Event-related Information Research Articles

The Utility of a Novel Neuropsychological Measurement to Analyze Event-Related Attentional Behaviors among Young Children with Attention Deficit Hyperactivity Disorder-a Pilot Study.

The identification and diagnosis of children with attention deficit hyperactivity disorder (ADHD) traits is challenging during the preschool stage. Neuropsychological measures may be useful in early assessments. Furthermore, analysis of event-related behavior appears to be an unmet need for clinical treatment planning. Conners' Kiddie Continuous Performance Test (K-CPT) is the most popular well-established neuropsychological measurement but lacks event markers to clarify the heterogeneous behaviors among children. This study utilized a novel commercially available neuropsychological measure, the ΣCOG, which was more game-like and provided definite event markers of individual trial in the test. Thirty-three older preschool children (14 were diagnosed with ADHD, mean age: 66.21 ± 5.48months; 19 demonstrated typical development, mean age: 61.16 ± 8.11months) were enrolled and underwent comprehensive medical and developmental evaluations. All participants underwent 2 versions of neuropsychological measures, including the K-CPT, Second Edition (K-CPT 2) and the ΣCOG, within a short interval. The study indicated the omissions and response time scores measured in this novel system correlated with clinical measurement of the behavioral scales in all participants and in the group with ADHD; additionally, associations with the traditional K-CPT 2 were observed in commissions and response time scores. Furthermore, this system provided a within-task behavioral analysis that identified the group differences in the specific trial regarding omission and commission errors. This innovative system is clinically feasible and can be further used as an alternative to the K-CPT 2 especially in research by revealing within-task event-related information analysis.

Is a Large Language Model a Good Annotator for Event Extraction?

Event extraction is an important task in natural language processing that focuses on mining event-related information from unstructured text. Despite considerable advancements, it is still challenging to achieve satisfactory performance in this task, and issues like data scarcity and imbalance obstruct progress. In this paper, we introduce an innovative approach where we employ Large Language Models (LLMs) as expert annotators for event extraction. We strategically include sample data from the training dataset in the prompt as a reference, ensuring alignment between the data distribution of LLM-generated samples and that of the benchmark dataset. This enables us to craft an augmented dataset that complements existing benchmarks, alleviating the challenges of data imbalance and scarcity and thereby enhancing the performance of fine-tuned models. We conducted extensive experiments to validate the efficacy of our proposed method, and we believe that this approach holds great potential for propelling the development and application of more advanced and reliable event extraction systems in real-world scenarios.

Non-interference assessment in colored net systems via integer linear programming

Event-related information leaks are a potential security hazard in information systems. Non-interference is a security property to describe event-related information security. Non-interference assessment is to detect whether a public observer can infer the occurrences of private events from the observation of public ones. To assess the non-interference of information systems, the utilization of formal modeling tools, especially Petri nets (PNs), is an effective way used in most previous works. However, for large-scale systems, non-interference assessment leads to the problem of state explosion in the context of basic net systems (NSs) defined by PNs. In this paper, considering the structural similarity of large-scale systems, colored PNs are used to model them more compactly and efficiently. We focus on the assessment of two typical non-interference properties, i.e., strong nondeterministic non-interference (SNNI) and bisimulation SNNI (BSNNI), in colored NSs (CNSs). Specifically, we propose a non-interference assessment method for bounded CNSs based on the definitions of SNNI and BSNNI in the context of CNSs. This method involves coarse and fine assessments. A coarse assessment is achieved via integer linear programming (ILP) by leveraging the structural similarity of systems. In contrast, the fine assessment can be fulfilled using ILP-based analysis or firing way analysis based on the results obtained by the coarse assessment, which is not essentially necessary. In particular, a fine assessment is necessary only if a coarse assessment is failed to obtain accurate assessment results. Finally, efficiency analysis reveals that our method reduces assessment redundancy and improves assessment efficiency.

MAPPING THE SKIES: ANALYZING AEROSPACE INDUSTRY TRENDS USING EVENT MAPS

Accurate event extraction and analysis are vital for understanding trends and making informed decisions in various domains. This paper presents a concise rule-based approach for event extraction in the aerospace domain, aiming to enhance decision-making processes. The proposed methodology utilizes rule-based pattern-matching techniques to identify and extract events from textual data, such as news articles and research papers. By leveraging linguistic features and syntactic structures, the approach effectively captures event-related information. Additionally, an event map visualization technique is introduced to provide a comprehensive overview of the extracted events and their relationships. The proposed approach, integrated into the Aerospace Expert System (AES), offers a powerful tool for tracking and analyzing events in the aerospace industry. Evaluation results demonstrate the high precision and recall of the rule-based approach, enabling stakeholders to gain valuable insights and make informed decisions in the aerospace domain.

Attention-related power and functional connectivity modulation associated with long-term alpha neurofeedback training

Electroencephalography (EEG) based Neurofeedback training (NFT) is a non-invasive, brain-modulation technique that can improve the subject’s attention. In this research, the effectiveness of long-term NFT on the subject’s attention is explored. Real-time frontal lobe alpha band (8–13 Hz) power was used as feedback. The subjects were divided into the NFT group (n = 25) which received training and the control group (n = 25) which did not. All the subjects participated in the attention network task (ANT) at three stages i.e., before, mid, and at the end of NFT sessions. The EEG and behavioral data (Response time (RT) of the ANT task) were recorded for all subjects. The EEG data were pre-processed using a manual artifact removal procedure to avoid event-related information loss. Alpha band modulation can affect other bands such as theta (4–8 Hz) and beta (13–30 Hz) hence event-related functional connectivity (FC) and band power (BP) were analyzed in these bands as well. In event-related analysis, a significant increase (ANOVA & T-Test: P < 0.05) in theta and alpha power and FC within the NFT group was observed after NFT sessions whereas the changes observed within the control group were not significant. The RT of the subjects in the NFT group decreased. The increase in event-related power and connectivity within the theta and alpha band and the decrease in RT in the NFT group indicate the effectiveness of NFT sessions in the enhancement of attention.

ST-CapsNet: Linking Spatial and Temporal Attention with Capsule Network for P300 Detection Improvement.

A brain-computer interface (BCI), which provides an advanced direct human-machine interaction, has gained substantial research interest in the last decade for its great potential in various applications including rehabilitation and communication. Among them, the P300-based BCI speller is a typical application that is capable of identifying the expected stimulated characters. However, the applicability of the P300 speller is hampered for the low recognition rate partially attributed to the complex spatio-temporal characteristics of the EEG signals. Here, we developed a deep-learning analysis framework named ST-CapsNet to overcome the challenges regarding better P300 detection using a capsule network with both spatial and temporal attention modules. Specifically, we first employed spatial and temporal attention modules to obtain refined EEG signals by capturing event-related information. Then the obtained signals were fed into the capsule network for discriminative feature extraction and P300 detection. In order to quantitatively assess the performance of the proposed ST-CapsNet, two publicly-available datasets (i.e., Dataset IIb of BCI Competition 2003 and Dataset II of BCI Competition III) were applied. A new metric of averaged symbols under repetitions (ASUR) was adopted to evaluate the cumulative effect of symbol recognition under different repetitions. In comparison with several widely-used methods (i.e., LDA, ERP-CapsNet, CNN, MCNN, SWFP, and MsCNN-TL-ESVM), the proposed ST-CapsNet framework significantly outperformed the state-of-the-art methods in terms of ASUR. More interestingly, the absolute values of the spatial filters learned by ST-CapsNet are higher in the parietal lobe and occipital region, which is consistent with the generation mechanism of P300.

Extraction of Event-Related Information from Text for the Representation of Cultural Heritage

In knowledge representation systems for Cultural Heritage (CH) there is a vast amount of curated textual information for CH objects and sites. However, the large-scale study of the accumulated knowledge is difficult as long as it is provided in the form of free text. By extracting the most significant pieces of information from textual descriptions of CH objects and sites and compiling them in a single comprehensive knowledge graph, conforming to a standard would facilitate its exploitation from multiple perspectives including study, presentation and narratives. The method proposed by this research work was to employ Natural Language Processing, and reinforcement learning for semantic knowledge extraction, and a knowledge representation standard of the CH domain for the knowledge graph thus making the extracted knowledge directly compatible with linked open data platforms and CH representation systems.

EVENT GRAPH CONSTRUCTION METHOD ON NATURAL DISASTER RESEARCH

Abstract. The analysis of natural hazards is still lacking in real-time and comprehensiveness. To enhance the intelligent analysis of disasters, reduce natural disasters, and provide more timely and accurate disaster warnings. The method of constructing a natural disaster research event graph using the abstracts of research literature related to natural disaster analysis as the data source is proposed. First, a rule-based matching algorithm combined with syntactic analysis is used to extract the events of the disaster analysis process and spatio-temporal information from the abstract; Then build an event storage model based on Neo4j to store the event chain and related information; Then use Word2vec to convert events into word vectors, and define the event similarity as a linear sum of the object similarity and predicate similarity of the two events, combine event similarity setting equation to calculate core nodes and perform event generalization and fusion of graphs. Based on the above method, soil erosion is used as an example to construct an event graph and provide the basis for decision making services for intelligent analysis of natural disasters.

Understanding the interplay between event communications and local business decision-making using signalling theory: the case of the 2018 Commonwealth Games

ABSTRACT Research question Governments often launch high profile and expensive bids to host large-scale sporting events to promote economic development. However, many studies have called into question the economic benefits of these events for host communities and businesses. This study examines the perceptions of host city businesses and their decision-making in relation to event leveraging, using the 2018 Gold Coast Commonwealth Games as a case study. Research methods Using in-depth interviews, a purposive study of 38 business professionals and experts based in the host city of the Gold Coast were conducted. Example mass media, social media, documents, and personal communications materials are presented to illustrate the messaging that businesses were exposed to. Data were analysed using an inductive thematic content approach through the software NVivo. Results and findings This study confirms that event-related information is vital for business leveraging behaviour. The data illustrated that local firms were at a point of information asymmetry between official forecasted information and their actual in-time experience. As businesses sought out information to inform their business decision-making, they found incomplete and inaccurate information which impacted their success in event leveraging decisions. Implications Our contribution to knowledge and practice is to propose that event mis/information that is often associated with large-scale sporting events is problematic for business planning and decision making. In applying signalling theory, we discern how official event messaging influences business performance, making an important contribution by highlighting that this mismanagement of official event messaging by event officials presents a critical inhibitor for event leveraging decisions.

Event temporal relation extraction with attention mechanism and graph neural network

Event temporal relation extraction is an important part of natural language processing. Many models are being used in this task with the development of deep learning. However, most of the existing methods cannot accurately obtain the degree of association between different tokens and events, and event-related information cannot be effectively integrated. In this paper, we propose an event information integration model that integrates event information through multilayer bidirectional long short-term memory (Bi-LSTM) and attention mechanism. Although the above scheme can improve the extraction performance, it can still be further optimized. To further improve the performance of the previous scheme, we propose a novel relational graph attention network that incorporates edge attributes. In this approach, we first build a semantic dependency graph through dependency parsing, model a semantic graph that considers the edges' attributes by using top-k attention mechanisms to learn hidden semantic contextual representations, and finally predict event temporal relations. We evaluate proposed models on the TimeBank-Dense dataset. Compared to previous baselines, the Micro-F1 scores obtained by our models improve by 3.9% and 14.5%, respectively.

Unique roles for the anterior and posterior retrosplenial cortices in encoding and retrieval of memory for context.

The rat retrosplenial cortex (RSC) makes critical contributions to learning and memory but these contributions may not be uniform along its rostro-caudal axis. Previous work suggests that event-related and context-related information are differentially encoded by anterior and posterior RSC subregions. Here, we further test this idea using a procedure in which spatial/environmental cues (context) and discrete event memories are acquired separately. All animals received a 5-min pre-exposure to the training context 24 h before contextual fear conditioning where shock was delivered immediately upon being placed in the chamber. Rats were tested for memory for the context the next day. We found that optogenetic inhibition of cells in only the posterior RSC during the pre-exposure phase, when spatial information is encoded, reduced behavioral responding during the subsequent memory test. However, similar inhibition of either the anterior or posterior RSC during shock delivery, when information about both the context and the shock become integrated, impaired memory. Finally, inhibiting cellular activity in only the posterior RSC during memory retrieval during testing reduced responding. Together, these results suggest that while activity in both subregions is needed during the period in which the event-related information becomes integrated with the context representation, the posterior RSC is important for both memory formation and retrieval or expression of memory for information about the context. These results add to a growing literature demonstrating a role for the RSC in integration of multiple aspects of memory, and provide information on how spatial representations reliant on the retrosplenial cortex interact with associative learning.

Second-victim experience and support among nurses in mainland China.

To investigate the experience and support of nurses as second victims in adverse events and explore factors. Adverse events have significant negative influences on healthcare professionals. However, there is still a lack of research on the impacts of individual and event factors. A cross-sectional survey via a self-report electronic questionnaire was sent to approximately 6400 nurses from six tertiary comprehensive hospitals in mainland China. The socio-demographic, adverse event-related information and second-victim experience and support questionnaires were used. Approximately 2897 (45.26%) of nurses were involved in at least one adverse event that mainly caused psychological distress. Male nurses, Grade I adverse events, public reports and discussions of adverse events were related to the second-victim reaction. Discussing with a respected colleague was the most strongly desired form of support. Chinese nurses have reported a high prevalence of psychological reactions. Follow-up studies must consider other potential factors. Nursing managers should consider the factors that affect second victims and provide support based on the expectations of nurses. Psychological first-aid and immediate and mid- to long-term support strategies should be provided to help the second victim alleviate distress.

Comparing Two Distinct Miliary Samples on Traumatic Events, Positive Coping Styles and Post Traumatic Growth

This study collected complete data traumatic event-related information, positive coping styles, and post traumatic growth variables from two different United States (US) military veteran samples: non-combat military veterans (n = 54) and combat military veterans (n = 84). Although both samples represent military veterans, only one sample experienced actual combat, i.e., active fighting in a war against an enemy. All data were collected via online survey. Demographically, both samples were predominately White male, with a four-year college degree being the highest education level frequency. The average participant age was 29 years and there was no significant mean age difference between the samples. In addition, there were no significant sample differences in the total number of traumatic events experienced or time since the most powerful traumatic event was experienced. The purposes of this study were to: (1) test if four positive coping strategies were related to Post Traumatic Growh (PTG), and (2) to determine if there were differences in the use of these four coping strategies or experienced PTG for non-combat versus combat military veterans. The four positive coping styles were measured, instrumental support, emotional support, religion, and acceptance. For the combined sample, two coping styles, instrumental support and religion were each significant positively related to post traumatic growth (PTG). Significant sample differences were found on instrumental support and religion such that the non-combat veterans perceived higher mean levels on both coping styles versus the combat veterans. No sample difference was found for PTG. Future research directions and study limitations are discussed.

The potential of data driven approaches for quantifying hydrological extremes

Recent droughts in Europe have shown that national water systems are facing increasing challenges when dealing with drought impacts. Especially the Netherlands has seen an increasing need to adapt their water management to improve preparedness for future drought events. Ideally, the necessary information needed for operational water management decisions should be readily available ahead of time and/or computed flexibly and efficiently to ensure sufficient time to evaluate the various management actions. In this study, we show that in addition to physically based hydrological models, the upcoming and promising trend of incorporating machine learning (ML) in hydrology can provide the basis for future efforts in supporting national operational water management by providing the needed information efficiently and with the required accuracy. As a precursor for their use in a forecasting system, we assessed the ability of five different data driven methods to simulate hydrological variables at a national-scale. We developed a unified workflow where we use limited information on hydro-meteorological variables and general water management policies to simulate historic timeseries of discharge, groundwater levels, surface water levels and surface water temperatures. We find that all ML methods, ranging from very simple to more complex ones, showed a generally good performance for stations and target levels which are closely linked to the input data and location (e.g. stations along main river network). For downstream stations and small rivers, the Random Forest method outperforms the other methods both for discharge and surface water levels. For surface water temperature no location dependency was observed and for groundwater levels, all methods were performing comparable with most stations ranging in nRMSE 0.2-0.3. Generally, the best performances were reached by the more advanced Random Forest and LSTM methods, which was also seen when simulating high and low flow events. High flow events were slightly better captured than low flow events but overall simulating extreme events based on a simple input data set remains challenging. Specific training sets, including event related information and additional input variables, could like improve future assessments. Including the feature importance of the methods allowed us to detect how and where water management influence played an important role. The addition of information on water management in the ML routines increases overall performance, although limited. We conclude that ML and other data driven approaches have potential in predicting different hydrological variables. We were able to capture and incorporate water management aspects in our analysis, creating a base for future experiments where scenario analysis might reveal ML based mitigation strategies. The combination of limited input data requirement and short computation times makes this new framework suitable for forecasting purposes.

Event-Based Clustering for Reducing Labeling Costs of Event-related Microposts

Automatically identifying the event type of event-related information in the sheer amount of social media data makes machine learning inevitable. However, this is highly dependent on (1) the number of correctly labeled instances and (2) labeling costs. Active learning has been proposed to reduce the number of instances to label. Albeit the thematic dimension is already used, other metadata such as spatial and temporal information that is helpful for achieving a more fine-grained clustering is currently not taken into account. In this paper, we present a novel event-based clustering strategy that makes use of temporal, spatial, and thematic metadata to determine instances to label. An evaluation on incident-related tweets shows that our selection strategy for active learning outperforms current state-of-the-art approaches even with few labeled instances.

Cross-Context News Corpus for Protest Event-Related Knowledge Base Construction

Abstract We describe a gold standard corpus of protest events that comprise various local and international English language sources from various countries. The corpus contains document-, sentence-, and token-level annotations. This corpus facilitates creating machine learning models that automatically classify news articles and extract protest event-related information, constructing knowledge bases that enable comparative social and political science studies. For each news source, the annotation starts with random samples of news articles and continues with samples drawn using active learning. Each batch of samples is annotated by two social and political scientists, adjudicated by an annotation supervisor, and improved by identifying annotation errors semi-automatically. We found that the corpus possesses the variety and quality that are necessary to develop and benchmark text classification and event extraction systems in a cross-context setting, contributing to the generalizability and robustness of automated text processing systems. This corpus and the reported results will establish a common foundation in automated protest event collection studies, which is currently lacking in the literature.

Differential encoding of place value between the dorsal and intermediate hippocampus

It is widely held that the hippocampus provides a cognitive map in which event-related information, such as an object, location, and their significance, is organized. However, how an organism's motivational experience is coded in the hippocampus is mostly unknown. Here, we investigated whether dorsal and intermediate regions of the hippocampus are differentially involved in representing changes in the motivational significance of a place. Rats were run in tasks in which various rewards with different degrees of palatability were associated with the same locations while single units were simultaneously recorded along the dorsoventral axis of the hippocampus. Place cells in the intermediate hippocampus remapped immediately after motivational significance decreased and shifted their fields dynamically toward high-value locations. In contrast, place cells in the dorsal hippocampus were mostly unresponsive to the same manipulations. Our findings suggest that the motivational significance of place is uniquely coded in the intermediate hippocampus in goal-directed tasks.

Modeling Collective Anticipation and Response on Wikipedia

The dynamics of popularity in online media are driven by a combination of endogenous spreading mechanisms and response to exogenous shocks including news and events. However, little is known about the dependence of temporal patterns of popularity on event-related information, e.g. which types of events trigger long-lasting activity. Here we propose a simple model that describes the dynamics around peaks of popularity by incorporating key features, i.e., the anticipatory growth and the decay of collective attention together with circadian rhythms. The proposed model allows us to develop a new method for predicting the future page view activity and for clustering time series. To validate our methodology, we collect a corpus of page view data from Wikipedia associated to a range of planned events, that are events which we know in advance will have a fixed date in the future, such as elections and sport events. Our methodology is superior to existing models in both prediction and clustering tasks. Furthermore, restricting to Wikipedia pages associated to association football, we observe that the specific realization of the event, in our case which team wins a match or the type of the match, has a significant effect on the response dynamics after the event. Our work demonstrates the importance of appropriately modeling all phases of collective attention, as well as the connection between temporal patterns of attention and characteristic underlying information of the events they represent.

Detecting Urban Events by Considering Long Temporal Dependency of Sentiment Strength in Geotagged Social Media Data

The development of location-based services facilitates the use of location data for detecting urban events. Currently, most studies based on location data model the pattern of an urban dynamic and then extract the anomalies, which deviate significantly from the pattern as urban events. However, few studies have considered the long temporal dependency of sentiment strength in geotagged social media data, and thus it is difficult to further improve the reliability of detection results. In this paper, we combined a sentiment analysis method and long short-term memory neural network for detecting urban events with geotagged social media data. We first applied a dictionary-based method to evaluate the positive and negative sentiment strength. Based on long short-term memory neural network, the long temporal dependency of sentiment strength in geotagged social media data was constructed. By considering the long temporal dependency, daily positive and negative sentiment strength are predicted. We extracted anomalies that deviated significantly from the prediction as urban events. For each event, event-related information was obtained by analyzing social media texts. Our results indicate that the proposed approach is a cost-effective way to detect urban events, such as festivals, COVID-19-related events and traffic jams. In addition, compared to existing methods, we found that accounting for a long temporal dependency of sentiment strength can significantly improve the reliability of event detection.

ED-SWE: Event detection based on scoring and word embedding in online social networks for the internet of people

Online social media networks are gaining attention worldwide, with an increasing number of people relying on them to connect, communicate and share their daily pertinent event-related information. Event detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of People. In this paper, a novel Event Detection model based on Scoring and Word Embedding (ED-SWE) is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k tweets. The proposed ED-SWE model can distill high-quality tweets, reduce the negative impact of the advent of spam, and identify latent events in the data streams automatically. Moreover, a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter data. In order to further improve the performance of the Expectation-Maximization (EM) iteration algorithm, a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and precisely. Finally, a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live event. Experiments conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models.