Reviewerly/exHarmony huggingface dataset:
Efficient relation extraction via quantum reinforcement learning

Authors:
raw_author_name: Xianchao Zhu
raw_affiliation_string: Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou, 450001, China
raw_author_name: Yashuang Mu
raw_affiliation_string: School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, China
raw_author_name: Xuetao Wang
raw_affiliation_string: School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, China
raw_author_name: William Zhu
raw_affiliation_string: Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China

Abstract: Abstract Most existing relation extraction methods only determine the relation type after identifying all entities, thus not fully modeling the interaction between relation-type recognition and entity mention detection. This article introduces a novel paradigm for relation extraction by treating relevant entities as parameters of relations and harnessing the strong expressive capabilities and acceleration advantages of quantum computing to address the relation extraction task. In this article, we develop a quantum hierarchical reinforcement learning approach to enhance the interaction between relation-type recognition and entity mention detection. The entire relation extraction process is broken down into a hierarchical structure of two layers of quantum reinforcement learning strategies dedicated to relation detection and entity extraction, demonstrating greater feasibility and expressiveness, especially when dealing with superimposed relations. Our proposed method outperforms existing approaches through experimental evaluations on commonly used public datasets, mainly showcasing its significant advantages in extracting superimposed relationships.
Intelligent real-time prediction for shield machine position on the basis of BWO-LSTM-GRU

Authors:
raw_author_name: Liu Xuanyu
raw_affiliation_string: School of Information and Control Engineering, Liaoning Petrochemical University, Fushun, 113001, People’s Republic of China
raw_author_name: Zhang Wenshuai
raw_affiliation_string: School of Information and Control Engineering, Liaoning Petrochemical University, Fushun, 113001, People’s Republic of China
raw_author_name: Wang Yudong
raw_affiliation_string: Zhongqing Construction Co, Ltd, Changchun, 130000, People’s Republic of China

Abstract: Abstract Due to the complexity and variability of shield machine working environment, it is very important to accurately control and regulate the position trajectory of shield machine. For that reason, an intelligent real-time prediction model of shield machine position based on BWO-LSTM-GRU (Beluga whale optimization-Long Short-term Memory-Gated recurrent unit) is proposed in this paper. Firstly, the real-time data of shield machine are processed based on Pearson correlation analysis, and the tunneling parameters presenting medium-strong correlation with the position parameters are filtered to obtain, which were used to be input variables for prediction models. Secondly, LSTM-GRU position prediction model was established separately for shield machine position parameters, and four hyperparameters of the model were optimized separately using BWO. Finally, BWO-LSTM-GRU position prediction models are used to realize the intelligent real-time prediction of the motion trajectories at four positions for shield machine. The simulation results indicate that the prediction deviation in the position prediction model is within 3 mm, and it can accurately complete the task of real-time prediction, providing real-time data support for shield machine drivers.
Segmentation, feature extraction and classification of leukocytes leveraging neural networks, a comparative study

Authors:
raw_author_name: Xiao Chen
raw_affiliation_string: School of Future Technology University of Chinese Academy of Sciences Beijing People's Republic of China; State Key Laboratory of Transducer Technology Aerospace Information Research Institute of Chinese Academy of Sciences Beijing People's Republic of China
raw_author_name: Jian Chen
raw_affiliation_string: School of Electronic Electrical and Communication Engineering of University of Chinese Academy of Sciences Beijing People's Republic of China; State Key Laboratory of Transducer Technology Aerospace Information Research Institute of Chinese Academy of Sciences Beijing People's Republic of China

Abstract: The gold standard of leukocyte differentiation is a manual examination of blood smears, which is not only time and labor intensive but also susceptible to human error. As to automatic classification, there is still no comparative study of cell segmentation, feature extraction, and cell classification, where a variety of machine and deep learning models are compared with home-developed approaches. In this study, both traditional machine learning of K-means clustering versus deep learning of U-Net, U-Net + ResNet18, and U-Net + ResNet34 were used for cell segmentation, producing segmentation accuracies of 94.36% versus 99.17% for the dataset of CellaVision and 93.20% versus 98.75% for the dataset of BCCD, confirming that deep learning produces higher performance than traditional machine learning in leukocyte classification. In addition, a series of deep-learning approaches, including AlexNet, VGG16, and ResNet18, was adopted to conduct feature extraction and cell classification of leukocytes, producing classification accuracies of 91.31%, 97.83%, and 100% of CellaVision as well as 81.18%, 91.64% and 97.82% of BCCD, confirming the capability of the increased deepness of neural networks in leukocyte classification. As to the demonstrations, this study further conducted cell-type classification of ALL-IDB2 and PCB-HBC datasets, producing high accuracies of 100% and 98.49% among all literature, validating the deep learning model used in this study.
Gamification in the Metaverse: Affordance, perceived value, flow state, and engagement

Authors:
raw_author_name: Seong‐Soo Cha
raw_affiliation_string: Department of Food Science &amp; Service, College of Bio‐Convergence Eulji University Seongnam Republic of Korea

Abstract: Abstract This study aims to empirically validate a theoretical model for the engagement of the performance Metaverse platform with other rapidly developing Metaverse platforms around the world. It innovatively examines the relationship between gamification affordance, perceived value, and flow state in the context of the Metaverse concert platform and the relationship that improves engagement in the tourism and entertainment industries. The proposed model incorporates five stages: gamification, affordance, perceived value, flow state, and engagement. A total of 671 usable questionnaires were obtained, and the results were analysed using confirmatory factor analysis, discriminant validity analysis, and path analysis. Social gain and enjoyment have a positive effect on the personal flow state and are influenced by identity affordance, competition affordance, and self‐expression affordance. Furthermore, the flow state increases engagement. However, the relationship between perceived value (effectiveness, social gain, and enjoyment) and flow state differ according to gender.
Man and the Machine: Effects of AI-assisted Human Labeling on Interactive Annotation of Real-Time Video Streams

Authors:
raw_author_name: Marko Radeta
raw_affiliation_string: Wave Labs, Funchal, MARE/ARNET/ARDITI, University of Madeira, Funchal, University of Belgrade, Belgrade, Portugal
raw_author_name: Ruben Freitas
raw_affiliation_string: Wave Labs, Funchal, MARE/ARNET/ARDITI, University of Madeira, Funchal, Portugal
raw_author_name: Agustin Zuniga
raw_affiliation_string: Department of Computer Science, University of Helsinki, Helsinki, Finland
raw_author_name: Ngoc Thi Nguyen
raw_affiliation_string: Department of Computer Science, University of Helsinki, Helsinki, Finland
raw_author_name: Huber Flores
raw_affiliation_string: Institute of Computer Science, University of Tartu, Tartu, Estonia
raw_author_name: Petteri Nurmi
raw_affiliation_string: Department of Computer Science, University of Helsinki, Helsinki, Finland

Abstract: AI-assisted interactive annotation is a powerful way to facilitate data annotation – a prerequisite for constructing robust AI models. While AI-assisted interactive annotation has been extensively studied in static settings, less is known about its usage in dynamic scenarios where the annotators operate under time and cognitive constraints, e.g., while detecting suspicious or dangerous activities from real-time surveillance feeds. Understanding how AI can assist annotators in these tasks and facilitate consistent annotation is paramount to ensure high performance for AI models trained on these data. We address this gap in interactive machine learning (IML) research, contributing an extensive investigation of the benefits, limitations, and challenges of AI-assisted annotation in dynamic application use cases. We address both the effects of AI on annotators and the effects of (AI) annotations on the performance of AI models trained on annotated data in real-time video annotations. We conduct extensive experiments that compare annotation performance at two annotator levels (expert and non-expert) and two interactive labelling techniques (with and without AI-assistance). In a controlled study with N = 34 annotators and a follow up study with 51963 images and their annotation labels being input to the AI model, we demonstrate that the benefits of AI-assisted models are greatest for non-expert users and for cases where targets are only partially or briefly visible. The expert users tend to outperform or achieve similar performance as AI model. Labels combining AI and expert annotations result in the best overall performance as the AI reduces overflow and latency in the expert annotations. We derive guidelines for the use of AI-assisted human annotation in real-time dynamic use cases.
Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems

Authors:
raw_author_name: Yongsu Ahn
raw_affiliation_string: University of Pittsburghm, Pittsburgh, United States
raw_author_name: Yu-Ru Lin
raw_affiliation_string: University of Pittsburgh, United States, United States

Abstract: Despite the benefits of personalizing items and information tailored to users’ needs, it has been found that recommender systems tend to introduce biases that favor popular items or certain categories of items, and dominant user groups. In this study, we aim to characterize the systematic errors of a recommendation system and how they manifest in various accountability issues, such as stereotypes, biases, and miscalibration. We propose a unified framework that distinguishes the sources of prediction errors into a set of key measures that quantify the various types of system-induced effects, both at the individual and collective levels. Based on our measuring framework, we examine the most widely adopted algorithms in the context of movie recommendation. Our research reveals three important findings: (1) Differences between algorithms: recommendations generated by simpler algorithms tend to be more stereotypical but less biased than those generated by more complex algorithms. (2) Disparate impact on groups and individuals: system-induced biases and stereotypes have a disproportionate effect on atypical users and minority groups (e.g., women and older users). (3) Mitigation opportunity: using structural equation modeling, we identify the interactions between user characteristics (typicality and diversity), system-induced effects, and miscalibration. We further investigate the possibility of mitigating system-induced effects by oversampling underrepresented groups and individuals, which was found to be effective in reducing stereotypes and improving recommendation quality. Our research is the first systematic examination of not only system-induced effects and miscalibration but also the stereotyping issue in recommender systems.
Exploring the Facets of the Multiplayer VR Gaming Experience

Authors:
raw_author_name: Sara Vlahovic
raw_affiliation_string: University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia
raw_author_name: Ivan Slivar
raw_affiliation_string: University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia
raw_author_name: Matko Silic
raw_affiliation_string: University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia
raw_author_name: Lea Skorin-Kapov
raw_affiliation_string: University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia

Abstract: While the topic of investigating user experience with immersive services, such as Social Virtual Reality (VR), is starting to gain traction in the research community, the unique case of multiplayer VR games requires a more specific approach. Attempts to investigate user experiences with this complex, multidimensional service are hindered by the absence of specific standards and guidelines going beyond what we know about non-immersive gaming. In this article, we present the results of a user study (N = 32) exploring participants’ experience of playing two competitive VR games of different genres (shooter, sports game), as we focus on three distinct facets of multiplayer VR gaming — network, interpersonal competitiveness, and social interaction. Furthermore, approaching the issue from the perspective of Quality of Experience researchers looking to conduct future user studies on the topic, we also present our findings as a way to shed light on factors that need further consideration, especially in the context of participant recruitment and methodology design, such as the choice of access network to be used for testing, and the issue of matchmaking study participants based on skill and prior relationship.
A Liquidity Analysis System for Large-Scale Video Streams in the Oilfield

Authors:
raw_author_name: Qiang Ma
raw_affiliation_string: Tsinghua University, Beijing, China
raw_author_name: Hao Yuan
raw_affiliation_string: Tsinghua University, Beijing, China
raw_author_name: Zhe Hu
raw_affiliation_string: Tsinghua University, Beijing, China
raw_author_name: Xu Wang
raw_affiliation_string: Tsinghua University, Beijing, China
raw_author_name: Zheng Yang
raw_affiliation_string: Tsinghua University, Beijing, China

Abstract: This article introduces LinkStream, a liquidity analysis system based on multiple video streams designed and implemented for oilfield. LinkStream combines a variety of technologies to solve several problems in computing power and network latency. First, the system adopts an edge-central architecture and tailoring based on spatio-temporal correlation, which greatly reduces computing power requirements and network costs, and enables real-time analysis of large-scale video stream on limited edge devices. Second, it designed a set of liquidity information to describe the liquidity status in the oilfield. Finally, it uses object tracking technology to design a counting algorithm for the unique tubing object in the oilfield. We have deployed LinkStream in an oilfield in Iraq. LinkStream can perform realtime inference on over 200 video streams with acceptable resource overhead.
Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flows

Authors:
raw_author_name: Vinayak Gupta
raw_affiliation_string: Paul G. Allen School of Computer Science and Engineering University of Washington, Washington Seattle, 98195, United States
raw_author_name: Srikanta Bedathur
raw_affiliation_string: Department of Computer Science and Engineering, Bharti Building Indian Institute of Technology Delhi, New Delhi Hauz Khas, 110016, India

Abstract: Human beings always engage in a vast range of activities and tasks that demonstrate their ability to adapt to different scenarios. These activities can range from the simplest daily routines, like walking and sitting, to multi-level complex endeavors such as cooking a four-course meal. Any human activity can be represented as a temporal sequence of actions performed to achieve a certain goal. Unlike the time series datasets extracted from electronics or machines, these action sequences are highly disparate in their nature – the time to finish a sequence of actions can vary between different persons. Therefore, understanding the dynamics of these sequences is essential for many downstream tasks such as activity length prediction, goal prediction, next-action recommendation, etc. Existing neural network-based approaches that learn a continuous-time activity sequence (or CTAS) are limited to the presence of only visual data or are designed specifically for a particular task, i.e. , limited to next action or goal prediction. In this paper, we present ProActive , a neural marked temporal point process (MTPP) framework for modeling the continuous-time distribution of actions in an activity sequence while simultaneously addressing three high-impact problems – next action prediction, sequence-goal prediction, and end-to-end sequence generation. Specifically, we utilize a self-attention module with temporal normalizing flows to model the influence and the inter-arrival times between actions in a sequence. Moreover, for time-sensitive prediction, we perform an early detection of sequence goal via a constrained margin-based optimization procedure. This in-turn allows ProActive to predict the sequence goal using a limited number of actions. In addition, we propose a novel addition over the ProActive model, called ProActive++ , that can handle variations in the order of actions, i.e. , different methods of achieving a given goal. We demonstrate that this variant can learn the order in which the person or actor prefers to do their actions. Extensive experiments on sequences derived from three activity recognition datasets show the significant accuracy boost of our ProActive and ProActive++ over the state-of-the-art in terms of action and goal prediction, and the first-ever application of end-to-end action sequence generation.
Bystanders’ collective responses set the norm against hate speech

Authors:
raw_author_name: Justin Sulik
raw_affiliation_string: Cognition, Values & Behaviour Lab, Faculty of Philosophy, Philosophy of Science and Religious Studies Ludwig-Maximilians-Universität München, Munich, Germany
raw_author_name: Ophelia Deroy
raw_affiliation_string: Munich Center for Neuroscience, Munich, Germany

Abstract: Abstract Hate speech incidents often occur in social settings, from public transport to football stadiums. To counteract a prevailing passive attitude towards them, governmental authorities, sociologists, and philosophers stress bystanders’ responsibility to oppose or block hate speech. Here, across two online experiments with UK participants using custom visual vignettes, we provide empirical evidence that bystanders’ expression of opposition can affect how harmful these incidents are perceived, but only as part of a collective response: one expressed by a majority of bystanders present. Experiment 1 ( N = 329) shows that the silence or intervention of three bystanders affects the harm caused by hate speech, but one bystander does not. Experiment 2 ( N = 269) shows this is not simply a matter of numbers but rather one of norms: only unanimous opposition reduces the public perception of the damage created by the incident. Based on our results, we advance an empirical norm account: group responses to hate speech modulate its harm by indicating either a permissive or a disapproving social norm. Our account and results, showing the need to consider responses to hate speech at a collective level, have direct implications for social psychology, the philosophy of language and public policies.
Spatial-spectral graph convolutional extreme learning machine for hyperspectral image classification

Authors:
raw_author_name: Qing Yu
raw_affiliation_string: Institute of Automation, Qilu University of Technoloy (Shandong Academy of Sciences), Jinan, China
raw_author_name: Xiangdong Li
raw_affiliation_string: Institute of Automation, Qilu University of Technoloy (Shandong Academy of Sciences), Jinan, China
raw_author_name: Hongrui Zhang
raw_affiliation_string: Institute of Automation, Qilu University of Technoloy (Shandong Academy of Sciences), Jinan, China
raw_author_name: Jinhuan Xu
raw_affiliation_string: Institute of Automation, Qilu University of Technoloy (Shandong Academy of Sciences), Jinan, China

Abstract: Hyperspectral image has excellent spectral information and abundant spatial information, and its feature quality is one of the key factors affecting the classification performance. Extreme learning machine (ELM) is widely used in hyperspectral classification problems. However, traditional ELM is usually based on regular Euclidean data, ignoring the inherent structured information between hyperspectral pixels, resulting in poor robustness. In this paper, we propose a new supervised extreme learning machine framework, termed spatial-spectral graph convolutional ELM (SSG-ELM), based on using graph convolution to extend ELM into the non-Euclidean domain. The method inherits all the advantages from ELM, and consists of a random graph convolutional layer followed by a graph convolutional regression layer, enabling it to model complex intra class variations. Otherwise, a local spectral-spatial context integration and reshaping mechanism is incorporated into the hidden layer feature representation by using a context-aware bilateral filtering procedure. Experimental results show that the proposed algorithm obtains a competitive performance and outperforms other state-of-the-art ELM-based methods.
A Multi-Path Semantic Segmentation Network Based on Convolutional Attention Guidance

Authors:
raw_author_name: Chenyang Feng
raw_affiliation_string: College of Computer Science, Sichuan University, Chengdu 610042, China
raw_author_name: Shu Hu
raw_affiliation_string: College of Computer Science, Sichuan University, Chengdu 610042, China
raw_author_name: Yi Zhang
raw_affiliation_string: College of Computer Science, Sichuan University, Chengdu 610042, China

Abstract: Due to the efficiency of self-attention mechanisms in encoding spatial information, Transformer-based models have recently taken a dominant position among semantic segmentation methods. However, Transformer-based models have the disadvantages of requiring a large amount of computation and lacking attention to detail, so we look back to the CNN model. In this paper, we propose a multi-path semantic segmentation network with convolutional attention guidance (dubbed MCAG). It has a multi-path architecture, and feature guidance from the main path is used in other paths, which forces the model to focus on the object’s boundaries and details. It also explores multi-scale convolutional features through spatial attention. Finally, it captures both local and global contexts in spatial and channel dimensions in an adaptive manner. Extensive experiments were conducted on popular benchmarks, and it was found that MCAG surpasses other SOTA methods by achieving 47.7%, 82.51% and 43.6% mIoU on ADE20K, Cityscapes and COCO-Stuff, respectively. Specifically, the experimental results prove that the proposed model has high segmentation precision for small objects, which demonstrates the effectiveness of convolutional attention mechanisms and multi-path strategies. The results show that the CNN model can achieve good segmentation effects with a lower amount of calculation.