Miracle-dz/UniFAR-FTUs huggingface dataset:
An influence maximization algorithm in social network using K-shell decomposition and community detection

The increasing use of services and different applications of social networks has led to a wide range of research and studies in the field of information technology and computer networks towards such networks. Creating a wide platform for advertising in social networks and attracting more customers in this way has created a variety of ways to maximize profits. Therefore, due to the high importance of the propagation speed and the extent of advertising, the issue of influence maximization is considered special. The influence maximization can be described as: determining a small set of nodes capable of operating large waterfalls of behavior that are spread across the network. In other words, selection of a set of K nodes from a social network is in such a way that the influence of the influence of the node in the network has maximum value. Due to the high sensitivity of the influence maximization process, in this study we try to reduce the strengths and problems of previous strategies in this field by decomposition K-shell and community detection based on SLPA algorithm. The proposed approach in this research is based on the recognition of community based on SLPA algorithm, to make a better result by flexible and optimizing the decision making in exploration and extraction of societies. In both methods, K-shell analysis and community detection are used to choose the more influential nodes, which are proportional to the graph of social networks. The proposed method is evaluated based on two fundamental criteria of execution time and number of active nodes, which have better efficiency and efficiency compared to previous methods.
When Do Politicians Use Populist Rhetoric? Populism as a Campaign Gamble

ABSTRACT Why do some politicians employ populist rhetoric more than others within the same elections, and why do the same politicians employ more of it in some elections? Building on a simple formal theoretical model of two-candidate elections informed by the ideational approach to populist communication, we argue that the initially less popular political actors are more likely to use populist rhetoric in a gamble to have at least some chance of winning. To test the empirical implications of our argument, we construct the most comprehensive corpus of U.S. presidential campaign speeches (1952–2016) and estimate the prevalence of populist rhetoric across these speeches with a novel automated text analysis method utilizing active learning and word embedding. Overall, we show the robustly greater use of populism among the presidential candidates with the lower polling numbers regardless of their partisanship or incumbency status.
Comparison of shared frailty models: A Bayesian approach

In this article, we propose gamma and inverse Gaussian frailty shared models with Akash distribution as baseline to analyze the bivariate survival data set of Mc Gilchrist and Aisbett (1991). Bayesian approach of Markov Chain Monte Carlo technique was employed to estimate the parameters involves in the models. The better model also suggested for the data.
A Wireless ASIC for Microscopic Ultrasound Neuromodulation

Peripheral neuropathy of the lower legs is a serious nervous system disorder that increases the risk of falls, mainly due to decreased sensation on the plantar surface of the feet. The objective of this study is to test our hypothesis that tactile augmentation on the plantar surface is more efficient than indirect compensatory sensory feedback in improving postural regulation when plantar cutaneous feedback is reduced. In our experiments, ten healthy human subjects stood on a lateral balance board and maintained their balance for as long as possible until the balance board contacted the ground for a fixed number of trials. The average balance time of the subjects was increased by plantar cutaneous augmentation, and further increased by the cognitive load. This result suggests that plantar cutaneous augmentation can be a promising way to improve postural regulation, particularly for individuals with compromised tactile feedback on the foot sole.
Development of modern methods for the diagnostics of murals in architectural monuments

The paper studies monitoring of the state of murals, retrieval of data pertaining to this state and management and storing of the said data. The possibility of integration of traditional methods of mural mapping and modern methods of data visualization, including new Google Project Tango device technology for fixation of complex textures of inner 3D volumes of architectural monuments has been investigated (for instance Assumption Cathedral). We further discuss the express-scanning of automated cartogramming for further comparison of states and methods of assessing the damage done to the mural. Results indicate that additional work is needed to improve the precision of the method.
Heterogeneous Catalytic Ozonation of Acid blue 62 Over Nanorod Fe-OMS-2

In this work, nanorod materials prepared by refluxed method (K-OMS-2) and by co-precipitation method (Fe-OMS-2) were analyzed by X-ray diffraction (XRD), Raman spectroscopy and scanning electron microscopy (SEM). Non-catalytic and heterogeneous catalytic ozonation processes of acid blue 62 (AB-62) were tested under the bubbling O3/air flow, at room temperature (RT). Comparing to ozonation over Fe-OMS-2, both adsorption and non-catalytic ozonation of AB-62 showed much lower removal efficiency. These results evidenced a positive role of nanorod Fe-OMS-2 in removal of (80% of AB-62 after 50 min of O3 bubbling. The pseudo first-order reaction with respect to the AB-62 was observed from ozonation over K-OMS-2 and Fe-OMS-2 catalysts. The reaction rate measured from Fe-OMS-2 is three times faster than that from K-OMS-2 probably contributed from a synergistic effect between O3 and defects on the surface of Fe-OMS-2.
N-Gram Graphs for Text Classification: A Distributed Approach

The power of n-gram models in capturing syntactic patterns using large text corpora made them the tool of choice for language modeling in machine translation, speech recognition, summarization and other tasks. N-grams have been frequently used for developing features for supervised classifiers and improved the performance of unigram features and recently n-gram graphs managed to capture significant language characteristics that go beyond mere vocabulary and grammar and have been established as a prominent representation for text classification. This work proposes a distributed implementation of the n-gram graph framework on Apache Spark, named ARGOT. The main operations of the n-gram graph framework (graph similarity, graph merging and update) have been redesigned in order to execute efficiently in the distributed environment. The implementation performance is evaluated on a demanding text classification task where the n-gram graphs are used for extracting features for a supervised classifier. The experimental results show the scalability of the proposed implementation to large text corpora and its ability to take advantage of a varying number of processing cores. Although the emphasis is on the scalability of algorithms, the classification algorithm also achieves state-of-the-art accuracy performance on a well-established multilingual dataset, which adds to the efficiency of the proposed approach.
STATs signalling in human bladder cancer : potential chemopreventive role for indoles

STATs signalling in human bladder cancer: Potential chemopreventive role for indoles Yiyang Sun While the transcription factors, signal transducers and activators of transcription (STATs) have been implicated in the development of cancer in a variety of tissues, no such information exists for bladder cancer. In patient tissues, STAT1, STAT3 and STAT5 were observed in normal bladder epithelium, while expression of phosphorylated proteins was essentially absent. In contrast, phospho-STAT3 in superficial tumours and phospho-STATl, 3 and 5 in muscle invasive tumours were present. Thus their increased expression, activation and nuclear localisation, may be associated with bladder tumour progression. Three epithelial bladder cancer lines (RT112, RT4, HT1376) had constitutive phospho-STAT5, while three mesenchymal lines (J82, T24, UMUC3) had activated STAT3. Constitutive expression and activation of STATs was EGF-independent. Specific knockdown of STAT3 in J82 cells increased STAT1 expression and phosphorylation. STAT1 and/or STAT3 knockdown led to inhibition of cell-matrix adhesion to fibronectin or collagen and suppression of cell migration. The dietary molecules, Indole-3-carbinol (I3C) (>100/zM) or 3, 3’-diindolylmethane (DIM) (>50/iM) induced apoptosis in J82 and RT112 cells by 48 hours. DEM-induced apoptosis in J82 cells was accompanied by downregulation of STAT1, 5a and 5b and inhibition of phospho-STATl and phospho-STAT3. In RT112 cells, DEM inhibited STAT5a, 5b and phospho-STAT5, but had no effect on STAT3. DEM ( >10/iM) inhibited J82 cell adhesion to fibronectin and collagen, accompanied by decreased N-cadherin, P-cadherin and /3-catenin expression. Furthermore, DEM (1/iM) was able to significantly decrease cell adhesion following STAT1 and/or STAT3 knockdown, suggesting inhibition of cell adhesion by DIM may involve the STAT pathway. This investigation in vivo and in vitro has provided an indication that phosphorylated STATs may be a potential marker of invasiveness in human bladder cancer. Elucidation of the effects of indoles and DEM in particular, at a molecular level has given an insight as to how these dietary agents may be useful in the therapy and/or prevention of bladder tumours.
Zero-shot Learning based Pedestrian Parsing

Pedestrian parsing is a fundamental problem for action recognition and behavior analysis. However, unlike indoor person parsing, it remains a challenging problem due to varying luminance, occlusion and clothing. In this paper, we propose a novel pedestrian parsing approach based zero-shot learning. Firstly, we learn an transferred model that extracts clothing parsing attributes from pedestrian images. Then we combine the attributes into higher level human parts, Finally we apply a seed-based segmentation approach to get the parsing results. We test the proposed approach on the Penne-Fudan and PPSS dataset, and achieve reasonablly good results.
Accounting for the Value of Earth Science Data

This paper considers a cost accounting framework for long-term preservation of Earth science information in governmental archives. It considers three standard approaches to valuing that information: net present value of economic benefits, probable cost of replicating information, or tracking the original cost of creating value. Only the latter appears viable, and even then, many of the original costs may be regarded as `sunk costs' by the time data collection begins. This means that the value of climate data is fundamentally determined by the original cost of creating the software to interpret the data and validating it. Furthermore, because this information does not depreciate in value over time, its treatment as an asset is governed by the contingent liability of loss. This conclusion suggests that the costs of preservation may be regarded as a form of 'insurance' against various kinds of losses. Because the requirements for long-term information preservation are extremely stringent, it appears appropriate to build a formal threat model for such losses. Such a model can then help prioritize various operational expenses.
GAUSSIAN FLUCTUATIONS FOR LINEAR SPECTRAL STATISTICS OF LARGE RANDOM COVARIANCE MATRICES By

are shown to be Gaussian, in the regime where both dimensions of matrix Σn go to infinity at the same pace and in the case where f is of class C, that is, has three continuous derivatives. The main improvements with respect to Bai and Silverstein’s CLT [Ann. Probab. 32 (2004) 553–605] are twofold: First, we consider general entries with finite fourth moment, but whose fourth cumulant is nonnull, that is, whose fourth moment may differ from the moment of a (real or complex) Gaussian random variable. As a consequence, extra terms proportional to
LocalRec 2019 workshop report: The Third ACM SIGSPATIAL Workshop on Location-Based Recommendations, Geosocial Networks and Geoadvertising

The amount of publicly available geo-referenced data has seen a dramatic explosion over the past few years. Many user activities generate data that are annotated with location and contextual information. Furthermore, it has become easier to collect and combine rich and diverse location information. In the context of geoadvertising, the use of geosocial data for targeted marketing is receiving significant attention from a wide spectrum of companies and organizations. With the advent of smartphones and online social networks, a multi-billion dollar industry that utilizes geosocial data for advertising and marketing has emerged. Geotagged social-media posts, GPS traces, data from cellular antennas and WiFi access points are used widely to directly access people for advertising, recommendations, marketing, and group purchases. Exploiting this torrent of geo-referenced data provides a tremendous potential to materially improve existing recommendation services and offer novel ones, with numerous applications in many domains, including social networks, marketing, and tourism.
AN ANALYSIS OF THE RELATIONSHIP BETWEEN ECONOMIC GROWTH AND FINANCIAL DEVELOPMENT FOR BRICS COUNTRIES

Many regions across the world are in dire need of financial development and economic growth. Tentatively, a relationship between the concepts exists. However, the direction of causality is of great interest, particularly in relation to the country’s level of development. Financial development in developed countries tends to be more progressive than those of developing countries. This research studies the existence of a relationship between financial development and economic growth using a sample of BRICS countries for the period of 1996 to 2016. A balanced panel data analysis was used to analyse secondary data from five BRICS countries (Brazil, Russia, India, China and South Africa). Variables used include: GDP growth, foreign direct investment, population, real interest rates, gross fixed capital formation, and domestic credit to private sector, and were selected on the availability of data. To analyse relationships between economic growth and financial development, this study used time series data obtained from the World Bank and International Monetary Fund (IMF). Findings of the study suggest that there is a longrun and short-run relationship between economic growth and financial development to some degree. BRICS countries should focus on those variables that are more suitable to generate more growth within their economy as it may differ from one economy to another when referring to financial statistics and resources.
Measuring competency-relevant knowledge in the competency-oriented student progress test

Background: Since 2013 a competency-oriented student progress test (SKPT) has been administered at a number of German medical schools. The questions are generated on the basis of a two-dimensional blueprint, on which one axis contains the five competency domains – communicative competence (CO), practical clinical competence (CP), theoretical clinical competence (CT), scientific competence (SC), and professional decision-making competence (PR) – that form part of the competency model of the National Competency-based Catalogue of Learning Objectives for Undergraduate Medicine (NKLM). The feedback for students is structured in part according to these domains. The aim of our study is to examine if the results differentiated by competency domain show adequate measurement accuracy and if the results for the different domains also contain different information. Methods: The SKPTs for the years 2013 to 2017, taken by a total of 3,027 students, were examined. The measurement accuracy was determined using the coefficient glb (greatest bound to reliability) and the standard error of measurement; discriminant analysis of the principal components was carried out to demonstrate differentiation between the competency domains. Results: The reliability of the competency domains was above 0.8 for all SKPTs; exceptions to this were seen in two of the tests for CO and PR that had a reliability of 0.7–0.8. The results for all of the individual competency domains differed in their informational content compared to the overall of the other domains; the same applies for all pairwise comparisons, with the exceptions of CP and CT. Discussion: The SKPT feedback for students that is differentiated by competency domains basically fulfills the requirements for measurement reliability and distinctness. An improvement of the measurement quality for CO and PR and a better differentiation between CP and CT is desirable.
Investigating the Gut Microbiota Composition of Individuals with Attention-Deficit/Hyperactivity Disorder and Association with Symptoms

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Given the growing evidence of gut microbiota being involved in psychiatric (including neurodevelopmental) disorders, we aimed to identify differences in gut microbiota composition between participants with ADHD and controls and to investigate the role of the microbiota in inattention and hyperactivity/impulsivity. Fecal samples were collected from 107 participants (NADHD = 42; Ncontrols = 50; NsubthreholdADHD = 15; range age: 13–29 years). The relative quantification of bacterial taxa was done using 16S ribosomal RNA gene amplicon sequencing. Beta-diversity revealed significant differences in bacterial composition between participants with ADHD and healthy controls, which was also significant for inattention, but showing a trend in case of hyperactivity/impulsivity only. Ten genera showed nominal differences (p < 0.05) between both groups, of which seven genera were tested for their association with ADHD symptom scores (adjusting for age, sex, body mass index, time delay between feces collection and symptoms assessment, medication use, and family relatedness). Our results show that variation of a genus from the Ruminococcaceae family (Ruminococcaceae_UCG_004) is associated (after multiple testing correction) with inattention symptoms and support the potential role of gut microbiota in ADHD pathophysiology.
Agency business cycles

We develop a theory of endogenous and stochastic ﬂuctuations in economic activity. Individual ﬁrms choose to randomize over ﬁring or keeping workers who performed poorly in the past to give them an ex ante incentive to exert effort. Different ﬁrms choose to correlate the outcome of their randomization to reduce the probability with which they ﬁre nonperforming workers. Correlated randomization leads to aggregate ﬂuctuations. Aggregate ﬂuctuations are endogenous— they emerge because ﬁrms choose to randomize and they choose to randomize in a correlated fashion—and they are stochastic—they are the manifestation of a randomization process. The hallmark of a theory of endogenous and stochastic ﬂuctuations is that the stochastic process for aggregate “shocks” is an equilibrium object.
Sentiment Analysis of Spanish Tweets Using a Ranking Algorithm and Skipgrams

espanolEn este articulo presentamos nuestra contribucion a la Tarea 1 (clasificacion de polaridad en 6 niveles) de la competicion TASS 2013. Esta contribucion esta formada por dos aproximaciones diferentes: una version modificada de un algoritmo de ranking (RA-SR) utilizando bigramas, y una nueva propuesta que utiliza un puntuador de skipgrams. Estas aproximaciones crean diccionarios de sentimientos capaces de mantener el contexto de los terminos. Todas nuestras aproximaciones aparecen en los primeros 10 mejores resultados entre los sistemas presentados a la competicion, y la combinacion de ambos consigue llegar a la primera posicion. EnglishIn this paper, we present our contribution for the Task 1 (6 levels polarity classification) of the TASS 2013 competition. This contribution consists on two different approaches: a modified version of a ranking algorithm (RA-SR) using bigrams, and new proposal using a skipgrams scorer. These approaches create sentiment lexicons able to retain the context of the terms. All our approaches appear in the top 10 best results of the systems presented to the competition, and the combination of them reaches the first position.
Estimating Supersenses with Conditional Random Fields

To interpret unstructured text information on the intranet or internet an interpretation of words and phrases in terms of an ontology is extremely helpful. However many ontologies, e.g. WordNet, are too fine-grained and even human annotators often have disagreements about the precise word sense. Therefore we propose to use coarse-grained supersenses of WordNet which allow to disambiguate most word senses but nevertheless can be assigned with higher reliability. We employ a sequential method for this task, conditional random fields, which allows to take into account the interaction of neighboring words. We use new features as inputs, especially topic models taking into account thematic information in an unsupervised way. With respects to previous results an increase of about 3% in F-value is achieved.
Multi-Camera Light Field Capture

The digital camera is the technological counterpart to the human eye, enabling the observation and recording of events in the natural world. Since modern life increasingly depends on digital systems, cameras and especially multiple-camera systems are being widely used in applications that affect our society, ranging from multimedia production and surveillance to self-driving robot localization. The rising interest in multi-camera systems is mirrored by the rising activity in Light Field research, where multi-camera systems are used to capture Light Fields the angular and spatial information about light rays within a 3D space. The purpose of this work is to gain a more comprehensive understanding of how cameras collaborate and produce consistent data as a multi-camera system, and to build a multi-camera Light Field evaluation system. This work addresses three problems related to the process of multi-camera capture: first, whether multi-camera calibration methods can reliably estimate the true camera parameters; second, what are the consequences of synchronization errors in a multi-camera system; and third, how to ensure data consistency in a multi-camera system that records data with synchronization errors. Furthermore, this work addresses the problem of designing a flexible multi-camera system that can serve as a Light Field capture testbed. The first problem is solved by conducting a comparative assessment of widely available multi-camera calibration methods. A special dataset is recorded, giving known constraints on camera ground-truth parameters to use as reference for calibration estimates. The second problem is addressed by introducing a depth uncertainty model that links the pinhole camera model and synchronization error to the geometric error in the 3D projections of recorded data. The third problem is solved for the color-and-depth multi-camera scenario, by using a proposed estimation of the depth camera synchronization error and correction of the recorded depth maps via tensor-based interpolation. The problem of designing a Light Field capture testbed is addressed empirically, by constructing and presenting a multi-camera system based on off-the-shelf hardware and a modular software framework. The calibration assessment reveals that target-based and certain target-less calibration methods are relatively similar at estimating the true camera parameters. The results imply that for general-purpose multi-camera systems, target-less calibration is an acceptable choice. For high-accuracy scenarios, even commonly used targetbased calibration approaches are insufficiently accurate. The proposed depth uncer-