Reviewerly/exHarmony huggingface dataset:
Website design of a spatial data dissemination system and soil classification based on geotechnical engineering (case study: Surabaya City)

The Indonesian Meteorology, Climatology, and Geophysics Agency (BMKG) provides information on meteorology, climatology, and geophysics related to soil classification and characteristics. This data is crucial for spatial mapping and needs to be easily understood by the public. Soil classification mapping is vital for urban planning and earthquake mitigation in densely populated areas. To address this, a website was created for easy access to soil classification mapping data in Surabaya. The website utilizes QGIS for raster map processing. Users select parameters to display interactive raster maps with measured values for assessing soil conditions. A survey evaluated the website and app, resulting in 77,2% to 85,7% user satisfaction. The study confirms their usability and effectiveness. This journal presents the system's design and implementation for disseminating spatial data and soil classification information. It highlights the potential for supporting efficient urban planning and earthquake risk mitigation, benefiting the public and authorities.
Implementation of Machine Learning for Text Classification Using the Naive Bayes Algorithm in Academic Information Systems at Sebelas Maret University Indonesia

This study implements machine learning using the Naive Bayes algorithm to create a text classification in an engineering professional program information system. The methods used include text data collection, preprocessing, feature extraction, Naive Bayes model training, and evaluation using data testing. This study made a classification model to predict text categories with a test accuracy rate of 0.975 and a training accuracy of 0.967. This research contributes to the development of text classification in information systems and can be used as a basis for further study.
Fast and lightweight network improves serial brain section stitching

High-resolution three-dimensional brain image reconstruction is crucial for understanding the brain. Light sheet microscopy combined with tissue clearing imaging plays a pivotal role in analyzing the micro-level structure of mammalian brains. However, the complex multi-level stitching process poses challenges such as non-overlapping areas, surface deformation, and tissue loss, resulting in incomplete or discontinuous tissue structures at the junctions. These issues not only impact the precision of the atlas but also complicate subsequent analyses like cell counting and neuron tracing. To address these issues, we propose a rapid deep learning-based image inpainting approach for accurate neuron reconstruction and analysis. Our approach involves initially employing conventional registration algorithms to preliminarily stitch brain sections together, followed by utilizing a neural network to predict and restore missing tissue with a thickness exceeding 10 µm. This process enhances the structural continuity and integrity between adjacent brain slices. Compared to the original 3D U-Net and ResNet models, our approach performs better and has a processing speed that is five times faster than the original 3D U-Net. Moreover, our method enables more accurate cell counting by repairing incomplete cell bodies, leading to an average improvement of 37.37% in the number of cell bodies accurately counted near the slice junction. By integrating this novel 3D image inpainting network into brain reconstruction processes, our research opens new avenues for a more detailed and accurate investigation of neural circuitry and neurological disorders.
Public Sentiment Analysis to Support Indonesian Government Induction Stove Program

This research focuses on a comprehensive examination of public sentiment surrounding PT PLN's pilot project to convert LPG stoves to induction stoves. By conducting sentiment analysis, the study aims to understand public perspectives and opinions, identify improvement areas, and enhance the quality of future projects. The research framework includes data collection, preprocessing, and analysis, using four algorithms for sentiment classification: Naïve Bayes classifier, logistic regression, support vector machine, and K-nearest neighbor. The accuracy of these algorithms varied, with logistic regression achieving the highest accuracy at 70%. This study's preliminary results indicate public sentiment toward the PLN induction stove project, with 50% positive, 26% negative, and 24% neutral sentiments. Word cloud visualization was utilized to highlight significant words based on frequency. The research emphasizes leveraging sentiment analysis to drive positive changes and align projects with community expectations. Further research can explore factors influencing sentiment, strategies to address concerns, and the long-term impact of incorporating public sentiment in decision-making processes.
A universal method for constructing n-dimensional polynomial hyperchaotic systems with any desired positive Lyapunov exponents

Abstract Most existing chaotic maps have many defects in engineering applications, such as discontinuous parameter range, uneven output of chaotic sequences and dynamic degradation. Based on this, a generalized n -dimensional polynomial chaotic map is proposed in this paper. By setting the coefficient of the linear term and the order of the highest order term of the polynomial, a series of n -dimensional polynomial chaotic maps of specific Lyapunov exponents can be obtained. The system solves the defects of the above system well, in addition, one can get the desired number of positive Lyapunov exponents, and one can get the desired value of positive Lyapunov exponents. Then, the effectiveness of the map is verified by a specific numerical example, and its dynamic analysis shows that the map has complex dynamic behavior. Finally, the map is applied to secure communication technology. Compared with other chaotic maps of the same dimension, the maps can obtain a smaller bit error rate, indicating that the chaotic map is more suitable for chaotic secure communication applications.
Self-Attention Mechanism-Based Head Pose Estimation Network with Fusion of Point Cloud and Image Features

Head pose estimation serves various applications, such as gaze estimation, fatigue-driven detection, and virtual reality. Nonetheless, achieving precise and efficient predictions remains challenging owing to the reliance on singular data sources. Therefore, this study introduces a technique involving multimodal feature fusion to elevate head pose estimation accuracy. The proposed method amalgamates data derived from diverse sources, including RGB and depth images, to construct a comprehensive three-dimensional representation of the head, commonly referred to as a point cloud. The noteworthy innovations of this method encompass a residual multilayer perceptron structure within PointNet, designed to tackle gradient-related challenges, along with spatial self-attention mechanisms aimed at noise reduction. The enhanced PointNet and ResNet networks are utilized to extract features from both point clouds and images. These extracted features undergo fusion. Furthermore, the incorporation of a scoring module strengthens robustness, particularly in scenarios involving facial occlusion. This is achieved by preserving features from the highest-scoring point cloud. Additionally, a prediction module is employed, combining classification and regression methodologies to accurately estimate head poses. The proposed method improves the accuracy and robustness of head pose estimation, especially in cases involving facial obstructions. These advancements are substantiated by experiments conducted using the BIWI dataset, demonstrating the superiority of this method over existing techniques.
Comparing Classical and Quantum Generative Learning Models for High-Fidelity Image Synthesis

The field of computer vision has long grappled with the challenging task of image synthesis, which entails the creation of novel high-fidelity images. This task is underscored by the Generative Learning Trilemma, which posits that it is not possible for any image synthesis model to simultaneously excel at high-quality sampling, achieve mode convergence with diverse sample representation, and perform rapid sampling. In this paper, we explore the potential of Quantum Boltzmann Machines (QBMs) for image synthesis, leveraging the D-Wave 2000Q quantum annealer. We undertake a comprehensive performance assessment of QBMs in comparison to established generative models in the field: Restricted Boltzmann Machines (RBMs), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DDPMs). Our evaluation is grounded in widely recognized scoring metrics, including the Fréchet Inception Distance (FID), Kernel Inception Distance (KID), and Inception Scores. The results of our study indicate that QBMs do not significantly outperform the conventional models in terms of the three evaluative criteria. Moreover, QBMs have not demonstrated the capability to overcome the challenges outlined in the Trilemma of Generative Learning. Through our investigation, we contribute to the understanding of quantum computing’s role in generative learning and identify critical areas for future research to enhance the capabilities of image synthesis models.
DoubleAUG: Single-domain Generalized Object Detector in Urban via Color Perturbation and Dual-style Memory

Object detection in urban scenarios is crucial for autonomous driving in intelligent traffic systems. However, unlike conventional object detection tasks, urban-scene images vary greatly in style. For example, images taken on sunny days differ significantly from those taken on rainy days. Therefore, models trained on sunny day images may not generalize well to rainy day images. In this paper, we aim to solve the single-domain generalizable object detection task in urban scenarios, meaning that a model trained on images from one weather condition should be able to perform well on images from any other weather conditions. To address this challenge, we propose a novel Double AUGmentation (DoubleAUG) method that includes image- and feature-level augmentation schemes. In the image-level augmentation, we consider the variation in color information across different weather conditions and propose a Color Perturbation (CP) method that randomly exchanges the RGB channels to generate various images. In the feature-level augmentation, we propose to utilize a Dual-Style Memory (DSM) to explore the diverse style information on the entire dataset, further enhancing the model’s generalization capability. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art methods. Furthermore, ablation studies confirm the effectiveness of each module in our proposed method. Moreover, our method is plug-and-play and can be integrated into existing methods to further improve model performance.
The Design and Simulation of Engine Rotating Speed Modulation of Circuit

Abstract Since the amplitude and frequency of engine rotating speed signals are changed with the engine rotating speed, data cannot be directly collected due to the high-frequency noise. In this work, an engine rotating speed modulation of the circuit was designed. It was used to retain the complete information of positive and negative periods in signals and convert rotating speed signals to rectangular wave signals with certain amplitude at the same frequency, which could be directly collected. Further, in order to investigate the applicability of our design, a simulation test on the developed circuit based on Multisim was performed. This indicates that the raw signal with high-frequency interference, different amplitudes, and frequencies could be accurately and efficiently converted to regular rectangular wave signals. The design is characterized by a compact structure, low difficulty in device selection, high stability, and low power consumption, which is of extreme significance for accurate rotating speed data collection.
High-quality 3D Object Detection Based on Instance-aware Sampling

Abstract Most existing 3D object detection that is researched on point clouds uses random down-sampling algorithms, which preserve the primary shape and feature information of the point clouds and extract robust features of sampling point clouds to decrease the consumption of computing resources and improve processing inference efficiency. On the other hand, this method of sampling may disregard significant areas within the point clouds, thereby diminishing the precision of detecting comparatively minute entities such as pedestrians and cyclists. In this research, an instance perception-based object detection method is presented that adaptively chooses additional foreground points in response to the properties of smaller objects in the initial point clouds. In addition, it offers end-to-end training and addresses the issue that the typical hand-designed multi-scale grouping receptive fields do not accurately reflect the size of the item. To effectively enhance the recall rate of foreground points, we present an instance-aware algorithm, which relies on farthest point sampling. Specifically, we employ dynamic gating networks to attain instance perception. This approach enables sample candidates to encompass a larger number of foreground objects. Then we use an aggregation layer to efficiently capture robust contextual features with rich spatial information. Through experiments performed on the KITTI dataset, we substantiate the effectiveness of our IAS when compared with current advanced 3D object detection methods. We have achieved a notable enhancement, especially in identifying diminutive objects.
An Optimized Byzantine Fault Tolerance Algorithm for Medical Data Security

Medical data are an intangible asset and an important resource for the entire society. The mining and application of medical data can generate enormous value. Currently, medical data management is mostly centralized and heavily relies on central servers, which are prone to malfunctions or malicious attacks, making it difficult to form a consensus among multiple parties and achieve secure sharing. Blockchain technology offers a solution to enhance medical data security. However, in medical data security sharing schemes based on blockchain, the widely adopted Practical Byzantine Fault-Tolerant (PBFT) algorithm encounters challenges, including intricate communication, limited scalability, and the inability to dynamically add or remove nodes. These issues make it challenging to address practical requirements effectively. In this paper, we implement an efficient and scalable consensus algorithm based on the PBFT consensus algorithm, referred to as Me-PBFT, which is more suitable for the field of medical data security. First, we design a reputation evaluation model to select more trusted nodes to participate in the system consensus, which is implemented based on a sigmoid function with adjustable difficulty. Second, we implement the division of node roles to construct a dual consensus layer structure. Finally, we design a node dynamic join and exit mechanism on the overall framework of the algorithm. Analysis shows that compared to PBFT and RAFT, ME-PBFT can reduce communication complexity, improve fault tolerance, and have good scalability. It can meet the need for consensus and secure sharing of medical data among multiple parties.
Discrepant Semantic Diffusion Boosts Transfer Learning Robustness

Transfer learning could improve the robustness and generalization of the model, reducing potential privacy and security risks. It operates by fine-tuning a pre-trained model on downstream datasets. This process not only enhances the model’s capacity to acquire generalizable features but also ensures an effective alignment between upstream and downstream knowledge domains. Transfer learning can effectively speed up the model convergence when adapting to novel tasks, thereby leading to the efficient conservation of both data and computational resources. However, existing methods often neglect the discrepant downstream–upstream connections. Instead, they rigidly preserve the upstream information without an adequate regularization of the downstream semantic discrepancy. Consequently, this results in weak generalization, issues with collapsed classification, and an overall inferior performance. The main reason lies in the collapsed downstream–upstream connection due to the mismatched semantic granularity. Therefore, we propose a discrepant semantic diffusion method for transfer learning, which could adjust the mismatched semantic granularity and alleviate the collapsed classification problem to improve the transfer learning performance. Specifically, the proposed framework consists of a Prior-Guided Diffusion for pre-training and a discrepant diffusion for fine-tuning. Firstly, the Prior-Guided Diffusion aims to empower the pre-trained model with the semantic-diffusion ability. This is achieved through a semantic prior, which consequently provides a more robust pre-trained model for downstream classification. Secondly, the discrepant diffusion focuses on encouraging semantic diffusion. Its design intends to avoid the unwanted semantic centralization, which often causes the collapsed classification. Furthermore, it is constrained by the semantic discrepancy, serving to elevate the downstream discrimination capabilities. Extensive experiments on eight prevalent downstream classification datasets confirm that our method can outperform a number of state-of-the-art approaches, especially for fine-grained datasets or datasets dissimilar to upstream data (e.g., 3.75% improvement for Cars dataset and 1.79% improvement for SUN dataset under the few-shot setting with 15% data). Furthermore, the experiments of data sparsity caused by privacy protection successfully validate our proposed method’s effectiveness in the field of artificial intelligence security.
Parallel Prediction Method of Knowledge Proficiency Based on Bloom’s Cognitive Theory

Knowledge proficiency refers to the extent to which students master knowledge and reflects their cognitive status. To accurately assess knowledge proficiency, various pedagogical theories have emerged. Bloom’s cognitive theory, proposed in 1956 as one of the classic theories, follows the cognitive progression from foundational to advanced levels, categorizing cognition into multiple tiers including “knowing”, “understanding”, and “application”, thereby constructing a hierarchical cognitive structure. This theory is predominantly employed to frame the design of teaching objectives and guide the implementation of teaching activities. Additionally, due to the large number of students in real-world online education systems, the time required to calculate knowledge proficiency is significantly high and unacceptable. To ensure the applicability of this method in large-scale systems, there is a substantial demand for the design of a parallel prediction model to assess knowledge proficiency. The research in this paper is grounded in Bloom’s Cognitive theory, and a Bloom Cognitive Diagnosis Parallel Model (BloomCDM) for calculating knowledge proficiency is designed based on this theory. The model is founded on the concept of matrix decomposition. In the theoretical modeling phase, hierarchical and inter-hierarchical assumptions are initially established, leading to the abstraction of the mathematical model. Subsequently, subject features are mapped onto the three-tier cognitive space of “knowing”, “understanding”, and “applying” to derive the posterior distribution of the target parameters. Upon determining the objective function of the model, both student and topic characteristic parameters are computed to ascertain students’ knowledge proficiency. During the modeling process, in order to formalize the mathematical expressions of “understanding” and “application”, the notions of “knowledge group” and “higher-order knowledge group” are introduced, along with a parallel method for identifying the structure of higher-order knowledge groups. Finally, the experiments in this paper validate that the model can accurately diagnose students’ knowledge proficiency, affirming the scientific and meaningful integration of Bloom’s cognitive hierarchy in knowledge proficiency assessment.
Semi-Markov Models for Process Mining in Smart Homes

Generally, these days people live longer but often with increased impairment and disabilities; therefore, they can benefit from assistive technologies. In this paper, we focus on the completion of activities of daily living (ADLs) by such patients, using so-called Smart Homes and Sensor Technology to collect data, and provide a suitable analysis to support the management of these conditions. The activities here are cast as states of a Markov-type process, while changes of state are indicated by sensor activations. This facilitates the extraction of key performance indicators (KPIs) in Smart Homes, e.g., the duration of an important activity, as well as the identification of anomalies in such transitions and durations. The use of semi-Markov models for such a scenario is described, where the state durations are represented by mixed gamma models. This approach is illustrated and evaluated using a publicly available Smart Home dataset comprising an event log of sensor activations, together with an annotated record of the actual activities. Results indicate that the methodology is well-suited to such scenarios.
Unsupervised Stereo Matching with Surface Normal Assistance for Indoor Depth Estimation

To obtain more accurate depth information with stereo cameras, various learning-based stereo-matching algorithms have been developed recently. These algorithms, however, are significantly affected by textureless regions in indoor applications. To address this problem, we propose a new deep-neural-network-based data-driven stereo-matching scheme that utilizes the surface normal. The proposed scheme includes a neural network and a two-stage training strategy. The neural network involves a feature-extraction module, a normal-estimation branch, and a disparity-estimation branch. The training processes of the feature-extraction module and the normal-estimation branch are supervised while the training of the disparity-estimation branch is performed unsupervised. Experimental results indicate that the proposed scheme is capable of estimating the surface normal accurately in textureless regions, leading to improvement in the disparity-estimation accuracy and stereo-matching quality in indoor applications involving such textureless regions.
Passive TDOA Emitter Localization Using Fast Hyperbolic Hough Transform

A fast Hough transform (HT)-based hyperbolic emitter localization system is proposed to process time difference of arrival (TDOA) measurements. The position-fixing problem is provided for cases where the source is known to be on a given plane (i.e., the elevation of the source is known), while the sensors can be deployed anywhere in the three-dimensional space. The proposed solution provides fast evaluation and guarantees the determination of the global optimum. Another favorable property of the proposed solution is that it is robust against faulty sensor measurements (outliers). A fast evaluation method involving the hyperbolic Hough transform is proposed, and the global convergence property of the algorithm is proven. The performance of the algorithm is compared to that of the least-squares solution, other HT-based solutions, and the theoretical limit (the Cramér–Rao lower bound), using simulations and real measurement examples.
Large Language Models and Information Retrieval

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