scholarly journals Unified approach to retrospective event detection for event- based epidemic intelligence

2021 ◽  
Author(s):  
Marco Fisichella
Keyword(s):  
Event Detection ◽  
Unified Approach ◽  
Epidemic Intelligence ◽  
Event Based

The Korean 3T practice: A New Biosurveillance Model utilizing New IT and Digital Tools (Preprint)

2021 ◽  
Author(s):  
HyunJung Kim
Keyword(s):  
Population Control ◽  
Digital Tools ◽  
Big Brother ◽  
Self Help ◽  
Ex Post ◽  
The Social ◽  
Epidemic Intelligence ◽  
Pandemic Response ◽  
The Government ◽  
Event Based

UNSTRUCTURED South Korea COVID-19 pandemic responses, namely the 3T (testing, tracing, and treating) strategy, come to the fore as a new biosurveillance regime utilizing new IT and digital tools actively. The 3T biosurveillance system is a developed version of the traditional biosurveillance systems (indicator-based or event-based systems), which can provide epidemic intelligence capabilities for both ex ante prevention/preparedness or ex post response/recovery missions. Epidemiological investigation efforts exploiting the use of new digital and IT tools are the ground of the Korean 3T system practicing test, trace, and treatment mission, which can be referred to as ‘contact-based biosurveillance system.’ However, critics argue that the Korea’s 3T strategy may violate individuals’ privacy and human rights in addressing that the Korean biosurveillance system would strengthen the social surveillance and population control by the government as a “digital big brother” in the cyber age. However, closer scrutiny reveals that the Korea’s digital-based biosurveillance system for pandemic response has evolved since the experience of the 2015 Middle East Respiratory Syndrome (MERS) outbreak, by citizen’s requests and self-help behaviors

scholarly journals A Unified Approach to Online Matching with Conflict-Aware Constraints

2019 ◽  
Vol 33 ◽  
pp. 2221-2228
Author(s):  
Pan Xu ◽  
Yexuan Shi ◽  
Hao Cheng ◽  
John Dickerson ◽  
Karthik Abinav Sankararaman ◽  
...  
Keyword(s):  
Online Algorithm ◽  
Broad Class ◽  
A Priori ◽  
Algorithm Design ◽  
Unified Approach ◽  
Bipartite Matching ◽  
Theoretical Predictions ◽  
Online Labor ◽  
Synthetic Datasets ◽  
Event Based

Online bipartite matching and allocation models are widely used to analyze and design markets such as Internet advertising, online labor, and crowdsourcing. Traditionally, vertices on one side of the market are fixed and known a priori, while vertices on the other side arrive online and are matched by a central agent to the offline side. The issue of possible conflicts among offline agents emerges in various real scenarios when we need to match each online agent with a set of offline agents.For example, in event-based social networks (e.g., Meetup), offline events conflict for some users since they will be unable to attend mutually-distant events at proximate times; in advertising markets, two competing firms may prefer not to be shown to one user simultaneously; and in online recommendation systems (e.g., Amazon Books), books of the same type “conflict” with each other in some sense due to the diversity requirement for each online buyer.The conflict nature inherent among certain offline agents raises significant challenges in both modeling and online algorithm design. In this paper, we propose a unifying model, generalizing the conflict models proposed in (She et al., TKDE 2016) and (Chen et al., TKDE 16). Our model can capture not only a broad class of conflict constraints on the offline side (which is even allowed to be sensitive to each online agent), but also allows a general arrival pattern for the online side (which is allowed to change over the online phase). We propose an efficient linear programming (LP) based online algorithm and prove theoretically that it has nearly-optimal online performance. Additionally, we propose two LP-based heuristics and test them against two natural baselines on both real and synthetic datasets. Our LP-based heuristics experimentally dominate the baseline algorithms, aligning with our theoretical predictions and supporting our unified approach.

scholarly journals Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks

2021 ◽  
Vol 15 (5) ◽  
pp. 1-33
Author(s):  
Hao Peng ◽  
Jianxin Li ◽  
Yangqiu Song ◽  
Renyu Yang ◽  
Rajiv Ranjan ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
Event Detection ◽  
Social Event ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Social Events ◽  
Meta Path ◽  
Social Event Detection ◽  
Event Based

Events are happening in real world and real time, which can be planned and organized for occasions, such as social gatherings, festival celebrations, influential meetings, or sports activities. Social media platforms generate a lot of real-time text information regarding public events with different topics. However, mining social events is challenging because events typically exhibit heterogeneous texture and metadata are often ambiguous. In this article, we first design a novel event-based meta-schema to characterize the semantic relatedness of social events and then build an event-based heterogeneous information network (HIN) integrating information from external knowledge base. Second, we propose a novel Pairwise Popularity Graph Convolutional Network, named as PP-GCN, based on weighted meta-path instance similarity and textual semantic representation as inputs, to perform fine-grained social event categorization and learn the optimal weights of meta-paths in different tasks. Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method. Comprehensive experiments on real-world streaming social text data are conducted to compare various social event detection and evolution discovery algorithms. Experimental results demonstrate that our proposed framework outperforms other alternative social event detection and evolution discovery techniques.

scholarly journals Cluster-discovery of twitter messages for event detection and trending.

2021 ◽  
Author(s):  
Shakira Banu Kaleel
Keyword(s):  
Event Detection ◽  
Large Scale ◽  
Feature Vector ◽  
Research Work ◽  
Locality Sensitive Hashing ◽  
Social Media Data ◽  
Speed Up ◽  
Event Based ◽  
Media Data ◽  
Cluster Quality

Social media data carries abundant hidden occurrences of real-time events in the world which raises the demand for efficient event detection and trending system. The Locality Sensitive Hashing (LSH) technique is capable of processing the large-scale big datasets. In this thesis, a novel framework is proposed for detecting and trending events from tweet clusters presence in Twitter1 dataset that are discovered using LSH. The experimental results obtained from this research work showed that the LSH technique took only 12.99% of the running time compared to that required for K-means to find all of the tweet clusters. Key challenges include: 1) construction of dictionary using incremental TF-IDF in high-dimensional data in order to create tweet feature vector 2) leveraging LSH to find truly interesting events 3) trending the behavior of event based on time, geo-locations and cluster size and 4) speed-up the cluster-discovery process while retaining the cluster quality.

scholarly journals On Event/Time Triggered and Distributed Analysis of a WSN System for Event Detection, Using Fuzzy Logic

2016 ◽  
Vol 2016 ◽  
pp. 1-28 ◽  
Author(s):  
Sofia Maria Dima ◽  
Christos Antonopoulos ◽  
Stavros Koubias
Keyword(s):  
Fuzzy Logic ◽  
Event Detection ◽  
Performance Metrics ◽  
Fuzzy Inference ◽  
Fire Detection ◽  
Inference System ◽  
Network Nodes ◽  
Event Time ◽  
Wireless Environments ◽  
Event Based

Event detection in realistic WSN environments is a critical research domain, while the environmental monitoring comprises one of its most pronounced applications. Although efforts related to the environmental applications have been presented in the current literature, there is a significant lack of investigation on the performance of such systems, when applied in wireless environments. Aiming at addressing this shortage, in this paper an advanced multimodal approach is followed based on fuzzy logic. The proposed fuzzy inference system (FIS) is implemented on TelosB motes and evaluates the probability of fire detection while aiming towards power conservation. Additionally to a straightforward centralized approach, a distributed implementation of the above FIS is also proposed, aiming towards network congestion reduction while optimally distributing the energy consumption among network nodes so as to maximize network lifetime. Moreover this work proposes an event based execution of the aforementioned FIS aiming to further reduce the computational as well as the communication cost, compared to a periodical time triggered FIS execution. As a final contribution, performance metrics acquired from all the proposed FIS implementation techniques are thoroughly compared and analyzed with respect to critical network conditions aiming to offer realistic evaluation and thus objective conclusions’ extraction.

Extracting Criminal-Related Events from Arabic Tweets

2017 ◽  
Vol 10 (3) ◽  
pp. 34-47
Author(s):  
Feriel Abdelkoui ◽  
Mohamed-Khireddine Kholladi
Keyword(s):  
Social Networks ◽  
Natural Hazards ◽  
Real World ◽  
Event Detection ◽  
Temporal Information ◽  
Automated Extraction ◽  
Temporal Expressions ◽  
Spatio Temporal ◽  
Event Based ◽  
F Measure

Recently, Twitter as one of social networks has been considered as a rich source of spatio-temporal information and significant revenue for mining data. Event detection from tweets can help to predict more serious real-world events. Such as: criminal events, natural hazards, and the spread of epidemics. Etc. This paper deals with event-based extraction for criminal incidents from Arabic tweets. It presents a framework that supports automated extraction of spatial and temporal information from tweets. The proposed approach is based on combining various indicators, including the names of places and temporal expressions that appear in the tweet message, related tweeting time, and additional locations from the user's profile. The effectiveness of the system was evaluated in term of recall, precision and f-measure.

scholarly journals CASIE: Extracting Cybersecurity Event Information from Text

2020 ◽  
Vol 34 (05) ◽  
pp. 8749-8757 ◽  
Author(s):  
Taneeya Satyapanich ◽  
Francis Ferraro ◽  
Tim Finin
Keyword(s):  
Neural Networks ◽  
Event Detection ◽  
Deep Neural Networks ◽  
Knowledge Graph ◽  
Semantic Model ◽  
Word Embeddings ◽  
Linguistic Features ◽  
Semantic Roles ◽  
Event Information ◽  
Event Based

We present CASIE, a system that extracts information about cybersecurity events from text and populates a semantic model, with the ultimate goal of integration into a knowledge graph of cybersecurity data. It was trained on a new corpus of 1,000 English news articles from 2017–2019 that are labeled with rich, event-based annotations and that covers both cyberattack and vulnerability-related events. Our model defines five event subtypes along with their semantic roles and 20 event-relevant argument types (e.g., file, device, software, money). CASIE uses different deep neural networks approaches with attention and can incorporate rich linguistic features and word embeddings. We have conducted experiments on each component in the event detection pipeline and the results show that each subsystem performs well.

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