Interactive information disclosure and non-penalty regulatory review risk

Using monthly data from the Shenzhen Stock Exchange's‘Hudongyi’platform and comment letters from December 2014 to December 2018,this study investigates the influence of interactive information disclosure on non-penalty regulatory review risk.The findings reveal that the richness and activeness of interactive information disclosure are positively associated with regulatory review risk.Moreover,the non-penalty regulatory review is effective as it significantly reduces the probability of receiving a comment letter in the subsequent three periods.The timeliness of interactive information disclosure is negatively associated with regulatory review risks.Additionally,we find that newspaper media coverage partially mediates the relationship between interactive information disclosure and regulatory review risk.For companies with low levels of internal governance,in low-competitive industries,and state-owned companies,the positive relationship between the number of investor questions and regulatory review risk is strengthened.These findings enrich the literature on the determinants of regulatory review risk and the economic consequences of interactive information disclosure in emerging markets.

Design and Realization of Meteorological Service and Meteorological Information Officer Management Business Platform Based on WebGIS Information Interaction

A meteorological service and meteorological information officer management business platform based on WebGIS information interaction is designed and realized.Firstly,the goals of system construction are introduced,and then the features of the system is analyzed.Finally,the subsystems of the system are studied,such as geographic information subsystem,information officer management subsystem,information release subsystem,information feedback and evaluation subsystem,radar analysis subsystem,time traceback subsystem,integrated display subsystem,etc.

Information Interaction Is the Fifth Type of Fundamental Interactions

Information is an integral part of the Universe. By its physical essence information is heterogeneity of matter and energy. Therefore information is inseparably connected with matter and energy. The universal measure of information in physical heterogeneity is the Shannon information entropy. An information approach along with a physical one allows to obtain new, sometimes more general data in relation to data obtained on the ground of physical rules only. The results presented in this paper show the effectiveness of informational approach for studying the interactions in the Universe. The paper shows that, along with the physical interactions the gravitational, electromagnetic, strong, weak interactions exists fifth type of fundamental interactions--information interaction, whose magnitude is not dependent on distance. The existence of information interaction is determined by the entanglement of quantum states, of quantum subsystems. The magnitude of information interaction is measured in bits.

AR Technology-Based Game for Finding Treasures in Museums

With the vigorous development of tourism and entertainment industry, the traditional way of museum information display has been increasingly unable to meet people’s growing entertainment needs. Benefiting from the development of AR technology, AR museum games, a method of combining traditional museums with emerging information technology, can transform the knowledge display of museums from boring learning to active exploration, thereby improving the fun of the journey. By combining the museum auxiliary guide with AR games, the story of the museum exhibits is processed with interest, and the knowledge display of the serious museum becomes more vivid. The design is based on Unity 3d platform, and the Vuforia plug-in and UGUI interface controls that can be stable and efficient for image recognition are used to complete the development of museum AR games.

Investigating the relationships between facets of work task and selection and query-related behavior

Purpose: This study aims to explore the relationships between different facets of work task and selection and query-related behavior.Design/methodology/approach:An experiment was conducted to explore the issue. The researcher recruited 24 participants and assigned six simulated work task situations to each of them. Each experiment lasted around 2 hours and was recorded by the software tool Morae.Findings: Time(frequency) and time(length) are more closely related to user’s selection and query-related behavior compared to the facet ‘process’ of work task. Knowledge level of work task topic, degree of work task difficulty, and subjective work task complexity are significantly correlated with selection and query-related behavior. Work task difficulty and work task complexity are different concepts. Subjective work task complexity, work task difficulty, and knowledge of work task topic are significantly correlated with user’s selection and query-related behavior.Research limitations/implications: The limitations of this study include a small sample size,limited work task situations, and possible spurious relationships. This study has implications in informing task-based information seeking/search/retrieval research and interactive information retrieval(IIR) systems design.Originality/values: Previous studies usually did not touch upon how different facets of work tasks affected interactive activities. Some studies examining task complexity and information behavior were concerned with how work tasks affect users’ behavior at information-seeking level, rather than at information search level. This study makes contribution to interactive information retrieval,task-based information search and retrieval, and personalization of IR.

U-shaped Vision Transformer and Its Application in Gear Pitting Measurement

Although convolutional neural networks have become the mainstream segmentation model,the locality of convolution makes them cannot well learn global and long-range semantic information.To further improve the performance of segmentation models,we propose U-shaped vision Transformer(UsViT),a model based on Transformer and convolution.Specifically,residual Transformer blocks are designed in the encoder of UsViT,which take advantages of residual network and Transformer backbone at the same time.What is more,transpositions in each Transformer layer achieve the information interaction between spatial locations and feature channels,enhancing the capability of feature learning.In the decoder,for enhancing receptive field,different dilation rates are introduced to each convolutional layer.In addition,residual connections are applied to make the information propagation smoother when training the model.We first verify the superiority of UsViT on automatic portrait matting public dataset,which achieves 90.43%accuracy(Acc),95.56%Dice similarity coefficient,and 94.66%Intersection over Union with relatively fewer parameters.Finally,UsViT is applied to gear pitting measurement in gear contact fatigue test,and the comparative results indicate that UsViT can improve the Acc of pitting detection.

Optimized Deep Learning Model for Fire Semantic Segmentation

Recent convolutional neural networks(CNNs)based deep learning has significantly promoted fire detection.Existing fire detection methods can efficiently recognize and locate the fire.However,the accurate flame boundary and shape information is hard to obtain by them,which makes it difficult to conduct automated fire region analysis,prediction,and early warning.To this end,we propose a fire semantic segmentation method based on Global Position Guidance(GPG)and Multi-path explicit Edge information Interaction(MEI).Specifically,to solve the problem of local segmentation errors in low-level feature space,a top-down global position guidance module is used to restrain the offset of low-level features.Besides,an MEI module is proposed to explicitly extract and utilize the edge information to refine the coarse fire segmentation results.We compare the proposed method with existing advanced semantic segmentation and salient object detection methods.Experimental results demonstrate that the proposed method achieves 94.1%,93.6%,94.6%,95.3%,and 95.9%Intersection over Union(IoU)on five test sets respectively which outperforms the suboptimal method by a large margin.In addition,in terms of accuracy,our approach also achieves the best score.

Digital Twin-Driven Intelligent Construction:Features and Trends

Digital twin(DT)can achieve real-time information fusion and interactive feedback between virtual space and physical space.This technology involves a digital model,real-time information management,comprehensive intelligent perception networks,etc.,and it can drive the rapid conceptual development of intelligent construction(IC)such as smart factories,smart cities,and smart medical care.Nevertheless,the actual use of DT in IC is partially pending,with numerous scientific factors still not clarified.An overall survey on pending issues and unsolved scientific factors is needed for the development of DT-driven IC.To this end,this study aims to provide a comprehensive review of the state of the art and state of the use of DT-driven IC.The use of DT in planning,design,manufacturing,operation,and maintenance management of IC is demonstrated and analyzed,following which the driving functions of DT in IC are detailed from four aspects:information perception and analysis,data mining and modeling,state assessment and prediction,intelligent optimization and decision-making.Furthermore,the future direction of research,using DT in IC,is presented with some comments and suggestions.This work will help researchers gain in-depth and systematic understanding of the use of DT,and help practitioners to better promote its implementation in IC.

Gate Feature Interaction Network for Relation Prediction in Knowledge Graph

Recently,many knowledge graph embedding models for knowledge graph completion have been proposed,ranging from the initial translation-based model such as TransE to recent CNN-based models such as ConvE.These models fill in the missing relations between entities by focusing on capturing the representation features to further complete the existing knowledge graph(KG).However,the above KG-based relation prediction research ignores the interaction information among entities in KG.To solve this problem,this work proposes a novel model called Gate Feature Interaction Network(GFINet)with a weighted loss function that takes the benefit of interaction information and deep expressive features together.Specifically,the proposed GFINet consists of a gate convolution block and an interaction attention module,corresponding to catching deep expressive features and interaction information based on these valid features respectively.Our method establishes state-of-the-art experimental results on the standard datasets for knowledge graph completion.In addition,we make ablation experiments to verify the effectiveness of the gate convolution block and the interaction attention module.

A geographical and operational deep graph convolutional approach for flight delay prediction

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning,flight scheduling,airport operation,and passenger service.Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity,which brings critical challenges to accurate flight delay prediction.In recent years,Graph Convolutional Networks(GCNs)have become popular in flight delay prediction due to the advantage in extracting complicated relationships.However,most of the existing GCN-based methods have failed to effectively capture the spatial-temporal information in flight delay prediction.In this paper,a Geographical and Operational Graph Convolutional Network(GOGCN)is proposed for multi-airport flight delay prediction.The GOGCN is a GCN-based spatial-temporal model that improves node feature representation ability with geographical and operational spatial-temporal interactions in a graph.Specifically,an operational aggregator is designed to extract global operational information based on the graph structure,while a geographical aggregator is developed to capture the similar nature among spatially close airports.Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.

Multitarget tracking control algorithm under local information selection interaction mechanism

This study focuses on the problem of multitarget tracking.To address the existing problems of current tracking algorithms,as manifested by the time consumption of subgroup separation and the uneven group size of unmanned aerial vehicles(UAVs)for target tracking,a multitarget tracking control algorithm under local information selection interaction is proposed.First,on the basis of location,number,and perceived target information of neighboring UAVs,a temporary leader selection strategy is designed to realize the local follow-up movement of UAVs when the UAVs cannot fully perceive the target.Second,in combination with the basic rules of cluster movement and target information perception factors,distributed control equations are designed to achieve a rapid gathering of UAVs and consistent tracking of multiple targets.Lastly,the simulation experiments are conducted in two-and three-dimensional spaces.Under a certain number of UAVs,clustering speed of the proposed algorithm is less than 3 s,and the equal probability of the UAV subgroup size after group separation is over 78%.

Augmenting Trigger Semantics to Improve Event Coreference Resolution

Due to the small size of the annotated corpora and the sparsity of the event trigger words, the event coreference resolver cannot capture enough event semantics, especially the trigger semantics, to identify coreferential event mentions. To address the above issues, this paper proposes a trigger semantics augmentation mechanism to boost event coreference resolution. First, this mechanism performs a trigger-oriented masking strategy to pre-train a BERT (Bidirectional Encoder Representations from Transformers)-based encoder (Trigger-BERT), which is fine-tuned on a large-scale unlabeled dataset Gigaword. Second, it combines the event semantic relations from the Trigger-BERT encoder with the event interactions from the soft-attention mechanism to resolve event coreference. Experimental results on both the KBP2016 and KBP2017 datasets show that our proposed model outperforms several state-of-the-art baselines.

Flexible thermochromic fabrics enabling dynamic colored display

Color-changeable fbers can provide diverse functions for intelligent wearable devices such as novel information displays and human-machine interfaces when woven into fabric.This work develops a low-cost,efective,and scalable strategy to produce thermochromic fbers by wet spinning.Through a combination of diferent thermochromic microcapsules,fexible fbers with abundant and reversible color changes are obtained.These color changes can be clearly observed by the naked eye.It is also found that the fbers exhibit excellent color-changing stability even after 8000 thermal cycles.Moreover,the thermochromic fbers can be fabricated on a large scale and easily woven or implanted into various fabrics with good mechanical performance.Driven by their good mechanical and physical characteristics,applications of thermochromic fbers in dynamic colored display are demonstrated.Dynamic quick response(QR)code display and recognition are successfully realized with thermochromic fabrics.This work well confrms the potential applications of thermochromic fbers in smart textiles,wearable devices,fexible displays,and human-machine interfaces.