Reviewerly/exHarmony huggingface dataset:
Dynamic adaptive control of boiler combustion based on improved GNG algorithm

The boiler combustion process contains complex physicochemical changes, which is a nonlinear time-varying industrial process with strong interference and multivariate strong coupling. For this kind of boiler combustion process with typical nonlinear characteristics and complex mechanism, it is difficult to establish an accurate mechanism model by conventional modeling methods, so it is difficult to meet the new requirements for the optimal control of the boiler. The large amount of data accumulated in the operation process contains rich system information, and the data-driven modeling and control method provides an effective way for the operation optimization of the unit. Data dynamic characterization and control technology is an important means of data mining, coal-fired boiler data has obvious temporal and drift characteristics, for the current data tracking and supervision algorithms mostly lack of dynamics, real-time and stability issues, design an adaptive clustering model based on the improved growth of neural gas model (GNG), the establishment of nodes based on the probability, the range of the search, the average distance of node A node generation and deletion mechanism based on probability, range search and average distance of nodes is established to realize real-time monitoring of drift data. Finally, the experiments are carried out by analyzing the dynamic data of coal-fired boiler, and the experimental results show that the model and algorithm have stronger real-time tracking ability for dynamic drift data, and can accurately and effectively monitor and control the dynamic data of coal-fired boiler.
ATBBC: Named entity recognition in emergency domains based on joint BERT-BILSTM-CRF adversarial training

With the rapid growth of Internet penetration, identifying emergency information from network news has become increasingly significant for emergency monitoring and early warning. Although deep learning models have been commonly used in Chinese Named Entity Recognition (NER), they require a significant amount of well-labeled training data, which is difficult to obtain for emergencies. In this paper, we propose an NER model that combines bidirectional encoder representations from Transformers (BERT), bidirectional long-short-term memory (BILSTM), and conditional random field (CRF) based on adversarial training (ATBBC) to address this issue. Firstly, we constructed an emergency dataset (ED) based on the classification and coding specifications of the national emergency platform system. Secondly, we utilized the BERT pre-training model with adversarial training to extract text features. Finally, BILSTM and CRF were used to predict the probability distribution of entity labels and decode the probability distribution into corresponding entity labels.Experiments on the ED show that our model achieves an F1-score of 85.39% on the test dataset, which proves the effectiveness of our model.
Critique of the paper, ‘Public's perception on nursing education during the COVID-19 Pandemic: Sentiment Analysis of Twitter Data’

Textual data derived from social media platforms constitutes a valuable resource for scholars, policymakers, and governmental entities seeking real-time insights into the prevailing sentiments of the general populace across diverse subject matters. In this article, we have presented our critique of the paper titled 'Public's Perception in Nursing Education during the COVID-19 Pandemic: Sentiment Analysis of Twitter Data' by highlighting the challenges unique to analyzing textual data and the issues that arise from relying on lexicon-based pre-defined libraries in textual data analysis. Further in the article, we have discussed the importance of providing reliable metrics like F1 score, accuracy, precision, and recall to ensure that the model used in the study to analyze the data is suitable. Due to the absence of an attention mechanism, a vital aspect in advanced deep learning techniques, pre-defined lexicon-based sentiment analysis dictionaries often fail in unstructured, complicated textual data that is filled with variations, nuanced references, and subtle emotional nuances. We have further analyzed the significance of addressing potential data bias in the article.
KEPA-CRF: Knowledge expansion prototypical amortized conditional random field for few-shot event detection

Event Detection (ED) has long struggled with the ambiguous definition of event categories, making it challenging to accurately classify events. Previous endeavors aimed to tackle this problem by constructing prototypes for specific event categories. However, they overlooked potential correlations among instances of distinct event categories, resulting in trigger misclassifications across event types. In this research, we introduce KEPA-CRF to train enhanced event prototypes and address the issue of limited samples in few-shot event detection. By integrating external knowledge from the Glove knowledge base into the model training process, we augment synonymous examples, mitigating the problem of insufficient samples in few-shot event detection. Additionally, through prototype adversarial training, we contrast prototypes of different event categories to augment the representational capabilities of prototype vectors. Experimental results showcase that our approach attains superior performance on the benchmark dataset FewEvent, surpassing comparative models with reduced training time.
When Evolutionary Computation Meets Privacy

Recently, evolutionary computation (EC) has experienced significant advancements due to the integration of machine learning, distributed computing, and big data technologies. These developments have led to new research avenues in EC, such as distributed EC and surrogate-assisted EC. While these advancements have greatly enhanced the performance and applicability of EC, they have also raised concerns regarding privacy leakages, specifically the disclosure of optimal results and surrogate models. Consequently, the combination of evolutionary computation and privacy protection becomes an increasing necessity. However, a comprehensive exploration of privacy concerns in evolutionary computation is currently lacking, particularly in terms of identifying the object, motivation, position, and method of privacy protection. To address this gap, this paper aims to discuss three typical optimization paradigms, namely, centralized optimization, distributed optimization, and data-driven optimization, to characterize optimization modes of evolutionary computation and proposes BOOM (i.e., oBject, mOtivation, pOsition, and Method) to sort out privacy concerns related to evolutionary computation. In particular, the centralized optimization paradigm allows clients to outsource optimization problems to a centralized server and obtain optimization solutions from the server. The distributed optimization paradigm exploits the storage and computational power of distributed devices to solve optimization problems. On the other hand, the data-driven optimization paradigm utilizes historical data to address optimization problems without explicit objective functions. Within each of these paradigms, BOOM is used to characterize the object and motivation of privacy protection. Furthermore, this paper discuss the potential privacy-preserving technologies that strike a balance between optimization performance and privacy guarantees. Finally, this paper outlines several new research directions for privacy-preserving evolutionary computation.
How to Build Self-Explaining Fuzzy Systems: From Interpretability to Explainability [AI-eXplained]

Fuzzy systems are known to provide not only accurate but also interpretable predictions. However, their explainability may be undermined if non-semantically grounded linguistic terms are used. Additional non-trivial challenges would arise if a prediction were to be explained counterfactually, i.e., in terms of hypothetical, non-predicted outputs. In this paper, we explore how both factual and counterfactual automated explanations can justify the output of fuzzy rule-based classifiers, and thus contribute to making them more trustworthy. Moreover, we demonstrate how end user preferences can be handled by customizing automated explanations, making them interactive, personalized, and therefore, human-centric. The full immersive article at IEEE Xplore provides detailed interactive examples for better understanding.
MFCANet: A road scene segmentation network based on Multi-Scale feature fusion and context information aggregation

Road scene segmentation is the basic task of autonomous driving. Recent representative scene segmentation methods adopt the full convolutional network based on the encoder-decoder. However, the framework can cause the loss of image fine-grained information in the process of down-sampling, feature extraction and feature fusion, resulting in blurred boundary details and chaotic segmentation effect. In this work, a road scene segmentation network based on multi-scale feature fusion and context information aggregation is proposed, in which context information is used to guide feature fusion and enhance semantic feature extraction. Three plug-and-play modules are designed to extract multi-scale features with strong semantic information from high-level features, which compensate for the loss of spatial information in the upper sampling stage, and capture the information dependence among pixels to improve pixel-by-pixel segmentation. Experimental results on Camvid and Cityscapes show that the proposed multi-scale feature fusion and context information aggregation network (MFCANet) can achieve satisfactory performance compared with the state-of-the-art segmentation methods.
Surveillance video synopsis framework base on tube set

Video synopsis technology can shorten the length of the video, which has attracted wide attention. However, due to the limitation of object extraction technology and the difficulty of preserving interactivity, synopsis videos will lose the semantic information of the original video. To address the above problems, we propose a video synopsis framework based on tube sets. Firstly, we propose a video tracking algorithm based on Yolov4 and Kalman Filter, which can effectively alleviate the problem that it is difficult for object tracking technology to extract occluded objects. Secondly, we propose a method that combines moving direction and dynamic threshold to judge interactivity. Accurately judging interactivity can ensure that the interactive objects are not separated in the synopsis video. Finally, we propose a tube set mapping model (TSMM) that can rearrange with the tube set as the basic unit. Experimental results demonstrate that our method can achieve superior performance than other state-of-the-art methods.
FedAGA: A federated learning framework for enhanced inter-client relationship learning

Exploring relationships is important for effective model aggregation in Personalized Federated Learning (PFL). However, state-of-the-art (SOTA) PFL methods primarily focus on simple model aggregation through inter-client relationships within Euclidean space, overlooking the intricate nature of unstructured relationships. To remedy this, we propose a novel framework named Federated Learning with Adaptive Graph-based Aggregation (FedAGA) tailored for non-Euclidean space. FedAGA captures inter-client relationships through similarities based on accumulated gradients of various client models, constructing a dynamic graph topology. To enhance the reliability and accuracy of graph topology, we introduce two criteria from the perspectives of model convergence and model divergence. Our proposed approach is extensively evaluated on two PFL benchmark datasets and four real medical datasets. The results demonstrate that FedAGA accurately captures inter-client relationships across diverse models, leading to superior performance compared to the SOTA baselines. In particular, we observe notable accuracy improvements of 6.8% on CIFAR10 and 4.0% on PathMNIST compared to the existing methods.
GeneConnector: Unlocking the full potential of Genbank metadata

Genbank currently stands as one of the most significant global repositories of genetic information. However, despite its vast quantity and diversity of data, a considerable portion of the existing records suffer from disjointed and often lacking metadata, failing to provide the necessary context of their acquisition. In light of this, we propose GeneConnector, a tool that harnesses shared information among multiple records of the same specimen in Genbank, aiming to enhance the completeness of poorly annotated nodes across various information domains. To demonstrate the tools capabilities, we conducted a comprehensive review and aggregation of available data using the Genbank database of Genera of Phytopathogenic Fungi (GOPHY). Through our evaluation, we observed substantial gains in information by analyzing shared data among nodes connecting Genbank specimen records, resulting in impressive increments ranging from 2% to a remarkable 60%. Our approach empowers users to make precise, straightforward, and accurate assessments of the context associated to results, facilitated by two metrics that gauge the current level of data annotation and the potential information gain achievable following our evaluation.
Generation of probabilistic synthetic data for serious games: A case study on cyberbullying

Synthetic data generation has been a growing area of research in recent years. However, its potential applications in serious games have yet to be thoroughly explored. Advances in this field could anticipate data modeling and analysis, as well as speed up the development process. To fill this gap in the literature, we propose a simulator architecture for generating probabilistic synthetic data for decision-based serious games. This architecture is designed to be versatile and modular so that it can be used by other researchers on similar problems (e.g., multiple choice exam, political surveys, any type of questionnaires). To simulate the interaction of synthetic players with the game, we use a cognitive testing model based on the Item Response Theory framework. We also show how probabilistic graphical models (in particular, Bayesian networks) can introduce expert knowledge and external data into the simulation. Finally, we apply the proposed architecture and methods in the case of a serious game focused on cyberbullying. We perform Bayesian inference experiments using a hierarchical model to demonstrate the identifiability and robustness of the generated data.
Daily Video: A tool for quality of experience (QoE) in long-term context research

This paper presents ‘Daily Video’, an innovative application designed to facilitate longitudinal studies in the realm of quality of experience (QoE). The application simulates real-life video service consumption and collects daily subjective quality assessments from users, promoting an in-depth understanding of user engagement and habits over time. Key features include scheduling, customised queries, user feedback, device orientation tracking and instant access to results, all of which enhance the application’s adaptability and usability for a variety of experimental conditions. The application, available as open-source code, supports state-of-the-art QoE research.
Integrating Machine Learning in Legal Management for Intelligent Supply Chain Governance

The integration of machine learning in legal management and the development of intelligent supply chain governance represents transformative approaches to address the evolving complexities of the legal and supply chain domains. This chapter summarizes the key findings and insights drawn from the outlined sections. The problem statement underscores the growing need for data-driven strategies to navigate the intricate legal landscape and meet the challenges of modern supply chains. With a focus on transparency, accountability, and risk management, the significance of supply chain governance is highlighted, emphasizing its critical role in resilient and efficient supply chain operations. Legal management emerges as a pivotal player in this transformation, tasked with utilizing machine learning to navigate a data-rich and complex legal environment.
LiDeNeRF: Neural radiance field reconstruction with depth prior provided by LiDAR point cloud

Neural Radiance Fields (NeRF) is a technique for reconstructing real-world scenes from multiple views. However, existing methods mostly focus on the visual effects of scene reconstruction while neglecting geometric accuracy, which is crucial in photogrammetry and remote sensing. In this paper, we propose a method called LiDeNeRF which uses LiDAR point cloud to provide depth priors for NeRF reconstruction. The goal of LiDeNeRF is to achieve real-time rendering of NeRF scenes and 3D reconstruction results with high geometric accuracy. In this method, first, the LiDAR point cloud is projected onto images to generate a sparse depth map. Then, by triangulating the sparse depth and using a multi-view image depth propagation method, a dense depth map with high accuracy is quickly generated as the depth prior for NeRF. Finally, a new depth correction module is designed and embedded into the NeRF rendering pipeline to improve the accuracy of scene depth estimation. The experimental results evince that our methodology has attained paramount performance in both novel view synthesis and 3D reconstruction tasks. Our source code is made available at https://github.com/WPC-WHU/LiDeNeRF.
Exploration of glitch phenomena in Holographic art

The concept of imperfection, where perfection itself is an imperfection, aligns with the aesthetic of glitch art in holographic art. Originating in the 1960s as an integration of science, technology, and art, the creation of holograms requires meticulous craftsmanship in optical laboratories, blending specific technical skills with extensive practical experience. This paper introduces glitch art within the realm of holographic art and explores its historical significance in holograms. It identifies 4 key factors, stability during exposure, control of exposure parameters, path length difference control and post-processing techniques, that commonly induce glitches. Additionally, the paper examines recent glitch-infused holograms encountered by the author, thereby enhancing the understanding of glitches as a unique artistic expression within holographic art. This exploration sheds light on the technical aspects of hologram creation and contributes to the evolving narrative of digital art in contemporary practice.
DOMAS: a data management software framework for advanced light sources

In recent years, China's advanced light sources have entered a period of rapid construction and development. As modern X-ray detectors and data acquisition technologies advance, these facilities are expected to generate massive volumes of data annually, presenting significant challenges in data management and utilization. These challenges encompass data storage, metadata handling, data transfer and user data access. In response, the Data Organization Management Access Software (DOMAS) has been designed as a framework to address these issues. DOMAS encapsulates four fundamental modules of data management software, including metadata catalogue, metadata acquisition, data transfer and data service. For light source facilities, building a data management system only requires parameter configuration and minimal code development within DOMAS. This paper firstly discusses the development of advanced light sources in China and the associated demands and challenges in data management, prompting a reconsideration of data management software framework design. It then outlines the architecture of the framework, detailing its components and functions. Lastly, it highlights the application progress and effectiveness of DOMAS when deployed for the High Energy Photon Source (HEPS) and Beijing Synchrotron Radiation Facility (BSRF).
Abstract TP31: Identifying Multivariate Nonlinear Bias in Sammpris Using Graph-Based AI Analysis

Background: Clinical trials are the most rigorous way of testing how novel treatments compare with existing ones regarding outcomes. The randomized controlled clinical trial has become the standard means of providing proof of safety and effectiveness across the board in medicine, including stroke. Yet, the risk factors among patients that are randomized might not always be equally distributed such that a bias may occur. In this study, we focus on the SAMMPRIS clinical trial aimed to assess the safety and efficacy of aggressive medical management versus stenting for preventing recurrent stroke in patients with symptomatic intracranial arterial stenosis. Methods: We developed and evaluated a machine learning model to identify potential multivariate bias present in the dataset that could not have been identified using standard statistical tests. Our approach utilizes a machine learning model based on the spectral regression model (SR-KDA) to identify biases created by combinations of variables. SR-KDA is based on a graph representation of the data that is processed via discriminative analysis to identify groups of patients that share common characteristics. By using both the treatment and outcome variables as constraints, the model is able to identify nonlinear relationships between clinical variables and the outcome. It can also be used to identify bias as part of the randomization that could have inadvertently skewed the statistical results with standard methodologies. Results: Our approach was successfully applied to the SAMMPRIS clinical dataset, which includes 121 clinical variables. The algorithm was able to automatically identify 6 clusters of patients that had a different response to treatment when considering the clinical variables. Each group was then assigned a set of the variables' cut-off values that best describe them. Conclusion: We have developed a sophisticated system that is capable of detecting non-linear correlations in clinical data collected during trials. This solution has the potential to mitigate any biases that may arise during the randomization process, which could ultimately improve recruitment efforts. Moving forward, this innovative tool could be applied proactively to future clinical trials.
Two-photon quantum state tomography of photonic qubits

We provide a tool for measuring the Stokes parameters and the degree of polarization of single photons by employing second-order interference, namely, the Hong-Ou-Mandel (HOM) interferometer. It is shown that the technique is able to distinguish a partially polarized photon where the polarization state is coupled to an internal degree of freedom, such as time of arrival, from partial polarization due to external entanglement with the environment. The method does not directly resort to any kind of polarization-selective components and therefore is not limited by the extinction ratio of polarizers. Moreover, the technique can be generalized to any two-level encoding of quantum information in single photons, such as time-bin or orbital angular momentum qubits.