abstract: Graphical Abstract Highlights of ggVennDiagram include: (1) Subset/Region filling Venn diagram up to seven sets; (2) Upset plot with unlimited sets; (3) Venn Calculator for two or more sets; (4) Provide as R package, Shiny App, and TBtools plugin.
Prediction of permeability in a tight sandstone reservoir using a gated network stacking model driven by data and physical models

Authors:
raw_author_name: Chuang Han
raw_affiliation_string: Exploration and Production Research Institute, China

Abstract: Introduction: Permeability is one of the most important parameters for reservoir evaluation. It is commonly measured in laboratories using underground core samples. However, it cannot describe the entire reservoir because of the limited number of cores. Therefore, petrophysicists use well logs to establish empirical equations to estimate permeability. This method has been widely used in conventional sandstone reservoirs, but it is not applicable to tight sandstone reservoirs with low porosity, extremely low permeability, and complex pore structures. Methods: Machine learning models can identify potential relationships between input features and sample labels, making them a good choice for establishing permeability prediction models. A stacking model is an ensemble learning method that aims to train a meta-learner to learn an optimal combination of expert models. However, the meta-learner does not evaluate or control the experts, making it difficult to interpret the contribution of each model. In this study, we design a gate network stacking (GNS) model, which is an algorithm that combines data and model-driven methods. First, an input log combination is selected for each expert model to ensure the best performance of the expert model and selfoptimization of the hyperparameters. Petrophysical constraints are then added to the inputs of the expert model and meta-learner, and weights are dynamically assigned to the output of the expert model. Finally, the overall performance of the model is evaluated iteratively to enhance its interpretability and robustness. Results and discussion: The GNS model is then used to predict the permeability of a tight sandstone reservoir in the Jurassic Ahe Formation in the Tarim Basin. The case study shows that the permeability predicted by the GNS model is more accurate than that of other ensemble models. This study provides a new approach for predicting the parameters of tight sandstone reservoirs.
Hybrid Crow Search and RBFNN: A Novel Approach to Medical Data Classification

Authors:
raw_author_name: Marai Ali
raw_affiliation_string: University of Agriculture Peshawar, Pakistan
raw_author_name: Faisal Khan
raw_affiliation_string: University of Agriculture Peshawar, Pakistan
raw_author_name: Muhammad Nouman Atta
raw_affiliation_string: University of Agriculture Peshawar, Pakistan
raw_author_name: Abdullah Khan
raw_affiliation_string: University of Agriculture Peshawar, Pakistan
raw_author_name: Asfandyar Khan
raw_affiliation_string: University of Agriculture Peshawar, Pakistan

Abstract: The Radial Basis Function Neural Network (RBFNN) is frequently employed in artificial neural networks for diverse classification tasks, yet it encounters certain limitations, including issues related to network latency and local minima. To tackle these challenges, researchers have explored various algorithms to enhance learning performance and alleviate local minima problems. This study introduces a novel approach that integrates the Crow Search Algorithm (CSA) with RBFNN to augment the learning process and address the local minima issue associated with RBFNN. The study evaluates the performance of this innovative model by comparing it to state-of-the-art models like Flower-pollination-RBNN (FP-NN), Artificial Neural Network (ANN), and the conventional RBFNN. To assess the efficacy of the proposed model, the study employs specific datasets, such as the Breast Cancer and Thyroid Disease datasets from the UCI Machine Repository. The simulation results illustrate that the proposed model surpasses other models in terms of accuracy, exhibiting lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. Specifically, for the Breast Cancer dataset, the proposed model attains an accuracy of 99.9693%, MSE of 0.000307024, and MAE of 0.00789449. Likewise, for the Thyroid Disease dataset, the proposed model achieves an accuracy of 99.9535%, along with MSE of 0.000464932 and MAE of 0.0057098. For the diabetes dataset, the proposed model demonstrates an accuracy of 98.8073%, MSE of 0.003024, and MAE of 0.009449. In summary, this analysis underscores the enhanced accuracy and effectiveness of the proposed model when compared to traditional approaches.
Staged cluster transformers for intracranial aneurysms segmentation from structure fused <scp>3D MRA</scp>

Authors:
raw_author_name: Lilin Guo
raw_affiliation_string: School of Medical Technology and Information Engineering Zhejiang Chinese Medical University Hangzhou China
raw_author_name: Ruichao Guo
raw_affiliation_string: School of Electronic Information and Electrical Engineering Shanghai Jiaotong University Shanghai China
raw_author_name: Jianming Ye
raw_affiliation_string: The First Affiliated Hospital Gannan Medical University Ganzhou China
raw_author_name: Xiaobo Lai
raw_affiliation_string: School of Medical Technology and Information Engineering Zhejiang Chinese Medical University Hangzhou China

Abstract: Abstract Intracranial aneurysms (IAs) manifest as atypical dilatation within the intracranial arterial structures, the rupture of which accounts for high mortality and morbidity rates. Current clinical protocols require radiologists to manually annotate IAs on Magnetic Resonance Angiography (MRA) images, which is inherently subjective and time‐consuming. Given these limitations, there is an urgent need to explore methods for automated and accurate segmentation of IAs from MRA images. In particular, recent years have witnessed the proliferation of sophisticated computational techniques, with deep learning algorithms—especially the 3D U‐Net and its derivatives—gaining prominence in segmentation works. Nevertheless, convolutional neural network (CNN)‐based models have an inherent limitation in capturing long‐range spatial dependencies, which inadvertently compromises the retention of global features critical for segmentation. In response to this challenge, we introduce an avant‐garde architectural design, dubbed staged cluster transformers (SCTR), which incorporates cluster mechanism into vision transformers to perform volumetric MRA image segmentation. In addition to the MRA clustering branch, the spatially aligned brain Magnetic Resonance Imaging (MRI) representation branch is also combined to extract the structural features and assist the network in learning richer contextual and boundary information for accurate voxel prediction. For validation, we utilized both a publicly available challenge dataset and an internal clinical dataset in this study. Our proposed model achieves dice similarity coefficients (DSC) of 0.5587 and 0.8110 on these two datasets, respectively, outperforming other state‐of‐the‐art approaches. The results suggest that SCTR is a promising method for automatic segmentation of IAs. Our code is available at https://github.com/guolilin/SCTR .
Classification Comparison Performance of Supervised Machine Learning Random Forest and Decision Tree Algorithms Using Confusion Matrix

Authors:
raw_author_name: Ellya Helmud
raw_author_name: Ellya Helmud
raw_author_name: Fitriyani Fitriyani
raw_affiliation_string: ISB Atma Luhur
raw_author_name: Parlia Romadiana

Abstract: The classification method is part of data mining which is used to predict existing problems and also as predictions for the future. The form of dataset used in the classification method is supervised data. The random forest classification method is processed by forming several decision trees and then combining them to get better and more precise predictions. while a decision tree is the concept of changing a pile of data into a decision tree that presents the rules of a decision. From these two classification methods, researchers will compare the level of accuracy of predictions from both methods with the same dataset, namely the employee dataset in India, to predict the level of accuracy of employees who leave their jobs or still remain to work at their company. The number of records available is 4654 records. Of the existing data, 90% was used as training data and 10% was used as test data. From the results of testing this method, it was found that the accuracy level of the random forest method was 86.45%, while the decision tree method was 84.30% accuracy level. Then, by using the confusion matrix, you can see the magnitude of the distribution of experimental validity visually to calculate precision, recall and F1-Score. The random forest algorithm obtained precision of: 96.7%, sensitivity of: 84.7%, specificity of: 91.4%, and F1-Score of: 90.2%. Meanwhile, the decision tree algorithm obtained precision of: 95.7%, sensitivity of: 82.9%, specificity of: 88.4%, and F1-Score of: 88.8%.