nils-herrmann/clean_scirepeval huggingface dataset:
DICO: A Graph DB Framework for Community Detection on Big Scholarly Data
Year: 2019

The widespread use of Online Social Networks has also involved the scientific field in which researchers interact each other by publishing or citing a given paper. The huge amount of information about scientific research documents has been described through the term Big Scholarly Data. In this paper we propose a framework, namely Discovery Information using COmmunity detection (DICO) for identifying overlapped communities of authors from Big Scholarly Data by modeling authors' interactions through a novel graph based data model combining jointly document metadata with semantic information. In particular, DICO presents three distinctive characteristics:i) the co authorship network has been built from publication records using a novel approach for estimating relationships weight between users;ii) a new community detection algorithm based on Node Location Analysis has been developed to identify overlapped communities;iii) some built in queries are provided to browse the generated network, though any graph traversal query can be implemented through the Cypher query language. The experimental evaluation has been carried out to evaluate the efficacy of the proposed community detection algorithm on benchmark networks.Finally, DICO has been tested on a real world Big Scholarly Dataset to show its usefulness working on the DBLP+AMiner dataset, that contains 1.7M+ distinct authors, 3M+ papers, handling 25M+ citation relationships.
Fake News Classification Based on Subjective Language
Venue: iiWAS
Year: 2019

While many works investigate spread patterns of fake news in social networks, we focus on the textual content. Instead of relying on syntactic representations of documents (aka Bag of Words) as many works do, we seek more robust representations that may better differentiate fake from legitimate news. We propose to consider the subjectivity of news under the assumption that the subjectivity levels of legitimate and fake news are significantly different. For computing the subjectivity level of news, we rely on a set subjectivity lexicons built by Brazilian linguists. We then build subjectivity feature vectors for each news article by calculating the Word Mover's Distance (WMD) between the news and these lexicons considering the embedding the news words lie in, in order to classify the documents. The results demonstrate that our method is more robust than classical text classification approaches, especially in scenarios where training and test domains are different.
The Rise and Impact of COVID 19 in India
Venue: Frontiers in Medicine
Year: 2020

The coronavirus disease (COVID 19) pandemic, which originated in the city of Wuhan, China, has quickly spread to various countries, with many cases having been reported worldwide. As of May 8th, 2020, in India, 56,342 positive cases have been reported. India, with a population of more than 1.34 billion the second largest population in the world will have difficulty in controlling the transmission of severe acute respiratory syndrome coronavirus 2 among its population. Multiple strategies would be highly necessary to handle the current outbreak; these include computational modeling, statistical tools, and quantitative analyses to control the spread as well as the rapid development of a new treatment. The Ministry of Health and Family Welfare of India has raised awareness about the recent outbreak and has taken necessary actions to control the spread of COVID 19. The central and state governments are taking several measures and formulating several wartime protocols to achieve this goal. Moreover, the Indian government implemented a 55 days lockdown throughout the country that started on March 25th, 2020, to reduce the transmission of the virus. This outbreak is inextricably linked to the economy of the nation, as it has dramatically impeded industrial sectors because people worldwide are currently cautious about engaging in business in the affected regions.
Synthesis of temperature/pH dual responsive mesoporous silica nanoparticles by surface modification and radical polymerization for anti cancer drug delivery
Year: 2021

In this study, mesoporous silica nanoparticles were synthesized from rice husk (RMSN D) as a natural, available, economical, and non toxic source for cancer treatment. Also, to increase the drug delivery efficiency, after modifying the surface of nanoparticles by vinyl groups (RMSN DV) acrylic acid and n isopropyl acrylamide were polymerized on the RMSN DV surface by a well tuned monomer ratio to create a biocompatible smart dual responsive drug delivery system (RMSN DAN) The structure and physicochemical properties of the nanoparticles were characterized by XRD, FT IR, SEM, TEM, EDS, BET, HNMR, and EDS techniques. The results indicated that monomers were successfully grafted onto the RMSN D. Doxorubicin (Dox) was exploited as a model drug, and in vitro Dox release was investigated. The result exhibited an excellent temperature and pH responsive controlled release behavior. The biocompatibility and safety of natural silica source and RMSN DAN were demonstrated Via MTT assay. Dox loaded RMSN DAN demonstrated cytotoxic activity similar to free Dox to MCF 7 cell line. Moreover, apoptosis of the MCF 7 cell line was investigated by flow cytometric analysis and morphological study. Therefore, this dual responsive biocompatible and economic RMSN DAN nanocarrier could be applied in targeting and stimuli responsive drug delivery due to negligible drug leakage during blood circulation whilst having a rapid release upon reaching tumor tissues.
Stacked Deep Learning Structure with Bidirectional Long Short Term Memory for Stock Market Prediction
Year: 2020

The rapid growth of deep learning research has introduced numerous methods to solve real world applications. In the financial market, the stock price prediction is one of the most challenging topics. This paper presents design and implementation of a stacked system to predict the stock price of the next day. This approach is a method that considers the historical data of the real stock prices from Yahoo Finance. This model uses the wavelet transform technique to reduce the noise of market data, and stacked auto encoder to filter unimportant features from preprocessed data. Finally, recurrent neural network and bidirectional long short term memory are used to predict the future stock price. We evaluate our model by analyzing the performance of different models with time series evaluation criteria.
Nonparametric spatial regression under near epoch dependence
Year: 2012

This paper establishes asymptotic normality and uniform consistency with convergence rates of the local linear estimator for spatial near epoch dependent (NED) processes. The class of the NED spatial processes covers important spatial processes, including nonlinear autoregressive and infinite moving average random fields, which generally do not satisfy mixing conditions. Apart from accommodating a larger class of dependent processes, the proposed asymptotic theory allows for triangular arrays of heterogeneous random fields located on unevenly spaced lattices and sampled over regions of arbitrary configuration. All these features make the results applicable in a wide range of empirical settings.
Civil Law Partnership in the System of Counteracting Money Laundering and Terrorism Financing (AML/CFT)
Year: 2020

This article addresses the problems of legal status of the so called civil law partnership, as specified in Art. 860 SS 1 of the Polish Civil Code, from the point of view of performing the obligations in the area of counteracting money laundering and terrorism financing. First, the author provides a detailed characterization of this civil law institution and resolves that the civil law partnership does not have legal subjectivity separate from its partners, and then points to the consequences of the above facts in the area of counteracting money laundering and terrorism financing. In conclusion, the author formulates a general conclusion that the obligations in respect of counteracting money laundering and terrorism financing, including financial safeguards, should be applied to the civil law partnership partners, as customers in the understanding of Art. 2(2) item 10 of the Polish AML Act.
Pre Exercise Infrared Low Level Laser Therapy (810 nm) in Skeletal Muscle Performance and Postexercise Recovery in Humans, What Is the Optimal Dose? A Randomized, Double Blind, Placebo Controlled Clinical Trial.
Venue: Photomedicine and laser surgery
Year: 2016

This study aimed to evaluate the medium term effects of low level laser therapy (LLLT or photobiomodulation) in postexercise skeletal muscle recovery and performance enhancement and to identify the optimal dose of 810 nm LLLT.  A randomized, double blind, placebo controlled trial was performed, with voluntary participation of 28 high level soccer athletes. We analyzed maximum voluntary contraction (MVC) delayed onset muscle soreness (DOMS) creatine kinase (CK) activity, and interleukin 6 (IL 6) expression. The assessments were performed before exercise protocols, after 1 min, and 1, 24, 48, 72, and 96 h after the end of eccentric exercise protocol used to induce fatigue. LLLT was applied before eccentric exercise protocol with a cluster with five diodes, and dose of 10, 30, or 50 J (200 mW and 810 nm) in six sites of quadriceps.  LLLT increased (p 0.05) MVC from immediately after exercise to 24 h with 50 J dose, and from 24 to 96 h with 10 J dose. Both 10 J then 50 J dose decreased (p 0.05) CK and IL 6 with better results in favor of 50 J dose. However, LLLT had no effect in decreasing DOMS. No differences (p 0.05) were found for 30 J dose in any of the outcomes measured.  Pre exercise LLLT, mainly with 50 J dose, significantly increases performance and improves biochemical markers related to skeletal muscle damage and inflammation.
Implementacion de un sistema automatizado de analisis vibracional para el mantenimiento predictivo en motores electricos.
Year: 2019

El presente trabajo tuvo como objetivo, implementar un sistema automatizado de analisis vibracional como herramienta de diagnostico para el mantenimiento predictivo en motores electricos a traves de procesamiento de senales mediante el uso de un banco de pruebas. Este sistema permite diagnosticar las principales fallas generadas por vibraciones en motores electricos. Las vibraciones son adquiridas usando un acelerometro triaxial instalado en el motor y una tarjeta de adquisicion de datos (NI DAQ USB 6009) Los datos adquiridos son enviados a un ordenador, y mediante el software LabVIEW se realiza el procesamiento de la senal con el fin de obtener la forma de onda en el tiempo, acondicionando y transformando la senal en un espectro de frecuencia utilizando la transformada rapida de Fourier (FFT) Al proyecto se anade un sistema global para comunicaciones moviles (GSM) utilizando un controlador logico programable (PLC) permitiendo al usuario obtener mensajes de monitoreo acerca del estado del motor y sus niveles de vibracion sin necesidad de verificar continuamente dichos valores en la interfaz humano maquina (HMI) Los resultados obtenidos fueron comparados a traves de un analisis visual y estadistico con el equipo VIBRAcheck 200. A partir de los valores globales de vibracion se obtuvo errores RMS relativos menores al 2% mientras que respecto a los valores de los componentes frecuenciales se encontro un valor de media similar entre los dos equipos. Este analisis fue utilizado para elaborar un plan de mantenimiento predictivo basado en el diagnostico de los espectros de acuerdo con lo establecido en las cartas de Charlotte y en base a los niveles de vibracion se establece la severidad en base a la norma ISO 2372. Se recomienda el uso del sistema implementado como alternativa en los procesos de diagnostico y elaboracion de planes de mantenimiento en motores electricos.
The Challenges to Leverage User Generated Contents in B2B Marketing
Venue: HCI
Year: 2020

This study explores the emerging phenomenon of User Generated Content (UGC) in Business to Business (B2B) digital marketing. It offers deep insights into the challenges in UGC generation in B2B context which can help develop frameworks, to leverage UGC for marketing outcomes. Exploratory field research approach was adopted to examine the state of UGC and other similar practises in the B2B industry. Semi structured interviews were conducted with key B2B practitioners in Asia. Interview data was analyzed through thematic analysis to understand the challenges and possibilities for UGC in B2B, through the words and meanings attached to them by B2B practitioners. Iterations between data analysis and reference to relevant literature, was done to guide theory development and subsequent data collection. Based on this understanding a UGC Leverage Framework was developed, which is grounded in practice and relevant theories. This study has established a basic theoretical framework about the challenges for UGC generation in B2B industry. As this is the first research study to explore UGC in B2B contexts, it's findings fill a gap in current literature and offer suggestions for further research. The outcome of this study gives B2B marketing practitioners and researchers, a sound understanding of the challenges involved. Thus helping them find ways to leverage UGC to effectively approach potential customers, at a time when traditional ways are losing effectiveness over the technology empowered B2B buyers.
Facebook privacy management strategies: A cluster analysis of user privacy behaviors
Venue: Comput. Hum. Behav.
Year: 2017

Online privacy management research related to e commerce mainly focuses on whether or not to disclose information. Online social networking on the other hand, offers a broader set of privacy management behaviors such as using vendor provided privacy settings, limiting self disclosures, and managing network size. While much prior research has examined these privacy behaviors individually, we investigate how Facebook users combine these behaviors to form privacy management strategies. Using communications privacy management and impression management theories we first develop four propositions about possible user strategies that combine differing levels of disclosure, network size, and privacy setting use. One strategy is the most public, whereas two others deviate from this public strategy by either limiting disclosures, or using privacy settings to restrict access. The final strategy is the most private. We then survey two samples of Facebook users (college students and the general population) about their privacy management behaviors, privacy perceptions, technology usage perceptions, and demographics. Using cluster analysis we find some support for all four propositions, and for an additional strategy that beginning users may employ. The findings also reveal intriguing differences among the privacy management strategies in terms of perceptions and demographics like privacy concern, usefulness, enjoyment, and age, gender, and experience. Implications for future research are discussed.
Investigating nurses' quality of life and work life balance statuses in Singapore
Venue: International nursing review
Year: 2019

To investigate the key determinants of nurses' quality of life and work life balance statuses in a tertiary hospital in Singapore.  Nurses' quality of life can directly and indirectly impact patients' safety and quality of care. Therefore, identifying key factors that influence nurses' quality of life is essential in the healthcare delivery system.  A descriptive quantitative study design was adopted, and validated questionnaires were used. Data were collected in a period of 3 months (March to May 2014) at a 600 bed tertiary hospital in Singapore. One thousand and forty nurses participated in the study.  Social support and sense of coherence were found to be significant predictors for high quality of life in all domains. Most nurses in this study spent more time on work than their private lives. However, there was no significant difference in job satisfaction among the four groups of nurses' proportions of percentages of actual time spent on work and private life.  Cultivating social support from family, friends/colleagues and supervisors can help an individual cope with stress and enhance a nurse's quality of life.  Even though nurses who spent more time at work were still satisfied with their job, they might need to be aware of their physical health and work environment. Nursing policy related to nurses' physical health and environment should be established. Health promotion programmes such as physical exercise and mindfulness interventions should be conducted to promote nurses' well being and healthy workplace environments to enhance nurses' quality of life.
Learning transform aware attentive network for object tracking
Venue: Neurocomputing
Year: 2019

Existing trackers often decompose the task of visual tracking into multiple independent components, such as target appearance sampling, classifier learning, and target state inferring. In this paper, we present a transform aware attentive tracking framework, which uses a deep attentive network to directly predict the target states via spatial transform parameters. During off line training, the proposed network learns generic motion patterns of target objects from auxiliary large scale videos. These leaned motion patterns are then applied to track target objects on test sequences. Built on the Spatial Transform Network (STN) the proposed attentive network is fully differentiable and can be trained in an end to end manner. Notably, we only fine tune the pre trained network in the initial frame. The proposed tracker requires neither online model update nor appearance sampling during the tracking process. Extensive experiments on OTB 2013, OTB 2015, VOT 2014 and UAV 123 datasets demonstrate the competitive performance of our method against state of the art attentive tracking methods.
Attitudes, Motivations and Beliefs about L2 Reading in the Filipino Secondary School Classroom: A Mixed methods Study
Year: 2016

This study is a two part investigation. The first part focuses on ESL learners' attitudes and motivations for reading in the target language. The second part deals with ESL teachers' beliefs about motivating L2 learners to read. The study involved 100 ESL learners N =100) and 30 teachers N =30) from rural schools in Mindanao, the Philippines. All the participants were recruited through convenience sampling. In other words, participants were selected based on their convenient accessibility and proximity. The current study is a mixed methods project. Both quantitative and qualitative methods were employed to collect different types of data. The instruments used were: a L2 reading attitude survey, a questionnaire dealing with motivations for L2 reading, a survey on beliefs about motivating L2 learners to read in English, a semi structured interview and a L2 reading lesson observation. The quantitative data were statistically analysed. Whenever appropriate, the data were presented in tables and on graphs. The qualitative data were analysed through thematic coding and used to support the quantitative data. The findings show that students have both positive and negative attitudes towards various aspects of L2 reading. They also have different levels of motivation for reading in English, with female participants having higher scores than male participants. The teachers, on the other hand, hold diverse beliefs about motivating learners to read in English. No significant correlation was found between teacher beliefs and students' motivations for reading in English. After the findings have been described, implications for teacher education and instructional practice are offered.
The Practitioner Scholar: Journal of Counseling and Professional Psychology 19 Volume 7, 2018 Trauma Focused Interventions: A Clinical Practice Analysis
Year: 2018

This paper presents a systematic review of trauma treatment interventions, which have different theoretical perspectives on trauma etiology and its application. The empirical findings of these trauma treatment therapies are presented. The extant literature identifies five therapies as the most effective treatment modalities for PTSD: cognitive therapy (CT) cognitive behavioral therapy (CBT) cognitive processing therapy (CPT) prolonged exposure (PE) therapy, and eye movement desensitization and reprocessing (EMDR) therapy. They are all validated with some nuances on treatment approach and effectiveness. However, to use these five interventions, practitioners need to consider and apply the research findings that indicate which intervention is best for which population. Based on the findings, suggestions are made for which population the different interventions are best suited.
IDENTIFICATION IN TEXT MINING FOR USED IN FORENSICS
Year: 2013

We have reviewed into text data and e mail content mining for author identification, or authorship categorization, for the purpose of forensic investigation. We have focused our discussion on the ability to discriminate between authors for the case of both e mails as well as across the simple text data. An set of e mail document features including structural characteristics and linguistic patterns which can be derived with a Support Vector Machine learning algorithm, which can be used for mining the e mail content. The experiments using a number of e mail documents and the text data generated by different authors on a set of topics gave good results for both author identification categorization.
Effects of nano encapsulated curcumin chrysin on telomerase, MMPs and TIMPs gene expression in mouse B16F10 melanoma tumour model
Venue: Artificial cells, nanomedicine, and biotechnology
Year: 2018

Due to the high rate of drug resistance among malignant melanoma cases, it seems necessary to introduce an efficient pharmaceutical approach to melanoma treatment. For this purpose, Curcumin (Cur) and Chrysin (Chr) two natural anti cancers, were co encapsulated in PLGA PEG nanoparticles (NPs) characterized by DLS, FTIR and FE SEM and investigated for their effects on MMPs, TIMPs and TERT genes expression in C57B16 mice bearing B16F10 melanoma tumours. The results showed that the expression of MMP 9, MMP 2 and TERT genes were significantly decreased in all treated groups compared to the control. This reduction had the highest amount in CurChr NPs group and then CurChr group for each three genes. Likewise, the expression of TIMP 1 and TIMP 2 genes was significantly increased in all treated groups, compared to the control. Combination groups showed the highest rise in expression of these two genes and the observed increase was greater in nano groups. Moreover, the highest melanoma tumour growth inhibition was detected for CurChr NPs, followed by CurChr Cur NPs Cur Chr NP Chr. Overall, it is speculated that the nano combination of Cur and Chr into polymeric NPs with a one step fabricated co delivery system may be a promising and convenient approach to improve their efficiency in melanoma cancer therapy.
Modeling of the Eigenfield of a Prestressed Hyperelastic Membrane Encapsulating a Liquid
Year: 2006

A spectral boundary problem on the eigenfield of an inflated/deflated stretched circular membrane, which is clamped to a circular cylindrical cavity filled with a liquid, is examined. The paper presents an operator formulation of the problem and proposes a new semi analytical approximate method. The method captures singular behavior of the solution in the pole and at the fastening contour of the membrane.