scholarly journals News Monitor: A Framework for Exploring News in Real-Time

Data ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 3
Author(s):  
Nikolaos Panagiotou ◽  
Antonia Saravanou ◽  
Dimitrios Gunopulos
Keyword(s):  
Knowledge Base ◽  
Real Time ◽  
State Of The Art ◽  
Online News ◽  
Fake News ◽  
Analysis Framework ◽  
Detection Techniques ◽  
News Analysis ◽  
Open Information Extraction ◽  
Source Of Information

News articles generated by online media are a major source of information. In this work, we present News Monitor, a framework that automatically collects news articles from a wide variety of online news portals and performs various analysis tasks. The framework initially identifies fresh news (first stories) and clusters articles about the same incidents. For every story, at first, it extracts all of the corresponding triples and, then, it creates a knowledge base (KB) using open information extraction techniques. This knowledge base is then used to create a summary for the user. News Monitor allows for the users to use it as a search engine, ask their questions in their natural language and receive answers that have been created by the state-of-the-art framework BERT. In addition, News Monitor crawls the Twitter stream using a dynamic set of “trending” keywords in order to retrieve all messages relevant to the news. The framework is distributed, online and performs analysis in real-time. According to the evaluation results, the fake news detection techniques utilized by News Monitor allow for a F-measure of 82% in the rumor identification task and an accuracy of 92% in the stance detection tasks. The major contribution of this work can be summarized as a novel real-time and scalable architecture that combines various effective techniques under a news analysis framework.

Cross-lingual Named Entity Recognition

2007 ◽  
Vol 30 (1) ◽  
pp. 135-162 ◽  
Author(s):  
Ralf Steinberger ◽  
Bruno Pouliquen
Keyword(s):  
State Of The Art ◽  
Named Entity Recognition ◽  
Online News ◽  
Entity Recognition ◽  
Minimal Amount ◽  
Document Similarity ◽  
News Analysis ◽  
Named Entity ◽  
Analysis Application ◽  
Cross Lingual

Named Entity Recognition and Classification (NERC) is a known and well-explored text analysis application that has been applied to various languages. We are presenting an automatic, highly multilingual news analysis system that fully integrates NERC for locations, persons and organisations with document clustering, multi-label categorisation, name attribute extraction, name variant merging and the calculation of social networks. The proposed application goes beyond the state-of-the-art by automatically merging the information found in news written in ten different languages, and by using the aggregated name information to automatically link related news documents across languages for all 45 language pair combinations. While state-of-the-art approaches for cross-lingual name variant merging and document similarity calculation require bilingual resources, the methods proposed here are mostly language-independent and require a minimal amount of monolingual language-specific effort. The development of resources for additional languages is therefore kept to a minimum and new languages can be plugged into the system effortlessly. The presented online news analysis application is fully functional and has, at the end of the year 2006, reached average usage statistics of 600,000 hits per day.

scholarly journals Event identification in social networks

2017 ◽  
Vol 01 (01) ◽  
pp. 1630002 ◽  
Author(s):  
Fattane Zarrinkalam ◽  
Ebrahim Bagheri
Keyword(s):  
Social Networks ◽  
Event Detection ◽  
State Of The Art ◽  
Recent Event ◽  
Event Identification ◽  
Detection Techniques ◽  
The Social ◽  
Structured Documents ◽  
Source Of Information ◽  
Emerging Topics

Social networks enable users to freely communicate with each other and share their recent news, ongoing activities or views about different topics. As a result, they can be seen as a potentially viable source of information to understand the current emerging topics/events. The ability to model emerging topics is a substantial step to monitor and summarize the information originating from social sources. Applying traditional methods for event detection which are often proposed for processing large, formal and structured documents, are less effective, due to the short length, noisiness and informality of the social posts. Recent event detection techniques address these challenges by exploiting the opportunities behind abundant information available in social networks. This article provides an overview of the state of the art in event detection from social networks.

A new method for QRS detection in ECG signals using QRS-preserving filtering techniques

2018 ◽  
Vol 63 (2) ◽  
pp. 207-217 ◽  
Author(s):  
Tanushree Sharma ◽  
Kamalesh K. Sharma
Keyword(s):  
Real Time ◽  
Wavelet Transforms ◽  
State Of The Art ◽  
Qrs Detection ◽  
Detection Techniques ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Improved Performance ◽  
Better Than

AbstractDetection of QRS complexes in ECG signals is required for various purposes such as determination of heart rate, feature extraction and classification. The problem of automatic QRS detection in ECG signals is complicated by the presence of noise spectrally overlapping with the QRS frequency range. As a solution to this problem, we propose the use of least-squares-optimisation-based smoothing techniques that suppress the noise peaks in the ECG while preserving the QRS complexes. We also propose a novel nonlinear transformation technique that is applied after the smoothing operations, which equalises the QRS amplitudes without boosting the supressed noise peaks. After these preprocessing operations, the R-peaks can finally be detected with high accuracy. The proposed technique has a low computational load and, therefore, it can be used for real-time QRS detection in a wearable device such as a Holter monitor or for fast offline QRS detection. The offline and real-time versions of the proposed technique have been evaluated on the standard MIT-BIH database. The offline implementation is found to perform better than state-of-the-art techniques based on wavelet transforms, empirical mode decomposition, etc. and the real-time implementation also shows improved performance over existing real-time QRS detection techniques.

Current Issues: Advanced Digital Technology for Supervising Graduate Clinicians

2010 ◽  
Vol 20 (1) ◽  
pp. 9-13 ◽  
Author(s):  
Glenn Tellis ◽  
Lori Cimino ◽  
Jennifer Alberti
Keyword(s):  
Real Time ◽  
Digital Technology ◽  
Clinical Training ◽  
State Of The Art ◽  
Clinical Skills ◽  
Slow Motion ◽  
Video Capture ◽  
Microsoft Excel ◽  
Clinical Supervisors ◽  
Training And Supervision

Abstract The purpose of this article is to provide clinical supervisors with information pertaining to state-of-the-art clinic observation technology. We use a novel video-capture technology, the Landro Play Analyzer, to supervise clinical sessions as well as to train students to improve their clinical skills. We can observe four clinical sessions simultaneously from a central observation center. In addition, speech samples can be analyzed in real-time; saved on a CD, DVD, or flash/jump drive; viewed in slow motion; paused; and analyzed with Microsoft Excel. Procedures for applying the technology for clinical training and supervision will be discussed.

Real-time error detection techniques based on FPGA

2013 ◽  
Vol 33 (5) ◽  
pp. 1459-1462
Author(s):  
Xiaoming JU ◽  
Jiehao ZHANG ◽  
Yizhong ZHANG
Keyword(s):  
Real Time ◽  
Error Detection ◽  
Time Error ◽  
Detection Techniques

An expert system for real-time well field management

2012 ◽  
Vol 12 (5) ◽  
pp. 699-706 ◽  
Author(s):  
B. S. Marti ◽  
G. Bauser ◽  
F. Stauffer ◽  
U. Kuhlmann ◽  
H.-P. Kaiser ◽  
...  
Keyword(s):  
Drinking Water ◽  
Expert System ◽  
Knowledge Base ◽  
Real Time ◽  
Urban Areas ◽  
Industrial Sites ◽  
Water Wells ◽  
Well Field ◽  
Field Management ◽  
Historical Management

Well field management in urban areas faces challenges such as pollution from old waste deposits and former industrial sites, pollution from chemical accidents along transport lines or in industry, or diffuse pollution from leaking sewers. One possibility to protect the drinking water of a well field is the maintenance of a hydraulic barrier between the potentially polluted and the clean water. An example is the Hardhof well field in Zurich, Switzerland. This paper presents the methodology for a simple and fast expert system (ES), applies it to the Hardhof well field, and compares its performance to the historical management method of the Hardhof well field. Although the ES is quite simplistic it considerably improves the water quality in the drinking water wells. The ES knowledge base is crucial for successful management application. Therefore, a periodic update of the knowledge base is suggested for the real-time application of the ES.

Browser Security Attacks and Detection Techniques: A Case of Tabnabbing

2020 ◽  
Vol 8 (1) ◽  
pp. 33-41
Author(s):  
Dr. S. Sarika ◽  
Keyword(s):  
Credit Card ◽  
State Of The Art ◽  
Experimental Results ◽  
Detection Technique ◽  
Security Attacks ◽  
Agent Based ◽  
Detection Techniques ◽  
Browser Security ◽  
Cyber Threats ◽  
Multi Agent

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.

Putting up barriers

2017 ◽  
Author(s):  
David J. Lobina
Keyword(s):  
Knowledge Base ◽  
Real Time ◽  
Language Processing ◽  
Cognitive Domain ◽  
Linguistic Knowledge ◽  
Levels Of Explanation ◽  
Real Time Processing ◽  
Time Processing ◽  
Neural Underpinnings ◽  
Different Levels

The study of cognitive phenomena is best approached in an orderly manner. It must begin with an analysis of the function in intension at the heart of any cognitive domain (its knowledge base), then proceed to the manner in which such knowledge is put into use in real-time processing, concluding with a domain’s neural underpinnings, its development in ontogeny, etc. Such an approach to the study of cognition involves the adoption of different levels of explanation/description, as prescribed by David Marr and many others, each level requiring its own methodology and supplying its own data to be accounted for. The study of recursion in cognition is badly in need of a systematic and well-ordered approach, and this chapter lays out the blueprint to be followed in the book by focusing on a strict separation between how this notion applies in linguistic knowledge and how it manifests itself in language processing.

Hybrid Approach and Architecture to Detect Fake News on Twitter in Real-Time using Neural Networks

2020 ◽  
Author(s):  
Madusha Prasanjith Thilakarathna ◽  
Vihanga Ashinsana Wijayasekara ◽  
Yasiru Gamage ◽  
Kavindi Hanshani Peiris ◽  
Chanuka Abeysinghe ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Hybrid Approach ◽  
Fake News

scholarly journals Real-Time PCR as an Alternative Technique for Detection of Dermatophytes in Cattle Herds

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1662
Author(s):  
Dominik Łagowski ◽  
Sebastian Gnat ◽  
Aneta Nowakiewicz ◽  
Aleksandra Trościańczyk
Keyword(s):  
Light Microscopy ◽  
Real Time ◽  
Real Time Pcr ◽  
Direct Analysis ◽  
Carrier Status ◽  
The Real ◽  
Detection Techniques ◽  
Direct Light ◽  
Microscopy Analysis ◽  
Cattle Herds

Dermatophytes are filamentous fungi with the ability to digest and grow on keratinized substrates. The ongoing improvements in fungal detection techniques give new scope for clinical implementations in laboratories and veterinary clinics, including the monitoring of the disease and carrier status. The technologically advanced methods for dermatophyte detection include molecular methods based on PCR. In this context, the aim of this study was to carry out tests on the occurrence of dermatophytes in cattle herds using qPCR methods and a comparative analysis with conventional methods. Each sample collected from ringworm cases and from asymptomatic cattle was divided into three parts and subjected to the real-time PCR technique, direct light microscopy analysis, and culture-based methods. The use of the real-time PCR technique with pan-dermatophyte primers detected the presence of dermatophytes in the sample with a 10.84% (45% vs. 34.17%) higher efficiency than direct analysis with light microscopy. Moreover, a dermatophyte culture was obtained from all samples with a positive qPCR result. In conclusion, it seems that this method can be used with success to detect dermatophytes and monitor cowsheds in ringworm cases and carriers in cattle.

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