Reviewerly/exHarmony huggingface dataset:
On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression

Random forest (RF) is a widely used data prediction and variable selection technique. However, the variable selection aspect of RF can become unreliable when there are more irrelevant variables than relevant ones. In response, we introduced the Bayesian random forest (BRF) method, specifically designed for high-dimensional datasets with a sparse covariate structure. Our research demonstrates that BRF possesses the oracle property, which means it achieves strong selection consistency without compromising the efficiency or bias.
An underwater image enhancement method based on Swin transformer

An underwater image enhancement model based on Swin Transformer is designed to solve the problems of color shift, low contrast, detail loss and noise in underwater images caused by absorption and scattering effects during the propagation of light underwater. The model consists of two parts: feature extraction and image reconstruction. First, image features are extracted using a set of improved residual Swin Transformer modules, which allows the model to process images with fewer parameters and establish long-term dependencies between image features. Further, a parallel attention mechanism is introduced into the model to focus on both semantic and spatial information contained in underwater images to improve image enhancement. Experimental results show that compared with existing popular underwater image enhancement methods, the proposed Underwater Image Enhancement Method in this paper has advantages in both the number of network parameters and image enhancement accuracy and has made significant progress in correcting color bias, improving contrast, enhancing details and eliminating noise.
Research on underwater image enhancement algorithm based on physical model

In this research work, an improved underwater image enhancement algorithm is presented that is based on physical modeling techniques. It aims to improve the quality of underwater images by addressing color differences and blur. The proposed algorithm performs better than other advanced and classical underwater image enhancement methods. The method is composed of a two-step process. First, a coarse transfer map is estimated, which optimizes the contrast and minimizes the loss of information during the image mapping process. Dark channel preprocessing and guided filter refinement are used to improve transmission map accuracy. Second, the occluded light is estimated by considering the differential attenuation of the red, green, and blue light underwater, thereby mitigating the color differences caused by the attenuation effects and solving the blur problem based on the image models. In addition, the gray world method is used for color correction, resulting in a deblurred and color corrected underwater image. In particular, it addresses color differences and blurriness in underwater imagery caused by light attenuation at different wavelengths, visible light diffusion, and diffusion from plankton and debris. The ability to significantly improve the quality of underwater images is the significance of this enhanced algorithm. This enhancement gives researchers a more apparent and reliable basis for understanding and analyzing underwater environments and phenomena. It has far-reaching implications for various fields, including marine biology, underwater archaeology, and oceanography, as it improves the accuracy of research and applications in these domains.
Road target detection under complex weather based on YOLOv5-CGα algorithm

Accuracy of the detection algorithm is very important for the safety of autonomous driving in complex road target detection and recognition. Therefore, this study proposes a novel lightweight YOLOv5-CGα target identification algorithm that focuses on optimizing the algorithm's lightness and accuracy. First, the algorithm adds the CBAM (Convolutional Block Attention Module) to improve the network's capacity to extract target characteristics and detection accuracy; secondly, by replacing the ordinary convolution module with the Ghost Bottleneck module in order to compress the parameters of the network and reduce the overall amount of model computation; finally, the original C-IoU (Complete-Intersection Over Union) model's loss function is changed by the α-IoU (α-Intersection Over Union) loss function, leading the model training process to converge faster. The mAP of the YOLOv5-CGα algorithm for the detection of road targets can reach 97.4%, according to experimental results on the BDD100K dataset. The simulation results show that compared with the performance of YOLOv3 and YOLOv5 algorithms, the YOLOv5-CGα algorithm improves the mAP of detecting road targets by 2.7% and by 1.4%. This algorithm only needs half of the parameters in order to enhance on-road target recognition accuracy, which may balance the algorithm's small weight with the issue of accurate road target detection.
SwinGAN: a generative adversarial network-based algorithm for generating Qin bamboo slips character images

This paper presents a generative adversarial network (GAN)-based algorithm for generating Qin bamboo slips character images, specifically addressing the issues of limited samples and a high occurrence of fragmented characters. To mitigate the interference caused by the image background on the network, a global thresholding binary segmentation method is employed to separate the foreground and background of Qin bamboo slips character images. Additionally, we propose the SwinGAN network model based on the DCGAN architecture. The SwinGAN generator network incorporates a windowed multi-head attention mechanism and a Qin Transformer module that combines convolutional neural networks. To prevent gradient varnish, spectral normalization is applied to the convolutional layers of the discriminator, constraining the weight variations. Furthermore, to ensure stable model training, the Wasserstein distance is adopted as the objective function to measure the difference between the generated data distribution and the real data distribution.
Examining the Impact of Digital Human Gaze Expressions on Engagement Induction

With advancements in technology, digital humans are becoming increasingly sophisticated, with their application scope widening to include interactions with real people. However, research on expressions that facilitate natural engagement in interactions between real people and digital humans is scarce. With this study, we aimed to examine the differences in user engagement as measured by subjective evaluations, eye tracking, and electroencephalogram (EEG) responses relative to different gaze expressions in various conversational contexts. Conversational situations were categorized as face-to-face, face-to-video, and digital human interactions, with gaze expressions segmented into eye contact and gaze avoidance. Story stimuli incorporating twelve sentences verified to elicit positive and negative emotional responses were employed in the experiments after validation. A total of 45 participants (31 females and 14 males) underwent stimulation through positive and negative stories while exhibiting eye contact or gaze avoidance under each of the three conversational conditions. Engagement was assessed using subjective evaluation metrics in conjunction with measures of the subjects' gaze and brainwave activity. The findings revealed engagement disparities between the face-to-face and digital-human conversation conditions. Notably, only positive stimuli elicited variations in engagement based on gaze expression across different conversation conditions. Gaze analysis corroborated the engagement differences, aligning with prior research on social sensitivity, but only in response to positive stimuli. This research departs from traditional studies of un-natural interactions with digital humans, focusing instead on interactions with digital humans designed to mimic the appearance of real humans. This study demonstrates the potential for gaze expression to induce engagement, regardless of the human or digital nature of the conversational dyads.
Invisible Shield: Unveiling an Efficient Watermarking Solution for Medical Imaging Security

Securing medical imaging poses a significant challenge in preserving the confidentiality of healthcare data. Numerous research efforts have focused on fortifying these images, with encryption emerging as a primary solution for maintaining data integrity without compromising confidentiality. However, applying conventional encryption techniques directly to e-health data encounters hurdles, including limitations in data size, redundancy, and capacity, particularly in open-channel patient data transmissions. As a result, the unique characteristics of images, marked by their risk of data loss and the need for confidentiality, make preserving the privacy of data contents a complex task. This underscores the pressing need for innovative approaches to ensure the security and confidentiality of sensitive healthcare information within medical images. The proposed algorithm outperforms referenced algorithms in both image fidelity and steganographic capacity across diverse medical imaging modalities. It consistently achieves higher Peak Signal-to-Noise Ratio (PSNR) values, indicating superior image fidelity, reduced noise, and preserved signal quality in CT, MRI, ultrasound, and X-ray modalities. The experimental results demonstrate a considerable improvement in both the Peak Signal-to-Noise Ratio (PSNR) and maximum embedding capacity. Specifically, the average PSNR value for the X-ray modality reached a notable 73 dB, signifying superior image quality. Moreover, the CT modality exhibited the highest maximum embedding capacity, measured at 0.52, showcasing its efficiency in accommodating data within the images. Moreover, the algorithm consistently offers increased steganographic data hiding capacity in these images without perceptibly degrading their quality or integrity.
The effect of old water making time and heating temperature on moisture content and NaCl on traditional salt production

Abstract The manufacture of salt from old water is only done in salt ponds by geomembrane methods and prisms with the help of evaporation from sunlight. This study uses experimental methods. Working procedures in this study include sampling, followed by characterizing water samples including pH, NaCl, salinity and clarity tests. Then continued with the manufacture of salt samples. After that, each sample was tested on the water content and NaCl levels. The data to be obtained will be analyzed using the Duncan and Tukey Test tests with the SPSS 25.0. While the sensory test using Krustal Wallis if there is a noticeable difference (p&lt;0.05) it will be continued with the Mann Whitney test and compared to the quality standard of SNI 3556:2010 iodine consumption salts. Sensorically covering the color, taste and texture of salt with the method of making using hotplate has met the quality 1 and 2. The chemical characteristics of the moisture content parameters have met the standard SNI 3556: 2010 salt consumption of iodine. Some salt samples do not meet the SNI’s standards on NaCl levels. Salt quality is affected by the NaCl levels of old water, impurities and the process of making salt.
Based on Zabbix-Prometheus Group Classification Alarm System

Abstract The efficient operation of the system is the foundation for improving business level in the rapid development of information technology. Based on the data integration of the Zabbix-Prometheus system, it achieves information data storage, data collection, grouping and classification linkage alarm functions. The Zabbix-Prometheus grouping and classification alarm integration system can effectively solve the problems of a large amount of redundant alarm information, no priority of alarm information, and inability to silence. It has established a multi-platform integrated alarm service system. It has implemented linkage alarm functions such as email, SMS, and WeChat.
Efficient Federated Learning for Feature Aggregation with Heterogenous Edge Devices

Abstract Federated learning is a powerful distributed machine learning paradigm for feature aggregation and learning from multiple heterogenous edge devices, due to its ability to keep the privacy of data. However, the training is inefficient for heterogenous devices with considerable communication. Progressive learning is a promising approach for improving the efficiency. Since progressive learning partitions the training process into multiple stages, it is necessary to determine the number of rounds for each stage, and balance the trade-off between saving the energy and improving the model accuracy. Through pilot experiments, we find that the profile which reflects the relationship between round allocation and model quality remains similar in different hyper-parameter configurations, and also observe that the model quality is lossless if the complete model gets sufficient training. Based on the phenomena, we formulate an optimization problem which minimizes the energy consumption of all devices, under the constraint of model quality. We then design a polynomial-time algorithm for the problem. Experimental results demonstrate the superiority of our proposed algorithm under various settings.
Application research of machine manufacturing process monitoring based on neural network

Abstract In the process of mechanical manufacturing, parameters such as geometry, size and surface quality are important factors affecting product quality. The existing technology makes it difficult to conduct product monitoring and data monitoring during machine manufacturing. At the same time, the uncertainty of the manufacturing process also brings great challenges. Therefore, it is necessary to design corresponding monitoring schemes according to different situations. Therefore, neural network based machinery manufacturing process monitoring technology is of great significance. The neural network model established in this paper can automatically learn and establish complex mapping relationships, and can predict and detect anomalies according to historical data. In addition, deep learning technology can be used to carry out adaptive feature learning on a large amount of monitoring data, and complete efficient training and real-time decision-making in a distributed environment. Compared with the common mechanical manufacturing process, neural network monitoring has a lower loss rate and error rate. The neural network based machinery manufacturing process monitoring technology will become an important direction of the future development of machinery manufacturing industry, and will have great practical application value in improving production efficiency, reducing production cost and ensuring product quality.
Enhancing Cybersecurity: The Crucial Role of Self-Regulation, Information Processing, and Financial Knowledge in Combating Phishing Attacks

In today’s technologically advanced world, the escalating prevalence of phishing attacks necessitates an urgent exploration of effective countermeasures. This study delves into the crucial investigation of how self-regulation influences phishing susceptibility, while also examining the mediating role of information processing and the moderating influence of financial knowledge. A comprehensive survey was meticulously crafted, targeting 370 recent university graduates. The collected data underwent rigorous analysis using Hayes’ PROCESS macro, unveiling a mediated moderation model. The findings reveal that self-regulation does not directly predict phishing susceptibility. However, information processing, whether systematic or heuristic, significantly impacts phishing susceptibility. Additionally, information processing serves as a mediator, connecting self-regulation to phishing susceptibility. Notably, the interaction between systematic information processing and financial knowledge emerged as a significant determinant of phishing susceptibility. By establishing a comprehensive mediated moderation model, this study provides invaluable insights for individuals and organizations seeking to bolster their defenses against phishing attacks. Furthermore, it emphasizes the vital role of financial knowledge and employees’ heuristic information processing in mitigating cyber threats. This research serves as a crucial scientific contribution, offering compelling evidence that proactive management of contributing factors can effectively mitigate the escalating threats posed by phishing attacks.
Impact of IT General Control and Application Control in Increasing Information Quality for Auditor

The development of information technology (IT), monitoring whether activities in the value chain are carried out in accordance with the planned goals, principles, laws and regulations and standards has made the use of autonomous intelligent decision support systems widespread in the audit process. The development of information technology makes a significant contribution to the development of auditing. One of them is the role of IT general control and application control. IT General Control is a procedure related to information system applications and supports the function of Application Control by ensuring the continuity of information systems. The purpose of this study is to examine and analyze the role of IT general control and Application Control in improving the quality of information for auditors. The form of research used in this research is quantitative research using survey methods. The sample used in this study is the probability sampling technique chosen, namely Proportionate Stratified Random Sampling. Then the data collection was carried out using a questionnaire. The data analysis method used is validity and reliability testing, then the data that has been obtained is tested statistically using bivariate and univariate analysis tests which include partial tests, simultaneous tests and tests of the coefficient of determination.
Auditor Acceptance of Metaverse: Approach from Technology Acceptance Model

Currently, in the era of industry 4.0, the digital world is experiencing rapid changes. The rapid changes marked by various developments in the world of technology, include the phenomenon of the metaverse world. Many companies already have digital assets in the metaverse world, such as: land, property and also have deposits in cryptocurrency. This creates new challenges for accounting and auditing. It is urgent to be prepared by accountants and auditors regarding this metaverse world, which triggers us to conduct research. Our research is a quantitative study using a descriptive statistical approach. We examine the auditor's willingness to study the metaverse audit using the technology acceptance model approach. Our research object is an auditor who works in a public accounting firm. We use statistical data processing using SMART PLS software. The results of our study state that perceived usefulness, perceived ease of use able each has a significant influence on auditors' intention to adopt metaverse technology. Audit firm size is also able to moderate or strengthen the influence of perceived usefulness, perceived ease of use for auditors' intention to adopt metaverse technology.
Development of an Intelligent Chatbot for an Intraosseous Access Serious Game (WIP Paper)

This work-in-progress paper outlines the development of a virtual assistant (chatbot) that supports natural language interaction for a serious game focusing on training for the intraosseous access medical procedure whereby fluids or medications are infused directly into the marrow of a bone when conventional venous access is not feasible. The main objective of our IO chatbot is to offer an interactive and effective learning experience by providing the trainee with contextualized feedback, suggestions and instructions as the trainee executes the procedure. In this paper we outline our IO chatbot and provide technical details regarding its implementation.
Effective Skill Learning via Autonomous Goal Representation Learning

A long standing goal of robotics researchers is to develop robots that are able to develop in an autonomous open-ended manner through lifelong learning and interactions. If we are to see robots learning in an autonomous and open-ended manner, we need to develop methods for incremental and autonomous skill discovery and trial-and-error learning. In other words, we want our robots to be able to autonomously select their goals according to their current capabilities and learn controllers or policies to achieve those goals. In this paper, we take a step towards solving this challenge and propose a novel pipeline, called AGRL, that effectively combines deterministic simulations, Variational Auto-Encoders (VAEs) and Reinforcement Learning (RL) and enables robots to learn goal-conditioned policies suited to their capabilities. Our main intuition is that we can use effective exploration strategies in order to learn a good goal representation and distribution, and then use this distribution to generate effective and reachable goals for fast skill learning. We extensively evaluate the proposed method in simulation with a 7DOF manipulator and a differential drive mobile robot.
Applications of Artificial Intelligence in Game Theory

This topic is an area of research related to the application of artificial intelligence methods in game theory. The aim of the research is to create effective algorithms and models to analyze various game scenarios and predict optimal decisions for players. To achieve this goal, various methods of machine learning are used, as well as algorithms for optimization and statistical analysis of data. The relevance of this topic is due to the increasing popularity and importance of games in our lives, as well as the possibility of applying the results in various fields such as economics, business, politics, etc. The study was conducted with the help of computer implementation, which allows us to test the algorithms on different game scenarios and evaluate their results. The results confirm the effectiveness of the developed methods and algorithms.The conclusion of the work summarizes the results of the study and discusses possible directions for further work in this area. This work may be useful for specialists in the field of artificial intelligence and game theory, as well as for developers of game applications.
Planning Mobile Robot Behavior in an Uncertain Multi-agent Environment

The paper proposes an approach for safe navigation when changing lanes in a road scenario. The aim of this approach is to create the baseline value of the decision making module for the lane changing task with multiple agents to avoid collisions. The paper describes three models of the project. Model-1 is a working method for implementing modeling. Model-2 is a way to build a decision-making module using safe zones. Model-3 is the proposed way to build a decision-making module using value functions.The paper presents a multi-agent approach to adjust traffic lights depending on traffic situations to reduce average delay time. The traffic lights of each intersection are controlled by a mobile agent. This approach creates a classical non-stationary environment, as each agent's decision affects neighboring agents. Therefore, each agent must not only learn from past experience, but also take into account the decisions of its neighbors to rule out dynamic changes in the traffic network. Fuzzy Q-learning and game theory are used to develop new policies based on previous experiences and decisions of neighboring agents. The results obtained from the simulation show the advantage of the proposed method over fixed time management, fuzzy Q-learning and fuzzy Q-learning methods.