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Online Social Media (OSM) platforms such as Twitter, Facebook are extensively exploited by the users of these platforms for spreading the (mis)information to a large audience effortlessly at a rapid pace. It has been observed that the misinformation can cause panic, fear, and financial loss to society. Thus, it is important to detect and control the misinformation in such platforms before it spreads to the masses. In this work, we focus on rumors, which is one type of misinformation (other types are fake news, hoaxes, etc). One way to control the spread of the rumors is by identifying users who are possibly the rumor spreaders, that is, users who are often involved in spreading the rumors. Due to the lack of availability of rumor spreaders labeled dataset (which is an expensive task), we use publicly available PHEME dataset, which contains rumor and non-rumor tweets information, and then apply a weak supervised learning approach to transform the PHEME dataset into rumor spreaders dataset. We utilize three types of features, that is, user, text, and ego-network features, before applying various supervised learning approaches. In particular, to exploit the inherent network property in this dataset (user-user reply graph), we explore Graph Convolutional Network (GCN), a type of Graph Neural Network (GNN) technique. We compare GCN results with the other approaches: SVM, RF, and LSTM. Extensive experiments performed on the rumor spreaders dataset, where we achieve up to 0.864 value for F1-Score and 0.720 value for AUC-ROC, shows the effectiveness of our methodology for identifying possible rumor spreaders using the GCN technique., Comment: Published at The International Joint Conference on Neural Networks 2021 (IJCNN2021). Please cite the IJCNN version

In the last decade, Social Networks (SNs) have deeply changed many aspects of society, and one of the most widespread behaviours is the sharing of pictures. However, malicious users often exploit shared pictures to create fake profiles leading to the growth of cybercrime. Thus, keeping in mind this scenario, authorship attribution and verification through image watermarking techniques are becoming more and more important. In this paper, firstly, we investigate how 13 most popular SNs treat the uploaded pictures, in order to identify a possible implementation of image watermarking techniques by respective SNs. Secondly, on these 13 SNs, we test the robustness of several image watermarking algorithms. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is robust enough in spite of the fact that the pictures get downgraded during the uploading process by SNs. The results of our analysis on a real dataset of 8,400 pictures show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs., Comment: 43 pages, 6 figures

Using lobbying data from OpenSecrets.org, we offer several experiments applying machine learning techniques to predict if a piece of legislation (US bill) has been subjected to lobbying activities or not. We also investigate the influence of the intensity of the lobbying activity on how discernible a lobbied bill is from one that was not subject to lobbying. We compare the performance of a number of different models (logistic regression, random forest, CNN and LSTM) and text embedding representations (BOW, TF-IDF, GloVe, Law2Vec). We report results of above 0.85% ROC AUC scores, and 78% accuracy. Model performance significantly improves (95% ROC AUC, and 88% accuracy) when bills with higher lobbying intensity are looked at. We also propose a method that could be used for unlabelled data. Through this we show that there is a considerably large number of previously unlabelled US bills where our predictions suggest that some lobbying activity took place. We believe our method could potentially contribute to the enforcement of the US Lobbying Disclosure Act (LDA) by indicating the bills that were likely to have been affected by lobbying but were not filed as such.

In the last decade, humanity has faced many different pandemics such as SARS, H1N1, and presently novel coronavirus (COVID-19). On one side, scientists are focusing on vaccinations, and on the other side, there is a need to propose models that can help us in understanding the spread of these pandemics as it can help governmental and other concerned agencies to be well prepared, especially from pandemics, which spreads faster like COVID-19. The main reason for some epidemic turning into pandemics is the connectivity among different regions of the world, which makes it easier to affect a wider geographical area, often worldwide. In addition, the population distribution and social coherence in the different regions of the world is non-uniform. Thus, once the epidemic enters a region, then the local population distribution plays an important role. Inspired by these ideas, we proposed a mobility-based SIR model for epidemics, which especially takes into account pandemic situations. To the best of our knowledge, this model is first of its kind, which takes into account the population distribution and connectivity of different geographic locations across the globe. In addition to presenting the mathematical proof of our model, we have performed extensive simulations using synthetic data to demonstrate our model's generalizability. To demonstrate the wider scope of our model, we used our model to forecast the COVID-19 cases for Estonia.

Over 90 % of all chemical manufacture uses a solid catalyst. Related work thus responds to major societal demand. This study is of water-gas shift on platinum for hydrogen production. The close-packed Pt(111) surface catalyses this process. Many chemical reactions involve bond-dissociation. This is also true for reactions at solid surfaces, in which the dissociation step is often limiting but facilitated in comparison to gas phase reaction channels. Since bond-breaking is poorly described by Hartree-Fock and DFT methods, this work adopts Quantum Monte Carlo (QMC) methodology. QMC is a stochastic approach to solving the Schr\"odinger equation recently came of age for heterogeneous systems involving solids. The present work considers co-adsorption of water and carbon monoxide on Pt(111). The water is partially dissociated while its oxygen atom binds to CO losing a hydrogen atom. This concerted step is rate-limiting. The resulting adsorbed formate species then decomposes to readily eliminated carbon dioxide and the clean-fuel product is H$_2$. The Transition-State geometry can be optimized using molecular Quantum Monte Carlo force constants, on the basis of our earlier work using the CASINO software. Our embedded active site approach is used. This allows a high-level configuration interaction (CI) wave-function to be used, expanded in plane-waves and embedded in the metal lattice exposing its close-packed face. The resulting periodic function is used to guide the Quantum Monte Carlo calculation. Results are given here on mechanism and QMC activation barrier for water addition to CO pre-adsorbed on Pt(111) of 17 +/- 0.2 c.f. apparent measured value of 17.05 kcal/mol. They are encouraging for investigating similar or complex catalytic systems., Comment: 23 pages, 1 figure

Identifying semantically identical questions on, Question and Answering social media platforms like Quora is exceptionally significant to ensure that the quality and the quantity of content are presented to users, based on the intent of the question and thus enriching overall user experience. Detecting duplicate questions is a challenging problem because natural language is very expressive, and a unique intent can be conveyed using different words, phrases, and sentence structuring. Machine learning and deep learning methods are known to have accomplished superior results over traditional natural language processing techniques in identifying similar texts. In this paper, taking Quora for our case study, we explored and applied different machine learning and deep learning techniques on the task of identifying duplicate questions on Quora's dataset. By using feature engineering, feature importance techniques, and experimenting with seven selected machine learning classifiers, we demonstrated that our models outperformed previous studies on this task. Xgboost model with character level term frequency and inverse term frequency is our best machine learning model that has also outperformed a few of the Deep learning baseline models. We applied deep learning techniques to model four different deep neural networks of multiple layers consisting of Glove embeddings, Long Short Term Memory, Convolution, Max pooling, Dense, Batch Normalization, Activation functions, and model merge. Our deep learning models achieved better accuracy than machine learning models. Three out of four proposed architectures outperformed the accuracy from previous machine learning and deep learning research work, two out of four models outperformed accuracy from previous deep learning study on Quora's question pair dataset, and our best model achieved accuracy of 85.82% which is close to Quora state of the art accuracy., Comment: 11 pages, 8 figures, 8 tables

The present letter describes an atomic scale investigation of a chemical reaction for selective hydrogen production. This clean fuel is a sustainable energy source. Because electron transfer is the key to such reactions, accurate methods based on quantum theory are used. The reaction between water and carbon monoxide has been used industrially with metal catalysts, usually Platinum. There is a considerable economic and environmental challenge underpinning this application of a fundamental process limited by bond dissociation. That is the process often limiting reaction rates for industrial catalysis. Most mainstream quantum approaches fail to greater or lesser degree in the description of bond dissociation. The present work presents a promising alternative: the initial analysis of a considerable mass of statistical data generated by the atomic-scale Quantum Monte Carlo method to very stringent statistical accuracy for essential information on the hydrogen production via the water-gas shift reaction with platinum catalyst. This is encouraging for establishing less well-known benchmark values of industrial reaction barriers on Pt(111)., Comment: 8 pages 2 figures

The online social networks facilitate naturally for the users to share information. On these platforms, each user shares information based on his or her interests. The particular information being shared by a user may be legitimate or fake. Sometimes a misinformation, propagated by users and group can create chaos or in some cases, might leads to cases of riots. Nowadays the third party like ALT news and Cobrapost check the information authenticity, but it takes too much time to validate the news. Therefore, a robust and new system is required to check the information authenticity within the network, to stop the propagation of misinformation. In this paper, we propose a blockchain based framework for sharing the information securely at the peer level. In the blockchain model, a chain is created by combining blocks of information. Each node of network propagates the information based on its credibility to its peer nodes. The credibility of a node will vary according to the respective information. Trust is calculated between sender and receiver in two ways:(i) Local trust used for sharing information at the peer level and (ii) global trust is used for a credibility check of each user in the network. We evaluate our framework using real dataset derived from Facebook. Our approach achieves an accuracy of 83% which shows the effectiveness of our proposed framework.