Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features

Satirical news is considered to be entertainment, but it is potentially deceptive and harmful. Despite the embedded genre in the article, not everyone can recognize the satirical cues and therefore believe the news as true news. We observe that satirical cues are often reflected in certain paragraphs rather than the whole document. Existing works only consider document-level features to detect the satire, which could be limited. We consider paragraph-level linguistic features to unveil the satire by incorporating neural network and attention mechanism. We investigate the difference between paragraph-level features and document-level features, and analyze them on a large satirical news dataset. The evaluation shows that the proposed model detects satirical news effectively and reveals what features are important at which level.Comment: EMNLP 2017, 11 page

A Markovian approach to the mathematical control of NPD projects

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Spectral filtering as a method of visualising and removing striped artefacts in digital elevation data

Spectral filtering was compared with traditional mean spatial filters to assess their ability to identify and remove striped artefacts in digital elevation data. The techniques were applied to two datasets: a 100 m contour derived digital elevation model (DEM) of southern Norway and a 2 m LiDAR DSM of the Lake District, UK. Both datasets contained diagonal data artefacts that were found to propagate into subsequent terrain analysis. Spectral filtering used fast Fourier transformation (FFT) frequency data to identify these data artefacts in both datasets. These were removed from the data by applying a cut filter, prior to the inverse transform. Spectral filtering showed considerable advantages over mean spatial filters, when both the absolute and spatial distribution of elevation changes made were examined. Elevation changes from the spectral filtering were restricted to frequencies removed by the cut filter, were small in magnitude and consequently avoided any global smoothing. Spectral filtering was found to avoid the smoothing of kernel based data editing, and provided a more informative measure of data artefacts present in the FFT frequency domain. Artefacts were found to be heterogeneous through the surfaces, a result of their strong correlations with spatially autocorrelated variables: landcover and landsurface geometry. Spectral filtering performed better on the 100 m DEM, where signal and artefact were clearly distinguishable in the frequency data. Spectrally filtered digital elevation datasets were found to provide a superior and more precise representation of the landsurface and be a more appropriate dataset for any subsequent geomorphological applications

Mathematical modelling of a hierarchical framework for controlling NPD projects under a hard time constraint

Based on the hierarchical framework introduced in Dragut, Bertrand (2002) for the control of New Product Development (NPD) projects under a hard time constraint, we formulate mathematically the project control. The relationships with the well-known mathematical project models are discussed. The framework splits the project horizon into a number of review periods. At the start of each review period the project state is reviewed in order incorporate the new information about the customer needs, and about the progress the engineers made in working on design tasks. This leads to the addition/deletion of design tasks, and to a stochastic solving time of the design tasks. The paper contributes to the area of mathematical models for the organization of work in an NPD, and to the development of management-related control concepts in the NPD projects, both areas that present research opportunities according to Brown, Eisenhardt (1995)

Mathematical modelling of a hierarchical framework for controlling NPD projects under a hard time constraint

Based on the hierarchical framework introduced in Dragut, Bertrand (2002) for the control of New Product Development (NPD) projects under a hard time constraint, we formulate mathematically the project control. The relationships with the well-known mathematical project models are discussed. The framework splits the project horizon into a number of review periods. At the start of each review period the project state is reviewed in order incorporate the new information about the customer needs, and about the progress the engineers made in working on design tasks. This leads to the addition/deletion of design tasks, and to a stochastic solving time of the design tasks. The paper contributes to the area of mathematical models for the organization of work in an NPD, and to the development of management-related control concepts in the NPD projects, both areas that present research opportunities according to Brown, Eisenhardt (1995)

TOURIST MАRKET IN THE CURRENT POST-PANDEMIC CONTEXT

The negаtive effects of the COVID -19 pаndemic on the tourist trаffic аnd the tourism sector hаve been felt globаlly, but to different extents, depending on the tourism regions аnd countries. In Romаniа, the decreаse recorded in tourist trаffic during the pаndemic period hаs been severe, but within the limits of the аverаge recorded in Europeаn Union Member Stаtes аnd with considerаbly different vаlues, depending on the cаtegories of tourist destinаtions in the country. The mаin chаllenge for Romаniа remаins cаrrying out а lаrge-scаle promotion cаmpаign on the foreign mаrkets relаted to its tourist offer, given the modest results obtаined so fаr in аttrаcting foreign tourists, despite the tourism potentiаl thаt it hаs. The post-COVID-19 period could represent а reset of tourism in Romаniа

Choosing the nutritional intervention to overweight and obese patients

Weight problems occur in 1.5 billion people and these are a risk factor for type 2 diabetes, cardiovascular, pulmonary and periodontal diseases, cancer and osteoporosis. Our study aimed to evaluate the caloric intake, vitamins and minerals from food before a nutritional intervention to overweight and obese patients

Aligning Comments to News Articles on a Budget

Disagreement among text annotators as a part of a human (expert) labeling process produces noisy labels, which affect the performance of supervised learning algorithms for natural language processing. Using only high agreement annotations introduces another challenge: the data imbalance problem. We study this challenge within the problem of relating user comments to the content of a news article. We show that traditional techniques for learning from imbalanced data, such as oversampling, using weighted loss functions, or assigning weak labels using crowdsourcing, may not be sufficient for modeling complex temporal relationships between news articles and user comments. In this study, we propose a framework for aligning comments and articles 1) from imbalanced news data characterized with 2) different degrees of annotator agreement, under 3) a constrained budget for human labeling and computing resources. Within the framework, we propose a Semi-Automatic Labeling solution based on Human-AI collaboration. We compare our proposed technique with traditional data imbalance handling techniques and synthetic data generation on the article-comment alignment problem, where the goal is to determine a category of an article-comment pair that represents how relevant the comment is to the article. Finding an effective and efficient solution is essential because it is time-consuming and prohibitively costly to manually label a sufficiently large amount of article-comment pairs based on the semantic understanding of an article and its comments. We discover that the Human-AI collaboration outperforms all alternative techniques by 17% of article-comment alignment accuracy. When there is no time or budget for re-labeling some article-comment pairs, we found that synonym augmentation is a reasonable alternative. We also provide a detailed analysis of the effect of humans in the loop and the use of unlabeled data.Temple University. College of Science and TechnologyComputer and Information Science