Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing nurse care for a child with extreme cardiac failure in the Intensive Care and Post-Operative Care Unit based on the International Classification of Nursing Practice (ICNP®)

Abstract Aim. The purpose of the work is to present nursing care for a pediatric patient with extreme circulatory failure, assisted by a device to Extracorporeal membrane oxygenation (ECMO) in the Intensive Care and Post-Operative Care Unit (ICPOCU) based on the International Classification of Nursing Practice (ICNP®). Material and methods. The work uses the individual case method based on the following techniques: analysis of medical records, observation and measurement of vital signs. Results. The most common and most dangerous care problem that may occur during therapy in extreme circulatory failure is the risk of cardiac dysfunction [10037314]. In this situation, the only life-saving method is often to support the circulatory and respiratory systems with the ECMO method. Another care problem is the risk of pressure sores [10027337]. The main goal of nursing care is to prevent their creation by minimizing risk factors and monitoring the patient’s skin condition. The problem of bleeding risk was also identified in the analyzed case [10017268] due to the need for heparin treatment. The goal of care is to minimize the risk and possible effects of bleeding. Because of taking care of the newborn baby, it is very important in planning activities to take into account the needs and problems of carers. One of the diagnosed problems is caregiver stress [10024773]. Conclusions. Nursing care for a pediatric patient with extreme circulatory failure supported by ECMO apparatus due to the impact of blood circulation on the functioning of the whole body does not focus solely on cardiovascular problems.
Evaluation of the Efficiency of Public Support for Cluster Organizations in the Czech Republic

Abstract The research presented in the paper aims to find out whether the public support (subsidies) received by cluster organizations for their development and activities is efficient. That means whether the state receives a return on investment in the form of increased revenues to public budgets. The research was conducted on a sample of seven cluster organizations that include the following sectors: furniture, packaging and textile production, engineering, the automotive industry, IT and nanotechnology. For each cluster organization, the data on subsidies was drawn from the moment of their establishment until 2017. At the same time, a list of the cluster member organizations was drawn up, with financial data being collected only for business entities. For each enterprise, information about paid corporate income tax, income tax on employment from employee wages, and social and health insurance paid by the companies and their employees was collected. In the next phase, increases in taxes and insurances were monitored and compared to the year in which the cluster organization drew a subsidy for the first time. Subsequently, the public support efficiency rate and payback period were calculated. The research results show that public support for cluster organizations is efficient, with a relatively short payback period.
Mass Automated Internet Analysis and Cyberspace Transparency

Abstract One of the roles of media research is to explain social phenomena. The Internet became a place where society expresses itself and where society could be influenced or even manipulated. Therefore, online communication analysis becomes a tool that is expected to guarantee the transparency of the social communication process. Unfortunately, the size of the Internet makes analysis difficult, and traditional methods of analysing communication are not always enough or force the researcher to focus on a fragmentary data. The author asks a question which research methods are suitable for Internet research and allow to improve transparency. It focuses on the method group referred to in the article as Mass Automated Internet Analysis. In the final part, the author shows examples of several – existing or being developed – research methods and techniques (including data collection and data analysis field), what research methods can improve the quality of digital communications research.
Extraction of User Demands Based on Similar Tweets Graph

Twitter is used by many users, and posted tweets include user's straightforward real intention. Therefore, we can obtain various opinions on items and events by collecting tweets. However, since the tweets are posted one after another over time and are represented by characters, it is difficult to grasp the overall picture of opinions on items. Therefore, by visualizing opinions on items, it is easier to grasp the whole picture more clearly. In this study, we collect tweets including item names and construct a graph connecting similar tweets. Then, from the connected component, we attempt to extract expressions related to user demands. Also, when constructing a similar tweet graph, it is necessary to appropriately set the similarity threshold. If the threshold is too low, unrelated tweets will be connected and a connected component will consist of different demand expressions. On the other hand, if the threshold value is too high, the demand expression of the same meaning will be divided as other connected components due to some notation fluctuation. In this paper, by focusing on the occurrence probability of the demand expression appearing in each connected component and defining the purity and the cohesiveness, we propose a method of setting the apropriate similarity threshold. In our experimental evaluations using a lot of tweets for two games “Mario tennis ace” and “Dairanto smash brothers SPECIAL”, we confirmed that opinions such as “interesting” or “difficult” can be extracted from similar tweets graph constructed by the appropriate similarity threshold value. We also confirmed that we can overlook the demands related to items.
Detection of Topical Influence in Social Networks via Granger-Causal Inference: A Twitter Case Study

With the ever-increasing importance of computer-mediated communication in our everyday life, understanding the effects of social influence in online social networks has become a necessity. In this work, we argue that cascade models of information diffusion do not adequately capture attitude change, which we consider to be an essential element of social influence. To address this concern, we propose a topical model of social influence and attempt to establish a connection between influence and Granger-causal effects on a theoretical and empirical level. While our analysis of a social media dataset finds effects that are consistent with our model of social influence, evidence suggests that these effects can be attributed largely to external confounders. The dominance of external influencers, including mass media, over peer influence raises new questions about the correspondence between objectively measurable information diffusion and social influence as perceived by human observers.
On the Causal Relation between Users' Real-World Activities and their Affective Processes

Research in social network analytics has already extensively explored how engagement on online social networks can lead to observable effects on users' real-world behavior (e.g., changing exercising patterns or dietary habits), and their psychological states. The objective of our work in this paper is to investigate the flip-side and examine whether engaging in or disengaging from real-world activities would reflect itself in users' affective processes such as anger, anxiety, and sadness, as expressed in users' posts on online social media. We have collected data from Foursquare and Twitter and found that engaging in or disengaging from a real-world activity, such as frequenting at bars or stopping going to a gym, have direct impact on the users' affective processes. In particular, we report that engaging in a routine real-world activity leads to expressing less emotional content online, whereas the reverse is observed when users abandon a regular real-world activity.
A Novel Methodology for Improving Election Poll Prediction Using Time-Aware Polling

Multiple poll forecasting solutions, based on statistics and economic indices, have been proposed over time, but, as we better understand diffusion phenomena, we know that temporal characteristics provide even more uncertainty. As such, current literature is not yet able to define truly reliable models for the evolution of political opinion, marketing preferences, or social unrest. Inspired by micro-scale opinion dynamics, we develop an original time-aware (TA) methodology which is able to improve the prediction of opinion distribution, by modeling opinion as a function which spikes up when opinion is expressed, and slowly dampens down otherwise. After a parametric analysis, we validate our TA method on survey data from the US presidential elections of 2012 and 2016. By comparing our time-aware method (TA) with classic survey averaging (SA), and cumulative vote counting (CC), we find our method is substantially closer to the real election outcomes. On average, we measure that SA is 6.3% off, CC is 5.6% off, while TA is only 1.5% off from the final registered election outcomes; this difference translates into an ≈ 75% prediction improvement of our TA method. As our work falls in line with studies on the microscopic temporal dynamics of social networks, we find evidence of how macroscopic prediction can be improved using time-awareness.
Detecting Depressed Users in Online Forums

Depression is the most common mental illness in the U.S., with 6.7% of all adults who have experienced a major depressive episode. Unfortunately, depression extends to teens and young users as well, and researchers observed an increasing rate in the recent years (from 8.7% in 2005 to 11.3% in 2014 in adolescents and from 8.8% to 9.6% in young adults), especially among girls and women. People themselves are a barrier to fight this disease as they tend to hide their symptoms and do not receive treatments. However, protected by anonymity, they share their sentiments on the Web, looking for help. In this paper, we address the problem of detecting depressed users in online forums. We analyze user behavior in the Rea-chOut.com online forum, a platform providing a supportive environment for young people to discuss their everyday issues, including depression. We examine the linguistic style of user posts in combination with network-based features modeling how users connect in the forum. Our results show that network features are strong predictors of depressed users and, by combining them with user post linguistic features, we can achieve an average precision of 0.78 (vs. 0.47 of a random classifier and 0.71 of linguistic features only) and perform better than related work (F1-measure of 0.63 vs. 0.50).
Basketball Lineup Performance Prediction Using Network Analysis

Winning a game in professional sports is the most significant matter for a team. All teams strive to bring their best performance to a game, and this requires considering all the possible lineups which coaches have available. Therefore, determining the lineup is more and more significant for a team in their winning endeavour. The ongoing result during a game defines the next decision coaches have to make to maintain or improve the outcome. Adaptive changes in a lineup of a team requires a complex decision making system. This system must consider the advantages, drawbacks, and previous experience about both teams' performance under similar situations. In order to analyze and predict lineups' performance, the authors create a directed, weighted, and signed network of all lineups that teams use against each other from 2007–2016 seasons in National Basketball Association (NBA) games. The proposed model uses machine learning and network analysis techniques to predict the performance of a lineup under a given situation by utilizing graph theory and Inverse Squared Metric. In order to evaluate the performance of the proposed method, several baseline models are established and results are compared. The final results over the span of ten years show that the proposed method in this paper improves the baseline results by 10% accuracy. The average of the best baseline results has an accuracy of 58% in lineup outcome prediction; however, the new method yields accuracy of 68%.
Modeling the Dynamics of Resource Exchange Networks

Understanding the evolution of large-scale cooperation is important for the social welfare and stability of economic and social networks. Therefore, there is a need to model real-world scenarios that involve a trade-off between self-interest and social welfare with minimal artificial assumptions or constraints in a versatile framework. In this paper, we build an agent-based model to simulate the dynamics of a multi-agent, bilateral, resource-exchange network. We analyze how various strategies employed by communities can improve or hurt community payoffs as well as the overall social welfare of the network. We also analyze the role of common knowledge in inducing cooperation in the network. Our experimental evidence from simulations confirms that carefully-designed trading mechanisms can indeed encourage cooperation among communities with various motivations.
Thousands of Small, Constant Rallies: A Large-Scale Analysis of Partisan WhatsApp Groups

There is growing concern about the use of social platforms to push political narratives during elections. One very recent case is Brazil's, where WhatsApp is now widely perceived as a key enabler of the far-right's rise to power. In this paper, we perform a large-scale analysis of partisan WhatsApp groups to shed light on how both right-wingers and left-wingers used the platform in the 2018 Brazilian presidential election. Across its two rounds, we collected +2.8M messages from +45k users in 232 public groups (175 right-wing vs. 57 left-wing). After describing how we obtained a sample that is many times larger than previous works, we contrast right-wingers and left-wingers on their social network metrics, regional distribution of users, content-sharing habits, and most characteristic news sources.
Next Cashtag Prediction on Social Trading Platforms with Auxiliary Tasks

Social trading platforms provide a forum for investors to share their analysis and opinions. Posts on these platforms are characterized by narrative styles which are much different from posts on general social platforms, for instance tweets. As a result, recommendation systems for social trading platforms should leverage tailor-made latent features. This paper presents a representation for these latent features in both textual data and market information. A real-world dataset is adopted to conduct experiments involving a novel task called next cashtag prediction. We propose a joint learning model with an attentive capsule network. Experimental results show positive results with the proposed methods and the corresponding auxiliary tasks.
Theory and Use Case of Game-theoretic Lexical Link Analysis

We demonstrate a machine learning method, namely lexical link analysis (LLA), which can be used to discover high-value information from financial data. LLA is an unsupervised learning method that does not require manually labeled training data. We also demonstrate how to form LLA in a game-theoretic framework. We show that with game theory: high-value information selected by LLA reaches a Nash equilibrium by superpositioning popular and anomalous information and at the same time generates high social welfare, therefore containing higher intrinsic value. We show the results of LLA of two sets of financial data validating and correlating with the ground truth.
A Hierarchical Insurance Recommendation Framework Using GraphOLAM Approach

Graph has been widely used for modeling complex relationship datasets in different application fields. Social networks based recommendation system have obtained satisfactory results in Business Intelligence(BI). However, current personalized recommendation methods based on graph structure generally lack interactivity and seldom consider efficient data management. To address these problems, Graph On-Line Analytical Mining (GraphOLAM) is a promising method, which combines OLAP technology with social networks. We first propose an efficient recommendation framework based on GraphOLAM data cube technology for the recommendation in the insurance service. Based on this framework, a new algorithm framework named RU-GOLAM for insurance is proposed, which combines GraphOLAM dimensional aggregation operation and specific recommendation methods. A series of graphs can be generated by GraphOLAM dimensional aggregation operations, which reflect the relationships of nodes under the constraints of different hierarchical dimensions. Node similarities are calculated to generate the Top-N sequential recommendation based on all of these graphs, which can achieve the balance between the topology of the original graph and high-dimensional information of the nodes. Experiments show that our approach outperforms other baseline algorithms on an insurance service dataset.
RiskCast: Social Sensing based Traffic Risk Forecasting via Inductive Multi-View Learning

Road traffic accidents are a major challenge in urban transportation systems. An effective countermeasure to address this problem is to accurately forecast the traffic risks in a city before accidents actually happen. Current traffic accident prediction solutions largely rely on accurate data collected from infrastructure-based sensors, which is not always available due to various resource constraints or privacy and legal concerns. In this paper, we address this limitation by exploring social sensing, a new sensing paradigm that uses humans as sensors to report the states of the physical world. In particular, we consider two types of publicly available social sensing data sources: social media data (e.g., traffic posts on Twitter) and open city data (e.g., traffic data from the city web portal). In this paper, we develop the RiskCast, an inductive multi-view learning approach to accurately forecast the traffic risk by exploiting the social sensing data under a principled co-regularization framework. The evaluation results on a real world dataset from New York City show that RiskCast significantly outperforms the state-of-the-art baselines in forecasting the traffic risks in a city.
Text Mining for Malware Classification Using Multivariate All Repeated Patterns Detection

Mobile phones have become nowadays a commodity to the majority of people. Using them, people are able to access the world of Internet and connect with their friends, their colleagues at work or even unknown people with common interests. This proliferation of the mobile devices has also been seen as an opportunity for the cyber criminals to deceive smartphone users and steel their money directly or indirectly, respectively, by accessing their bank accounts through the smartphones or by blackmailing them or selling their private data such as photos, credit card data, etc. to third parties. This is usually achieved by installing malware to smartphones masking their malevolent payload as a legitimate application and advertise it to the users with the hope that mobile users will install it in their devices. Thus, any existing application can easily be modified by integrating a malware and then presented it as a legitimate one. In response to this, scientists have proposed a number of malware detection and classification methods using a variety of techniques. Even though, several of them achieve relatively high precision in malware classification, there is still space for improvement. In this paper, we propose a text mining all repeated pattern detection method which uses the decompiled files of an application in order to classify a suspicious application into one of the known malware families. Based on the experimental results using a real malware dataset, the methodology tries to correctly classify (without any misclassification) all randomly selected malware applications of 3 categories with 3 different families each.
Fraudulent User Detection on Rating Networks Based on Expanded Balance Theory and GCNs

Rating platforms provide users with useful information on products or other users. However, fake ratings are sometimes generated by fraudulent users. In this paper, we tackle the task of fraudulent user detection on rating platforms. We propose an end-to-end framework based on Graph Convolutional Networks (GCNs) and expanded balance theory, which properly incorporates both the signs and directions of edges. Experimental results on four real-world datasets show that the proposed framework performs better, or even best, in most settings. In particular, this framework shows remarkable stability in inductive settings, which is associated with the detection of new fraudulent users on rating platforms. Furthermore, using expanded balance theory, we provide new insight into the behavior of users in rating networks, that fraudulent users form a faction to deal with the negative ratings from other users. The owner of a rating platform can detect fraudulent users earlier and constantly provide users with more credible information by using the proposed framework.
Surveying public opinion using label prediction on social media data

In this study, a procedure is proposed for surveying public opinion from big social media domain-specific textual data to minimize the difficulties associated with modeling public behavior. Strategies for labeling posts relevant to a topic are discussed. A two-part framework is proposed in which semiautomatic labeling is applied to a small subset of posts, referred to as the “seed” in further text. This seed is used as bases for semi-supervised labeling of the rest of the data. The hypothesis is that the proposed method will achieve better labeling performance than existing classification models when applied to small amounts of labeled data. The seed is labeled using posts of users with a known and consistent view on the topic. A semi-supervised multi-class prediction model labels the remaining data iteratively. In each iteration, it adds context-label pairs to the training set if softmax-based label probabilities are above the threshold. The proposed method is characterized on four datasets by comparison to the three popular text modeling algorithms (n-grams + tfidf, fastText, VDCNN) for different sizes of labeled seeds (5,000 and 50,000 posts) and for several label-prediction significance thresholds. Our proposed semi-supervised method outperformed alternative algorithms by capturing additional contexts from the unlabeled data. The accuracy of the algorithm was increasing by (3-10%) when using a larger fraction of data as the seed. For the smaller seed, lower label probability threshold was clearly a better choice, while for larger seeds no predominant threshold was observed. The proposed framework, using fastText library for efficient text classification and representation learning, achieved the best results for a smaller seed, while VDCNN wrapped in the proposed framework achieved the best results for the bigger seed. The performance was negatively influenced by the number of classes. Finally, the model was applied to characterize a biased dataset of opinions related to gun control/rights advocacy. The proposed semi-automatic seed labeling is used to label 8,448 twitter posts of 171 advocates for guns control/rights. On this application, our approach performed better than existing models and it achieves 96.5% accuracy and 0.68 F1 score.