介绍了Kirk—Othmer Encyclopedia of Chemical Technology（化工技术百科全书）（第五版）电子图书网络版数据库，并对该数据库使用方法和检索途径作出了说明，且结合实例简单地介绍了该数据库的检索方法。

Kirk-Othmer Encyclopedia of Chemical Technology数据库介绍及实例

Convex bodies: the brunn–minkowski theory

Poised to become the leading reference in the field, the Handbook of Finite Fields is exclusively devoted to the theory and applications of finite fields. More than 80 international contributors compile state-of-the-art research in this definitive handbook. Edited by two renowned researchers, the book uses a uniform style and format throughout and each chapter is self contained and peer reviewed. The first part of the book traces the history of finite fields through the eighteenth and nineteenth centuries. The second part presents theoretical properties of finite fields, covering polynomials, special functions, sequences, algorithms, curves, and related computational aspects. The final part describes various mathematical and practical applications of finite fields in combinatorics, algebraic coding theory, cryptographic systems, biology, quantum information theory, engineering, and other areas. The book provides a comprehensive index and easy access to over 3,000 references, enabling you to quickly locate up-to-date facts and results regarding finite fields.

Handbook of Finite Fields

The explosion of social media allowed individuals to spread information without cost, with little investigation and fewer filters than before. This amplified the old problem of fake news, which became a major concern nowadays due to the negative impact it brings to the communities. In order to tackle the rise and spreading of fake news, automatic detection techniques have been researched building on artificial intelligence and machine learning. The recent achievements of deep learning techniques in complex natural language processing tasks, make them a promising solution for fake news detection too. This work proposes a novel hybrid deep learning model that combines convolutional and recurrent neural networks for fake news classification. The model was successfully validated on two fake news datasets (ISO and FA-KES), achieving detection results that are significantly better than other non-hybrid baseline methods. Further experiments on the generalization of the proposed model across different datasets, had promising results.

Fake news detection: A hybrid CNN-RNN based deep learning approach

As the COVID-19 virus quickly spreads around the world, unfortunately, misinformation related to COVID-19 also gets created and spreads like wild fire. Such misinformation has caused confusion among people, disruptions in society, and even deadly consequences in health problems. To be able to understand, detect, and mitigate such COVID-19 misinformation, therefore, has not only deep intellectual values but also huge societal impacts. To help researchers combat COVID-19 health misinformation, therefore, we present CoAID (Covid-19 heAlthcare mIsinformation Dataset), with diverse COVID-19 healthcare misinformation, including fake news on websites and social platforms, along with users' social engagement about such news. CoAID includes 4,251 news, 296,000 related user engagements, 926 social platform posts about COVID-19, and ground truth labels. The dataset is available at: this https URL.

CoAID: COVID-19 Healthcare Misinformation Dataset

Most currently available fake news datasets revolve around US politics, entrainment news or satire. They are typically scraped from fact-checking websites, where the articles are labeled by human experts. In this paper, we present FA-KES, a fake news dataset around the Syrian war. Given the specific nature of news reporting on incidents of wars and the lack of available sources from which manually-labeled news articles can be scraped, we believe a fake news dataset specifically constructed for this domain is crucial. To ensure a balanced dataset that covers the many facets of the Syrian war, our dataset consists of news articles from several media outlets representing mobilisation press, loyalist press, and diverse print media. To avoid the difficult and often-subjective task of manually labeling news articles as true or fake, we employ a semi-supervised fact-checking approach to label the news articles in our dataset. With the help of crowdsourcing, human contributors are prompted to extract specific and easy-to-extract information that helps match a given article to information representing “ground truth” obtained from the Syrian Violations Documentation Center. The information extracted is then used to cluster the articles into two separate sets using unsupervised machine learning. The result is a carefully annotated dataset consisting of 804 articles labeled as true or fake and that is ideal for training machine learning models to predict the credibility of news articles. Our dataset is publicly available at https://doi.org/10.5281/zenodo.2607278. Although our dataset is focused on the Syrian crisis, it can be used to train machine learning models to detect fake news in other related domains. Moreover, the framework we used to obtain the dataset is general enough to be used to build other fake news datasets around military conflicts, provided there is some corresponding ground-truth available.

/pdf/fa-kes-a-fake-news-dataset-around-the-syrian-war-3e1mvgbt61.pdf

FA-KES: A Fake News Dataset around the Syrian War

We introduce a new approach to multivariate polynomial factorisation which incorporates ideas from polyhedral geometry, and generalises Hensel lifting. Our main contribution is to present an algorithm for factoring bivariate polynomials which is able to exploit to some extent the sparsity of polynomials. We give details of an implementation which we used to factor randomly chosen sparse and composite polynomials of high degree over the binary field.

/pdf/factoring-polynomials-via-polytopes-wa39eh7fcq.pdf

Factoring polynomials via polytopes

Cadmium (Cd) is a highly toxic heavy metal with low permissible limits. This study aims at conducting a health risk assessment for Cd and examining potential sources of pollution in springs of the Mount-Lebanon Governorate, a semi-urbanized area in Lebanon. A total of 31 spring samples were collected during the wet and dry seasons and tested for water quality. Graphite furnace atomic absorption spectrometry was used to quantify Cd levels in water. Risk assessment was performed by means of a survey on water consumption and usage, and followed the USEPA Hazard Quotient (HQ) model. Correlations with different human activities were examined using different linear regression models and spatial analysis tools. Results highlight springs within El Metn and Keserwan Districts as posing a non-carcinogenic health risk in (1) Ain Saadeh both in adults (HQwet = 1.94) and iron-deficient children (HQwet = 1.29), and (2) Faraya both in healthy (HQwet = 1.33; HQdry = 1.19) and iron-deficient children (HQwet = 2.67; HQdry = 2.37), as well as in iron-deficient adults (HQwet = 1.57; HQdry = 1.39). In addition, Cd levels increased significantly with proximity to upstream open dumps (Pearson’s r = − 0.511; p = 0.003), agricultural land surface area, increased upstream industrial activity, mainly jewelry and fine stones industries in both bivariate (r = 0.33; p = 0.001) and multivariate analyses (r = 0.41; p = 0.002), plastic industries (Pearson’s r = 0.36; p = 0.05), and furniture manufacturing (Pearson’s r = 0.38; p = 0.038). Our findings constitute the first report assessing Cd health risk and identifying sources of pollution in Mount-Lebanon, and make a strong basis for a risk management strategy.

Cadmium Health Risk Assessment and Anthropogenic Sources of Pollution in Mount-Lebanon Springs

Let C be an arbitrary smooth algebraic curve of genus g over a large finite field K. We revisit fast addition algorithms in the Jacobian of C due to Khuri-Makdisi (math.NT/0409209, to appear in Math. Comp.). The algorithms, which reduce to linear algebra in vector spaces of dimension O(g) once |K| >> g, and which asymptotically require O(g^{2.376}) field operations using fast linear algebra, are shown to perform efficiently even for certain low genus curves. Specifically, we provide explicit formulae for performing the group law on Jacobians of C_{3,4} curves of genus 3. We show that, typically, the addition of two distinct elements in the Jacobian of a C_{3,4} curve requires 117 multiplications and 2 inversions in K, and an element can be doubled using 129 multiplications and 2 inversions in K. This represents an improvement of approximately 20% over previous methods.

Fatima K. Abu Salem

Papers

FA-KES: A Fake News Dataset around the Syrian War

Factoring polynomials via polytopes

Cadmium Health Risk Assessment and Anthropogenic Sources of Pollution in Mount-Lebanon Springs

Fast Jacobian group operations for C_{3,4} curves over a large finite field

An efficient sparse adaptation of the polytope method over Fp and a record-high binary bivariate factorisation