Reviewerly/exHarmony huggingface dataset:
ATBBC: Named entity recognition in emergency domains based on joint BERT-BILSTM-CRF adversarial training

Authors:
raw_author_name: Buqing Cai
raw_affiliation_string: School of Software, University of Xinjiang, Xinjiang, China
raw_author_name: Shengwei Tian
raw_affiliation_string: School of Software, University of Xinjiang, Xinjiang, China
raw_author_name: Long Yu
raw_affiliation_string: College of Network Center, University of Xinjiang, Xinjiang, China
raw_author_name: Jun Long
raw_affiliation_string: Institute of Big Data Research, University of Central South, Changsha, China
raw_author_name: Tiejun Zhou
raw_affiliation_string: School of Software, University of Xinjiang, Xinjiang, China
raw_author_name: Bo Wang
raw_affiliation_string: School of Software, University of Xinjiang, Xinjiang, China

Abstract: 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’

Authors:
raw_author_name: Praveen Sv
raw_affiliation_string: Department of Data Science, Mudra Institute of Communications, Ahmedabad, India

Abstract: 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

Authors:
raw_author_name: Rong Wu
raw_affiliation_string: School of Software, University of Xinjiang, Xinjiang, China
raw_author_name: Long Yu
raw_affiliation_string: Network and Information Center, University of Xinjiang, Xinjiang, China
raw_author_name: Shengwei Tian
raw_affiliation_string: Key Laboratory of Software Engineering Technology, University of Xinjiang, Xinjiang, China
raw_author_name: Jun Long
raw_affiliation_string: Institute of Big Data Research, University of Central South, Changsha, China
raw_author_name: Tiejun Zhou
raw_affiliation_string: Internet Information Security Centre, Xinjiang, China
raw_author_name: Bo Wang
raw_affiliation_string: School of Software, University of Xinjiang, Xinjiang, China

Abstract: 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

Authors:
raw_author_name: Bowen Zhao
raw_affiliation_string: Xidian University, China
raw_author_name: Wei-Neng Chen
raw_affiliation_string: South China University of Technology, China
raw_author_name: Xiaoguo Li
raw_affiliation_string: Singapore Management University, Singapore
raw_author_name: Ximeng Liu
raw_affiliation_string: Fuzhou University, China
raw_author_name: Qingqi Pei
raw_affiliation_string: Xidian University, China
raw_author_name: Jun Zhang
raw_affiliation_string: Hanyang University ERICA, South Korea; Nankai University, China

Abstract: 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]

Authors:
raw_author_name: Ilia Stepin
raw_affiliation_string: Universidade de Santiago de Compostela, Santiago de Compostela, Spain
raw_author_name: Muhammad Suffian
raw_affiliation_string: Universit&#x00E0; degli Studi di Urbino Carlo Bo, Urbino, Italy
raw_author_name: Alejandro Catala
raw_affiliation_string: Universidade de Santiago de Compostela, Santiago de Compostela, Spain
raw_author_name: Jose M. Alonso-Moral
raw_affiliation_string: Universidade de Santiago de Compostela, Santiago de Compostela, Spain

Abstract: 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

Authors:
raw_author_name: Yunfeng Wang
raw_affiliation_string: College of Electromechanics Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
raw_author_name: Yi Zhou
raw_affiliation_string: College of Electromechanics Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
raw_author_name: Hao Wu
raw_affiliation_string: College of Electromechanics Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
raw_author_name: Xiyu Liu
raw_affiliation_string: School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
raw_author_name: Xiaodi Zhai
raw_affiliation_string: School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
raw_author_name: Chengliang Tian
raw_affiliation_string: College of Computer Science and Technology, Qingdao University, Qingdao 266000, China
raw_author_name: Haixia Zhao
raw_affiliation_string: College of Electromechanics Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
raw_author_name: Tao Li
raw_affiliation_string: College of Electromechanics Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
raw_author_name: Yan Zhang
raw_affiliation_string: College of Electromechanics Engineering, Qingdao University of Science and Technology, Qingdao 266061, China; School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China

Abstract: 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

Authors:
raw_author_name: Yunzuo Zhang
raw_affiliation_string: School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
raw_author_name: Pengfei Zhu
raw_affiliation_string: School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
raw_author_name: Tingting Zheng
raw_affiliation_string: School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
raw_author_name: Puze Yu
raw_affiliation_string: School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
raw_author_name: Jianming Wang
raw_affiliation_string: School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, China

Abstract: 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

Authors:
raw_author_name: Jiaqi Ge
raw_affiliation_string: Department of Computer Science and Technology, Jilin University, Changchun 130012, PR China
raw_author_name: Gaochao Xu
raw_affiliation_string: Department of Computer Science and Technology, Jilin University, Changchun 130012, PR China
raw_author_name: Jianchao Lu
raw_affiliation_string: School of Computing, Macquarie University, Sydney, Australia
raw_author_name: Chenhao Xu
raw_affiliation_string: School of Information Technology, Deakin University, Australia
raw_author_name: Quan Z. Sheng
raw_affiliation_string: School of Computing, Macquarie University, Sydney, Australia
raw_author_name: Xi Zheng
raw_affiliation_string: School of Computing, Macquarie University, Sydney, Australia

Abstract: 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.
Generation of probabilistic synthetic data for serious games: A case study on cyberbullying

Authors:
raw_author_name: Mario Castro
raw_affiliation_string: Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, 28015, Spain; Institute for Research in Technology (IIT), ICAI Engineering School, Universidad Pontificia Comillas, Madrid, 28015, Spain
raw_author_name: Edmond Awad
raw_affiliation_string: Department of Economics, University of Exeter, Exeter, EX4 4PU, United Kingdom

Abstract: 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

Authors:
raw_author_name: Natalia Cieplińska
raw_affiliation_string: Faculty of Computer Science, Electronics and Telecommunications, AGH University of Krakow, al. Adama Mickiewicza 30, 30-059 Kraków, Poland
raw_author_name: Lucjan Janowski
raw_affiliation_string: Faculty of Computer Science, Electronics and Telecommunications, AGH University of Krakow, al. Adama Mickiewicza 30, 30-059 Kraków, Poland

Abstract: 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

Authors:
raw_author_name: Edwin Ramirez-Asis
raw_affiliation_string: Universidad Señor de Sipán, Chiclayo, Peru

Abstract: 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

Authors:
raw_author_name: Pengcheng Wei
raw_affiliation_string: School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Rd, Wuhan, 430079, China
raw_author_name: Li Yan
raw_affiliation_string: School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Rd, Wuhan, 430079, China
raw_author_name: Hong Xie
raw_affiliation_string: Luojia Laboratory Hubei, 129 Luoyu Rd, Wuhan, 430079, China; School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Rd, Wuhan, 430079, China
raw_author_name: Hao Wu
raw_affiliation_string: School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Rd, Wuhan, 430079, China
raw_author_name: Yinghao Zhao
raw_affiliation_string: School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Rd, Wuhan, 430079, China
raw_author_name: Xiao Hu
raw_affiliation_string: School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Rd, Wuhan, 430079, China

Abstract: 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

Authors:
raw_author_name: Xiaofan Liu
raw_affiliation_string: Holographic Arts Center, Beijing Institute of Graphic Communication (China)
raw_author_name: Wenbin Bai
raw_affiliation_string: Holographic Arts Center, Beijing Institute of Graphic Communication (China)
raw_author_name: Jie Yang
raw_author_name: Shuo Wang
raw_affiliation_string: Holographic Arts Center, Beijing Institute of Graphic Communication (China)
raw_author_name: Zhilin Zhu
raw_affiliation_string: Holographic Arts Center, Beijing Institute of Graphic Communication (China)
raw_author_name: Ye Li
raw_affiliation_string: Holographic Arts Center, Beijing Institute of Graphic Communication (China)

Abstract: 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.